Method and device for determining thermal parameters of undisturbed root planting soil and storage medium

Abstract

The invention relates to the technical field of soil thermal parameter acquisition, in particular to a method and device for determining thermal parameters of undisturbed root planting soil and a storage medium. The method comprises the following steps: periodically acquiring soil temperature data at different depths based on a preset time interval; based on the soil temperature data, the soil thermal diffusivity is determined by adopting an implicit differential numerical inversion algorithm; according to the soil thermal diffusivity and the soil volumetric specific heat, a characteristic curve of the soil heat conductivity coefficient and the soil water content is determined; fitting the characteristic curve to obtain a function relation between the heat conductivity coefficient of the soil and the water content of the soil; according to the function relation, the thermal parameters of the target soil are determined, and the accuracy and convenience of determining the thermal parameters of the undisturbed rooted soil are improved.

Description

Technical Field

[0001] This application relates to the technical field of obtaining soil thermal parameters, and particularly relates to a method, device, and storage medium for determining the thermal parameters of undisturbed rooted soil. Background Art

[0002] As a porous medium, the thermal parameters of soil are crucial for understanding and predicting the thermal behavior of soil. In the natural state, the composition of soil is complex, usually in a state of coexistence of solid, liquid, and gas phases, and contains a large number of plant roots, organic matter, etc. The composition and spatial structure of soil determine its thermal parameters, which are crucial in the applications of geotechnical engineering, underground building design, environmental assessment, etc. Existing methods for measuring soil thermal conductivity usually cannot balance the accuracy and convenience of measurement. For example, when using the steady-state method for measurement, soil specimens need to be dug out and transferred to the laboratory, and a steady heat source is used to form a stable heat conduction process to calculate the soil thermal conductivity. This method has the problem of disturbing the soil structure, and due to the deterioration of organic matter or damage to roots during the transfer process, it is impossible to effectively ensure the consistency between the measured specimen and the undisturbed soil mass. The transient method has the advantage of being convenient and fast, but since the transient method requires the use of a specific thermal property analyzer, due to the short measurement time, it is impossible to effectively obtain the characteristic curve of thermal conductivity under different water content states, and limited by the measurement range, it cannot accurately reflect the soil thermal parameters under the actual state in this area. Therefore, how to improve the accuracy and convenience of determining the thermal parameters of undisturbed rooted soil has become a technical problem to be solved urgently. Summary of the Invention

[0003] The main purpose of this application is to provide a method, device, and storage medium for determining the thermal parameters of undisturbed rooted soil, aiming to solve the technical problem of how to improve the accuracy and convenience of determining the thermal parameters of undisturbed rooted soil.

[0004] To achieve the above purpose, this application provides a method for determining the thermal parameters of undisturbed rooted soil, and the method includes the following steps:

[0005] Periodically obtain soil temperature data at different depths based on a preset time interval;

[0006] Based on the soil temperature data, use the implicit difference numerical inversion algorithm to determine the soil thermal diffusivity;

[0007] According to the soil thermal diffusivity and the soil volumetric specific heat, determine the characteristic curve of soil thermal conductivity and soil water content;

[0008] Fit the characteristic curve to obtain the functional relationship between the soil thermal conductivity and the soil water content;

[0009] According to the functional relationship, the target soil thermal parameters are determined.

[0010] In one embodiment, after the step of periodically acquiring soil temperature data at different depths based on a preset time interval, the method further includes:

[0011] Acquire soil monitoring data, and determine, based on the soil monitoring data, whether there is rainfall and / or irrigation when the soil temperature data is collected;

[0012] If so, the soil temperature data is marked as invalid data and discarded.

[0013] In one embodiment, the step of determining the soil thermal diffusivity based on the soil temperature data using an implicit difference numerical inversion algorithm comprises:

[0014] Based on the soil temperature data, determining the temperature change of each measuring point within each preset time interval;

[0015] Based on the temperature change, the implicit difference numerical inversion algorithm is used to determine the soil temperature at the next time point;

[0016] Comparing the soil temperature at the next time point with the measured temperature, and determining the sum of square errors according to the comparison result;

[0017] Based on the error square sum, the initial soil thermal diffusivity is iteratively optimized to obtain the soil thermal diffusivity.

[0018] In one embodiment, the step of iteratively optimizing the initial soil thermal diffusivity based on the error sum of squares to obtain the soil thermal diffusivity includes:

[0019] Based on the sum of squared errors, setting an objective function;

[0020] The soil thermal diffusivity is obtained by adopting a preset optimization algorithm, minimizing the objective function, iteratively optimizing the initial soil thermal diffusivity until the error sum of squares meets a preset threshold.

[0021] In one embodiment, the step of determining a characteristic curve of soil thermal conductivity and soil water content based on the soil thermal diffusivity and soil volume specific heat comprises:

[0022] Determining the thermal conductivity of the soil within each preset time interval according to the product of the soil thermal diffusivity and the soil volume specific heat;

[0023] Acquire soil moisture content, and generate a scatter plot of the soil thermal conductivity and the soil moisture content based on the relationship between the soil thermal conductivity and the soil moisture content;

[0024] Based on a preset data fitting algorithm, fit the scatter plot to obtain the characteristic curve of the soil thermal conductivity and the soil water content.

[0025] In one embodiment, the step of fitting the characteristic curve to obtain the functional relationship between the soil thermal conductivity and the soil water content includes:

[0026] According to the characteristic curve, determine the preliminary relationship diagram between the soil thermal conductivity and the soil water content;

[0027] Based on the preset data fitting algorithm, fit the preliminary relationship diagram to obtain the functional relationship between the soil thermal conductivity and the soil water content. During the fitting process, adjust the parameters of the fitting function according to the soil thermal conductivity at different water contents to reduce the fitting error.

[0028] In one embodiment, after the step of fitting the characteristic curve to obtain the functional relationship between the soil thermal conductivity and the soil water content, it further includes:

[0029] Take the difference between the measured soil thermal conductivity and the fitted soil thermal conductivity as the residual;

[0030] Perform statistical analysis on the mean value, standard deviation, and maximum residual of the residuals;

[0031] Based on the statistical analysis results, evaluate the deviation between the fitted curve and the actual data;

[0032] If the evaluation result exceeds the preset deviation threshold, adjust the fitting model parameters and refit.

[0033] In one embodiment, the step of determining the target soil thermal parameters according to the functional relationship includes:

[0034] Obtain the input water content value;

[0035] According to the input water content value and the functional relationship, determine the target soil thermal conductivity;

[0036] According to the relationship between the target soil thermal conductivity and the soil thermal parameters, determine the target soil thermal parameters.

[0037] In addition, to achieve the above object, the present application also proposes a device for determining the thermal parameters of undisturbed rooted soil. The device for determining the thermal parameters of undisturbed rooted soil includes:

[0038] A data acquisition module for periodically acquiring soil temperature data at different depths based on a preset time interval;

[0039] A thermal diffusivity determination module, configured to determine the soil thermal diffusivity based on the soil temperature data by using an implicit difference numerical inversion algorithm;

[0040] A curve determination module, configured to determine a characteristic curve of the soil thermal conductivity and the soil water content according to the soil thermal diffusivity and the soil volumetric specific heat;

[0041] A fitting module, configured to fit the characteristic curve to obtain a functional relationship between the soil thermal conductivity and the soil water content;

[0042] A target module, configured to determine the target soil thermophysical parameters according to the functional relationship.

