Coastal saline-alkali soil infiltration calculation method based on temperature tracing method

Abstract

The invention discloses a coastal saline-alkali soil infiltration calculation method based on a temperature tracing method. Acquiring data such as soil temperature data, soil water content data, water level data and soil property parameters of different soil layers; aiming at the target depth, establishing a heat transfer model considering the soil salinity, estimating initial heat parameters of the model and determining boundary conditions; discretizing the model domain by using a finite difference format, determining a total simulation period, dividing stress periods, and discretizing each stress period into a plurality of time nodes; predicting the temperature and the water content of the target depth, and establishing a loss function by minimizing the residual sum of squares of the measured value and the calculated value of the temperature; iteratively correcting the heat conductivity coefficient and the water flux in each stress period by adopting a self-adaptive optimization algorithm; the optimization parameter of the previous stress period is used as the initial condition of the subsequent period, and full-period recursive calculation is achieved; finally, the water flux of the target depth is obtained through convergence criteria and serves as quantitative characterization of the soil infiltration rate.

Description

Technical Field

[0001] The present invention belongs to the intersecting field of environmental monitoring technology and soil hydrology, and particularly relates to a method for calculating soil infiltration in coastal saline-alkali land based on a temperature tracing method. Background Art

[0002] Due to factors such as high salt content, shallow groundwater levels, and seawater intrusion, coastal saline-alkali lands have complex soil structures. The water infiltration process is significantly affected by the degree of soil dryness and wetness, salt concentration, and soil porosity. Traditional methods have difficulty accurately quantifying the impact of salinization on infiltration in the saturated-unsaturated zone. Currently, 1) traditional soil water infiltration measuring instruments are at risk of sensor failure when used in saline-alkali lands: devices such as tensiometers based on electrochemical principles are prone to data distortion due to electrode corrosion or salt crystallization blocking pores in saline-alkali environments; 2) contact measurement methods such as the ring knife method are limited by scale effects: the porosity and salt distribution of saline-alkali soils are uneven, and local measurements cannot reflect the overall characteristics; 3) sampling methods lack dynamic monitoring: laboratory centrifugation methods require destructive sampling and cannot obtain in-situ continuous data. The temperature tracer method is a technical means of indirectly deriving soil water movement parameters. Its core principle is to achieve this by monitoring changes in the soil temperature field. This method offers several significant advantages: First, it does not require disrupting the original soil structure, making it suitable for long-term dynamic monitoring scenarios; second, it relies on high-precision temperature sensors to accurately capture minute differences in heat conduction; and third, the temperature signal is less affected by soil salinity, maintaining good applicability even in highly salinized soil environments. However, existing technologies still have shortcomings: the impact of salt is not considered in the hydrothermal coupling inversion mechanism. Salt type, concentration, and salinity density flow can affect the hydrothermal coupling inversion effect.

[0003] Therefore, how to infer the soil infiltration of coastal saline-alkali land through soil temperature data, soil moisture data, water level data, soil property parameters and soil salinity data is the problem that the present invention focuses on solving. Summary of the Invention

[0004] To solve the problems existing in the prior art, the present invention provides a method for calculating soil infiltration in coastal saline-alkali land based on the temperature tracing method. The method obtains soil temperature data, soil moisture data, water level data, and soil property parameters of different soil layers. The thermal conductivity and water flux are optimized according to the soil heat transfer equation to fit the internal temperature of the soil, and the optimized water flux is used as the soil infiltration.

[0005] To achieve the above object, the present invention provides the following solutions:

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

[0007] Obtain soil temperature data, soil moisture data, water level data, soil property parameters and meteorological data of different soil layers in 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 fields are determined;

[0009] The model domain is discretized using the finite difference scheme to determine the total simulation period and divide it into stress cycles. Each stress cycle is discretized into multiple time nodes.

