A soil water flux monitoring method, device, equipment and storage medium

Abstract

The present invention provides a soil water flux monitoring method, apparatus, device, and storage medium. The method includes: deploying an active heat source and monitoring equipment, and acquiring moisture content and temperature data of the soil to be tested; constructing soil hydrodynamic constraints based on the Richard equation and the moisture content data of the soil to be tested, and training a pre-constructed water flux monitoring model; constructing soil thermodynamic constraints based on the convection-conduction-diffusion equation and the temperature data of the soil to be tested, and training the water flux monitoring model; and constructing soil hydrothermal coupling constraints, training the water flux monitoring model, and obtaining a target water flux monitoring model. The present invention addresses the difficulty of existing monitoring methods in achieving high-precision soil water flux monitoring in different scenarios at a low data cost.

Description

Technical Field

[0001] The present invention relates to the technical field of soil water flux monitoring, and in particular to a soil water flux monitoring method, device, equipment and storage medium. Background Art

[0002] Soil water flux plays a key role in multiple fields, including agricultural production, ecological protection, and geological disaster warning. However, there is currently no universal direct soil water flux monitoring equipment. Existing indirect measurement methods have strict requirements on the environment or equipment conditions. Furthermore, the strong temporal and spatial variability of the physical and chemical properties of soils in my country poses a significant challenge to soil water flux monitoring. Therefore, a high-precision and convenient soil water flux measurement method is urgently needed to meet practical needs.

[0003] Soil moisture content and soil temperature are commonly used soil monitoring indicators. Currently, soil moisture content can be combined with the Richard equation to estimate soil water flux. However, under high flux or variable saturation conditions, moisture content changes minimally, making it difficult for moisture content data to contain valid water flux information, thereby reducing the accuracy of water flux monitoring. Soil temperature, on the other hand, can be combined with the soil convection-conduction-diffusion equation to calculate soil water flux. Thermal pulse active heat source technology has great application potential, but its strict control over heat generation significantly reduces its accuracy in heterogeneous soils and thermally non-closed environments. Hydrothermal coupling methods have also been used to estimate soil water flux, but these methods currently require prior knowledge of the soil's hydrothermal properties, limiting their further promotion and application.

[0004] There is currently no effective solution to the problem that existing monitoring methods are difficult to achieve high-precision monitoring of soil water flux in different scenarios at a low data cost. Summary of the Invention

[0005] The present invention provides a soil water flux monitoring method, device, equipment and storage medium to address the defect that existing monitoring methods are difficult to achieve high-precision monitoring of soil water flux at a low data cost under different scenarios.

[0006] In a first aspect, the present invention provides a soil water flux monitoring method, comprising:

[0007] Deploy active heat sources and monitoring equipment, and obtain moisture content and temperature data of the soil to be tested;

[0008] Based on the Richard equation and the moisture content data of the soil to be tested, soil hydrodynamic constraints are constructed, and a pre-constructed water flux monitoring model is trained;

[0009] Based on the convection-conduction-diffusion equation and the temperature data of the soil to be measured, soil thermodynamic constraints are constructed, and the water flux monitoring model is trained;

[0010] Constructing soil water-heat coupling constraints, training the water flux monitoring model, and obtaining a target water flux monitoring model;

[0011] The spatiotemporal coordinates of a position to be measured of the soil to be measured are obtained, and the soil water flux of the position to be measured of the soil to be measured is determined by using the target water flux monitoring model.

[0012] According to a soil water flux monitoring method provided by the present invention, an active heat source and monitoring equipment are arranged, and moisture content data and temperature data of the soil to be tested are obtained, including:

[0013] Determining a monitoring area of ​​the soil to be tested, and deploying an active heat source and monitoring equipment in the monitoring area of ​​the soil to be tested;

[0014] Determining a measurement point set and a matching point set for training based on the monitoring area and the monitoring position of the monitoring device; the measurement point set includes a plurality of measurement point coordinates, and the matching point set includes a plurality of matching point coordinates;

[0015] The moisture content data and temperature data of the soil measuring point to be measured are obtained.

[0016] According to a soil water flux monitoring method provided by the present invention, before obtaining the moisture content data and temperature data of the position where the measuring point coordinates of the soil to be measured are located, the method includes: normalizing the measuring point coordinates and the matching point coordinates of the soil to be measured.

