A method for calculating soil infiltration in coastal saline-alkali land based on temperature tracing

Abstract

This invention discloses a method for calculating soil infiltration in coastal saline-alkali land based on temperature tracing. It acquires soil temperature, moisture content, water level, and soil property parameters for different soil layers. For the target depth, a heat transfer model considering soil salinity is established, and the initial thermal parameters and boundary conditions are estimated. The model domain is discretized using a finite difference scheme to determine the total simulation period and divide it into stress periods, each stress period being discretized into multiple time nodes. The temperature and moisture content at the target depth are predicted, and a loss function is established by minimizing the sum of squared residuals between the measured and calculated temperature values. An adaptive optimization algorithm is used to iteratively correct the thermal conductivity and water flux within each stress period. The optimized parameters of the previous stress period are used as initial conditions for subsequent periods to achieve full-cycle recursive calculation. Finally, the water flux at the target depth is obtained through a convergence criterion as a quantitative characterization of the soil infiltration rate.

Description

Technical Field

[0001] This invention belongs to the interdisciplinary field of environmental monitoring technology and soil hydrology, specifically involving a method for calculating soil infiltration in coastal saline-alkali land based on temperature tracing. Background Technology

[0002] Coastal saline-alkali lands have complex soil structures due to high salinity, shallow water tables, and seawater intrusion. The water infiltration process is significantly affected by soil moisture levels, salinity, and soil porosity, making it difficult to accurately quantify the impact of salinization on infiltration in saturated and unsaturated zones using traditional methods. Currently, 1) traditional soil moisture infiltration measurement instruments face the risk of sensor failure in saline-alkali land applications: devices such as tensiometers based on electrochemical principles are prone to data distortion in saline-alkali environments due to electrode corrosion or salt crystallization clogging pores; 2) contact measurement methods such as the ring sampler method have limitations due to scale effects: the uneven distribution of soil porosity and salinity in saline-alkali land makes local measurements difficult to reflect overall characteristics; 3) sampling methods lack dynamic monitoring: laboratory centrifugation methods require destructive sampling, making it impossible to obtain in-situ continuous data. Temperature tracing is a technique for indirectly deriving soil moisture movement parameters, its core principle being the monitoring of changes in the soil temperature field. This method possesses several significant advantages: firstly, it does not require disruption of the original soil structure, making it suitable for long-term dynamic monitoring scenarios; secondly, relying on high-precision temperature sensors, it can accurately capture minute differences in heat conduction; and thirdly, the temperature signal is less affected by soil salinity, maintaining good applicability even in soil environments with high salinity. However, existing technologies still have shortcomings: the influence of salinity is not considered in the hydrothermal coupling inversion mechanism; salinity type, concentration, and salinity density flow all affect the hydrothermal coupling inversion results.

[0003] Therefore, how to deduce the soil infiltration of coastal saline-alkali land using data such as soil temperature, soil moisture content, water level, soil property parameters, and soil salinity is the key problem that this invention aims to solve. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides a method for calculating soil infiltration in coastal saline-alkali land based on temperature tracing. This method acquires soil temperature data, soil moisture content data, water level data, and soil property parameters for different soil layers. Based on the soil heat transfer equation, it optimizes the thermal conductivity and water flux to fit the internal soil temperature, and uses the optimized water flux as the soil infiltration rate.

[0005] To achieve the above objectives, the present invention provides the following solution:

[0006] A method for calculating soil infiltration in coastal saline-alkali land based on temperature tracing, the method comprising:

[0007] Acquire soil temperature data, soil moisture content data, water level data, soil property parameters, and meteorological data for the surrounding time period for different soil layers within the study area;

[0008] For the target depth, a one-dimensional soil heat transfer model is established, the initial thermal parameters of the one-dimensional soil heat transfer model are estimated, and the boundary conditions and initial field are determined.

[0009] The model domain is discretized using a finite difference scheme to determine the total simulation period and to divide it into stress periods, with each stress period discretized into multiple time nodes.

[0010] Predict the temperature and water content at the target depth by establishing a loss function that minimizes the sum of squared residuals between the measured and calculated temperature values.

[0011] An adaptive optimization algorithm is used to iteratively correct the thermal conductivity and water flux in each stress cycle.

[0012] The optimized parameters of the previous stress cycle are used as the initial conditions for the subsequent cycles to achieve full-cycle recursive calculation.