[0043] In addition, to achieve the above object, the present application further provides a storage medium, on which a program for determining the thermophysical parameters of undisturbed rooted soil is stored, and when the program for determining the thermophysical parameters of undisturbed rooted soil is executed by a processor, the steps of the method for determining the thermophysical parameters of undisturbed rooted soil as described above are implemented.

[0044] The present application periodically acquires soil temperature data at different depths based on a preset time interval; determines the soil thermal diffusivity based on the soil temperature data by using an implicit difference numerical inversion algorithm; determines a characteristic curve of the soil thermal conductivity and the soil water content according to the soil thermal diffusivity and the soil volumetric specific heat; fits the characteristic curve to obtain a functional relationship between the soil thermal conductivity and the soil water content; and determines the target soil thermophysical parameters according to the functional relationship. By acquiring soil temperature data at different depths periodically, calculating the soil thermal diffusivity by using an implicit difference numerical inversion algorithm, and combining with the soil volumetric specific heat, the present application accurately determines the characteristic curve between the soil thermal conductivity and the soil water content. Compared with the traditional method, it does not require soil disturbance or destruction, can be directly measured in the on-site natural environment, avoids the errors caused by sample extraction in the traditional method, and obtains an accurate functional relationship between the soil thermal conductivity and the soil water content by fitting the characteristic curve, improving the accuracy and convenience of determining the thermophysical parameters of undisturbed rooted soil. Description of the Drawings

[0045] Figure 1 It is a schematic flowchart of the first embodiment of the method for determining the thermophysical parameters of undisturbed rooted soil of the present application;

[0046] Figure 2 It is a schematic sub-flowchart of the second embodiment of the method for determining the thermophysical parameters of undisturbed rooted soil of the present application;

[0047] Figure 3 It is a schematic sub-flowchart of the third embodiment of the method for determining the thermophysical parameters of undisturbed rooted soil of the present application;

[0048] Figure 4Schematic diagram of the method for obtaining soil moisture content and temperature in an embodiment of the method for determining the thermophysical parameters of undisturbed planted soil in the original state of the present application;

[0049] Figure 5 Schematic diagram of characteristic curve fitting in an embodiment of the method for determining the thermophysical parameters of undisturbed planted soil in the original state of the present application;

[0050] Figure 6 Schematic diagram of the module structure of the device for determining the thermophysical parameters of undisturbed planted soil in the original state in an embodiment of the present application;

[0051] Figure 7 Schematic diagram of the device structure of the hardware operating environment involved in the method for determining the thermophysical parameters of undisturbed planted soil in the original state in an embodiment of the present application.

[0052] The realization, functional features and advantages of the purpose of the present application will be further described with reference to the embodiments and the accompanying drawings. Detailed implementation manners

[0053] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0054] In order to better understand the technical solution of the present application, the following will be described in detail in conjunction with the accompanying drawings of the specification and specific implementation manners.

[0055] It should be noted that soil, as a porous medium, its thermophysical parameters are crucial for understanding and predicting the thermal behavior of soil. In the natural state, the soil has a complex composition and is usually in a state of coexistence of solid, liquid and gas phases, and contains a large number of plant roots, organic matter and other components. The composition and spatial structure of the soil determine its thermophysical parameters, and these parameters are crucial in the applications in the fields of geotechnical engineering, underground building design, environmental assessment, etc. The existing methods for measuring the soil thermal conductivity usually cannot take into account both the accuracy and convenience of the measurement. For example, when using the steady-state method for measurement, it is necessary to dig out soil specimens and transfer them to the laboratory, and use a steady heat source to form a stable heat conduction process to calculate the soil thermal conductivity. This method has the problem of disturbing the soil structure, and due to the deterioration of organic matter or damage to roots during the transfer process, it is impossible to effectively ensure the consistency between the measured specimen and the undisturbed soil mass. The transient method has the advantage of being convenient and fast, but since the transient method requires the use of a specific thermal property analyzer, due to the short measurement time, it is impossible to effectively obtain the characteristic curve of the thermal conductivity under different water content states, and limited by the interval distance of the probes, it is impossible to accurately reflect the thermophysical parameters of the soil in the actual state in this area. Therefore, how to improve the accuracy and convenience of determining the thermophysical parameters of undisturbed planted soil has become an urgent technical problem to be solved.

[0056] The main solution of this application is as follows: Based on a preset time interval, periodically obtain soil temperature data at different depths; based on the soil temperature data, use an implicit difference numerical inversion algorithm to determine the soil thermal diffusivity; according to the soil thermal diffusivity and the soil volume specific heat, determine the characteristic curve of the soil thermal conductivity and the soil water content; fit the characteristic curve to obtain the functional relationship between the soil thermal conductivity and the soil water content; according to the functional relationship, determine the target soil thermophysical parameters.

[0057] In this application, based on the periodically obtained soil temperature data at different depths, an implicit difference numerical inversion algorithm is used to calculate the soil thermal diffusivity, and combined with the soil volume specific heat, the characteristic curve between the soil thermal conductivity and the soil water content is accurately determined. Compared with traditional methods, it does not require soil disturbance or destruction, can be directly measured in the natural field environment, avoids the errors caused by sample extraction in traditional methods, and through the fitting of the characteristic curve, an accurate functional relationship between the soil thermal conductivity and the soil water content is obtained, improving the accuracy and convenience of determining the thermophysical parameters of undisturbed rooted soil.

[0058] It should be noted that the execution subject of the method in this embodiment can be a computing service device with data processing, network communication, and program running functions, or the above-mentioned device for determining the thermophysical parameters of undisturbed rooted soil with the same or similar functions. This embodiment and the following embodiments will be described by taking the device for determining the thermophysical parameters of undisturbed rooted soil as an example.

[0059] Based on this, a first embodiment of the method for determining the thermophysical parameters of undisturbed rooted soil in this application is proposed. Please refer to Figure 1 , Figure 1 which is a schematic flowchart of the first embodiment of the method for determining the thermophysical parameters of undisturbed rooted soil in this application.

[0060] In this embodiment, the method for determining the thermophysical parameters of undisturbed rooted soil includes the following steps:

[0061] S1: Based on a preset time interval, periodically obtain soil temperature data at different depths;

[0062] It should be noted that the preset time interval refers to performing periodic measurements at a pre-set time interval during the data collection process. The soil temperature data refers to the soil temperature values collected by temperature sensors. These data reflect the temperature changes of the soil at different times and depths.

[0063] Specifically, first, a suitable time interval needs to be set, and this interval is adjusted according to actual needs. By periodically obtaining the soil temperature data, continuous monitoring of the soil temperature can be ensured, thereby capturing the temperature changes of the soil at different time periods.

[0064] Furthermore, select appropriate measurement positions. Generally, multiple measurement points need to be set at different depths of the soil (such as the surface layer, deep layer, etc.). These measurement points usually use temperature sensors to collect data. Each sensor records the temperature data at a specific depth. By periodically obtaining these temperature data, the trend of the soil temperature change with time at each depth position can be obtained, providing basic data support for the subsequent calculation of thermal parameters.