[0017] According to a soil water flux monitoring method provided by the present invention, based on the Richard equation and the moisture content data of the soil to be tested, soil hydrodynamic constraints are constructed, and a pre-constructed water flux monitoring model is trained, including:

[0018] Predicting the moisture content, matrix potential, and hydraulic conductivity of the measured point coordinates and the matching point coordinates of the soil to be tested using the water flux monitoring model to obtain a moisture content prediction value;

[0019] Based on the measurement point set and the matching point set of the soil to be measured and the moisture content prediction value, constructing soil hydrodynamic constraints and determining the soil hydrodynamic constraint loss of the water flux monitoring model;

[0020] With the goal of minimizing the soil hydrodynamic constraint loss of the water flux monitoring model, the water flux monitoring model is trained and its parameters are updated.

[0021] According to a soil water flux monitoring method provided by the present invention, based on the convection-conduction-diffusion equation and the temperature data of the soil to be measured, soil thermodynamic constraints are constructed, and the water flux monitoring model is trained, including:

[0022] Obtaining the temperature, thermal diffusion coefficient, thermal conductivity and volume heat capacity of the measuring point coordinates and the matching point coordinates of the soil to be measured through the water flux monitoring model to obtain a temperature prediction value;

[0023] Based on the measurement point set and the matching point set of the soil to be measured and the temperature prediction value, a soil thermodynamic constraint is constructed, and a soil thermodynamic constraint loss of the water flux monitoring model is determined;

[0024] With the goal of minimizing the soil thermodynamic constraint loss of the water flux monitoring model, the water flux monitoring model is trained and its parameters are updated.

[0025] According to a soil water flux monitoring method provided by the present invention, a soil water-heat coupling constraint is constructed, the water flux monitoring model is trained, and a target water flux monitoring model is obtained, including:

[0026] determining the water-thermal coupling constraint loss based on the soil hydrodynamic constraint loss and the soil thermodynamic constraint loss of the water flux monitoring model;

[0027] With the goal of minimizing the water-heat coupling constraint loss of the water flux monitoring model, the water flux monitoring model is trained and parameters are updated to obtain a target water flux monitoring model.

[0028] According to a soil water flux monitoring method provided by the present invention, determining the soil water flux at a position to be measured of the soil to be measured by using the target water flux monitoring model includes:

[0029] Inputting the spatiotemporal coordinates of the position to be measured of the soil to be measured into the target water flux monitoring model to obtain the moisture content and soil temperature of the soil to be measured at the position to be measured;

[0030] The soil water flux of the soil to be tested at the position to be tested is determined based on the moisture content and soil temperature of the soil to be tested at the position to be tested.

[0031] In a second aspect, the present invention further provides a soil water flux monitoring device, comprising:

[0032] The data acquisition module is used to deploy active heat sources and monitoring equipment, and obtain moisture content and temperature data of the soil to be tested;

[0033] A hydrodynamic training module, for constructing soil hydrodynamic constraints based on the Richard equation and the moisture content data of the soil to be tested, and training a pre-built water flux monitoring model;

[0034] A thermodynamic training module, configured to construct soil thermodynamic constraints based on the convection-conduction-diffusion equation and the temperature data of the soil to be measured, and to train the water flux monitoring model;

[0035] A coupling training module is used to construct soil water-heat coupling constraints, train the water flux monitoring model, and obtain a target water flux monitoring model;

[0036] The monitoring module is used to obtain the spatiotemporal coordinates of the position to be measured of the soil to be measured, and determine the soil water flux of the position to be measured of the soil to be measured by using the target water flux monitoring model.

[0037] In a third aspect, the present invention further provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the soil water flux monitoring method as described in the first aspect above is implemented.

[0038] In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the soil water flux monitoring method as described in the first aspect above.

[0039] In a fifth aspect, the present invention further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the soil water flux monitoring method as described in the first aspect above.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] The soil water flux monitoring method provided by the present invention only uses soil moisture and temperature data, combined with an active heat source, to achieve high-precision monitoring of soil water flux, with lower data costs. Moreover, this method has higher monitoring accuracy in variable saturation and high-flux scenarios. Compared with traditional active heat source technologies such as heat pulses, this method does not need to strictly control the amount of heat source heating, and uses hydrothermal coupling technology to make up for the shortcomings of heat pulse technology in considering soil heterogeneity; compared with existing hydrothermal coupling technology, this method does not require the hydrothermal characteristics of the soil as prior information, and has better generalization performance. Through this method, it is possible to solve the problem that existing monitoring methods are difficult to achieve high-precision monitoring of soil water flux at a lower data cost in different scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are 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.