[0010] Predict the temperature and water content at the target depth and establish a loss function by minimizing the sum of squares of the residuals between the measured and calculated temperature values;

[0011] Adaptive optimization algorithm is used to iteratively correct thermal conductivity and water flux in each stress cycle;

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

[0013] The water flux at the target depth is obtained by the convergence criterion as a quantitative representation of the soil infiltration rate.

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

[0015] The initial estimation method of thermal conductivity λ is determined based on measured soil temperature data, soil moisture data, water level data, soil property parameters, and soil salinity data. The formula is:

[0016] λ=(A+Bθ+Cρ)*DS

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

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

[0019]

[0020] where k(θ) can be obtained through empirical formulas or using models, Δψ is the matrix potential difference, Δz is the depth difference, P is the rainfall, and ET is the evaporation.

[0021] Preferably, the model domain is discretized, including: discretizing the target segment into a plurality of spatial nodes, determining a total simulation period, and dividing the period into stress periods, and discretizing each stress period into a plurality of time nodes;

[0022] The target segment is discretized into multiple spatial nodes as follows: temperature sensors are labeled 1, 2, 3, etc. from top to bottom along the vertical direction. Assuming that three temperature sensors numbered s-1, s, and s+1 are continuously distributed underground along the soil profile, to calculate the soil infiltration at the target depth, i.e., the depth where temperature sensor number s is located, the one-dimensional vertical region Z between temperature sensors s-1 and s+1 is discretized into N nodes. The number of nodes N is calculated as follows:

[0023] N=Z / Δz

[0024] Where: Z is the distance between temperature sensors s-1 and s+1, Δz is the spatial node size;

[0025] The discrete stress cycle process is as follows: the entire simulation period P is divided into p stress cycles according to the flow conditions, where the length of one stress cycle is Q, and it is further discretized into n time steps, where:

[0026] n=Q / Δt

[0027] Where: Δt is the time step.

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

[0029]

[0030] Where: a1, a2 are weighting coefficients; is the calculated value of temperature; is the temperature measurement value; i is the calculated value of thermal conductivity; Thermal conductivity value optimized for the last stress cycle.

[0031] Preferably, the temperature at the target depth is predicted by:

[0032]

[0033] Where: is the spatiotemporal temperature distribution at any spatial node i and time node j;

[0034] The calculation method of m1 is:

[0035]

[0036] Where: i-1 ,λ i is the thermal conductivity of spatial node i-1, i, C p is the volumetric heat capacity of moist soil, C w is the volume heat capacity of liquid water, C v is the volumetric heat capacity of water vapor, q lis the liquid water flux, q v is the water vapor flux;

[0037] The calculation method of m2 is:

[0038]

[0039] The calculation method of m3 is:

[0040]

[0041] The calculation method of m4 is:

[0042]

[0043] Preferably, the optimal thermal conductivity λ and water flux q in each stress cycle are the thermal conductivity λ and water flux q when the loss function L reaches a minimum value in the region Z.

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

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] The present invention discloses a method for calculating soil infiltration in coastal saline-alkali land based on a temperature tracer method, comprising the following steps: obtaining soil temperature data, soil moisture data, water level data, and soil property parameters of different soil layers; establishing a heat transfer equation taking soil salinity into account for a target depth, estimating the initial thermal parameters of the model, and determining boundary conditions; discretizing the model domain using a finite difference format to determine the total simulation period and divide it into stress cycles, with each stress cycle being discretized into multiple time nodes; predicting the temperature and moisture content at the target depth, and establishing a loss function by minimizing the residual sum of squares between the measured and calculated temperature values; iteratively correcting the thermal conductivity and water flux within each stress cycle using an adaptive optimization algorithm; using the optimized parameters of the previous stress cycle as the initial conditions for the subsequent cycle to achieve full-cycle recursive calculation; and finally obtaining the water flux at the target depth through a convergence criterion as a quantitative representation of the soil infiltration rate. The technical solution provided by the present invention breaks through 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 migration laws of saline-alkali land, and can provide a scientific basis for coastal ecological restoration and soil improvement projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