[0065] By obtaining soil temperature data at different depths at regular intervals, the temperature changes of the soil at multiple levels and multiple time points can be comprehensively and continuously reflected. This periodic monitoring not only improves the timeliness and accuracy of the data, but also can capture the dynamic changes of the soil. Further analysis can reveal the heat transfer characteristics between different depths of the soil, providing accurate basic data for the inversion calculation of subsequent soil thermal parameters, ensuring that the measurement results are more representative and scientific. Therefore, this process significantly improves the accuracy of determining soil thermal parameters and provides stable and reliable data support for applications in different fields.

[0066] S2: Based on the soil temperature data, use the implicit difference numerical inversion algorithm to determine the soil thermal diffusivity;

[0067] It should be noted that the implicit difference numerical inversion algorithm is a method of numerical calculation, which is often used to solve heat transfer problems involving time or space changes. Different from the explicit method, the implicit method solves the stability problem in the calculation process through more efficient iterative steps and is suitable for dealing with complex and irregular physical phenomena. The soil thermal diffusivity is an important parameter describing the heat conduction ability of the soil, reflecting the diffusion speed of heat in the soil. The level of the thermal diffusivity determines the propagation rate of heat in the soil and is an important item in the soil thermal parameters.

[0068] Specifically, collect the soil temperature data within a certain time period. These data are periodically collected by temperature sensors at different depth points. The temperature data of each collection point will be used as the basis for the inversion calculation. According to the known soil temperature data and the set calculation model, assume an initial value of the thermal diffusivity. Based on this assumed value, use the implicit difference method to gradually calculate the temperature changes of the soil at different times. The implicit difference method transforms the complex heat conduction process into a solvable linear equation by introducing the difference formulas of time and space, and through multiple iterations, optimizes the calculation results to minimize the error.

[0069] Furthermore, adjust the thermal diffusivity through multiple iterations until the difference (error) between the calculated temperature and the actual measured temperature is minimized, so as to determine the most accurate value of the soil thermal diffusivity. This process eliminates the possible errors and instabilities in the traditional methods through precise numerical calculation methods, ensuring the reliability of the inversion results of the thermal diffusivity.

[0070] By adopting the implicit difference numerical inversion algorithm, the thermal diffusivity of the soil can be accurately calculated from the soil temperature data effectively. It not only overcomes the possible stability problems of traditional explicit calculation methods, but also can handle complex soil heat conduction processes, providing a more accurate estimation of the thermal diffusivity. Through this process, the calculation result of the soil thermal diffusivity can be ensured to be more reliable, and basic data for subsequent calculation of thermal parameters can be provided, significantly improving the accuracy and stability of soil thermal parameter inversion. In addition, the introduction of the implicit difference method makes this method have strong adaptability, capable of dealing with various different soil structures and environmental conditions, thus enhancing the wide applicability and reliability of soil thermal parameter determination.

[0071] S3: Determine the characteristic curve of soil thermal conductivity and soil water content according to the soil thermal diffusivity and soil volumetric specific heat;

[0072] It should be noted that the soil volumetric specific heat represents the heat absorbed or released by unit volume of soil when the temperature changes. It depends on the specific heat capacities of various components (such as water, organic matter, and minerals) in the soil and their volume ratios. The soil thermal conductivity is a thermal conductivity parameter of the soil, indicating the ability of heat flow per unit area to pass through the soil per unit time. It is affected by factors such as the structure, composition (such as water content), and temperature of the soil. The soil water content represents the proportion of water in the soil in the total volume of the soil. The characteristic curve is a curve describing the relationship between soil thermal conductivity and soil water content, reflecting the thermal conductivity characteristics of the soil under different water content conditions.

[0073] Specifically, according to the soil thermal diffusivity and volumetric specific heat data, calculate the thermal properties of the soil at different water contents. The soil thermal diffusivity and volumetric specific heat are usually known, obtained through experimental measurement or calculation. Based on these data, calculate the soil thermal conductivity. Using the relationship between the known thermal diffusivity and volumetric specific heat, combined with different soil water content conditions, determine the characteristic curve between soil thermal conductivity and soil water content. This process requires the calculation and analysis of data at multiple measurement points to ensure that the variation trend of soil thermal conductivity under different soil water content conditions is correctly expressed.

[0074] Furthermore, calculate the thermal conductivities of multiple groups of soil samples at different water contents, compare them with the water content data, and plot the relationship curve between soil thermal conductivity and soil water content. This curve reflects how the soil thermal conductivity changes with the water content, providing the thermal conduction characteristics of the soil under different water conditions. Finally, a precise characteristic curve is formed, providing a scientific basis for subsequent calculation of soil thermal parameters.

[0075] By combining the thermal diffusivity and volumetric specific heat of the soil to determine the characteristic curve of the soil thermal conductivity and soil water content, the change of the soil thermal conductivity under different water contents can be accurately reflected. This process combines the thermal properties of the soil with the actual moisture conditions to ensure the accuracy of the soil thermal conductivity in the real environment. Through this method, accurate thermal conductivity data can be provided for soils with different water contents, further improving the accuracy of soil thermal parameters, especially in the variable natural environment. Finally, this characteristic curve can not only provide reliable data for the calculation of soil thermal parameters, but also provide effective thermal parameter support for fields such as underground engineering, agriculture, and environmental analysis, thus enabling more accurate engineering design and environmental management.

[0076] S4: Fit the characteristic curve to obtain the functional relationship between the soil thermal conductivity and the soil water content;

[0077] It should be noted that the functional relationship between the soil thermal conductivity and the soil water content refers to the mathematical formula obtained by fitting the characteristic curve, which describes how the soil thermal conductivity changes with the change of the soil water content. It provides a mathematical model that can predict the soil thermal conductivity based on the water content of the soil.

[0078] Specifically, based on the characteristic curve of the soil thermal conductivity and the soil water content obtained in the previous step, collect and organize the thermal conductivity data measured under different water contents. These data points show a certain trend. Usually, as the soil water content increases, the thermal conductivity shows a certain change. Use a pre-selected mathematical method to fit these data points. Common fitting methods include the least squares method, exponential fitting, or power-law fitting, etc. The purpose of fitting is to find a functional relationship that can accurately describe the relationship between the soil thermal conductivity and the soil water content as much as possible. For example, the least squares method can be used to obtain the optimal function parameters by minimizing the square difference between the predicted value and the actual observed value. Through this process, a smooth fitting curve can be obtained, and the functional relationship between the soil thermal conductivity and the soil water content can be derived from this curve. This functional relationship will provide a reliable mathematical basis for the subsequent calculation and analysis of soil thermal parameters.

[0079] By fitting the characteristic curve of soil thermal conductivity and soil water content, an accurate functional relationship can be obtained, which provides a mathematical model for subsequent calculation of soil thermal parameters. This fitting process not only reduces the volatility of data but also improves the accuracy of the relationship between soil thermal conductivity and soil water content. Through the obtained functional relationship, the thermal conductivity under different soil water content conditions can be effectively predicted, providing an accurate and reproducible calculation basis for the analysis and design of soil thermal parameters in practical engineering. This method not only improves the calculation accuracy but also provides reliable support for modeling the soil thermal behavior under different environmental conditions, ensuring the stability and scientificity of soil thermal parameters.

[0080] S5: Determine the target soil thermal parameters according to the said functional relationship.