[0043] Figure 1 is a flow chart of the soil water flux monitoring method provided by the present invention;

[0044] Figure 2 Schematic diagram of the water flux acquisition process in an embodiment of the present invention;

[0045] Figure 3 is a generalized diagram of the training process of the water flux monitoring model in an embodiment of the present invention;

[0046] Figure 4 is a comparative bar graph of water flux monitoring errors under different flow regimes using this method and using only water content data in an embodiment of the present invention;

[0047] Figure 5 3 is a comparison chart of the water flux field estimation and its error using the present method and using only water content data in sandy soil and loamy sandy soil in an embodiment of the present invention;

[0048] Figure 6 This is a rendering of the soil water and heat parameters inversion model according to an embodiment of the present invention;

[0049] Figure 7 This is a structural block diagram of a soil water flux monitoring device provided by the present invention;

[0050] Figure 8 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0051] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0052] The present invention provides a soil water flux monitoring method. Figure 1 is a flow chart of the soil water flux monitoring method provided by the present invention, such as Figure 1 As shown, the method includes the following steps:

[0053] Step S101: deploy active heat sources and monitoring equipment, and obtain moisture content data and temperature data of the soil to be tested;

[0054] Step S102: constructing soil hydrodynamic constraints based on the Richard equation and the moisture content data of the soil to be tested, and training a pre-built water flux monitoring model;

[0055] Step S103: constructing soil thermodynamic constraints based on the convection-conduction-diffusion equation and the temperature data of the soil to be measured, and training a water flux monitoring model;

[0056] Step S104: constructing soil water-heat coupling constraints, training a water flux monitoring model, and obtaining a target water flux monitoring model;

[0057] Step S105 , obtaining the spatiotemporal coordinates of the position to be measured of the soil to be measured, and determining the soil water flux of the position to be measured of the soil to be measured by using the target water flux monitoring model.

[0058] In this method, active heat sources and soil moisture and temperature monitoring equipment are first deployed. The monitoring equipment then acquires real-world moisture and temperature data at the soil locations to be monitored. Then, based on the Richard equation and the soil moisture data, soil hydrodynamic constraints are constructed, and a pre-built water flux monitoring model is trained to initially resolve the hydraulic state of the soil profile. The water flux monitoring model's underlying architecture can utilize physics-informed neural networks (PINNs). Then, using the convection-conduction-diffusion equation and soil temperature data, soil thermodynamic constraints are constructed to train the water flux monitoring model and initialize soil thermodynamic parameters. Next, coupled soil hydrothermal constraints are constructed, and the water flux monitoring model is trained to update all soil hydrothermal parameters and states. Finally, the spatiotemporal coordinates of the desired monitoring location are input into the trained target water flux monitoring model to obtain the soil water flux. In this process, using only soil moisture and temperature data, combined with an active heat source, high-precision soil water flux monitoring can be achieved with reduced data costs. Furthermore, this method offers higher monitoring accuracy in variable saturation and high-flux scenarios. Compared to traditional active heat source techniques like heat pulses, this method eliminates the need for strict control of heat source heating output. Its hydrothermal coupling compensates for the heat pulse's inadequate consideration of soil heterogeneity. Compared to existing hydrothermal coupling techniques, this method eliminates the need for prior information on soil hydrothermal properties, resulting in improved generalization. This method addresses the difficulty of existing monitoring methods in achieving high-precision soil water flux monitoring in diverse scenarios at a low data cost.

[0059] Figure 2 Schematic diagram of the water flux acquisition process in an embodiment of the present invention. Figure 2As shown, in some embodiments, step S101, deploying active heat sources and monitoring equipment, and obtaining moisture content data and temperature data of the soil to be tested, includes: determining a monitoring area of ​​the soil to be tested, and deploying active heat sources and monitoring equipment in the monitoring area of ​​the soil to be tested; determining a measurement point set and a matching point set for training based on the monitoring area and the monitoring positions of the monitoring equipment; the measurement point set includes a plurality of measurement point coordinates, and the matching point set includes a plurality of matching point coordinates; and obtaining moisture content data and temperature data of the locations where the measurement point coordinates of the soil to be tested are located.