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

[0049] Figure 2 This is a comparison chart of the predicted temperature and measured temperature data obtained after optimization of the L-BFGS-B optimization algorithm in an embodiment of the present invention;

[0050] Figure 3 This is a comparison chart of the predicted temperature and measured temperature data obtained after the optimization of the PSO optimization algorithm according to the embodiment of the present invention;

[0051] Figure 4 This is a comparison chart of the predicted temperature and measured temperature data obtained after optimization of the Nelder-Mead optimization algorithm in an embodiment of the present invention. DETAILED DESCRIPTION

[0052] 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 the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0053] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0054] Example 1

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

[0056] Obtain soil temperature data, soil moisture data, water level data, soil property parameters and meteorological data of different soil layers in 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 fields are determined;

[0058] The model domain is discretized using the finite difference scheme to determine the total simulation period and divide it into stress cycles. Each stress cycle is discretized into multiple time nodes.

[0059] Predict the temperature and water content at the target depth and establish a loss function by minimizing the sum of squares of the residuals between the measured and calculated temperature values;

[0060] Adaptive optimization algorithm is used to iteratively correct thermal conductivity and water flux in each stress cycle;

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

[0062] The water flux at the target depth is obtained by the convergence criterion as a quantitative representation of the soil infiltration rate.

[0063] The specific implementation process is as follows: Step 1, obtaining basic data; the basic data include soil temperature data of different soil layers in the study area, soil moisture data of different soil layers, water level data and soil property parameter data and meteorological data in the surrounding time period; the soil temperature data of different soil layers is the time-varying data of soil temperature of different soil layers in the same time period; the water level data is the time-varying data of water level elevation; the soil property parameter data includes soil salinity, soil type, and may include soil porosity and soil bulk density if conditions permit; the meteorological data in 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. The one-dimensional soil heat transfer model first needs to determine the boundary conditions and initial field for a given target depth and estimate the initial thermal parameters of the model. The boundary conditions are to assume the position of the sensor for a given target depth and input the time-varying temperature of two adjacent depths as the boundary conditions. The initial thermal parameters of the model include the volume heat capacity C of the solid. n , the volume heat capacity of liquid water C w 、The volume heat capacity of water vapor C v and the volumetric heat capacity of moist soil C p The initial field determination includes thermal conductivity λ and water flux q; the one-dimensional soil heat transfer model considering soil salinity is as follows:

[0065]

[0066]

[0067] λ=(A+Bθ+Cρ)*DS

[0068] Where S represents the salt concentration, C' w , C′ vHere, a, b, c, and d represent the correction coefficients for specific heat capacity related to salt concentration. These coefficients are determined experimentally. For normal-temperature seawater with a salinity range of 30–40 ppt, a = 4186, b = 5.6, c = 1000, and d = 0.8, respectively.

[0069] Step 3: Finite difference format of the heat transfer model; the finite difference format of the heat transfer model is to discretize the model domain; the discretization step includes discretizing the target segment into multiple spatial nodes, determining the total simulation cycle, and dividing it into stress cycles, and each stress cycle is discretized into multiple time nodes.

[0070] Step 4: Determine whether the stress cycle meets the requirements; if the stress cycle meets the requirements, it means that the stress cycle is greater than or equal to the total number of stress cycles preset by the discretization; if the requirements are met, the calculation is terminated directly, and the water flux q of the final optimization result is used as the soil infiltration at the given target depth; if not, proceed to step 5;

[0071] Step 5: Predict the time-varying temperature series T under the stress cycle, and use the residual sum of squares between the measured and calculated temperature values ​​as a loss function to estimate the difference between the measured and calculated values;

[0072] Step 6: Determine whether the loss function meets the requirements; if so, use the thermal conductivity λ and water flux q under the stress cycle as the initial field for the next stress cycle, and return to step 4; if not, proceed to step 7;

[0073] Step 7: Select an optimization algorithm; the optimization algorithm may be selected from different optimization algorithms as needed to optimize the thermal conductivity λ and water flux q within each stress cycle;

[0074] Step 8: Use the optimized thermal conductivity λ and water flux q as the initial input of the stress cycle and proceed to step 5.