[0081] Step S5 includes:

[0082] S51: Obtain the input water content value;

[0083] S52: Determine the target soil thermal conductivity according to the input water content value and the said functional relationship;

[0084] S53: Determine the target soil thermal parameters according to the relationship between the target soil thermal conductivity and soil thermal parameters.

[0085] It should be noted that the target soil thermal parameters refer to the thermal property parameters of the soil obtained through calculation or derivation, commonly including soil thermal conductivity, thermal diffusivity, specific heat capacity, etc. The target soil thermal parameters are the basic data required for engineering design, environmental analysis, and prediction of soil thermal behavior.

[0086] Specifically, use the functional relationship obtained by fitting, which has accurately described the mathematical relationship between soil thermal conductivity and soil water content. By substituting the actually measured or preset soil water content value into this functional relationship, the corresponding soil thermal conductivity can be calculated. Then, according to the known relationship between the known soil thermal conductivity and other thermal parameters, other relevant target soil thermal parameters are calculated. For example, if it is necessary to calculate the thermal diffusivity or specific heat capacity of the soil, these thermal parameters can be further deduced by combining data such as thermal conductivity, soil volumetric specific heat, and density. Finally, the obtained soil thermal parameters can be used for more refined underground engineering design, geological exploration, or environmental impact assessment and other work. This process ensures the accuracy and reliability of soil thermal parameters and provides effective support for practical applications.

[0087] By substituting the actual soil moisture value into the functional relationship obtained by fitting, the thermal conductivity of the soil can be calculated quickly and accurately. This process not only improves the accuracy of determining soil thermal parameters, but also makes the calculation of soil thermal parameters more efficient by providing a simple mathematical model. This method avoids tedious experimental measurements and directly predicts through mathematical models, which greatly improves calculation efficiency and reduces experimental costs. In addition, based on these parameters, other key soil thermal properties can be further derived, providing reliable decision-making basis and parameter support for underground engineering, agriculture, environmental analysis, etc., thereby ensuring the scientificity and accuracy of soil thermal behavior.

[0088] This embodiment periodically obtains soil temperature data at different depths based on a preset time interval; based on the soil temperature data, an implicit difference numerical inversion algorithm is used to determine the soil thermal diffusivity; based on the soil thermal diffusivity and the soil volume specific heat, a characteristic curve of soil thermal conductivity and soil water content is determined; the characteristic curve is fitted to obtain a functional relationship between soil thermal conductivity and soil water content; and the target soil thermal parameters are determined based on the functional relationship. This embodiment calculates soil thermal diffusivity based on periodically obtained soil temperature data at different depths using an implicit difference numerical inversion algorithm, and accurately determines the characteristic curve between soil thermal conductivity and soil water content in combination with the soil volume specific heat. Compared with traditional methods, there is no need to disturb or destroy the soil, and measurements can be performed directly in the natural environment on site, avoiding errors caused by sample extraction in traditional methods. By fitting the characteristic curve, an accurate functional relationship between soil thermal conductivity and soil water content is obtained, which improves the accuracy and convenience of determining the thermal parameters of the original rooted soil.

[0089] Based on the above first embodiment, a second embodiment of the method for determining thermal parameters of undisturbed rooted soil of the present application is proposed. Figure 2 , Figure 2 This is a schematic diagram of a sub-process in the second embodiment of the method for determining thermal parameters of original rooted soil of the present application.

[0090] like Figure 2 As shown, in this embodiment, after step S1, the following is further included:

[0091] S1a: Acquire soil monitoring data, and determine, based on the soil monitoring data, whether there is rainfall and / or irrigation when the soil temperature data is collected;

[0092] S1b: If yes, mark the soil temperature data as invalid data and discard it.

[0093] It should be noted that soil monitoring data refers to soil-related information collected through various sensors or monitoring devices, including but not limited to data such as soil temperature, humidity, and water content. These data are used to analyze the thermal properties of the soil and their changing trends. Invalid data refers to data that cannot truly reflect the soil state due to interference from external factors (such as rainfall, irrigation, etc.) on monitoring data such as soil temperature. If such data is not excluded, it will affect the subsequent analysis results and the accuracy of soil thermal parameters.

[0094] Specifically, soil temperature data for different time periods is obtained through a soil monitoring system. When obtaining the data, the rainfall and irrigation conditions need to be recorded synchronously. This information can be obtained through external meteorological data interfaces, ground monitoring devices, weather forecasting systems, etc., or through specially installed rainfall sensors and irrigation monitoring devices for real-time detection. Once the soil temperature data is collected and the relevant rainfall and irrigation information is obtained, the next step is to determine whether these data were affected by rainfall and / or irrigation during the collection. If a rainfall or irrigation event occurred during that time period, it is considered that the soil temperature data for that period may have been disturbed and cannot accurately reflect the true thermal properties of the soil. If it is confirmed that there is rainfall or irrigation, the data will be marked as invalid data and excluded from the dataset or the analysis process. The processed dataset only contains soil temperature data that is not disturbed by external factors, ensuring the accuracy and reliability of subsequent calculations.

[0095] By judging and excluding soil temperature data affected by rainfall and irrigation, the quality of soil temperature data can be significantly improved. Rainfall and irrigation can cause drastic fluctuations or short-term changes in soil temperature, which may be irrelevant to the actual heat conduction properties of the soil, thus interfering with the subsequent analysis of thermal parameters. Excluding invalid data can ensure the true representativeness of soil temperature data, reduce the influence of external interference, and improve the accuracy of analysis results. In addition, this process helps to maintain the stability of the data, enabling the calculation of soil thermal parameters to be based on a more accurate and reliable dataset, thereby enhancing the scientific nature and practical application value of the calculation results. For monitoring data over a long time period, excluding interfering data also helps to ensure the accurate prediction of the long-term trends and laws of soil thermal behavior, thus providing more reliable data support for fields such as underground engineering, agriculture, and climate change research.

[0096] Based on the above first embodiment, step S2 includes:

[0097] S21: Based on the soil temperature data, determine the temperature change at each measurement point within each preset time interval;

[0098] S22: Based on the temperature change, use the implicit difference numerical inversion algorithm to determine the soil temperature at the next time point;

[0099] S23: Compare the soil temperature at the next time point with the measured temperature, and determine the sum of squared errors according to the comparison result;

[0100] S24: Based on the sum of squared errors, iteratively optimize the initial soil thermal diffusivity to obtain the soil thermal diffusivity.

[0101] Step S24 includes:

[0102] S241: Set an objective function based on the sum of squared errors;

[0103] S242: Use a preset optimization algorithm to iteratively optimize the initial soil thermal diffusivity by minimizing the objective function until the sum of squared errors meets a preset threshold, and obtain the soil thermal diffusivity.

[0104] It should be noted that the sum of squared errors is an index obtained by summing the squares of the differences between the calculated temperature and the actual measured temperature, indicating the difference between the calculation result and the actual observation value. The objective function refers to a mathematical function that needs to be minimized or maximized during the optimization process. In this step, the objective function is usually the sum of squared errors, which is used to measure the deviation between the calculation result and the actual data. The optimization algorithm refers to an algorithm that adjusts parameters to gradually reduce the value of the objective function (such as the sum of squared errors) to obtain the optimal result. Commonly used ones include the gradient descent method, Newton's method, etc.