[0013] The water flux at the target depth is obtained through a convergence criterion, serving as a quantitative characterization of the soil infiltration rate.

[0014] Preferably, determining the initial field includes thermal conductivity. and water flux ;

[0015] Among them, thermal conductivity The initial estimation method was determined based on measured soil temperature data, soil moisture content data, water level data, soil property parameters, and soil salinity data. The formula is as follows:

[0016]

[0017] Where A, B, and C are parameters determined experimentally, D is a parameter related to salinity, and S represents the salt concentration;

[0018] water flux The initial estimation method is determined based on measured meteorological data, soil temperature data, soil moisture content data, water level data, and soil property parameters. The formula is as follows:

[0019]

[0020] in Obtained through empirical formulas or by using models, where For matrix potential difference, For depth difference, For rainfall, This represents the amount of evaporation.

[0021] Preferably, the model domain is discretized, including: discretizing the target segment into multiple spatial nodes, determining the total simulation period, and dividing the stress period into multiple time nodes.

[0022] Discretizing the target segment into multiple spatial nodes involves labeling the temperature sensors vertically from top to bottom as 1, 2, 3… (Assuming the three temperature sensors are numbered…) , , The sensors are continuously distributed along the soil profile underground, and are numbered to calculate the target depth, i.e., the temperature sensor. Soil infiltration at the specified depth will affect the temperature sensor. and Discretize the one-dimensional vertical region Z between them There are 10 nodes; the method for calculating the number of nodes N is as follows:

[0023]

[0024] In the formula: Temperature sensor and The distance between them Size of the spatial node;

[0025] The discrete stress periodic process is as follows: the entire simulation period P is divided into three parts based on the flow conditions. p There are n stress cycles, one of which has a length of Q, and these cycles are further discretized into n time steps, where:

[0026]

[0027] In the formula: Δt is the time step.

[0028] Preferably, the loss function is established, including:

[0029]

[0030] In the formula: , These are weighting coefficients; This is a calculated temperature value; This is a temperature measurement value; This is the calculated value for thermal conductivity; This is the thermal conductivity value optimized for the previous stress cycle.

[0031] The preferred method for predicting the temperature at the target depth is as follows:

[0032]

[0033] In the formula: Let be the spatiotemporal temperature distribution at any spatial node i and time node j;

[0034] in, The calculation method is as follows:

[0035]

[0036] In the formula: , For spatial nodes , Thermal conductivity at that point The volumetric heat capacity of moist soil. This is the volumetric heat capacity of liquid water. Let be the volumetric heat capacity of water vapor. For liquid water flux, For water vapor flux;

[0037] The calculation method is as follows:

[0038] ;

[0039] The calculation method is as follows:

[0040] ;

[0041] The calculation method is as follows:

[0042] .

[0043] Preferably, the optimal thermal conductivity within each stress cycle and water flux , is the loss function Thermal conductivity at which it reaches its minimum value in region Z and water flux .

[0044] Preferably, adaptive optimization algorithms include L-BFGS-B, PSO, Nelder-Mead, and TNC optimization algorithms.

[0045] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0046] This invention discloses a method for calculating soil infiltration in coastal saline-alkali land based on temperature tracer method, comprising the following steps: acquiring soil temperature data, soil moisture content data, water level data, and soil property parameters of different soil layers; establishing a heat transfer equation considering soil salinity for the target depth, estimating the initial thermal parameters of the model, and determining the boundary conditions; discretizing the model domain using a finite difference scheme, determining the total simulation period, and dividing it into stress periods, with each stress period discretized into multiple time nodes; predicting the temperature and moisture content at the target depth, and establishing a loss function by minimizing the sum of squared residuals between the measured and calculated temperature values; iteratively correcting the thermal conductivity and water flux in each stress period using an adaptive optimization algorithm; using the optimized parameters of the previous stress period as the initial conditions for subsequent periods to achieve full-cycle recursive calculation; and finally obtaining the water flux at the target depth through a convergence criterion as a quantitative characterization of the soil infiltration rate. The technical solution provided by this invention overcomes the limitations of traditional methods, significantly improves the accuracy and reliability of data, provides reliable technical support for in-depth research on the water and salt transport patterns in saline-alkali land, and can provide a scientific basis for coastal ecological restoration and soil improvement projects. Attached Figure Description

[0047] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a schematic diagram of a method for calculating soil infiltration in coastal saline-alkali land based on temperature tracing, according to an embodiment of the present invention.

