A method for irrigating saline-alkali land based on straw adsorption and water regulation

Abstract

The present application relates to irrigation saline-alkali land improvement technical field, specifically to a kind of irrigation saline-alkali land improvement method based on straw adsorption and water conservancy regulation, comprising: obtaining the multi-depth soil parameters and surface weather data of irrigation area, establish soil water salt dynamic migration coupling model;Utilize model to calculate vertical water flux and net salt flux of plough layer;Calculate cumulative risk index of salt return, obtain salt return risk grade;Using multi-depth soil parameters, construct straw decomposition index;And based on straw decomposition index, update soil water salt dynamic migration coupling model;In response to salt return risk grade being high risk, build optimization objective function;Solve optimization objective function, obtain optimal irrigation strategy;Output optimal irrigation strategy to irrigation control system, and it is used as irrigation rate feedback to model;The present application can identify threat before high-risk salt return event occurs, improve the timeliness and initiative of regulation, realize the balance of management benefit and economic cost.

Description

Technical Field

[0001] This invention relates to the field of irrigated saline-alkali land remediation technology, specifically to an irrigated saline-alkali land remediation method based on straw adsorption and water conservancy regulation. Background Technology

[0002] In irrigated agricultural areas, the remediation of saline-alkali land is crucial for ensuring food security and ecological balance. A key challenge in this process is controlling secondary salinization of the soil.

[0003] Returning straw to the field is a common technique for soil improvement, but its process is complex. The decomposition of straw dynamically alters the hydraulic properties of the soil, such as soil pore structure and capillary transport capacity. However, traditional irrigation management of saline-alkali land relies heavily on static soil models or empirical irrigation regimes, which typically treat soil parameters as fixed or slowly changing. This static management approach cannot couple the dynamic impact of straw decomposition on soil water and salt transport mechanisms in real time. Furthermore, irrigation decisions are often delayed or based on fixed schedules, lacking forward-looking risk prediction. This not only leads to low water resource utilization efficiency but also fails to effectively suppress high-risk salinization events before they occur, resulting in poor remediation effects. Therefore, how to dynamically couple the biological process of straw decomposition with the physical process of soil water and salt transport to accurately predict salinization risks and optimize irrigation strategies to balance remediation effects and water consumption has become a pressing technical problem in this field. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a method for the remediation of saline-alkali land through irrigation based on straw adsorption and water regulation. Specifically, the technical solution of this invention includes:

[0005] S1. Obtain multi-depth soil parameters and surface meteorological data of the irrigation area, and establish a coupled model of soil water and salt dynamic transport;

[0006] S2. Combining multi-depth soil parameters and surface meteorological data, the vertical water flux and topsoil net salt flux are calculated using a model; and based on the topsoil net salt flux, the cumulative salt return risk index is calculated to obtain the salt return risk level.

[0007] S3. Using multi-depth soil parameters, a straw decomposition index is constructed; and based on the straw decomposition index, the interlayer capillary transport coefficient and hydrodynamic dispersion coefficient of the model are recursively corrected to update the coupled model of soil water and salt dynamic transport.

[0008] S4. In response to the high risk level of salt return, construct an optimization objective function; solve the optimization objective function to obtain the optimal irrigation strategy; output the optimal irrigation strategy to the irrigation control system and feed it back to the model as the irrigation rate.

[0009] Preferably, in S2, the calculation of vertical water flux and net salt flux in the cultivated layer includes:

[0010] S11. Salt concentration is obtained by converting the soil electrical conductivity of multiple depths from the soil parameters of multiple depths.

[0011] S12. Based on the principle of water balance, combined with the soil moisture content at multiple depths in the soil parameters, the evaporation and irrigation rate in the surface meteorological data, and the interlayer capillary transport coefficient preset in the model, the vertical water flux is calculated.

[0012] S13. Based on the convection-dispersion equation of solute transport, combined with vertical water flux, salt concentration, and the hydrodynamic dispersion coefficient preset in the model, calculate the net salt flux of the cultivated layer.

[0013] Preferably, in S2, the cumulative salt return risk index is calculated, including:

[0014] S21. Set the prediction period and time step;

[0015] S22. The cumulative salt flux of the cultivated layer at each time step within the prediction period is summed to obtain the cumulative salt return risk index.

[0016] Preferably, in S2, obtaining the risk level of salt return also includes:

[0017] S23, preset crop tolerance thresholds and regulatory intervention thresholds;

[0018] S24. When the cumulative salt return risk index is less than or equal to the crop tolerance threshold, the salt return risk level is determined to be low risk, and the current control strategy is maintained.

[0019] S25. When the cumulative salt return risk index is greater than the crop tolerance threshold and less than or equal to the regulation intervention threshold, the salt return risk level is determined to be medium risk, and an early warning signal is issued.

[0020] S26. When the cumulative salt return risk index is greater than the regulatory intervention threshold, the salt return risk level is determined to be high risk.

[0021] Preferably, in S3, the straw decomposition index is constructed, including:

[0022] S31. Preset the minimum and optimal temperature and humidity thresholds required for microbial degradation activities;

[0023] S32. Based on the soil temperature and temperature threshold of the cultivated layer in the multi-depth soil parameters, and combined with the preset temperature weighting coefficient, calculate the temperature influence term.