[0075] In this embodiment, the volumetric heat capacity of the solid C n , the volume heat capacity of liquid water C w 、The volume heat capacity of water vapor C v and the volumetric heat capacity of moist soil C p Remains constant throughout the calculation period and varies in time.

[0076] In this embodiment, the initial field is determined to include thermal conductivity λ and water flux q;

[0077] The initial estimation method of thermal conductivity λ is determined based on measured soil temperature data, soil moisture data, water level data, soil property parameters, and soil salinity data. The formula is:

[0078] λ=(A+Bθ+Cρ)*DS

[0079] Among them, A, B, and C are parameters determined by experiments, D is a parameter related to salinity, and S represents the salt concentration;

[0080] The initial estimation method of water flux q is determined based on measured meteorological data, soil temperature data, soil moisture data, water level data and soil property parameters. The formula can be:

[0081]

[0082] where k(θ) can be obtained through empirical formulas or using models, Δψ is the matrix potential difference, Δz is the depth difference, P is the rainfall, and ET is the evaporation.

[0083] In this embodiment, the model domain is discretized, including: discretizing the target segment into multiple spatial nodes, determining the total simulation period, and dividing it into stress cycles, and discretizing each stress cycle into multiple time nodes;

[0084] The target segment is discretized into multiple spatial nodes as follows: temperature sensors are labeled 1, 2, 3, etc. from top to bottom along the vertical direction. Assuming that three temperature sensors numbered s-1, s, and s+1 are continuously distributed underground along the soil profile, to calculate the soil infiltration at the target depth, i.e., the depth where temperature sensor number s is located, the one-dimensional vertical region Z between temperature sensors s-1 and s+1 is discretized into N nodes. The number of nodes N is calculated as follows:

[0085] N=Z / Δz

[0086] Where: Z is the distance between temperature sensors s-1 and s+1, Δz is the spatial node size;

[0087] The discrete stress cycle process is as follows: the entire simulation period P is divided into p stress cycles according to the flow conditions, where the length of one stress cycle is Q, and it is further discretized into n time steps, where:

[0088] n=Q / Δt

[0089] Where: Δt is the time step.

[0090] In this embodiment, the loss function is established, including:

[0091]

[0092] Where: a1, a2 are weighting coefficients; is the calculated value of temperature; is the temperature measurement value; i is the calculated value of thermal conductivity; The thermal conductivity value optimized for the last stress cycle. The weighting coefficient a1+a2=1.

[0093] In this embodiment, the temperature at the target depth is predicted as follows:

[0094]

[0095] Where: is the spatiotemporal temperature distribution at any spatial node i and time node j;

[0096] The calculation method of m1 is:

[0097]

[0098] Where: i-1 ,λ i is the thermal conductivity of spatial node i-1, i, C p is the volumetric heat capacity of moist soil, C w is the volume heat capacity of liquid water, C v is the volumetric heat capacity of water vapor, q l is the liquid water flux, q v is the water vapor flux;

[0099] The calculation method of m2 is:

[0100]

[0101] The calculation method of m3 is:

[0102]

[0103] The calculation method of m4 is:

[0104]

[0105] Furthermore, the prediction method is to discretize the heat transfer equation considering soil salinity using the Crank-Nicolson difference method:

[0106]

[0107] In this embodiment, the optimal thermal conductivity λ and water flux q in each stress cycle are the thermal conductivity λ and water flux q when the loss function L reaches a minimum value in the region Z.

[0108] In this embodiment, the adaptive optimization algorithms include L-BFGS-B, PSO, Nelder-Mead and TNC optimization algorithms.