[0049] Figure 2 This is a comparison chart showing the effect of the optimized L-BFGS-B optimization algorithm in this embodiment of the invention, which yields predicted temperature and measured temperature data.

[0050] Figure 3 This is a comparison chart showing the effect of the optimized PSO optimization algorithm in this embodiment of the invention, which yields predicted temperature and measured temperature data.

[0051] Figure 4 This is a comparison chart showing the effect of the optimized Nelder-Mead optimization algorithm in this embodiment of the invention, resulting in predicted temperature and measured temperature data. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0054] Example 1

[0055] like Figure 1 As shown in the figure, this embodiment provides a method for calculating soil infiltration in coastal saline-alkali land based on the temperature tracing method. The method includes:

[0056] Acquire soil temperature data, soil moisture content data, water level data, soil property parameters, and meteorological data for the surrounding time period for different soil layers within the study area;

[0057] For the target depth, a one-dimensional soil heat transfer model is established, the initial thermal parameters of the one-dimensional soil heat transfer model are estimated, and the boundary conditions and initial field are determined.

[0058] The model domain is discretized using a finite difference scheme to determine the total simulation period and to divide it into stress periods, with each stress period discretized into multiple time nodes.

[0059] Predict the temperature and water content at the target depth by establishing a loss function that minimizes the sum of squared residuals between the measured and calculated temperature values.

[0060] An adaptive optimization algorithm is used to iteratively correct the thermal conductivity and water flux in each stress cycle.

[0061] The optimized parameters of the previous stress cycle are used as the initial conditions for the subsequent cycles to achieve full-cycle recursive calculation.

[0062] The water flux at the target depth is obtained through a convergence criterion, serving as a quantitative characterization of the soil infiltration rate.

[0063] The specific implementation process is as follows: Step 1, Obtain basic data; the basic data includes soil temperature data, soil moisture content data, water level data, and soil property parameter data for different soil layers within the study area, as well as meteorological data for the surrounding time period; the soil temperature data for different soil layers is time-varying data of soil temperature for different soil layers within the same time period; the water level data is time-varying data of water level elevation; the soil property parameter data includes soil salinity, soil type, and, where conditions permit, soil porosity and soil bulk density; the meteorological data for the surrounding time period includes atmospheric temperature, humidity, air pressure, and rainfall, etc.

[0064] Step 2: Construct a one-dimensional soil heat transfer model considering soil salinity. This one-dimensional soil heat transfer model first requires determining the boundary conditions and initial field for a given target depth, and estimating the initial thermal parameters of the model. The boundary conditions are, for a given target depth, assuming the sensor location, inputting the time-varying temperatures of two adjacent depths as boundary conditions. Determining the initial thermal parameters of the model includes the volumetric heat capacity of the solid. Volumetric heat capacity of liquid water Volumetric heat capacity of water vapor Volumetric heat capacity of moist soil The determination of the initial field includes thermal conductivity. and water flux The one-dimensional soil heat transport model considering soil salinity is as follows:

[0065]

[0066]

[0067]

[0068]

[0069] Where S represents the salt concentration. This represents the specific heat capacity correction factor related to salt concentration. , , , It is a coefficient determined through experiments, which can be used to estimate the salinity of seawater at room temperature with a salinity range of 30–40 ppt. , , , .

[0070] Step 3: Finite difference scheme of heat transfer model; The finite difference scheme of heat transfer model is to discretize the model domain; The discretization steps include discretizing the target segment into multiple spatial nodes, determining the total simulation period, and dividing the stress period, with each stress period discretized into multiple time nodes.

[0071] Step 4: Determine if the stress period meets the requirements; the stress period meets the requirements if it is greater than or equal to the total number of preset discretization stress periods; if the requirements are met, the calculation ends directly, and the final optimized result of the water flux is obtained. The soil infiltration depth is taken as the target depth; if the target depth is not met, proceed to step five.

[0072] Step 5: Predict the time-varying temperature sequence under stress period The sum of squared residuals between the measured and calculated temperature values ​​is used as a loss function to estimate the difference between the measured and calculated values.

[0073] Step 6: Determine if the loss function meets the requirements; if the requirements are met, then the thermal conductivity under that stress period is... and water flux As the initial field for the next stress cycle, return to step four; if the judgment does not meet the requirements, proceed to step seven.