[0024] S33. Based on the soil moisture content and humidity threshold of the topsoil layer in the multi-depth soil parameters, and combined with the preset humidity weighting coefficient, calculate the humidity influence item.

[0025] S34. Add the temperature effect item and the humidity effect item to obtain the straw decomposition index.

[0026] Preferably, in S3, the interlayer capillary transport coefficient and hydrodynamic dispersion coefficient of the model are recursively corrected, including:

[0027] S35. Based on the straw decomposition index and the preset capillary inhibition rate constant, calculate the corrected interlayer capillary transport coefficient.

[0028] S36. Based on the straw decomposition index and the preset pore evolution rate constant, calculate the corrected hydrodynamic dispersion coefficient.

[0029] Preferably, in S4, the objective function for optimization is constructed, including:

[0030] S41. Calculate the risk cost based on the cumulative salt return risk index and the preset risk cost weight;

[0031] S42. Calculate the water resource cost based on the total water consumption of the irrigation strategy and the preset water resource cost weight;

[0032] S43. Perform a linear weighted summation of the risk cost and the water resource cost to construct an optimization objective function.

[0033] Preferably, in step S4, solving the objective function to obtain the optimal irrigation strategy further includes:

[0034] S44. Under the constraints of preset total water volume limit and preset pump power limit, the minimum function value of the optimization objective function is solved iteratively using an optimization algorithm.

[0035] S45. Determine the irrigation strategy corresponding to the minimum function value as the optimal irrigation strategy;

[0036] The optimal irrigation strategy includes irrigation time, total irrigation depth, and irrigation method.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] 1. This method constructs a straw decomposition index and uses it to dynamically correct the key parameters of the soil water and salt transport model. This overcomes the limitations of traditional static models, enabling the model to reflect the evolution of soil structure under straw improvement in real time, and significantly improving the accuracy of water and salt transport simulation.

[0039] 2. This method calculates and accumulates the net salt flux in the topsoil, enabling forward-looking prediction and classification of future salt return risks. This transforms irrigation management from a passive response to an active early warning system, allowing for the identification of threats before high-risk salt return events occur, thus improving the timeliness and proactivity of regulation.

[0040] 3. This method only initiates optimization under high-risk conditions, and calculates the optimal irrigation strategy by balancing the risk of salt return and the cost of water resources through the objective function; this avoids unnecessary irrigation, significantly improves water resource utilization efficiency, and achieves a balance between governance benefits and economic costs;

[0041] 4. This method integrates real-time monitoring, dynamic model correction, risk prediction, and optimization feedback into a closed-loop control system; this design enables intelligent and automated management of irrigation for saline-alkali land, effectively suppressing secondary salt return and improving the long-term effects of the treatment. Attached Figure Description

[0042] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0043] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0045] Example 1:

[0046] Please see Figure 1 A method for irrigating saline-alkali land based on straw adsorption and water regulation, the specific steps of which include:

[0047] S1. Obtain multi-depth soil parameters and surface meteorological data of the irrigation area, and establish a coupled model of soil water and salt dynamic transport;

[0048] S2. Combining multi-depth soil parameters and surface meteorological data, the vertical water flux and topsoil net salt flux are calculated using a model; and based on the topsoil net salt flux, the cumulative salt return risk index is calculated to obtain the salt return risk level;

[0049] S3. Using multi-depth soil parameters, a straw decomposition index was constructed; and based on the straw decomposition index, the interlayer capillary transport coefficient and hydrodynamic dispersion coefficient of the model were recursively corrected to update the coupled model of soil water and salt dynamic transport.

[0050] S4. In response to the high risk level of salt return, construct an optimization objective function; solve the optimization objective function to obtain the optimal irrigation strategy; output the optimal irrigation strategy to the irrigation control system and feed it back to the model as the irrigation rate.

[0051] This embodiment provides a method for the treatment of saline-alkali land through irrigation based on straw adsorption and water conservancy regulation;

[0052] This study acquires multi-depth soil parameters and surface meteorological data of the irrigated area and establishes a coupled model for soil water and salt dynamic transport. Acquiring multi-depth soil parameters aims to provide the model with real-time, stratified input of the soil's internal state. In this embodiment, this is achieved by deploying a multi-depth soil sensor array within the irrigated area, with monitoring points set at different key depths, such as the topsoil layer. Subsurface and deep The real-time collected data includes: soil moisture content at each layer. Its dimensions are Soil electrical conductivity It is used for subsequent conversion of salt concentration. and soil temperature Acquiring surface meteorological data aims to provide the external environmental driving factors required by the model; in this embodiment, it is acquired through surface meteorological stations, including evaporation. Its dimensions are Rainfall or irrigation rate Its dimensions are The purpose of establishing a coupled model for dynamic transport of soil water and salt is to build a core simulation engine that can simulate the interaction and migration of water and salt in soil profiles. This model is the basis for subsequent risk prediction and strategy optimization, and it consists of two formulas for water flux calculation and salt flux calculation.