[0105] Specifically, based on the collected soil temperature data, calculate the temperature change at each measurement point within each preset time interval. By comparing the temperature differences between adjacent time points, the temperature change trend of the soil within a specific time period can be determined. These data provide the basis for subsequent calculation of the thermal diffusivity. Next, use the implicit difference numerical inversion algorithm to calculate the soil temperature at the next time point according to the known temperature change. The implicit difference method introduces difference formulas for time and space and gradually approaches the correct soil temperature during the iteration process. This method can effectively solve the stability problem in numerical calculation and is especially suitable for complex soil heat conduction models.

[0106] Furthermore, compare the calculated soil temperature at the next time point with the actual measured temperature, and evaluate the accuracy of the model by calculating the sum of squared errors. The sum of squared errors is an index that measures the difference between the predicted temperature of the model and the actual measured temperature. A smaller sum of squared errors indicates that the prediction of the model is more accurate. Based on the sum of squared errors, by setting an objective function and using a preset optimization algorithm (such as Newton's method or the gradient descent method), gradually optimize the value of the initial soil thermal diffusivity until the sum of squared errors meets the preset threshold. In this process, the optimization algorithm adjusts the value of the thermal diffusivity according to the magnitude of the error, gradually reducing the error until the difference between the calculated soil temperature and the measured temperature is minimized.

[0107] Through the above steps, the accurate inversion of the soil thermal diffusivity can be achieved. First, by calculating the temperature change and using the implicit difference numerical inversion algorithm to determine the temperature change trend of the soil, the soil temperature at the next time point can be accurately predicted. This provides stable basic data for subsequent calculations. By comparing the sum of the squared errors between the calculated temperature and the measured temperature, the initial value of the soil thermal diffusivity is optimized, ensuring the calculation accuracy and stability of the thermal diffusivity. Finally, the soil thermal diffusivity obtained through iterative optimization can accurately reflect the heat conduction characteristics of the soil, not only improving the accuracy of the inversion of soil thermal parameters, but also providing a reliable mathematical model for the long-term monitoring and analysis of soil thermal characteristics, ensuring the scientificity and practicality of soil thermal behavior.

[0108] In this embodiment, based on a preset time interval, soil temperature data at different depths are periodically obtained; based on the soil temperature data, the implicit difference numerical inversion algorithm is used to determine the soil thermal diffusivity; according to the soil thermal diffusivity and the soil volumetric specific heat, the characteristic curve of the soil thermal conductivity and the soil water content is determined; the characteristic curve is fitted to obtain the functional relationship between the soil thermal conductivity and the soil water content; according to the functional relationship, the target soil thermal parameters are determined. In this embodiment, by using the implicit difference numerical inversion algorithm to calculate the soil thermal diffusivity based on the periodically obtained soil temperature data at different depths and combining the soil volumetric specific heat, the characteristic curve between the soil thermal conductivity and the soil water content is accurately determined. Compared with the traditional method, there is no need for soil disturbance or destruction, and it can be directly measured in the natural field environment, avoiding the errors caused by sample extraction in the traditional method. By fitting the characteristic curve, the accurate functional relationship between the soil thermal conductivity and the soil water content is obtained, improving the accuracy and convenience of determining the thermal parameters of undisturbed rooted soil.

[0109] Based on the above second embodiment, a third embodiment of the method for determining the thermal parameters of undisturbed rooted soil of the present application is proposed. Please refer to Figure 3 , Figure 3 which is a schematic diagram of a sub-process in the third embodiment of the method for determining the thermal parameters of undisturbed rooted soil of the present application.

[0110] In this embodiment, step S3 includes:

[0111] S31: Determine the soil thermal conductivity within each preset time interval according to the product of the soil thermal diffusivity and the soil volumetric specific heat;

[0112] S32: Obtain the soil water content, and generate a scatter plot of the soil thermal conductivity and the soil water content based on the relationship between the soil thermal conductivity and the soil water content;

[0113] S33: Based on a preset data fitting algorithm, fit the scatter plot to obtain the characteristic curve of the soil thermal conductivity and the soil water content.

[0114] It should be noted that a scatter plot is a common data visualization chart that represents the relationship between two variables through points in the graph. In this step, the scatter plot is used to show the relationship between the soil thermal conductivity and the soil water content.

[0115] Specifically, according to the determined soil thermal diffusivity and soil volumetric heat capacity, calculate the soil thermal conductivity for each preset time interval. This calculation process is based on the known physical relationship between the soil thermal diffusivity and the volumetric heat capacity. By multiplying these two parameters, the soil thermal conductivity for each time period can be obtained, which provides important basic data for subsequent analysis. Based on the relationship between the calculated soil thermal conductivity and the soil water content, a scatter plot is generated. Each data point represents the thermal conductivity value at a specific soil water content. Through these scatter points, it is possible to intuitively observe how the soil thermal conductivity changes with the soil water content. This chart provides visual data support for the subsequent fitting work.

[0116] Furthermore, use a preset data fitting algorithm to fit the generated scatter plot above to obtain the characteristic curve of the soil thermal conductivity and the soil water content. The goal of the fitting process is to express the relationship between the scatter data as accurately as possible through a mathematical model. Common fitting methods include the least squares method, power law fitting, exponential fitting, etc. Through fitting, the obtained characteristic curve can more clearly describe the functional relationship between the soil thermal conductivity and the soil water content, and provide reliable model support for subsequent thermal parameter analysis.

[0117] By determining the soil thermal conductivity based on the product of the soil thermal diffusivity and the volumetric heat capacity, generating a scatter plot and then performing fitting, the relationship between the soil thermal conductivity and the soil water content can be accurately revealed. This process can transform the complex soil heat conduction characteristics into an operable mathematical model. Through the fitted characteristic curve, it is possible to more effectively predict the soil thermal conductivity under different water content conditions. This provides strong support for further soil thermal parameter calculation and analysis, especially when there are no complex equipment or laboratory conditions, this method can be relied on to quickly obtain accurate soil thermal parameters. In addition, the characteristic curve obtained through fitting not only improves the accuracy of the data, but also can be applied to a wide range of engineering and environmental analyses, and has important practical application value.

[0118] Based on the above second embodiment, in this embodiment, step S4 includes:

[0119] S41: According to the characteristic curve, determine the preliminary relationship diagram between the soil thermal conductivity and the soil water content;

[0120] S42: Based on the preset data fitting algorithm, fit the preliminary relationship graph to obtain the functional relationship between the soil thermal conductivity and the soil water content. During the fitting process, adjust the parameters of the fitting function according to the soil thermal conductivity at different water contents to reduce the fitting error.

[0121] It should be noted that the preliminary relationship graph is a preliminary visualization chart obtained from the characteristic curve, which shows the preliminary relationship between the soil thermal conductivity and the soil water content. This chart usually presents the basic trend or pattern between the two. The fitting error is the difference between the value calculated by the fitting function and the actual observed data, usually represented by the residual (the vertical distance between the data point and the fitting curve). During the fitting process, it is desired to reduce the fitting error to ensure a more accurate fitting result.

[0122] Specifically, based on the previously obtained characteristic curve, determine the preliminary relationship graph between the soil thermal conductivity and the soil water content. This graph will show the approximate relationship between the soil thermal conductivity and the water content, which may be a linear or non-linear relationship. By plotting the data points of the characteristic curve on the graph, it is possible to visually observe how the soil thermal conductivity changes with the soil water content. This preliminary relationship graph provides a basis for further fitting and helps to identify potential trends and patterns.