[0074] Step 7: Select an optimization algorithm; different optimization algorithms can be selected as needed to optimize the thermal conductivity in each stress cycle. and water flux ;

[0075] Step 8: Optimize the thermal conductivity and water flux This serves as the initial input for the stress cycle and proceeds to step five.

[0076] In this embodiment, the volumetric heat capacity of the solid Volumetric heat capacity of liquid water Volumetric heat capacity of water vapor Volumetric heat capacity of moist soil It remains constant throughout the calculation period, but changes over time.

[0077] In this embodiment, determining the initial field includes thermal conductivity. and water flux ;

[0078] Among them, thermal conductivity The initial estimation method is determined based on measured soil temperature data, soil moisture content data, water level data, soil property parameters, and soil salinity data, and the formula is as follows:

[0079]

[0080] Where A, B, and C are parameters determined experimentally, D is a parameter related to salinity, and S represents the salt concentration;

[0081] water flux The initial estimation method is determined based on measured meteorological data, soil temperature data, soil moisture content data, water level data, and soil property parameters. The formula can be:

[0082]

[0083] in It can be obtained through empirical formulas or by using models, where For matrix potential difference, For depth difference, For rainfall, This represents the amount of evaporation.

[0084] In this embodiment, the model domain is discretized, including: discretizing the target segment into multiple spatial nodes, determining the total simulation period, and dividing the stress period into multiple time nodes.

[0085] Discretizing the target segment into multiple spatial nodes involves labeling the temperature sensors vertically from top to bottom as 1, 2, 3… (Assuming the three temperature sensors are numbered…) , , The sensors are continuously distributed along the soil profile underground, and are numbered to calculate the target depth, i.e., the temperature sensor. Soil infiltration at the specified depth will affect the temperature sensor. and Discretize the one-dimensional vertical region Z between them There are 10 nodes; the method for calculating the number of nodes N is as follows:

[0086]

[0087] In the formula: Temperature sensor and The distance between them Size of the spatial node;

[0088] The discrete stress periodic process is as follows: the entire simulation period P is divided into three parts based on the flow conditions. p There are n stress cycles, one of which has a length of Q, and these cycles are further discretized into n time steps, where:

[0089]

[0090] In the formula: Δt is the time step.

[0091] In this embodiment, the loss function is established, including:

[0092]

[0093] In the formula: , These are weighting coefficients; This is a calculated temperature value; This is a temperature measurement value; This is the calculated value for thermal conductivity; This is the optimized thermal conductivity value for the previous stress cycle. Weighting coefficients. .

[0094] In this embodiment, the method for predicting the temperature at the target depth is as follows:

[0095]

[0096] In the formula: Let be the spatiotemporal temperature distribution at any spatial node i and time node j;

[0097] in, The calculation method is as follows:

[0098]

[0099] In the formula: , For spatial nodes , Thermal conductivity at that point The volumetric heat capacity of moist soil. This is the volumetric heat capacity of liquid water. Let be the volumetric heat capacity of water vapor. For liquid water flux, For water vapor flux;

[0100] The calculation method is as follows:

[0101] ;

[0102] The calculation method is as follows:

[0103] ;

[0104] The calculation method is as follows:

[0105] .

[0106] Furthermore, the prediction method is to discretize the heat transfer equation considering soil salinity using the Crank-Nicolson difference method:

[0107]

[0108] In this embodiment, the optimal thermal conductivity within each stress cycle and water flux , is the loss function Thermal conductivity at which it reaches its minimum value in region Z and water flux .

[0109] In this embodiment, the adaptive optimization algorithm includes L-BFGS-B, PSO, Nelder-Mead, and TNC optimization algorithms.

[0110] The four optimization algorithms—L-BFGS-B, PSO, Nelder-Mead, and TNC—each have their own advantages and disadvantages, and are applicable to different scenarios. L-BFGS-B is suitable for large-scale bounded constraint optimization, relies on gradient information, has high memory efficiency, and is suitable for smooth problems such as machine learning. PSO (Particle Swarm Optimization) is a gradient-free global optimizer that explores complex non-convex, multimodal problems through swarm intelligence, making it suitable for high-dimensional black-box optimization. Nelder-Mead (Simplified Method) is designed for low-dimensional non-differentiable problems, requires only function values, is simple to use, but is limited to local search. TNC (Truncation Newton's Method) combines Newton's method with constraint handling, is suitable for small- to medium-scale high-precision bounded optimization, requires gradients, and has a fast convergence speed. Table 1 below provides a comparative overview of the four algorithms.