[0109] The four optimization algorithms, L-BFGS-B, PSO, Nelder-Mead, and TNC, each have their own advantages and disadvantages and are applicable in different scenarios. L-BFGS-B is suitable for large-scale bounded constrained optimization, relies on gradient information, is highly memory-efficient, and is suitable for smooth problems such as machine learning. PSO (Particle Swarm Optimization) is a gradient-free global optimizer that uses swarm intelligence to explore complex non-convex and multimodal problems, making it suitable for high-dimensional black-box optimization. Nelder-Mead (Simple Form Method) is designed for low-dimensional non-differentiable problems, requiring only function values. It is simple and easy to use, but limited to local search. TNC (Truncation Newton Method) combines Newton's method with constraint processing, making it suitable for small- to medium-scale, high-precision bounded optimization. It requires gradients and converges quickly. Table 1 below provides an overview of the four algorithms.

[0110] Table 1

[0111]

[0112]

[0113] Example 2

[0114] The present invention also provides a coastal saline-alkali land soil infiltration calculation experimental device based on the temperature tracing method. The system is used to implement the method of embodiment 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 placed at equal intervals on both sides. The temperature sensors and soil moisture sensors are connected to a paperless recorder via data cables, enabling intuitive data reading and convenient computer processing. The temperature and soil moisture sensors are used to measure the temperature and soil moisture of different layers in the soil filling layer.

[0116] In practical application, the experimental setup involves starting the constant-temperature heating water tank, turning on the peristaltic pump, and maintaining constant power. Open the valve to allow water to flow through the hose at a constant rate into the soil filling layer. The water is then infiltrated for eight hours, maintaining a stable soil moisture content. This can be observed and recorded in real time using a paperless recorder. The heating rod in the constant-temperature heating water tank is turned on, and multiple temperature gradients are set according to experimental requirements to maintain a stable temperature field. This ensures that the water flows evenly into the soil.

[0117] Step 1: Obtain basic data. Basic data include soil temperature data of different soil layers in the soil column, soil moisture data of different soil layers, water level data, soil property parameter data, and laboratory environmental data. Soil temperature data of different soil layers are time-varying data of soil temperature of different soil layers in the same period, and water level data are time-varying data of water level elevation. The above data are monitored and recorded in real time using a paperless recorder. Soil property parameter data are measured before the experiment begins, including soil salinity, soil type, soil porosity, and soil bulk density. Laboratory environmental data within the surrounding period include 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 needs to determine the boundary conditions, the initial field, and the initial thermal parameters of the model for a given target depth. In this embodiment, the soil layer depth is selected as 20 cm from the soil layer surface. The boundary conditions and sensor positions are determined, and the time-varying temperature recorded by the sensor data at two adjacent depths is used as the boundary condition. The initial thermal parameters of the model are determined, including the volume heat capacity C of the solid. n , the volume heat capacity of liquid water C w 、The volume heat capacity of water vapor C v and the volumetric heat capacity of moist soil C p ; Determine the thermal conductivity λ and water flux q based on the initial laboratory environment data and experimental device presets;

[0119] Step 3: Finite difference scheme for the heat transfer model. Label the temperature sensors 1, 2, 3, and so on vertically, from top to bottom. Assume that three temperature sensors, numbered s-1s and s+1, are continuously distributed along the soil profile. To calculate soil infiltration at the depth of sensor s, the one-dimensional vertical region Z between sensors s-1 and s+1 is discretized into N nodes. The number of nodes N is calculated as: N = Z / Δz. Based on the flow conditions, the entire simulation period P is divided into p stress cycles, each of length Q. These cycles are further discretized into n time steps, where: n = Q / Δt.

[0120] Step 4: Determine whether the stress cycle meets the requirements; if the stress cycle meets the requirements, it means that the stress cycle is greater than or equal to the total number of stress cycles preset by the discretization; if the requirements are met, the calculation is terminated directly, and the water flux q of the final optimization result is used as the soil infiltration at the given target depth; if not, proceed to step 5;

[0121] Step 5: Predict the temperature time-varying sequence T under stress cycle and use the loss equation

[0122]

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

[0124] Step 6: Determine whether the loss function meets the requirements; if so, use the thermal conductivity λ and water flux q under the stress cycle as the initial field for the next stress cycle and return to step 4; if not, proceed to step 7;

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

[0126] Step 8: Use the optimized thermal conductivity λ and water flux q as the initial input of the stress cycle and proceed to step 5.