[0060] Furthermore, before obtaining the moisture content data and temperature data of the position where the measuring point coordinates of the soil to be measured are located, the method includes: normalizing the measuring point coordinates and the matching point coordinates of the soil to be measured.

[0061] For example, in determining the soil water flux monitoring area Then, if upward is defined as the positive direction of the spatial coordinate, the spatial coordinate is expressed as , the time coordinate is expressed as . Then place the active heat source at Soil moisture and temperature monitoring equipment are installed at In this embodiment, the active heat source is taken as the starting position line of the space at 0m, and soil moisture and temperature monitoring equipment are respectively arranged at -0.05, -0.45, and -0.85m.

[0062] According to monitoring area The coordinates of the measuring points used in training can be obtained by combining the soil moisture and temperature monitoring positions With point coordinates A set of points:

[0063]

[0064] in, is the coordinate of the measuring point, is the spatial coordinate of the original measuring point, is the time coordinate of the original measuring point; It is the point set consisting of the coordinates of all measuring points; are the coordinates of the points, is the original point space coordinate, is the original point time coordinate, is the super Latin cube sampling function. The obtained measurement point and collocation point coordinates need to be normalized:

[0065]

[0066]

[0067] in, is the spatial coordinate of the measured point after normalization; is the time coordinate of the measurement point after normalization; is the normalized coordinate of the measuring point; is the normalized point space coordinate; is the time coordinate of the measurement point after normalization; is the normalized coordinate of the collocation point; is the lower boundary position of the flux monitoring area (m); is the upper boundary position of the flux monitoring area (m); is the initial time of the flux monitoring area (day); is the end time (day) of the flux monitoring area. The corresponding soil volume moisture content , soil temperature The data were matched to produce the dataset required for soil water flux monitoring.

[0068] Figure 3 This is a generalized diagram of the training process of the water flux monitoring model in an embodiment of the present invention, such as Figure 3 As shown, in some embodiments, step S102, based on the Richard equation and the moisture content data of the soil to be tested, constructs a soil hydrodynamic constraint, and trains a pre-constructed water flux monitoring model, including: predicting the moisture content, matrix potential and hydraulic conductivity of the measuring point coordinates and the collocation point coordinates of the soil to be tested through the water flux monitoring model to obtain a moisture content prediction value; constructing a soil hydrodynamic constraint based on the measuring point set and the collocation point set of the soil to be tested and the moisture content prediction value, and determining the soil hydrodynamic constraint loss of the water flux monitoring model; and training and updating the parameters of the water flux monitoring model with the goal of minimizing the soil hydrodynamic constraint loss of the water flux monitoring model.

[0069] For example, first, a soil hydrodynamic neural network is constructed to estimate the matric potential of the soil profile. , moisture content , hydraulic conductivity :

[0070]

[0071]

[0072]

[0073] in, is the predicted value of soil matrix potential (m); To predict soil matric potential (m) neural network; is the predicted value of soil moisture (m3 m -3 ); To predict soil moisture content (m 3 m -3 ) neural network; is the predicted value of soil hydraulic conductivity (m·day -1 ); To predict soil hydraulic conductivity (m·day -1 )’s mapping function; is the updateable parameter of the neural network; Updateable parameters for the hydraulic conductivity mapping function.

[0074] Gather the measurement points , matching point set Input soil water dynamics neural network and based on the measured soil moisture content Data construction soil hydrodynamic constraints:

[0075]

[0076]

[0077]

[0078] in, and are the hydrodynamic observation loss and the hydrodynamic collocation residual respectively; is the serial number of the points in the point set; and are the number of measuring points and matching points respectively; It is the self-differentiation operation of the neural network; and are the weight coefficients of hydrodynamic observation loss and hydrodynamic collocation residual, respectively; is the soil hydrodynamic confinement loss.

[0079] Finally, according to the soil hydrodynamic constraint loss , train the water flux monitoring model and update the parameters:

[0080]

[0081] in, 、 and These are hydraulic parameters to be updated.