[0053] By combining soil parameters at multiple depths and surface meteorological data, the vertical water flux and net salt flux of the topsoil were calculated using a model. Based on the net salt flux of the topsoil, the cumulative salt return risk index was calculated to obtain the salt return risk level. The calculation of vertical water flux and net salt flux of the topsoil using the model aims to quantify the migration rate and direction of water and salt in the soil in the current and future period, especially the salt return situation in the topsoil.

[0054] This step utilizes the model established in the previous step and inputs the collected real-time parameters, solving the model's physical equations; net salt flux in the cultivated layer. It is a key indicator that characterizes whether salt accumulates in the topsoil (positive value) or is leached away (negative value). Based on the net salt flux in the topsoil, a cumulative salt return risk index is calculated to obtain the salt return risk level. Its purpose is to transform instantaneous flux data into an assessment of the overall salt return trend over a future forecast period. The cumulative salt return risk index... By summing up the net salt flux of the cultivated layer at each time step within the prediction period, i.e., numerical integration, its physical meaning is the net increase in salt mass per unit area of ​​the cultivated layer during the prediction period; by comparing this index with preset thresholds, such as crop tolerance thresholds and regulatory intervention thresholds, the risk level of salt return can be determined.

[0055] A straw decomposition index was constructed using soil parameters at multiple depths. Based on this index, the interlayer capillary transport coefficient and hydrodynamic dispersion coefficient of the model were recursively corrected to update the coupled soil water-salt dynamic transport model. The aim is to quantify the real-time rate of soil structure improvement resulting from the biochemical processes of straw return to the field; the core role of straw decomposition lies in reconstructing soil porosity, especially by blocking capillary connections that lead to salinization; this index is based on the topsoil temperature obtained in the previous step. and topsoil moisture content These two key environmental factors that influence microbial degradation activities are used to construct the system;

[0056] Based on the straw decomposition index, the interlayer capillary transport coefficient and hydrodynamic dispersion coefficient of the model are recursively corrected. The purpose is to dynamically feed back the long-term biological improvement effect of straw into the aforementioned physical model, so that the model can reflect the evolution of the soil ecosystem rather than a static simulation.

[0057] In this embodiment, the interlayer capillary transport coefficient and hydrodynamic dispersion coefficient These are the two core adjustable parameters in the aforementioned model; straw decomposition increases organic matter and aggregate structure, which is quantified as: reducing the interlayer capillary transport coefficient. Simulates the process of capillary connection being blocked and vertical backflow of saline water weakening; improves the hydrodynamic dispersion coefficient. This simulates the process of improved pore structure and more thorough solute mixing; this correction is recursive, meaning the parameter values ​​from the previous time step are used as the basis for the correction. Based on the currently calculated straw decomposition index Corrected to new parameter values This is used for model calculations at the next time step; updating the coupled soil water and salt dynamic transport model refers to applying the corrected new parameters. Write back to the aforementioned model;

[0058] In response to a high risk level of salt return, an optimization objective function is constructed; the optimization objective function is solved to obtain the optimal irrigation strategy; the optimal irrigation strategy is output to the irrigation control system and converted into an irrigation rate, which is then fed back to the model; a high risk level of salt return is the trigger condition for initiating optimization regulation; when the evaluation result of the previous step shows a high risk, it indicates that a significant secondary salt return is predicted to occur in the future, and the system automatically initiates this step.

[0059] Construct the optimization objective function The aim is to establish a mathematical model to balance the potentially conflicting goals of reducing the risk of salt return and conserving water resources; this function typically incorporates the risk cost, based on a cumulative salt return risk index. Calculation and water resource costs, based on total water consumption The calculation involves linear weighted summation; solving the objective function yields the optimal irrigation strategy. The aim is to find an irrigation strategy under given constraints. This includes irrigation time, total irrigation depth, irrigation method, etc., which makes the optimization objective function constructed in this step... Obtain the minimum function value;

[0060] The solution process is completed iteratively using a specific optimization algorithm. Each iteration requires calling the model from the previous step to perform a prospective simulation in order to evaluate the assumed irrigation strategy. The corresponding cumulative salt return risk index The final optimal irrigation strategy It is the solution that minimizes the overall cost; the optimal irrigation strategy is output to the irrigation control system to execute the regulation; the output instruction may be: start drip irrigation in area A for 4 hours in the next 72 hours, and apply the total irrigation depth. The conversion to irrigation rate is fed back into the model to form a closed-loop control; for example, the above strategy will be converted into a time series function. Within the specified 4 hours, ,in, For the duration of irrigation; at other times ;this The sequence will be used as the model in the next round of simulation. This means inputting the rainfall or irrigation rate to ensure that the model's simulation always remains consistent with actual regulation.

[0061] Example 2:

[0062] In S2, the vertical water flux and net salt flux of the cultivated layer are calculated, including:

[0063] S11. Salt concentration is obtained by converting the soil electrical conductivity from multiple depths of soil parameters.

[0064] S12. Based on the principle of water balance, and combining the multi-depth soil moisture content in multi-depth soil parameters, the evaporation and irrigation rate in surface meteorological data, and the interlayer capillary transport coefficient preset in the model, calculate the vertical water flux;

[0065] S13. Based on the convection-dispersion equation of solute transport, combined with vertical water flux, salt concentration, and the hydrodynamic dispersion coefficient preset in the model, calculate the net salt flux of the cultivated layer.

