Method and system for simultaneously measuring ice content and unfrozen water content in frozen soil pores

Abstract

The application provides a method and system for simultaneously measuring the ice content and unfrozen water content of frozen soil, and belongs to the technical field of frozen soil parameter measurement based on machine learning. The temperature response curve of frozen soil under a pulse heat source and specific frozen soil parameters are obtained. The specific frozen soil parameters include frozen soil particle density, frozen soil particle thermal conductivity, frozen soil particle specific heat capacity and frozen soil particle specific gravity. The obtained temperature response curve and specific frozen soil parameters are processed by using a pre-trained inversion analysis model to obtain the ice content and unfrozen water content of frozen soil. The application can simultaneously measure the ice content and unfrozen water content of frozen soil within one minute, greatly improves the measurement efficiency, and avoids the assumption conditions of an empirical model.

Description

Technical Field

[0001] The present invention relates to the technical field of frozen soil parameter measurement based on machine learning, and in particular to a method and system for simultaneously measuring the ice content and unfrozen water content in frozen soil pores. Background Art

[0002] A brief, constant pulse of heat is applied to the frozen soil. The temperature response curve (TRC) at a specific location is measured over time. The TRC is directly controlled by the thermal parameters of the frozen soil (i.e., λ and C). As the ice and water content in the frozen soil changes, the λ and C of the frozen soil also change, causing the characteristics of the TRC to change. Therefore, the TRC contains information about both ice and water. In other words, if the TRC can be accurately measured, it can be inverted and analyzed to obtain information about the ice content.

[0003] Currently, methods for measuring unfrozen water content and ice content each have their limitations. Methods for testing soil ice content can be divided into direct and indirect methods. Indirect methods typically require the total moisture content of the soil to be known first, followed by measurement of the unfrozen water content, and the ice content is calculated from the two. Examples include neutron scattering and nuclear magnetic resonance. These methods are only applicable to closed systems or situations where the total moisture content is known. However, frozen soil in nature is an open system, and the continuous migration of unfrozen water causes the total moisture content to constantly change. Therefore, the applicability of indirect methods is limited. Direct methods, such as the expansion method and the transient heat source method, do not require measurement of the total moisture content. These methods rely heavily on complex instrumentation, which reduces measurement efficiency and makes it difficult to quickly measure ice content and unfrozen water content in the field.

[0004] The transient heat source method has certain advantages in measuring ice content due to the sensitivity of frozen soil to temperature. The basic principle of the transient heat source method is to place a short pulse heat source in the soil and interpret the frozen soil parameters by analyzing the temperature response. This method can easily cause ice in the frozen soil to melt, thereby affecting the measurement results. However, the ice / water phase change requires a large amount of continuous energy input. As long as the power / time of the input energy is controlled, the impact of the phase change can be reduced to a certain extent. Li Xu [1] et al. proposed a pulse heat probe method based on the transient heat source method. By measuring the temperature response in the frozen soil, the ice content of the frozen soil was calculated. Wu Bing [2] et al. developed an actively heated fiber Bragg grating sensor based on the transient heat source method for measuring ice content.

[0005] The existing calculation models based on the transient heat source method are all empirical models, which not only lack theoretical basis but also contain many empirical parameters. In addition, these parameters need to be calibrated through a large number of laboratory experiments, which further reduces the measurement efficiency. There are two problems with the two measurement methods of Li Xu et al. [1] and Wu Bing et al. [2]: 1) The calculation models of ice content are all empirical models, which contain many assumptions and empirical parameters and lack a solid theoretical basis, resulting in certain calculation errors; 2) The undetermined parameters in the calculation model need to be calibrated through a large number of laboratory experiments, which limits the accuracy and efficiency of the measurement. Summary of the Invention

[0006] The object of the present invention is to provide a method and system for simultaneously measuring the ice content and unfrozen water content in frozen soil pores, so as to solve at least one technical problem existing in the above-mentioned background technology.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] In a first aspect, the present invention provides a method for simultaneously measuring the pore ice content and unfrozen water content of frozen soil, comprising:

[0009] Obtaining a temperature response curve of frozen soil under a pulse heat source and specific frozen soil parameters; the specific frozen soil parameters include frozen soil particle density, frozen soil particle thermal conductivity, frozen soil particle specific heat capacity, and frozen soil particle specific gravity;