[0082] Correspondingly, step S103 constructs soil thermodynamic constraints based on the convection-conduction-diffusion equation and the temperature data of the soil to be measured, and trains a water flux monitoring model, including: obtaining the temperature, thermal diffusion coefficient, thermal conductivity, and volume heat capacity of the measurement point coordinates and the collocation point coordinates of the soil to be measured through the water flux monitoring model to obtain a temperature prediction value; constructing soil thermodynamic constraints based on the measurement point set and collocation point set of the soil to be measured and the temperature prediction value, and determining the soil thermodynamic constraint loss of the water flux monitoring model; and training and updating the parameters of the water flux monitoring model with the goal of minimizing the soil thermodynamic constraint loss of the water flux monitoring model.

[0083] For example, first, a soil thermodynamic neural network is built to estimate the temperature of the soil profile. , thermal diffusion coefficient , thermal conductivity , volumetric heat capacity :

[0084]

[0085]

[0086]

[0087] in, is the predicted soil temperature (℃); For predicting soil temperature (℃) neural network, is the updateable parameter of the network; is the predicted value of soil thermal conductivity ( ); To predict soil thermal conductivity ( ), is the updateable parameter of the mapping function; is the predicted value of soil volume heat capacity ( ); is the volumetric heat capacity of water ( ); is the volume heat capacity of soil particles ( ); is the saturated volumetric water content of soil (m 3 m -3 ).

[0088] Then, the measurement points are collected , matching point set Input the soil thermodynamic neural network and based on the measured soil temperature Data construction soil thermodynamic constraints:

[0089]

[0090]

[0091]

[0092] in, and are thermodynamic observation loss and thermodynamic collocation residual respectively; and are the weight coefficients of thermodynamic observation loss and thermodynamic collocation residual, respectively; is the soil thermodynamic constraint loss, is the estimated value of thermal diffusion coefficient. Indicates the serial number i The predicted soil temperature at Indicates the serial number i The soil temperature at represents the updateable parameters of the hydraulic conductivity mapping function, Indicates the serial number i The thermal conductivity of the soil at Indicates the serial number i The hydraulic conductivity at Indicates the serial number i The soil matrix potential at Indicates the serial number i The spatial coordinates of .

[0093] Based on soil thermodynamic constraint losses , train the network and update the parameters to initialize the thermodynamic parameters:

[0094]

[0095] in, and These are thermodynamic parameters to be updated.

[0096] Based on the above embodiment, step S104 constructs soil hydrothermal coupling constraints, trains a water flux monitoring model, and obtains a target water flux monitoring model. The steps include: determining a hydrothermal coupling constraint loss based on the soil hydrodynamic constraint loss and the soil thermodynamic constraint loss of the water flux monitoring model; and training and updating the parameters of the water flux monitoring model with the goal of minimizing the hydrothermal coupling constraint loss of the water flux monitoring model to obtain the target water flux monitoring model.

[0097] For example, the water-thermal coupling constraint loss Losses due to hydrodynamic constraints and thermodynamic constraint losses The specific formula is as follows:

[0098]

[0099] in, is the water-thermal coupling constraint loss; is the hydrodynamic constraint loss weight coefficient; is the thermodynamic constraint loss weight coefficient. According to the water-thermal coupling constraint loss Update all model parameters:

[0100]

[0101] in, 、 、 、 and These are model parameters to be updated.

[0102] During the training process described above, parameter updates are divided into three phases. The first phase updates parameters solely related to hydrodynamic constraints, while the second phase updates parameters solely related to thermodynamic constraints. However, water or heat alone cannot accurately approximate the actual soil hydrothermal distribution. Therefore, in the third phase, these parameters are updated simultaneously to implement the concept of hydrothermal coupling. Furthermore, adding the first and second phases before the simultaneous updates can reduce the difficulty of training the water flux monitoring model and prevent overfitting or model collapse.

[0103] In some embodiments, step S105, determining the soil water flux at the position to be measured of the soil to be measured using the target water flux monitoring model, includes: inputting the spatiotemporal coordinates of the position to be measured of the soil to be measured into the target water flux monitoring model, obtaining the moisture content and soil temperature of the soil to be measured at the position to be measured; and determining the soil water flux at the position to be measured of the soil to be measured based on the moisture content and soil temperature of the soil to be measured at the position to be measured.

[0104] For example, specify any location , input into the trained model, obtain the soil hydrothermal state at the corresponding location, and calculate the soil water flux using Darcy's law:

[0105]

[0106] in, is the calculated water flux (m·day -1 ).