[0066] This embodiment illustrates the specific process of calculating vertical water flux and net salt flux in the topsoil using a model;

[0067] Multi-depth soil electrical conductivity from multi-depth soil parameters Conversion to obtain salt concentration Since conductivity is strongly correlated with the total dissolved solids in the solution, mainly salts, and their concentration, those skilled in the art can use pre-calibrated empirical formulas or physicochemical models to analyze the collected conductivity. Converted to Its dimensions are ;

[0068] Based on the principle of water balance, and combining multi-depth soil moisture content from soil parameters, evaporation and irrigation rate from surface meteorological data, and the interlayer capillary transport coefficient preset in the model, the vertical water flux is calculated; the vertical water flux... Designed to calculate moisture in soil layers The vertical velocity of motion; in this embodiment, its calculation model is as follows:

[0069] ;

[0070] The model aims to simulate water migration driven by surface meteorology, namely evaporation and precipitation, and interlayer moisture gradient, namely capillary action.

[0071] in, For the first Vertical water flux of soil layer, with dimensions of ; The atmospheric coupling coefficient is dimensionless and characterizes the effect of surface evaporation and precipitation on the first... The direct impact of water content in the layer is determined by the model's preset values, and varies with depth. The value increases or decreases, and the specific value is set according to the soil texture type and depth of the irrigated area; Evaporation amount, dimensionless The source is acquired surface meteorological data; For rainfall or irrigation rate, in units of The source is the obtained surface meteorological data or the irrigation strategy output from subsequent optimization steps; The interlayer capillary transport coefficient, dimensionless. Its physical meaning is the water flux driven by a unit difference in water content; this is a key preset parameter in the model, and its initial value is... The soil texture is determined based on a static input and then dynamically and recursively corrected in subsequent steps. and The first Layer and overlying layer Real-time soil moisture content, dimensionless, sourced from acquired multi-depth soil parameters;

[0072] This formula is based on the principle of water balance; the first term Representing the atmospheric driving term, the second term The term representing the capillary pressure gradient driving term is simplified here to the water content gradient. Those skilled in the art should understand that the model shown in S12 is a linear simplification approximation of the nonlinear Richards equations to achieve rapid, real-time risk assessment and closed-loop control. This model uses calibrable parameters... and This formula characterizes the main trends of water movement within a specific irrigation area, and its computational efficiency is far higher than solving a system of partial differential equations. Therefore, it is suitable for the real-time control system described in this invention. The formula is calculated in real time, and its output... It not only describes water movement, but is also a necessary input parameter for the next step of calculating salt flux, serving as the carrier velocity of salt migration;

[0073] Based on the convection-dispersion equation for solute transport, combined with vertical water flux, salt concentration, and the hydrodynamic dispersion coefficient preset in the model, the net salt flux of the topsoil is calculated; net salt flux In particular, when At that time, it represents the net salt flux of the cultivated layer, which is used to calculate the migration of salt with water flow, i.e., convection, and diffusion due to concentration gradient, i.e., dispersion; in this embodiment, its calculation model is as follows:

[0074] ;

[0075] This model is designed to simulate the migration of salts in porous media, namely soil;

[0076] in, For the first Net salt flux of the soil layer, dimensionless ; Vertical water flux, dimensional The source is the calculation result of the preceding water flux formula; and For the first Layer and overlying layer Salt concentration, dimensions The source is the conversion result of the previous conductivity; The hydrodynamic dispersion coefficient is dimensional. This is another key preset parameter in the model, and its initial value is... It is determined based on the static input of soil porosity and dynamically and recursively corrected by subsequent steps; Soil layer thickness, dimensionless Preset geometric parameters for the model;

[0077] This formula is derived from the standard solute transport convection-dispersion equation; the first term For the convection term, it represents the salinity of the water flow calculated in the preceding sequence. Carrying and migrating; the second item The diffusion term represents the salt concentration gradient. And the diffusion and dispersion that occur; this formula must be calculated in order to... Then it is executed in series; among them, the net salt flux of the topsoil The calculation results are the core basis for assessing the risk of salt return.

[0078] Example 3:

[0079] In S2, the cumulative salt return risk index is calculated, including:

[0080] S21. Set the prediction period and time step;

[0081] S22. The cumulative salt flux of the cultivated layer at each time step within the prediction period is summed to obtain the cumulative salt return risk index.

[0082] This embodiment illustrates the specific process of calculating the cumulative salt return risk index;

[0083] Set forecast period and time step Prediction cycle This refers to the future time span that this method aims to predict; its setting provides a scale for consideration of key crop growth cycles or the cycles of regulatory measures; time step. It is the minimum time interval for model simulation calculations, which determines the level of detail in the simulation; prediction cycle. and time step Together, they determine the total number of steps for subsequent cumulative summation. Their relationship is ;

[0084] The cumulative salt flux of the cultivated layer at each time step within the forecast period is summed to obtain the cumulative salt return risk index. This step aims to quantify the forecasting period. Inside, the cultivated layer, that is The layer represents the net accumulated total salt content; in this embodiment, its calculation model is as follows:

[0085] ;

[0086] The model assesses the total salt return within the prediction period by numerically integrating the instantaneous salt flux, specifically by the rectangular method or the Riemann sum.