[0123] Furthermore, based on the preset data fitting algorithm, fit the preliminary relationship graph to obtain the functional relationship between the soil thermal conductivity and the soil water content. The goal of this process is to minimize the error between the data points and the fitting curve through a fitting algorithm (such as the least squares method, exponential fitting, or polynomial fitting). During the fitting process, adjust the parameters of the fitting function according to the soil thermal conductivity at different water contents to reduce the fitting error and improve the fitting accuracy. This optimization process iteratively adjusts the fitting function to ensure that it can represent the relationship between the soil thermal conductivity and the soil water content as accurately as possible.

[0124] By determining the preliminary relationship graph based on the characteristic curve and performing fitting, the functional relationship between the soil thermal conductivity and the soil water content can be accurately obtained. This process can not only accurately express the complex soil thermal behavior through a mathematical model, but also effectively reduce the fitting error by adjusting the parameters of the fitting function, thereby improving the accuracy of the model. This enables a reliable quantitative description of the relationship between the soil thermal conductivity and the soil water content and provides an accurate mathematical basis for practical applications. Through this method, it is possible to ensure that the calculation results of soil thermal parameters are more stable and scientific, and provide effective data support for fields such as engineering design and environmental analysis, thereby achieving accurate prediction and optimization of soil thermal behavior.

[0125] Based on the above second embodiment, in this embodiment, after step S4, the following steps are further included:

[0126] S4a: Take the difference between the measured soil thermal conductivity and the fitted soil thermal conductivity as the residual;

[0127] S4b: Conduct statistical analysis on the mean value, standard deviation, and maximum residual of the residuals;

[0128] S4c: Evaluate the deviation between the fitted curve and the actual data based on the statistical analysis results;

[0129] S4d: If the evaluation result exceeds the preset deviation threshold, adjust the parameters of the fitting model and refit.

[0130] It should be noted that the measured soil thermal conductivity refers to the value of the soil thermal conductivity obtained through experiments or on-site measurements, representing the heat conduction ability of the actual soil. The fitted soil thermal conductivity refers to the predicted value of the soil thermal conductivity obtained through a fitting algorithm, which is calculated through a mathematical model and used for comparison with the measured value. The preset deviation threshold refers to the preset error standard. When the deviation of the residual exceeds this threshold, the fitting result is considered unsatisfactory and the model needs to be adjusted.

[0131] Specifically, by comparing the measured soil thermal conductivity with the fitted soil thermal conductivity, the difference between them is calculated. This difference is the residual, which reflects the deviation between the fitted value and the actual value. The residual is an important indicator for evaluating the fitting effect. A smaller residual indicates a more accurate fitting result, while a larger residual indicates a larger error in the fitting model. Calculate the mean value of the residuals, which represents the average difference between the fitting result and the actual data; calculate the standard deviation of the residuals, which describes the fluctuation range of the fitting error; calculate the maximum residual, that is, the maximum value of the residuals, representing the most serious deviation. Through these statistics, the difference between the fitting model and the measured data can be comprehensively evaluated.

[0132] Furthermore, based on the statistical analysis results, the deviation between the fitted curve and the actual data is evaluated. If it is found through statistical analysis such as the mean value, standard deviation, and maximum residual that the fitting error is too large, that is, the deviation exceeds the preset deviation threshold, it is considered that the current fitting model fails to accurately describe the actual data. If the evaluation result shows that the deviation exceeds the preset threshold, the parameters of the fitting model need to be adjusted. By adjusting the parameters of the fitting function (such as coefficients, exponents, etc.), the model can be optimized to better adapt to the actual data. The adjusted fitting model will be refitted until the fitting result meets the accuracy requirements, the residual is small enough, and the deviation no longer exceeds the preset threshold.

[0133] By calculating and analyzing the residuals, the deviation between the fitting model and the actual data can be clearly understood, thereby ensuring the accuracy of the fitting result. Statistical analysis provides a quantitative method, making the evaluation of the fitting error more scientific and objective. When the deviation exceeds the preset threshold, by adjusting the fitting model parameters and refitting, the fitting error can be effectively reduced and the accuracy of the fitting curve can be improved. This process ensures the accuracy and reliability of the model, avoids incorrect analysis results caused by inaccurate models, and thus provides more accurate data support for subsequent calculations of soil thermophysical parameters. In addition, this process also improves the stability and adaptability of the fitting algorithm, enabling the calculation of soil thermophysical parameters to adapt to different soil types and environmental conditions.

[0134] In this embodiment, based on a preset time interval, soil temperature data at different depths are periodically obtained; based on the soil temperature data, an implicit difference numerical inversion algorithm is used to determine the soil thermal diffusivity; according to the soil thermal diffusivity and the soil volumetric specific heat, the characteristic curve of the soil thermal conductivity and the soil water content is determined; the characteristic curve is fitted to obtain the functional relationship between the soil thermal conductivity and the soil water content; according to the functional relationship, the target soil thermophysical parameters are determined. In this embodiment, by using the implicit difference numerical inversion algorithm to calculate the soil thermal diffusivity based on the periodically obtained soil temperature data at different depths and combining with the soil volumetric specific heat, the characteristic curve between the soil thermal conductivity and the soil water content is accurately determined. Compared with the traditional method, there is no need for soil disturbance or destruction, and it can be directly measured in the natural field environment, avoiding the errors caused by sample extraction in the traditional method. By fitting the characteristic curve, an accurate functional relationship between the soil thermal conductivity and the soil water content is obtained, improving the accuracy and convenience of determining the thermophysical parameters of undisturbed rooted soil.

[0135] Exemplarily, to help understand the technical concept or technical principle of the method for determining the thermophysical parameters of undisturbed rooted soil in the above embodiment, please refer to Figure 4 and Figure 5 , Figure 4 which is a schematic diagram of the method for obtaining soil moisture content and temperature in an embodiment of the method for determining the thermophysical parameters of undisturbed rooted soil in this application; Figure 5 which is a schematic diagram of characteristic curve fitting in an embodiment of the method for determining the thermophysical parameters of undisturbed rooted soil in this application.

[0136] In one embodiment, the method for determining the thermophysical parameters of undisturbed rooted soil includes the following steps:

[0137] A1: Data collection and screening

[0138] Select a measurement location, and select three points at different depths at the measurement location as measurement points. Name the measurement points as point j-1, point j, and point j+1. Set the depth where point j is located as z, and the vertical interval distance between the measurement points as Δz. Use a temperature sensor to obtain the temperature measurement data of the measurement points, and the measurement time interval is Δt, which needs to satisfy Δt ≤ 1h. In this embodiment, z = 7cm, Δz = 3cm, and Δt = 10min are taken. Obtain long-term temperature data for the measurement location, and use 0:00-12:00 and 12:00-24:00 every day as a data acquisition period (12h). These temperature data are used as the inversion data of the soil thermal coefficient for this period. In particular, screen the data. The data for which the weather at the detection location is rainless during the data acquisition period and there is no irrigation behavior at the detection location during this period are screened as valid data.