[0111] Table 1

[0112]

[0113] Example 2

[0114] The present invention also provides an experimental device for calculating soil infiltration in coastal saline-alkali land based on temperature tracing method. The system is used to implement the method of Example 1 and includes: an experimental soil column, a paperless recorder, and a constant temperature heating water tank.

[0115] The experimental soil column is a tubular shell, with six temperature sensors and six soil moisture sensors evenly spaced on both sides. Both temperature and soil moisture sensors are connected to a paperless recorder via data cables, allowing for direct data reading and convenient computer processing. The temperature and soil moisture of different layers of the soil filling layer are measured using the temperature and soil moisture sensors.

[0116] In practical application, the experimental setup is as follows: Start the constant-temperature heating water tank, turn on the peristaltic pump, and under constant power, open the valve to allow water to flow at a constant speed through the hose into the soil filling layer, immersing the soil filling layer for 8 hours to maintain a stable soil moisture content. Real-time observation and recording can be performed using a paperless recorder. Turn on the heating rod in the constant-temperature heating water tank, setting multiple temperature gradients according to experimental requirements to maintain a stable temperature field. This ensures the water flows evenly into the soil layer.

[0117] Step 1: Obtain basic data. This basic data includes soil temperature data, soil moisture content data, water level data, and soil property parameter data for different soil layers within the soil column, as well as environmental data from the laboratory. The soil temperature data for different soil layers is time-varying data for the same soil layer over the same period. The water level data includes water level elevation and time-varying data. All of these data are monitored and recorded in real-time using a paperless recorder. Soil property parameter data is measured before the experiment begins, including soil salinity, soil type, soil porosity, and soil bulk density. Environmental data from the surrounding area includes atmospheric temperature, humidity, air pressure, and rainfall.

[0118] Step 2: Construct a one-dimensional soil heat transfer model. The one-dimensional soil heat transfer model first requires determining the boundary conditions, initial field, and estimated initial thermal parameters for a given target depth (in this embodiment, the soil depth is selected as 20cm from the soil surface). The boundary conditions and sensor locations are determined by using the time-varying temperatures recorded by sensors at two adjacent depths as boundary conditions. The initial thermal parameters of the model include the volumetric heat capacity of the solid. Volumetric heat capacity of liquid water Volumetric heat capacity of water vapor Volumetric heat capacity of moist soil The thermal conductivity was determined based on the initial laboratory environment data and the preset experimental setup. and water flux ;

[0119] Step 3: Finite difference scheme for the heat transfer model; The temperature sensors will be labeled 1, 2, 3… from top to bottom along the vertical direction. Assume the three temperature sensors are numbered… , The temperature sensors are numbered and continuously distributed underground along the soil profile. Soil infiltration at the specified depth will affect the temperature sensor. and Discretize the one-dimensional vertical region Z between them The method for calculating the number of nodes N is as follows: The entire simulation period P is divided into several parts based on the flow conditions. pThere are n stress cycles, one of which has a length of Q, and these cycles are further discretized into n time steps, where: .

[0120] Step 4: Determine if the stress period meets the requirements; the stress period meets the requirements if it is greater than or equal to the total number of preset discretization stress periods; if the requirements are met, the calculation ends directly, and the final optimized result of the water flux is obtained. The soil infiltration depth is taken as the target depth; if the target depth is not met, proceed to step five.

[0121] Step 5: Predict the time-varying temperature sequence under stress period And adopt the loss equation

[0122]

[0123] To estimate the difference between the measured and calculated values;

[0124] Step 6: Determine if the loss function meets the requirements; if it does, then determine the thermal conductivity under that stress period. and water flux As the initial field for the next stress cycle, return to step four; if the requirements are not met, proceed to step seven.

[0125] Step 7: Select an optimization algorithm; different optimization algorithms can be selected as needed. In this embodiment, the L-BFGS-B, PSO, and Nelder-Mead optimization algorithms are used to optimize the thermal conductivity in each stress cycle, respectively. and water flux ;

[0126] Step 8: Optimize the thermal conductivity and water flux This serves as the initial input for the stress cycle and proceeds to step five.