[0107] To verify the effectiveness of the above method, the following comparison is made between the estimated results of the water flux field using only water content data and free drainage, seepage surface, and groundwater in sand, loamy sand, sandy loam, and loam at a depth of 1 m within 2.5 days. Figure 4 As shown, Figure 4 This is a comparative bar graph of the water flux monitoring errors using this method and using only water content data in different flow scenarios in the embodiment of the present invention. In addition, this experiment focuses on the comparison of the inversion of the flux field in loam sand, such as Figure 5 As shown, Figure 5 The figure is a comparison of the water flux field estimated by this method and the error of using only water content data in sand and loam sand in the embodiment of the present invention. In addition, this experiment also compared the hydrothermal characteristics inverted by this method with the actual values. The results are as follows: Figure 6 As shown, Figure 6 This figure shows the effects of soil hydrothermal parameter inversion using the model in an embodiment of the present invention. The results demonstrate that the proposed water flux monitoring method using active thermal tracing has higher flux monitoring accuracy, performs well in different flow regimes and soils, and exhibits excellent generalization capabilities.

[0108] The present invention also provides a soil water flux monitoring device. The soil water flux monitoring device provided by the present invention is described below. The soil water flux monitoring device described below and the soil water flux monitoring method described above can be referred to each other. Figure 7 This is a structural diagram of a soil water flux monitoring device provided by the present invention, such as Figure 7 As shown, the device includes:

[0109] The data acquisition module 701 is used to deploy active heat sources and monitoring equipment, and obtain moisture content data and temperature data of the soil to be tested;

[0110] A hydrodynamic training module 702 is used to construct soil hydrodynamic constraints based on the Richard equation and the moisture content data of the soil to be tested, and to train a pre-built water flux monitoring model;

[0111] Thermodynamic training module 703 is used to construct soil thermodynamic constraints based on the convection-conduction-diffusion equation and the temperature data of the soil to be tested, and to train a water flux monitoring model;

[0112] The coupling training module 704 is used to construct soil water-heat coupling constraints, train the water flux monitoring model, and obtain the target water flux monitoring model;

[0113] The monitoring module 705 is used to obtain the time-space coordinates of the position to be measured of the soil to be measured, and determine the soil water flux of the position to be measured of the soil to be measured through the target water flux monitoring model.

[0114] When using this device, active heat sources and soil moisture and temperature monitoring equipment are deployed. The data acquisition module 701 uses the monitoring equipment to obtain real-world moisture and temperature data for the soil at the corresponding location to be measured. Next, the hydrodynamic training module 702 constructs soil hydrodynamic constraints based on the Richard equation and the soil moisture data, and trains a pre-built water flux monitoring model to initially solve the hydraulic state of the soil profile. The water flux monitoring model's underlying architecture can utilize a physics-informed neural network (PINN). The thermodynamic training module 703 then uses the convection-conduction-diffusion equation and soil temperature data to construct soil thermodynamic constraints to subsequently train the water flux monitoring model and initialize soil thermodynamic parameters. Next, the coupled training module 704 constructs soil hydrothermal coupled constraints and trains the water flux monitoring model to update all soil hydrothermal parameters and states. Finally, the monitoring module 705 inputs the spatiotemporal coordinates of the location to be monitored into the trained target water flux monitoring model to obtain the soil water flux. In the above process, only soil moisture and temperature data are used in conjunction with an active heat source to achieve high-precision monitoring of soil water flux, with lower data costs. Moreover, this device has higher monitoring accuracy in variable saturation and high-flux scenarios. Compared with traditional active heat source technologies such as heat pulses, this method does not require strict control of the heat source heating amount, and uses hydrothermal coupling technology to make up for the lack of consideration of soil heterogeneity by heat pulse technology; compared with existing hydrothermal coupling technology, this device does not require the hydrothermal characteristics of the soil as prior information and has better generalization performance. Through this device, it is possible to solve the problem that existing monitoring methods are difficult to achieve high-precision monitoring of soil water flux in different scenarios at a lower data cost.