[0139] A2: Calculate the soil thermal diffusivity a during the data acquisition period

[0140] A21: First, given a thermal diffusivity a and knowing the temperature value at the initial moment of the data acquisition period at depth j Then, through the temperature T obtained by actual measurement at each time interval j+1 and T j-1 , gradually calculate the temperature at point j at the (n + 1)-th moment. The specific calculation formula is as follows:

[0141]

[0142] In the above formula, the subscripts j, j + 1, and j - 1 represent the temperature measurement points at three depths with an interval distance of Δz, and the superscripts n and n + 1 represent two time intervals with a measurement time interval of Δt.

[0143] A22: Through the temperature T obtained by actual measurement at each time interval j+1 and T j-1 , gradually calculate the temperature at point j at the (n + 1)-th moment. Take the sum of the squares of the errors between the calculated temperature T j and the actually measured temperature at this depth as the objective function That is: Then, the soil thermal diffusivity during this period is the a that minimizes the objective function.

[0144] A22 includes:

[0145] A22-1: Assume an initial soil thermal diffusivity a0, and the sequentially searched thermal diffusivities are a1, a2, a3......a n-1 , a n , a n+1 ,...... The initial iteration step k = 0, H is the identity matrix, and H0 = H.

[0146] A22-2: Calculate the gradient Information matrix Thereby obtaining the iteration direction step size

[0147] A22-3: Judge the iteration step size d k Whether it is less than the specified value d min If satisfied, the thermal diffusivity a(i) within a data period can be obtained. If not, proceed to the next step A22-4.

[0148] A22-4: Perform data iteration

[0149]

[0150] A22-5: Let k = k + 1, and return to step A22-2

[0151] Through the above calculations, the thermal diffusivity a(i) within a data acquisition period can be obtained.

[0152] A3: Thermal conductivity calculation.

[0153] A3 includes:[[]]

[0154] A31 Record the soil moisture sensor

[0155] Taking the water content measured at point j as the reference standard, record the soil moisture value θ w (n) during the observation period through the soil moisture sensor, with a sampling frequency of f. Assume the sampling frequency is 1 time per 10 minutes. And calculate the average values θ w (i) of the two time periods from 0:00 to 12:00 and from 12:00 to 24:00 every day as the subsequent inversion calculation data.

[0156] A32 Calculate the volumetric specific heat of the soil at the detection site

[0157] Respectively use the core method, pycnometer method, and potassium dichromate oxidation method to determine the plant root, solid mineral, and organic matter contents. The soil moisture value is the average moisture content of a cycle calculated from the value recorded in S31. Use the following formula to obtain the volumetric specific heat c P (i) of the soil within a sampling period:[[]]

[0158] c P (i) = c m θ m + c o θ o + c r θ r + c w θ w (i) + c a θa

[0159] Among them, the first four items on the right side of the equal sign are the heat capacities of minerals, organic fertilizers, plant root tissues, and liquid water per unit volume respectively. Among them, c m is the specific heat capacity of soil minerals, usually taken as 1.92×10 6 J / m 3 / K, θ m is the content of minerals in the soil; c o is the specific heat capacity of soil organic matter, usually taken as 2.51×10 6 J / m 3 / K, θ o is the content of organic matter in the soil; c r is the specific heat capacity of soil plant root tissues, which is about 2.39×10 6 J / m 3 / K, θ r is the content of soil plant root tissues; c w is the specific heat capacity of soil liquid water, usually taken as 4.18×10 6 J / m 3 / K, θ w (i) is the content of liquid water in the soil; and the volume heat capacity of gas is much smaller than that of other substances, so this item can be ignored. Let c a θ a be 0. θ is the volume ratio of various substances, and their sum is 1.

[0160] A33 Draw a data graph and fit to obtain the functional relationship of the thermal conductivity

[0161] The thermal conductivity λ(i) in one sampling period can be obtained by multiplying the thermal diffusivity by the volume specific heat

[0162] λ(i) = a(i) × c p (i)

[0163] The corresponding θ w (i) and λ(i) in one period are used as the horizontal and vertical coordinates respectively, and the relationship graph between the thermal conductivity and the soil water content can be obtained. The following formula is used to fit the scatter data, and the functional relationship between the thermal conductivity λ and the water content θ w can be obtained.

[0164] λ(θ w ) = a + b×θ w + c×θ w 0.5 .

[0165] The embodiment of the present application also provides a device for determining the thermophysical parameters of undisturbed rooted soil. Please refer to Figure 6 , Figure 6It is a schematic diagram of the module structure of the device for determining the thermophysical parameters of undisturbed root-planting soil in the embodiment of the present application. The device for determining the thermophysical parameters of undisturbed root-planting soil includes:

[0166] A data acquisition module 601, configured to periodically acquire soil temperature data at different depths based on a preset time interval;

[0167] A thermal diffusivity determination module 602, configured to determine the soil thermal diffusivity based on the soil temperature data by using an implicit difference numerical inversion algorithm;

[0168] A curve determination module 603, configured to determine the characteristic curve of the soil thermal conductivity and the soil water content according to the soil thermal diffusivity and the soil volumetric specific heat;

[0169] A fitting module 604, configured to fit the characteristic curve to obtain the functional relationship between the soil thermal conductivity and the soil water content;

[0170] A target module 605, configured to determine the target soil thermophysical parameters according to the functional relationship.

[0171] The device for determining the thermophysical parameters of undisturbed root-planting soil provided in the embodiment of the present application adopts the method for determining the thermophysical parameters of undisturbed root-planting soil in the above embodiment, and can solve the technical problem of how to improve the accuracy and convenience of determining the thermophysical parameters of undisturbed root-planting soil. Compared with the prior art, the beneficial effects of the device for determining the thermophysical parameters of undisturbed root-planting soil provided in the embodiment of the present application are the same as those of the method for determining the thermophysical parameters of undisturbed root-planting soil provided in the above embodiment, and other technical features in the device for determining the thermophysical parameters of undisturbed root-planting soil are the same as those disclosed in the method of the above embodiment, and will not be elaborated here.

[0172] The present application provides a device for determining the thermophysical parameters of undisturbed root-planting soil. The device for determining the thermophysical parameters of undisturbed root-planting soil includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for determining the thermophysical parameters of undisturbed root-planting soil in the above embodiment.

[0173] Next, refer to Figure 7 , Figure 7 It is a schematic diagram of the device structure of the hardware operating environment involved in the method for determining the thermophysical parameters of undisturbed root-planting soil in the embodiment of the present application, which shows the schematic diagram of the structure of the device for determining the thermophysical parameters of undisturbed root-planting soil suitable for implementing the embodiment of the present application. Figure 7 The device for determining the thermophysical parameters of undisturbed root-planting soil shown is only an example, and should not bring any limitation to the functions and usage scope of the embodiment of the present application.

[0174] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product that includes a computer program carried on a computer-readable medium, and the computer program contains program codes for executing the methods shown in the flowcharts. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the methods of the embodiments disclosed in the present application are executed.

[0175] The in-situ root-embedded soil thermophysical parameter determination device provided by the present application adopts the in-situ root-embedded soil thermophysical parameter determination method in the above-mentioned embodiment, and can solve the technical problem of how to improve the accuracy and convenience of determining the in-situ root-embedded soil thermophysical parameters. Compared with the prior art, the beneficial effects of the in-situ root-embedded soil thermophysical parameter determination device provided by the present application are the same as those of the in-situ root-embedded soil thermophysical parameter determination method provided by the above-mentioned embodiment, and other technical features in the in-situ root-embedded soil thermophysical parameter determination device are the same as the features disclosed in the method of the previous embodiment, and will not be elaborated here.