[0087] in, The cumulative salt return risk index, physically represented as the net increase in salt mass per unit area of ​​the topsoil during the prediction period, has the dimension of: ; For the prediction period The total number of time steps within the time frame, dimensionless, derived from the previous settings; From arrive Discrete time step index; For the model in the first The net salt flux into the cultivated layer predicted at each time step, with dimensions of The source is the calculation result of the preceding salt flux formula; Let be the time step, and have dimensions of . The source is the previous setting;

[0088] Continuing from the previous output The model will be in the future Predicting each instantaneous salt flux within each step. Multiply by time step Obtain the cumulative salt amount within this step size, and then... The salt concentrations for each time period are summed to obtain the total salt concentration for the entire prediction period. The cumulative risk index of salt return within .

[0089] Example 4:

[0090] In S2, obtaining the risk level of salt return also includes:

[0091] S23, Preset crop tolerance thresholds and regulatory intervention thresholds;

[0092] S24. When the cumulative salt return risk index is less than or equal to the crop tolerance threshold, the salt return risk level is determined to be low risk, and the current control strategy is maintained;

[0093] S25. When the cumulative salt return risk index is greater than the crop tolerance threshold but less than or equal to the regulatory intervention threshold, the salt return risk level is determined to be medium risk, and an early warning signal is issued;

[0094] S26. When the cumulative salt return risk index is greater than the regulatory intervention threshold, the salt return risk level is determined to be high risk.

[0095] This embodiment continues to calculate the cumulative salt return risk index. The steps for obtaining the risk level of salt return are then explained.

[0096] Preset crop tolerance threshold and regulatory intervention threshold These two thresholds are the benchmarks for risk classification; crop tolerance thresholds This refers to the forecast period. Inside, salt accumulated in the topsoil When this value is reached, it will begin to exert significant stress on the growth of the target crop; this threshold is determined based on experimental data on the salt tolerance of the target crop and agronomic standards, and its dimensions are... Same, that is ; Adjusting intervention threshold This refers to the accumulation of salt. Reaching this threshold indicates impending severe salinization, necessitating proactive water management intervention. This threshold is determined based on local soil environmental capacity standards and remediation experience. Numerically greater than ;

[0097] When the cumulative risk index of salt return Less than or equal to the crop tolerance threshold The risk level of salt return was determined to be low, and the current control strategy was maintained. This situation indicates that the salt accumulation level predicted by the model is lower than the stress threshold of the crop, and the system judges it to be low risk, without the need for additional human intervention.

[0098] When the cumulative risk index of salt return Greater than the crop tolerance threshold And less than or equal to the regulatory intervention threshold The system determines the risk level of salt return to be medium and issues an early warning signal. This situation indicates that the predicted salt accumulation has exceeded the crop's tolerance level, but has not yet reached the critical point where mandatory intervention is necessary. The system determines this as a medium risk and issues an early warning signal to prompt enhanced monitoring.

[0099] When the cumulative risk index of salt return Greater than the regulatory intervention threshold The risk level of salt return was determined to be high; this situation indicates that the model predicts a significant secondary salt return, which the system judges as high risk; this level determination is the trigger signal for subsequent optimization and control steps.

[0100] Example 5:

[0101] In S3, the straw decomposition index is constructed, including:

[0102] S31. Preset the minimum and optimal temperature and humidity thresholds required for microbial degradation activities;

[0103] S32. Based on the topsoil temperature and temperature threshold in the multi-depth soil parameters, and combined with the preset temperature weighting coefficient, calculate the temperature influence term;

[0104] S33. Based on the topsoil moisture content and humidity threshold in multi-depth soil parameters, and combined with the preset humidity weighting coefficient, calculate the humidity influence term;

[0105] S34. Add the temperature effect item and the humidity effect item to obtain the straw decomposition index.

[0106] This embodiment illustrates the specific process of constructing the straw decomposition index;

[0107] Preset minimum and optimal temperature and humidity thresholds required for microbial degradation activity; temperature thresholds include minimum temperature thresholds. and optimal temperature threshold ; This refers to the lower temperature at which microorganisms begin to exhibit significant degradation activity; below this temperature, decomposition essentially ceases. This refers to the optimal temperature at which the microbial degradation rate reaches its peak; the humidity threshold includes the minimum humidity threshold. and optimal humidity threshold ; This refers to the situation where the activity of microorganisms is limited due to water shortage when the soil moisture content is below this value; This refers to the optimal moisture content that is most conducive to microbial activity; these thresholds are determined and preset based on relevant agricultural microbiology literature or experimental data for specific straw types.