[0115] Figure 8 An example of a physical structure diagram of an electronic device is shown below. Figure 8 As shown, the electronic device may include: a processor 801, a communication interface 802, a memory 803, and a communication bus 804, wherein the processor 801, the communication interface 802, and the memory 803 communicate with each other via the communication bus 804. The processor 801 may call the logic instructions in the memory 803 to execute a soil water flux monitoring method, which includes:

[0116] Deploy active heat sources and monitoring equipment, and obtain moisture content and temperature data of the soil to be tested;

[0117] Based on the Richard equation and the moisture content data of the soil to be tested, soil hydrodynamic constraints are constructed and a pre-built water flux monitoring model is trained;

[0118] Based on the convection-conduction-diffusion equation and the temperature data of the soil to be tested, soil thermodynamic constraints are constructed and a water flux monitoring model is trained;

[0119] Construct soil water-heat coupling constraints, train the water flux monitoring model, and obtain the target water flux monitoring model;

[0120] The spatiotemporal coordinates of a position to be measured of the soil to be measured are obtained, and the soil water flux of the position to be measured of the soil to be measured is determined by a target water flux monitoring model.

[0121] Furthermore, the logic instructions in the aforementioned memory 803 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0122] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can perform the soil water flux monitoring method provided by the above methods, which includes:

[0123] Deploy active heat sources and monitoring equipment, and obtain moisture content and temperature data of the soil to be tested;

[0124] Based on the Richard equation and the moisture content data of the soil to be tested, soil hydrodynamic constraints are constructed and a pre-built water flux monitoring model is trained;

[0125] Based on the convection-conduction-diffusion equation and the temperature data of the soil to be tested, soil thermodynamic constraints are constructed and a water flux monitoring model is trained;

[0126] Construct soil water-heat coupling constraints, train the water flux monitoring model, and obtain the target water flux monitoring model;

[0127] The spatiotemporal coordinates of a position to be measured of the soil to be measured are obtained, and the soil water flux of the position to be measured of the soil to be measured is determined by a target water flux monitoring model.

[0128] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the soil water flux monitoring method provided by the above methods, the method comprising:

[0129] Deploy active heat sources and monitoring equipment, and obtain moisture content and temperature data of the soil to be tested;

[0130] Based on the Richard equation and the moisture content data of the soil to be tested, soil hydrodynamic constraints are constructed and a pre-built water flux monitoring model is trained;

[0131] Based on the convection-conduction-diffusion equation and the temperature data of the soil to be tested, soil thermodynamic constraints are constructed and a water flux monitoring model is trained;

[0132] Construct soil water-heat coupling constraints, train the water flux monitoring model, and obtain the target water flux monitoring model;

[0133] The spatiotemporal coordinates of a position to be measured of the soil to be measured are obtained, and the soil water flux of the position to be measured of the soil to be measured is determined by a target water flux monitoring model.

[0134] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0135] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0136] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A soil water flux monitoring method, characterized in that: include: Deploy active heat sources and monitoring equipment, and obtain moisture content and temperature data of the soil to be tested; Based on the Richard equation and the moisture content data of the soil to be tested, soil hydrodynamic constraints are constructed, and a pre-constructed water flux monitoring model is trained; Based on the convection-conduction-diffusion equation and the temperature data of the soil to be measured, soil thermodynamic constraints are constructed, and the water flux monitoring model is trained; Constructing soil water-heat coupling constraints, training the water flux monitoring model, and obtaining a target water flux monitoring model; Acquiring the spatiotemporal coordinates of a position to be measured of the soil to be measured, and determining the soil water flux of the position to be measured of the soil to be measured using the target water flux monitoring model; Deploy active heat sources and monitoring equipment, and obtain moisture content and temperature data of the soil to be tested, including: Determining a monitoring area of ​​the soil to be tested, and deploying an active heat source and monitoring equipment in the monitoring area of ​​the soil to be tested; Determining a measurement point set and a matching point set for training based on the monitoring area and the monitoring position of the monitoring device; the measurement point set includes a plurality of measurement point coordinates, and the matching point set includes a plurality of matching point coordinates; Obtaining moisture content data and temperature data of the soil measurement point coordinates at the location to be measured; Based on the Richard equation and the moisture content data of the soil to be tested, soil hydrodynamic constraints are constructed and a pre-built water flux monitoring model is trained, including: Predicting the moisture content, matrix potential, and hydraulic conductivity of the measured point coordinates and the matching point coordinates of the soil to be tested using the water flux monitoring model to obtain a moisture content prediction value; Based on the measurement point set and the matching point set of the soil to be measured and the moisture content prediction value, constructing soil hydrodynamic constraints and determining the soil hydrodynamic constraint loss of the water flux monitoring model; With the goal of minimizing the soil hydrodynamic constraint loss of the water flux monitoring model, the water flux monitoring model is trained and its parameters are updated.