[0176] It should be understood that the various parts disclosed in the present application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in a suitable manner in any one or more embodiments or examples.

[0177] As described above, the above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0178] The present application provides a computer-readable storage medium with computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the in-situ root-embedded soil thermophysical parameter determination method in the above-mentioned embodiment.

[0179] The above computer-readable storage medium carries one or more programs, which, when executed by the in-situ root-planting soil thermophysical parameter determination device as they are, cause the in-situ root-planting soil thermophysical parameter determination device to: periodically obtain soil temperature data at different depths based on a preset time interval; determine the soil thermal diffusivity based on the soil temperature data by using an implicit difference numerical inversion algorithm; determine the characteristic curve of the soil thermal conductivity and the soil water content according to the soil thermal diffusivity and the soil volume specific heat; fit the characteristic curve to obtain the functional relationship between the soil thermal conductivity and the soil water content; and determine the target soil thermophysical parameters according to the functional relationship. Computer program code for performing the operations of the present application can be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0180] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that, in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0181] The modules involved in the embodiments described in the present application can be implemented in software or in hardware. Among them, the name of the module does not constitute a limitation on the unit itself in some cases.

[0182] The readable storage medium provided by this application is a computer-readable storage medium. The computer-readable storage medium stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned method for determining the thermophysical parameters of undisturbed planted soil, which can solve the technical problem of how to improve the accuracy and convenience of determining the thermophysical parameters of undisturbed planted soil. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by this application are the same as those of the method for determining the thermophysical parameters of undisturbed planted soil provided in the above embodiments, and will not be elaborated here.

[0183] An embodiment of this application provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the steps of the method for determining the thermophysical parameters of undisturbed planted soil as described above.

[0184] The computer program product provided by this application can solve the technical problem of how to improve the accuracy and convenience of determining the thermophysical parameters of undisturbed planted soil. Compared with the prior art, the beneficial effects of the computer program product provided in the embodiment of this application are the same as those of the method for determining the thermophysical parameters of undisturbed planted soil provided in the above embodiments, and will not be elaborated here.

[0185] The above are only the preferred embodiments of this application, and do not limit the patent scope of this application accordingly. Any equivalent structural or equivalent process transformation made by using the content of the specification and drawings of this application, or directly or indirectly applied in other related technical fields, shall be similarly included within the patent scope of this application.

Claims

1. A method for determining the thermophysical parameters of undisturbed root-embedded soil, characterized in that, The method includes: Periodically obtaining soil temperature data at different depths based on a preset time interval; Based on the soil temperature data, using an implicit difference numerical inversion algorithm to determine the soil thermal diffusivity; According to the soil thermal diffusivity and the soil volume specific heat, determining the characteristic curve of the soil thermal conductivity and the soil water content; Fitting the characteristic curve to obtain the functional relationship between the soil thermal conductivity and the soil water content; According to the functional relationship, determining the target soil thermophysical parameters.

2. The method according to claim 1, wherein After the step of periodically obtaining soil temperature data at different depths based on a preset time interval, it further includes: Obtaining soil monitoring data, and based on the soil monitoring data, determining whether there is rainfall and / or irrigation during the collection of the soil temperature data; If so, marking the soil temperature data as invalid data and deleting it.

3. The method according to claim 1, characterized in that The step of using an implicit difference numerical inversion algorithm based on the soil temperature data to determine the soil thermal diffusivity includes: Based on the soil temperature data, determining the temperature change at each measurement point within each preset time interval; Based on the temperature change, using the implicit difference numerical inversion algorithm to determine the soil temperature at the next time point; Comparing the soil temperature at the next time point with the measured temperature, and determining the sum of squared errors according to the comparison result; Based on the sum of squared errors, iteratively optimizing the initial soil thermal diffusivity to obtain the soil thermal diffusivity.

4. The method according to claim 3, wherein The step of iteratively optimizing the initial soil thermal diffusivity based on the sum of squared errors to obtain the soil thermal diffusivity includes: Based on the sum of squared errors, setting an objective function; Using a preset optimization algorithm, by minimizing the objective function, iteratively optimizing the initial soil thermal diffusivity until the sum of squared errors meets a preset threshold to obtain the soil thermal diffusivity.

5. The method according to claim 1, characterized in that, The step of determining the characteristic curve of the soil thermal conductivity and the soil water content according to the soil thermal diffusivity and the soil volume specific heat includes: According to the product of the soil thermal diffusivity and the soil volume specific heat, determining the soil thermal conductivity within each preset time interval; Obtaining the soil water content, and based on the relationship between the soil thermal conductivity and the soil water content, generating a scatter plot of the soil thermal conductivity and the soil water content; Based on a preset data fitting algorithm, fitting the scatter plot to obtain the characteristic curve of the soil thermal conductivity and the soil water content.

6. The method according to claim 1, wherein The step of fitting the characteristic curve to obtain the functional relationship between the soil thermal conductivity and the soil water content includes: According to the characteristic curve, determining a preliminary relationship diagram of the soil thermal conductivity and the soil water content; Based on the preset data fitting algorithm, fitting the preliminary relationship diagram to obtain the functional relationship between the soil thermal conductivity and the soil water content, wherein during the fitting process, according to the soil thermal conductivity at different water contents, adjusting the parameters of the fitting function to reduce the fitting error.

7. The method according to claim 6, wherein After the step of fitting the characteristic curve to obtain the functional relationship between the soil thermal conductivity and the soil water content, it further includes: The difference between the measured soil thermal conductivity and the fitted soil thermal conductivity is taken as the residual; Statistical analysis is performed on the mean, standard deviation, and maximum residual of the residual; Based on the results of the statistical analysis, the deviation between the fitted curve and the actual data is evaluated; If the evaluation result exceeds the preset deviation threshold, the fitting model parameters are adjusted and fitting is performed again.

8. The method according to claim 1, wherein The step of determining the target soil thermophysical parameters according to the functional relationship includes: Obtain the input water content value; Determine the target soil thermal conductivity according to the input water content value and the functional relationship; Determine the target soil thermophysical parameters according to the relationship between the target soil thermal conductivity and the soil thermophysical parameters.

9. An apparatus for determining the thermophysical parameters of undisturbed root-embedded soil, characterized in that, The device includes: A data acquisition module for periodically acquiring soil temperature data at different depths based on a preset time interval; A thermal diffusivity determination module for determining the soil thermal diffusivity by using an implicit difference numerical inversion algorithm based on the soil temperature data; A curve determination module for determining the characteristic curve of the soil thermal conductivity and the soil water content according to the soil thermal diffusivity and the soil volumetric specific heat; A fitting module for fitting the characteristic curve to obtain the functional relationship between the soil thermal conductivity and the soil water content; A target module for determining the target soil thermophysical parameters according to the functional relationship.

10. A storage medium, characterized in that, The storage medium stores a program for determining the thermophysical parameters of undisturbed rooted soil. When the program for determining the thermophysical parameters of undisturbed rooted soil is executed by a processor, the steps of the method for determining the thermophysical parameters of undisturbed rooted soil according to any one of claims 1 to 8 are implemented.

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