[0108] Soil temperature in the topsoil layer based on multi-depth soil parameters And temperature threshold, combined with preset temperature weighting coefficient Calculate the temperature effect term; topsoil temperature. Derived from real-time data collected by sensors in the tillage layer; temperature weighting coefficient It is a dimensionless parameter used to adjust the contribution of temperature to the total corrosion index; in this embodiment, the calculation of the temperature effect term is standardized as follows: ;

[0109] Soil moisture content in the topsoil layer based on multi-depth soil parameters And humidity threshold, combined with preset humidity weighting coefficient Calculate the influence of humidity; topsoil moisture content. Derived from real-time data collected by sensors in the tillage layer; humidity weighting coefficient It is a dimensionless parameter used to adjust the contribution of humidity to the total decomposition index; in this embodiment, the calculation of the humidity effect term is standardized as follows: ;

[0110] Temperature weighting coefficient Humidity weighting coefficient The source needs to be determined based on the type of straw, such as its carbon-to-nitrogen ratio. To compare, calibration is performed, usually by default. ;

[0111] The straw decomposition index is obtained by adding the temperature effect factor and the humidity effect factor. In this embodiment, the construction model is as follows:

[0112] ;

[0113] This model is a custom empirical model designed to incorporate previously collected real-time soil condition data based on environmental factor constraints. , Combined with a preset threshold, it forms a standardized, dimensionless index. The formula is passed through The function ensures that when environmental factors, such as temperature or humidity, are below the minimum threshold required for decomposition, this contribution is zero, thus reducing the straw decomposition index. The values ​​are always non-negative, preventing the model from generating negative exponents that contradict physical principles under extreme low temperatures or drought conditions. This model selects the two most important environmental factors affecting the decomposition rate: temperature and humidity. In other embodiments, those skilled in the art can also consider other influencing factors such as soil pH, aeration conditions, redox potential, or real-time carbon-nitrogen ratio of straw, as needed, to construct a higher-dimensional decomposition index model, thereby improving the model's applicability.

[0114] in, The straw decomposition index is dimensionless. For the temperature of the topsoil, The soil moisture content in the topsoil layer was collected in real time by sensors. The preset threshold; These are preset weighting coefficients;

[0115] When the real-time data is collected and The closer to their respective optimal thresholds , hour, The higher the value, the more intense the microbial degradation activity and the faster the straw improves the soil structure.

[0116] Example 6:

[0117] In S3, the interlayer capillary transport coefficient and hydrodynamic dispersion coefficient of the model are recursively corrected, including:

[0118] S35. Based on the straw decomposition index and the preset capillary inhibition rate constant, calculate the corrected interlayer capillary transport coefficient;

[0119] S36. Based on the straw decomposition index and the preset pore evolution rate constant, calculate the corrected hydrodynamic dispersion coefficient.

[0120] This embodiment continues to calculate the straw decomposition index. Next, the steps for recursively correcting the interlayer capillary transport coefficient and hydrodynamic dispersion coefficient of the model are described.

[0121] Based on straw decomposition index and the preset capillary inhibition rate constant Calculate the corrected interlayer capillary transport coefficient ; Capillary inhibition rate constant It is a key calibration parameter, and its dimensions are... ,For example Used to characterize the straw decomposition index Capillary transport coefficient Suppression rate; corrected interlayer capillary transport coefficient The calculation in this embodiment adopts the following recursive update rule:

[0122] ;

[0123] This model aims to simulate the blocking effect of straw decomposition on capillary connections;

[0124] in, The revised parameters will be used for the calculation of the preceding model in the next time step; These are the old parameters from the previous time step; The straw decomposition index is derived from previous calculations. The time interval for recursive parameter correction must have the same dimensions as... Dimensional matching, for example It should be noted that this correction interval... For example, the cumulative salt return risk index is revised daily, differing from the previous calculation. The simulation time step used in the model Its dimensions are usually 1 ;

[0125] Based on straw decomposition index and the preset pore evolution rate constant Calculate the corrected hydrodynamic dispersion coefficient ; Pore evolution rate constant It is another key calibration parameter, and its dimensions are also [missing information]. Used to characterize the straw decomposition index Hydrodynamic dispersion coefficient The rate of increase; the corrected hydrodynamic dispersion system The calculation of numbers uses a similar recursive update rule:

[0126] ;

[0127] This model aims to simulate the process by which straw decomposition improves soil pore structure and increases dispersion effects.

[0128] in, These are the revised parameters; These are the old parameters from the previous time step; The pore evolution rate constant; and The definition is the same as above;

[0129] To ensure the feasibility of the method described in this invention, the capillary suppression rate constant is... and pore evolution rate constant The calibration process is further clarified; these two constants are determined through field calibration experiments; specifically, several experimental fields can be set up, with different amounts of straw returned to the field, and within a controlled period, such as 180 days, the actual changes in the interlayer capillary transport coefficient of each experimental field are measured and recorded periodically, denoted as . The actual variation data of the hydrodynamic dispersion coefficient are denoted as... Based on this calibrated dataset, regression analysis techniques, such as least squares, are used to fit and solve the parameters in the above recursive update rule. and To make the model predictions consistent with the experimental data and The residual is the smallest;

[0130] Calculated and It will be written back to the preceding coupled model and replaced and This is used for the next round of water and salt transport calculations.

[0131] Example 7:

[0132] In S4, the objective function for optimization is constructed, including:

[0133] S41. Calculate the risk cost based on the cumulative salt return risk index and the preset risk cost weights;

[0134] S42. Calculate the water resource cost based on the total water consumption of the irrigation strategy and the preset water resource cost weights;

[0135] S43. Perform a linear weighted summation of the risk cost and the water resource cost to construct an optimization objective function.