[0010] The obtained temperature response curve and specific frozen soil parameters are processed using a pre-trained inversion analysis model to obtain the frozen soil ice content and unfrozen water content; wherein, the inversion analysis model is trained using a training set, and the training set includes multiple groups of data, each group of data includes a temperature response curve, and each temperature response curve is labeled with the frozen soil ice content, the frozen soil unfrozen water content, the frozen soil particle density, the frozen soil particle thermal conductivity, the frozen soil particle specific heat capacity and the frozen soil particle specific gravity; a machine learning algorithm is used, with the temperature response curve and different frozen soil parameters as input, and the frozen soil ice content and the frozen soil unfrozen water content as output, to train the training set and extract features, compare the prediction effects when different frozen soil parameters are used as input, and obtain the optimal inversion analysis model.

[0011] Furthermore, a set of parameters including the ice content of frozen soil, the unfrozen water content of frozen soil, the density of frozen soil particles, the thermal conductivity of frozen soil particles, the specific heat capacity of frozen soil particles and the specific gravity of frozen soil particles are set, and a set of frozen soil thermal conductivity and frozen soil specific heat capacity are calculated according to the set parameters; based on the transient heat conduction numerical simulation model of frozen soil, the thermal conductivity and the specific heat capacity of frozen soil under heat pulse are set to obtain the corresponding temperature response curve.

[0012] Furthermore, Monte Carlo sampling was performed on the set frozen soil ice content, frozen soil unfrozen water content, frozen soil particle density, frozen soil particle thermal conductivity, frozen soil particle specific heat capacity and frozen soil particle specific gravity, and multiple sets of frozen soil thermal conductivity and frozen soil specific heat capacity were calculated. The obtained multiple sets of frozen soil thermal conductivity and frozen soil specific heat capacity were input into the transient heat conduction numerical simulation model to obtain corresponding multiple sets of temperature response curves.

[0013] Furthermore, the following four equations are used to calculate the heat conduction parameters and density of frozen soil:

[0014]

[0015] ρ f =ρ s +0.92θ i +θ w

[0016]

[0017] Among them, ρ f is the density of frozen soil, C f is the specific heat capacity of frozen soil, λ f is the thermal conductivity of frozen soil; ρ s is the density of soil particles; θ is the volume fraction; φ is the mass fraction, and Gs is the specific gravity of soil particles.

[0018] Furthermore, in the transient heat conduction numerical simulation model of frozen soil, the following equation is used as the control equation for heat conduction simulation: Where T is temperature, t is time, u is velocity, and Q is the heat source.

[0019] Furthermore, the BP model is used to i ,θu) to perform inversion analysis and establish the following mapping relationship:

[0020]

[0021] Among them, [θ i ,θ u ],TRC=[T f0 ,T f1 ,T f2 …T f60 ],S=[ρ s ,λ s ,C s ].T fi is the temperature response data of frozen soil within 60s, with a measurement frequency of 1s; θ i represents the ice content of frozen soil, θ u represents water content; ρ s represents the density of frozen soil particles; s represents the thermal conductivity of frozen soil particles; Cs represents the specific heat capacity of frozen soil particles.

[0022] In a second aspect, the present invention provides a system for simultaneously measuring the pore ice content and unfrozen water content of frozen soil, comprising:

[0023] An acquisition module is used to obtain a temperature response curve of frozen soil under a pulse heat source and specific frozen soil parameters; the specific frozen soil parameters include frozen soil particle density, frozen soil particle thermal conductivity, frozen soil particle specific heat capacity and frozen soil particle specific gravity;

[0024] The processing module is used to use a pre-trained inversion analysis model to process the obtained temperature response curve and specific frozen soil parameters to obtain the frozen soil ice content and unfrozen water content; wherein, the inversion analysis model is trained using a training set, and the training set includes multiple groups of data, each group of data includes a temperature response curve, and each temperature response curve is labeled with the frozen soil ice content, the frozen soil unfrozen water content, the frozen soil particle density, the frozen soil particle thermal conductivity, the frozen soil particle specific heat capacity and the frozen soil particle specific gravity; a machine learning algorithm is used, with the temperature response curve and different frozen soil parameters as input, and the frozen soil ice content and the frozen soil unfrozen water content as output, to train the training set and extract features, compare the prediction effects when different frozen soil parameters are used as input, and obtain the optimal inversion analysis model.