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

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

[0129] Table 2

[0130] Optimization Algorithm L-BFGS-B, PSO Nelder-Mead RMSE 0.38 0.6 0.81 Calculate flow rate 8.25 8.29 8.26

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

Claims

1. A method for calculating soil infiltration in coastal saline-alkali land based on temperature tracing method, characterized in that: The method comprises: Obtain soil temperature data, soil moisture data, water level data, soil property parameters and meteorological data of different soil layers in 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 fields are determined; The model domain is discretized using the finite difference scheme to determine the total simulation period and divide it into stress cycles. Each stress cycle is discretized into multiple time nodes. Predict the temperature and water content at the target depth and establish a loss function by minimizing the sum of squares of the residuals between the measured and calculated temperature values; Adaptive optimization algorithm is used to iteratively correct thermal conductivity and water flux in each stress cycle; The optimized parameters of the previous stress cycle are used as the initial conditions of the subsequent cycles to achieve full-cycle recursive calculation; The water flux at the target depth is obtained by the convergence criterion as a quantitative representation of the soil infiltration rate.

2. The method according to claim 1, characterized in that Determine the initial field including thermal conductivity λ and water flux q; The initial estimation method of thermal conductivity λ is determined based on measured soil temperature data, soil moisture data, water level data, soil property parameters, and soil salinity data. The formula is: λ=(A+Bθ+Cρ)*DS Among them, A, B, and C are parameters determined by experiments, D is a parameter related to salinity, and S represents the salt concentration; The initial estimation method of water flux q is determined based on measured meteorological data, soil temperature data, soil moisture data, water level data and soil property parameters. The formula is: where k(θ) can be obtained through empirical formulas or using models, Δψ is the matrix potential difference, Δz is the depth difference, P is the rainfall, and ET is the evaporation.

3. The method according to claim 1, characterized in that Discretize the model domain, including: discretizing the target segment into multiple spatial nodes, determining the total simulation period, and dividing it into stress cycles, and discretizing each stress cycle into multiple time nodes; The target segment is discretized into multiple spatial nodes as follows: temperature sensors are labeled 1, 2, 3, etc. from top to bottom along the vertical direction. Assuming that three temperature sensors numbered s-1, s, and s+1 are continuously distributed underground along the soil profile, to calculate the soil infiltration at the target depth, i.e., the depth where temperature sensor number s is located, the one-dimensional vertical region Z between temperature sensors s-1 and s+1 is discretized into N nodes. The number of nodes N is calculated as follows: N=Z / Δz Where: Z is the distance between temperature sensors s-1 and s+1, Δz is the spatial node size; The discrete stress cycle process is as follows: the entire simulation period P is divided into p stress cycles according to the flow conditions, where the length of one stress cycle is Q, and it is further discretized into n time steps, where: n=Q / Δt Where: Δt is the time step.

4. The method according to claim 3, characterized in that Establish a loss function, including: Where: a1, a2 are weighting coefficients; is the calculated value of temperature; is the temperature measurement value; i is the calculated value of thermal conductivity; Thermal conductivity value optimized for the last stress cycle.

5. The method according to claim 4, characterized in that The temperature at the target depth is predicted as follows: Where: is the spatiotemporal temperature distribution at any spatial node i and time node j; The calculation method of m1 is: Where: i-1 ,λ i is the thermal conductivity of spatial node i-1, i, C p is the volumetric heat capacity of moist soil, C w is the volume heat capacity of liquid water, C v is the volumetric heat capacity of water vapor, q l is the liquid water flux, q v is the water vapor flux; The calculation method of m2 is: The calculation method of m3 is: The calculation method of m4 is:

6. The method according to claim 1, characterized in that The optimal thermal conductivity λ and water flux q in each stress cycle are the thermal conductivity λ and water flux q when the loss function L reaches the minimum value in region Z.

7. 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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