[0136] This embodiment illustrates the specific process of constructing the optimization objective function;

[0137] Based on the cumulative salt return risk index and preset risk cost weights Calculate the risk cost; here is the cumulative salt return risk index. Not referring to the currently calculated Rather, it refers to: when assuming the execution of a certain pending irrigation strategy. At that time, the predictions made through forward-looking simulations using a preceding model for future prediction cycles The cumulative risk index of salt return within the range; the preset risk cost weight. It is a key decision input parameter, referring to the ecological or economic cost of an increase of one unit of salinity per unit area, with dimensions of . This weight The source can be determined based on the estimated loss of the target crop or the penalty standards for ecological restoration; the calculation method for risk cost is as follows: ;

[0138] Total water consumption per unit area based on irrigation strategies and preset water resource cost weights Calculate the cost of water resources; the total water consumption per unit area for irrigation strategies. This refers to the irrigation strategy to be determined. The total irrigation water volume per unit area, in units of depth Preset water resource cost weights It is another key decision input parameter, which refers to the price per unit volume of water resources, with dimensions of This weight The source of water costs is usually determined by local water prices or the cost of water scarcity; the calculation method for water resource costs is as follows: ;

[0139] The risk cost and water resource cost are linearly weighted and summed to construct an optimization objective function. This step aims to merge the two objectives, risk cost and water resource cost, which have different dimensions, into a single scalar objective function to facilitate subsequent solution. In this embodiment, a linear weighted sum method is used to construct the model as follows:

[0140] ;

[0141] The model aims to find an irrigation strategy. This strategy The resulting cumulative salt return risk index cost With the implementation of this strategy The cost of water resources required The sum, i.e., the comprehensive cost To reach the minimum ;

[0142] in, irrigation strategies The overall cost, measured in units of comprehensive cost, for example ; It is the irrigation strategy to be optimized, which is a strategy vector that includes irrigation time, irrigation rate, and irrigation method; and The definition is the same as above; and These are preset weighting coefficients;

[0143] This objective function is the core of subsequent optimization decisions; weights and This reflects decision-making preferences; for example, during seasons of water scarcity, managers can adjust [their policies / measures]. This makes the optimization algorithm more inclined to find water-saving strategies; and during the critical period of crop growth, the water content can be adjusted upwards. This makes the algorithm more inclined to reduce the cumulative risk index of salt return. .

[0144] Example 8:

[0145] In S4, the objective function is solved to obtain the optimal irrigation strategy, and the following steps are also included:

[0146] S44. Under the constraints of preset total water volume limits and preset pump power limits, the minimum function value of the objective function is solved iteratively using an optimization algorithm;

[0147] S45. Determine the irrigation strategy corresponding to the minimum function value as the optimal irrigation strategy;

[0148] The optimal irrigation strategy includes irrigation time, total irrigation depth, and irrigation method.

[0149] This embodiment continues by constructing the optimization objective function. Next, the steps for solving and optimizing the objective function to obtain the optimal irrigation strategy are explained;

[0150] Under the constraints of preset total water volume limits per unit area and preset pump power limits, an optimization algorithm is used to iteratively solve for the minimum function value of the objective function. The constraints are the boundary conditions of the optimization solution. The preset total water volume limit per unit area, for example, means that the total water consumption per unit area within this irrigation cycle must not exceed [a certain limit]. And preset pump power limits, such as the maximum water delivery rate of the irrigation system must not exceed These are the necessary real-world conditions that must be met. The preset total water volume limit per unit area is determined based on local water resource allocation quotas, and the preset pump set power limit is determined based on the rated power of the irrigation equipment. The optimal solution is found through iterative optimization algorithms. The process; due to the optimization objective function In It requires complex model simulations to obtain. This is typically a complex, nonlinear function; therefore, this embodiment employs heuristic optimization algorithms, such as genetic algorithms, simulated annealing algorithms, or particle swarm optimization algorithms, to iteratively solve the problem. The minimum function value;

[0151] The solution process is as follows: the optimization algorithm generates a set or a candidate irrigation strategy. For each candidate strategy The system converts it into irrigation rate. As a preceding model Input, run the complete pre-simulation process, and calculate the strategy. Corresponding future cumulative salt return risk index And calculate the strategy simultaneously. Corresponding total water consumption per unit area ; In examining the strategy Meet the constraints set in this step, for example Then, according to the preceding formula Calculate its overall cost ;Optimize the algorithm and then based on Value, iterative adjustment strategy This generates new candidate strategies, and the process is repeated until a strategy is found that makes the strategy effective. The minimum strategy; the irrigation strategy corresponding to the minimum function value is determined as the optimal irrigation strategy. The optimal irrigation strategy That is, after the above iterative solution process converges, the one found that enables... An irrigation strategy that yields the minimum function value while satisfying all constraints. ;

[0152] Among them, the optimal irrigation strategy This includes irrigation time, total irrigation depth, and irrigation method; it is a specific implementation prescription, for example: the irrigation time is set 72 hours from now; the total irrigation depth is set to... =15mm, duration =4 hours; Irrigation mode set to start. The district's drip irrigation system; this strategy, when fed back to the model, will be converted into instantaneous irrigation rates as described above. .