[0025] In a third aspect, the present invention provides a non-transitory computer-readable storage medium, which is used to store computer instructions. When the computer instructions are executed by a processor, the method of simultaneously measuring the pore ice content and unfrozen water content of frozen soil as described in the first aspect is implemented.

[0026] In a fourth aspect, the present invention provides a computer device comprising a memory and a processor, wherein the processor and the memory communicate with each other, the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the method for simultaneously measuring the pore ice content and unfrozen water content of frozen soil as described in the first aspect.

[0027] In a fifth aspect, the present invention provides an electronic device comprising: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to execute instructions for implementing the method for simultaneously measuring the pore ice content and unfrozen water content of frozen soil as described in the first aspect.

[0028] Explanation of terms:

[0029] 1. Ice content and unfrozen water content of frozen soil (θ i and θ u):Due to the effect of suction in the pore of frozen soil, ice and water coexist. The ice-water phase transition in frozen soil is one of the important factors leading to the instability of its properties. Ice content and unfrozen water content are basic parameters for describing the properties of frozen soil, which directly affect the key processes such as thermal properties, mechanical behavior and water migration of frozen soil. It is of great significance for the construction of cold regions to quickly and accurately measure the ice content and unfrozen water content of frozen soil.

[0030] 2. Temperature response curve: In geotechnical materials, input heat of constant time (t0) and constant power (P0), and then the geotechnical material at a constant distance (r0) from the heat source will gradually heat up. The temperature (t i ,T i ) of the point at different times is measured, wherein the time when heating starts is t=0. Further, according to ΔT i =T i -T0, (wherein T0 is the initial temperature), the temperature change (t i , ΔT i ) of the point at different times is calculated. The curve described by t i as the horizontal coordinate and ΔT i as the vertical coordinate is the thermal response curve. With the change of geothermal parameters of geotechnical materials, the curve will change synchronously.

[0031] 3. Back analysis: Starting from the measured structural response (such as displacement, strain, temperature field, etc.), the input condition or model parameter is inversely calculated. In the present application, the ice content and unfrozen water content are inversely calculated by measuring the temperature response curve.

[0032] 4. Monte Carlo numerical simulation: It is a method of carrying out a large number of random numerical simulations. A large number of numerical simulations can be carried out by this method to generate a large data set required for machine learning.

[0033] 5. Back propagation algorithm: Backpropagation Algorithm, abbreviated as BP algorithm, is a machine learning algorithm. BP algorithm calculates the gradient of loss function with respect to network parameters, and uses these gradients to update the weights and biases of the network in reverse, so as to minimize the prediction error of the loss function.

[0034] The present application has the advantages that the ice content and unfrozen water content of frozen soil can be measured simultaneously within one minute, the measurement efficiency is greatly improved, and the assumption conditions of the empirical model are avoided.

[0035] The advantages of the additional aspects of the present application will be more apparent from the following description or understood through the practice of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments description. Obviously, the drawings in the following description only some of the embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0037] Figure 1 The numerical simulation model described in the embodiments of the present application is shown in the schematic diagram. Among them, Figure 1 (a) is a schematic diagram under laboratory conditions; Figure 1 (b) is a numerical simulation model schematic diagram.

[0038] Figure 2 The Monte Carlo sampling schematic diagram described in the embodiments of the present application.

[0039] Figure 3 The BP model structure diagram described in the embodiments of the present application. DETAILED DESCRIPTION

[0040] The embodiments of the present application will be described in detail below, and the examples of the embodiments are shown in the drawings, wherein the same or similar reference signs represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below through the drawings are exemplary and are only used to explain the present application, and cannot be interpreted as a limitation on the present application.

[0041] Those skilled in the art can understand that, unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meaning as that generally understood by those skilled in the art in the field to which the present application belongs.

[0042] It should also be understood that terms such as those defined in a general dictionary should be understood to have meanings consistent with those in the context of the prior art, and unless defined as such, should not be interpreted in an idealized or overly formal sense.

[0043] Those skilled in the art can understand that, unless otherwise stated, the singular forms "a", "an" and "the" used herein also include the plural forms. It should be further understood that the phrase "comprising" used in the specification of the present application means that the features, integers, steps, operations, elements and / or groups exist, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements and / or groups thereof.