2. The soil water flux monitoring method according to claim 1, characterized in that: Before obtaining the moisture content data and temperature data of the position where the measuring point coordinates of the soil to be measured are located, the method includes: normalizing the measuring point coordinates and the matching point coordinates of the soil to be measured.

3. The soil water flux monitoring method according to claim 1, characterized in that: Based on the convection-conduction-diffusion equation and the temperature data of the soil to be measured, soil thermodynamic constraints are constructed, and the water flux monitoring model is trained, including: Obtaining the temperature, thermal diffusion coefficient, thermal conductivity and volume heat capacity of the measuring point coordinates and the matching point coordinates of the soil to be measured through the water flux monitoring model to obtain a temperature prediction value; Based on the measurement point set and the matching point set of the soil to be measured and the temperature prediction value, a soil thermodynamic constraint is constructed, and a soil thermodynamic constraint loss of the water flux monitoring model is determined; With the goal of minimizing the soil thermodynamic constraint loss of the water flux monitoring model, the water flux monitoring model is trained and its parameters are updated.

4. The soil water flux monitoring method according to claim 3, characterized in that: Constructing soil water-heat coupling constraints, training the water flux monitoring model, and obtaining a target water flux monitoring model, including: determining the water-thermal coupling constraint loss based on the soil hydrodynamic constraint loss and the soil thermodynamic constraint loss of the water flux monitoring model; With the goal of minimizing the water-heat coupling constraint loss of the water flux monitoring model, the water flux monitoring model is trained and parameters are updated to obtain a target water flux monitoring model.

5. The soil water flux monitoring method according to claim 1, characterized in that: Determining the soil water flux at the position to be tested of the soil to be tested by the target water flux monitoring model includes: Inputting the spatiotemporal coordinates of the position to be measured of the soil to be measured into the target water flux monitoring model to obtain the moisture content and soil temperature of the soil to be measured at the position to be measured; The soil water flux of the soil to be tested at the position to be tested is determined based on the moisture content and soil temperature of the soil to be tested at the position to be tested.

6. A soil water flux monitoring device, characterized in that: include: The data acquisition module is used to deploy active heat sources and monitoring equipment, and obtain moisture content and temperature data of the soil to be tested; A hydrodynamic training module, for constructing soil hydrodynamic constraints based on the Richard equation and the moisture content data of the soil to be tested, and training a pre-built water flux monitoring model; A thermodynamic training module, configured to construct soil thermodynamic constraints based on the convection-conduction-diffusion equation and the temperature data of the soil to be measured, and to train the water flux monitoring model; A coupling training module is used to construct soil water-heat coupling constraints, train the water flux monitoring model, and obtain a target water flux monitoring model; a monitoring module, configured to obtain the spatiotemporal coordinates of a position to be measured of the soil to be measured, and determine the soil water flux of the position to be measured of the soil to be measured by using the target water flux monitoring model; Deploy active heat sources and monitoring equipment, and obtain moisture content and temperature data of the soil to be tested, including: Determining a monitoring area of ​​the soil to be tested, and deploying an active heat source and monitoring equipment in the monitoring area of ​​the soil to be tested; Determining a measurement point set and a matching point set for training based on the monitoring area and the monitoring position of the monitoring device; the measurement point set includes a plurality of measurement point coordinates, and the matching point set includes a plurality of matching point coordinates; Obtaining moisture content data and temperature data of the soil measurement point coordinates at the location to be measured; Based on the Richard equation and the moisture content data of the soil to be tested, soil hydrodynamic constraints are constructed and a pre-built water flux monitoring model is trained, including: Predicting the moisture content, matrix potential, and hydraulic conductivity of the measured point coordinates and the matching point coordinates of the soil to be tested using the water flux monitoring model to obtain a moisture content prediction value; Based on the measurement point set and the matching point set of the soil to be measured and the moisture content prediction value, constructing soil hydrodynamic constraints and determining the soil hydrodynamic constraint loss of the water flux monitoring model; With the goal of minimizing the soil hydrodynamic constraint loss of the water flux monitoring model, the water flux monitoring model is trained and its parameters are updated.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the soil water flux monitoring method according to any one of claims 1 to 5 is implemented.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the soil water flux monitoring method according to any one of claims 1 to 5 is implemented.

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