[0153] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for irrigating saline-alkali land based on straw adsorption and water regulation, characterized in that, The specific steps include: S1. Obtain multi-depth soil parameters and surface meteorological data of the irrigation area, and establish a coupled model of soil water and salt dynamic transport; S2. Combining multi-depth soil parameters and surface meteorological data, the vertical water flux and topsoil net salt flux are calculated using a model; and based on the topsoil net salt flux, the cumulative salt return risk index is calculated to obtain the salt return risk level. S3. Construct a straw decomposition index using soil parameters at multiple depths; Based on the straw decomposition index, the interlayer capillary transport coefficient and hydrodynamic dispersion coefficient of the model are recursively corrected to update the coupled model of soil water and salt dynamic transport. S4. In response to the high risk level of salt return, construct an optimization objective function; solve the optimization objective function to obtain the optimal irrigation strategy; output the optimal irrigation strategy to the irrigation control system and feed it back to the model as the irrigation rate; In S3, the straw decomposition index is constructed, including: S31. Preset the minimum and optimal temperature and humidity thresholds required for microbial degradation activities; S32. Based on the soil temperature and temperature threshold of the cultivated layer in the multi-depth soil parameters, and combined with the preset temperature weighting coefficient, calculate the temperature influence term. S33. Based on the soil moisture content and humidity threshold of the topsoil layer in the multi-depth soil parameters, and combined with the preset humidity weighting coefficient, calculate the humidity influence item. S34. Add the temperature effect item and the humidity effect item to obtain the straw decomposition index; Straw decomposition index The construction model is as follows: ； in, The straw decomposition index is dimensionless. For the temperature of the topsoil, The soil moisture content in the topsoil layer was collected in real time by sensors. The preset threshold; These are preset weighting coefficients; In S3, the interlayer capillary transport coefficient and hydrodynamic dispersion coefficient of the model are recursively corrected, including: S35. Based on the straw decomposition index and the preset capillary inhibition rate constant, calculate the corrected interlayer capillary transport coefficient. S36. Based on the straw decomposition index and the preset pore evolution rate constant, calculate the corrected hydrodynamic dispersion coefficient; Interlayer capillary transport coefficient The calculation uses the following recursive update rule: ； This model aims to simulate the blocking effect of straw decomposition on capillary connections; in, The revised parameters will be used for the calculation of the preceding model in the next time step; These are the old parameters from the previous time step; The straw decomposition index is derived from previous calculations. The time interval for recursive parameter correction must have the same dimensions as... Dimensional matching, this time interval Its dimensions are .

2. The method for irrigating saline-alkali land based on straw adsorption and water regulation according to claim 1, characterized in that, In S2, the vertical water flux and net salt flux of the cultivated layer are calculated, including: S11. Salt concentration is obtained by converting the soil electrical conductivity of multiple depths from the soil parameters of multiple depths. S12. Based on the principle of water balance, combined with the soil moisture content at multiple depths in the soil parameters, the evaporation and irrigation rate in the surface meteorological data, and the interlayer capillary transport coefficient preset in the model, the vertical water flux is calculated. S13. Based on the convection-dispersion equation of solute transport, combined with vertical water flux, salt concentration, and the hydrodynamic dispersion coefficient preset in the model, calculate the net salt flux of the cultivated layer.

3. The method for irrigating saline-alkali land based on straw adsorption and water regulation according to claim 1, characterized in that, In S2, the cumulative salt return risk index is calculated, including: S21. Set the prediction period and time step; S22. The cumulative salt flux of the cultivated layer at each time step within the prediction period is summed to obtain the cumulative salt return risk index.

4. The method for irrigating saline-alkali land based on straw adsorption and water regulation according to claim 3, characterized in that, In S2, obtaining the risk level of salt return also includes: S23, preset crop tolerance thresholds and regulatory intervention thresholds; S24. When the cumulative salt return risk index is less than or equal to the crop tolerance threshold, the salt return risk level is determined to be low risk, and the current control strategy is maintained. S25. When the cumulative salt return risk index is greater than the crop tolerance threshold and less than or equal to the regulation intervention threshold, the salt return risk level is determined to be medium risk, and an early warning signal is issued. S26. When the cumulative salt return risk index is greater than the regulatory intervention threshold, the salt return risk level is determined to be high risk.

5. The method for irrigating saline-alkali land based on straw adsorption and water regulation according to claim 3, characterized in that, In S4, the objective function for optimization is constructed, including: S41. Calculate the risk cost based on the cumulative salt return risk index and the preset risk cost weight; S42. Calculate the water resource cost based on the total water consumption of the irrigation strategy and the preset water resource cost weight; S43. Perform a linear weighted summation of the risk cost and the water resource cost to construct an optimization objective function.

6. The method for irrigating saline-alkali land based on straw adsorption and water regulation according to claim 5, characterized in that, In S4, the objective function is solved to obtain the optimal irrigation strategy, and the following steps are also included: S44. Under the constraints of preset total water volume limit and preset pump power limit, the minimum function value of the optimization objective function is solved iteratively using an optimization algorithm. S45. Determine the irrigation strategy corresponding to the minimum function value as the optimal irrigation strategy; The optimal irrigation strategy includes irrigation time, total irrigation depth, and irrigation method.

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