[0044] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. The person skilled in the art can combine and combine the different embodiments or examples described in the present specification and the features of the different embodiments or examples without contradiction.

[0045] In order to facilitate the understanding of the present application, the present application will be further explained and described in specific embodiments in combination with the accompanying drawings, and the specific embodiments do not constitute a limitation on the embodiments of the present application.

[0046] The person skilled in the art should understand that the drawings are only schematic diagrams of the embodiments, and the components in the drawings are not necessarily necessary for the implementation of the present application.

[0047] Embodiment 1

[0048] In this embodiment 1, a system for simultaneously measuring the ice content and unfrozen water content of frozen soil is provided, comprising: an acquisition module for acquiring a temperature response curve of frozen soil under a pulse heat source and specific frozen soil parameters; the specific frozen soil parameters include frozen soil particle density, frozen soil particle thermal conductivity, frozen soil particle specific heat capacity and frozen soil particle specific gravity; a processing module for processing the acquired temperature response curve and specific frozen soil parameters by using a pre-trained inversion analysis model to obtain the ice content and unfrozen water content of the frozen soil; wherein the inversion analysis model is trained using a training set, and the training set includes multiple groups of data, each group of data including a temperature response curve and each temperature response curve labeled with frozen soil ice content, frozen soil unfrozen water content, frozen soil particle density, frozen soil particle thermal conductivity, frozen soil particle specific heat capacity and frozen soil particle specific gravity; a machine learning algorithm is used, the temperature response curve and different frozen soil parameters are used as input, the frozen soil ice content and the frozen soil unfrozen water content are used as output, the training set is trained and features are extracted, the prediction effect when different frozen soil parameters are used as input is compared, and the optimal inversion analysis model is obtained.

[0049] In this embodiment, the above-mentioned system is used to implement a method for simultaneously measuring the ice content and unfrozen water content of frozen soil pores, including: using an acquisition module to obtain the temperature response curve and specific frozen soil parameters of frozen soil under a pulse heat source; the specific frozen soil parameters include the density of frozen soil particles, the thermal conductivity of frozen soil particles, the specific heat capacity of frozen soil particles and the specific gravity of frozen soil particles; using a processing module to process the obtained temperature response curve and specific frozen soil parameters using a pre-trained inversion analysis model to obtain the frozen soil ice content and unfrozen water content; wherein, the inversion analysis model is trained using a training set. The invention provides an inversion analysis model, wherein the training set includes multiple groups of data, each group of data includes a temperature response curve, and each temperature response curve is labeled with the frozen soil ice content, the frozen soil unfrozen water content, the frozen soil particle density, the frozen soil particle thermal conductivity, the frozen soil particle specific heat capacity and the frozen soil particle specific gravity; a machine learning algorithm is used, with the temperature response curve and different frozen soil parameters as input, and the frozen soil ice content and the frozen soil unfrozen water content as output, to train the training set and extract features, compare the prediction effects when different frozen soil parameters are used as input, and obtain the optimal inversion analysis model.

[0050] Among them, a set of parameters including frozen soil ice content, frozen soil unfrozen water content, frozen soil particle density, frozen soil particle thermal conductivity, frozen soil particle specific heat capacity and frozen soil particle specific gravity are set, and a set of frozen soil thermal conductivity and frozen soil specific heat capacity are calculated according to the set parameters; based on the transient heat conduction numerical simulation model of frozen soil, the thermal conductivity and frozen soil specific heat capacity under heat pulse are set to obtain the corresponding temperature response curve; Monte Carlo sampling is performed on the set frozen soil ice content, frozen soil unfrozen water content, frozen soil particle density, frozen soil particle thermal conductivity, frozen soil particle specific heat capacity and frozen soil particle specific gravity, and multiple sets of frozen soil thermal conductivity and frozen soil specific heat capacity are calculated. The obtained multiple sets of frozen soil thermal conductivity and frozen soil specific heat capacity are input into the transient heat conduction numerical simulation model to obtain corresponding multiple sets of temperature response curves.

[0051] The following four equations are used to calculate the thermal conductivity parameters and density of frozen soil:

[0052]

[0053] ρ f =ρ s +0.92θ i +θ w

[0054]

[0055] Among them, ρ f is the density of frozen soil, C f is the specific heat capacity of frozen soil, λ f is the thermal conductivity of frozen soil; ρ s is the density of soil particles; θ is the volume fraction; φ is the mass fraction, and Gs is the specific gravity of soil particles.