[0127] Figures 2 to 4 The figures show a comparison of the predicted and measured temperature data obtained after optimization using the L-BFGS-B, PSO, and Nelder-Mead optimization algorithms in this embodiment of the invention. The basic setup for this example is as follows: a tubular shell, 55cm high, 15cm inner diameter, and 20cm outer diameter, is filled from bottom to top. Six temperature sensors and six soil moisture sensors, spaced 5cm apart, are installed on both sides of the tubular shell. The first temperature sensor and the soil moisture sensor are both 5cm from the soil surface and connected to a paperless recorder via a data cable. A single type of soil is used in the experiment to simplify the experimental soil conditions.

[0128] Table 2 shows the results obtained using the method proposed in this invention. It can be seen that the optimized flow velocity is close to the actual flow velocity and has a smaller RMSE. In summary, this demonstrates that the method proposed in this invention for calculating soil infiltration in coastal saline-alkali land based on the temperature tracer method is effective.

[0129] Table 2

[0130]

[0131] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A temperature-tracing method-based coastal saline-alkali soil infiltration calculation method, characterized in that, The method includes: Acquire soil temperature data, soil moisture content data, water level data, soil property parameters, and meteorological data for the surrounding time period for different soil layers within the study area; For the target depth, a one-dimensional soil heat transfer model is established, the initial thermal parameters of the one-dimensional soil heat transfer model are estimated, and the boundary conditions and initial field are determined. The model domain is discretized using a finite difference scheme to determine the total simulation period and to divide it into stress periods, with each stress period discretized into multiple time nodes. Predict the temperature and water content at the target depth by establishing a loss function that minimizes the sum of squared residuals between the measured and calculated temperature values. An adaptive optimization algorithm is used to iteratively correct the thermal conductivity and water flux in each stress cycle. The optimized parameters of the previous stress cycle are used as the initial conditions for the subsequent cycles to achieve full-cycle recursive calculation. The water flux at the target depth is obtained through the convergence criterion, serving as a quantitative characterization of the soil infiltration rate. Discretizing the model domain includes: discretizing the target segment into multiple spatial nodes, determining the total simulation period, and dividing it into stress periods, with each stress period discretized into multiple time nodes. Discretizing the target segment into multiple spatial nodes involves labeling the temperature sensors vertically from top to bottom as 1, 2, 3… (Assuming the three temperature sensors are numbered…) , , The sensors are continuously distributed along the soil profile underground, and are numbered to calculate the target depth, i.e., the temperature sensor. Soil infiltration at the specified depth will affect the temperature sensor. and Discretize the one-dimensional vertical region Z between them There are 1,000 nodes; the method for calculating the number of nodes N is as follows: In the formula: Temperature sensor and The distance between them Size of the spatial node; The discrete stress periodic process is as follows: the entire simulation period P is divided into three parts based on the flow conditions. p There are n stress cycles, one of which has a length of Q, and these cycles are further discretized into n time steps, where: In the formula: Δt is the time step; The method for predicting temperature at the target depth is as follows: In the formula: Let be the spatiotemporal temperature distribution at any spatial node i and time node j; in, The calculation method is as follows: In the formula: , For spatial nodes , Thermal conductivity at that point The volumetric heat capacity of moist soil. This is the volumetric heat capacity of liquid water. Let be the volumetric heat capacity of water vapor. For liquid water flux, For water vapor flux; The calculation method is as follows: ； The calculation method is as follows: ； The calculation method is as follows: 。 2. The method according to claim 1, characterized in that, Determining the initial field includes thermal conductivity and water flux ; Among them, thermal conductivity The initial estimation method was determined based on measured soil temperature data, soil moisture content data, water level data, soil property parameters, and soil salinity data. The formula is as follows: Where A, B, and C are parameters determined experimentally, D is a parameter related to salinity, and S represents the salt concentration; water flux The initial estimation method is determined based on measured meteorological data, soil temperature data, soil moisture content data, water level data, and soil property parameters. The formula is as follows: in Obtained through empirical formulas or by using models, where For matrix potential difference, For depth difference, For rainfall, This represents the amount of evaporation.

3. The method according to claim 1, characterized in that, Establish the loss function, including: In the formula: , These are weighting coefficients; This is a calculated temperature value; This is a temperature measurement value; This is the calculated value for thermal conductivity; This is the thermal conductivity value optimized for the previous stress cycle.

4. The method according to claim 1, characterized in that, Optimal thermal conductivity within each stress cycle and water flux , is the loss function Thermal conductivity at which it reaches its minimum value in region Z and water flux .

5. The method according to claim 1, characterized in that, Adaptive optimization algorithms include L-BFGS-B, PSO, Nelder-Mead, and TNC optimization algorithms.

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