[0056] In the transient heat conduction numerical simulation model of frozen soil, the following equation is used as the control equation for heat conduction simulation: Where T is temperature, t is time, u is velocity, and Q is the heat source.

[0057] Using BP model to (θ i ,θu) to perform inversion analysis and establish the following mapping relationship:

[0058]

[0059] Among them, [θ i ,θ u ],TRC=[T f0 ,T f1 ,T f2 …T f60 ],s=[ρ s ,λ s ,C s ].T fi is the temperature response data of frozen soil within 60s, with a measurement frequency of 1s; θ i represents the ice content of frozen soil, θ u represents water content; ρ s represents the density of frozen soil particles; s represents the thermal conductivity of frozen soil particles; C s represents the specific heat capacity of frozen soil particles.

[0060] Example 2

[0061] In this embodiment 2, a method for quickly and simultaneously measuring the pore ice content and unfrozen water content of frozen soil is provided. First, the TRC and specific frozen soil parameters of the frozen soil under a pulse heat source are measured and obtained, and then they are input into the inversion analysis model to quickly predict the ice content θi and the unfrozen water content θu.

[0062] First, select the calculation model of thermal conductivity and specific heat capacity of frozen soil. Set a set of (θ i ,θ u , ρ s ,λ s 、C s , G s ) parameters and input them into the calculation model to obtain a set of frozen soil (λ f and C f ).

[0063] Secondly, a transient heat conduction numerical simulation model of frozen soil was established under laboratory conditions. In this model, a heating probe was inserted into the frozen soil to provide a brief heat pulse. By setting the geothermal parameters of the frozen soil (λf and Cf), the TRC under these parameters could be obtained.

[0064] Next, Monte Carlo sampling is performed on (θi, θu, ρs, λs, Cs, Gs), calculating tens of thousands of sets of (λf and Cf) and inputting them into the numerical model. Each time (λf and Cf) is input, a set of TRCs is obtained. Ultimately, a database of tens of thousands of TRCs is constructed, each labeled with (θi, θu, ρs, λs, Cs, Gs).

[0065] Finally, a machine learning algorithm was used to train the dataset and extract features, using TRC and different frozen soil parameters (ρs, λs, Cs, and Gs) as inputs and θu and θi as outputs. The prediction performance of the different frozen soil parameters was compared, resulting in the optimal inversion analysis model.

[0066] In this embodiment, constructing a numerical simulation model includes the following steps:

[0067] The following four equations are used to calculate the thermal conductivity parameters and density of frozen soil:

[0068]

[0069] ρ f =ρ s +0.92θ i +θ w (3)

[0070]

[0071] Among them, ρ f is the density of frozen soil; C f is the specific heat capacity of frozen soil; f is the thermal conductivity of frozen soil; ρ s is the density of soil particles; θ is the volume fraction; φ is the mass fraction, and Gs is the particle specific gravity.

[0072] In the above four equations, six independent variables or parameters need to be determined in frozen soil: θ i (ice content), θ u (unfrozen water content), ρ s (soil particle density), μ s (Parameters related to soil texture, including λ s (particle thermal conductivity), C s (specific heat capacity of frozen soil particles), G s (Specific gravity of particles). Once the above six variables or parameters are determined, the thermal parameters of frozen soil can be determined, thereby simulating the transient heat conduction process of frozen soil.

[0073] In this example, the laboratory conditions for the numerical simulation model are as follows: a Perspex cylinder is used as the soil container, with dimensions of inner diameter Φ = 10 cm, thickness δ = 5 mm, and height h = 10 cm. The inside of the container is filled with frozen soil, and a temperature sensor is inserted into it, as shown in Figure 1 (a).

[0074] The sensor has three probes: a heating needle on the left, which provides a short pulse of heat. The heating needle is composed of polypropylene filler and a steel shell. There is a pt100 type temperature probe (referred to as the temperature probe) 1 cm to the right of the heating needle, made of platinum. Another temperature probe is located immediately to the left of the heating needle, used to monitor the temperature of the heating needle. The radius of the three needle tips is 2 mm. The thermal physical parameters of the materials used in the numerical model are shown in Table 1. A larger pulse of heat can cause the frozen soil to undergo a drastic phase change. The appropriate pulse of heat time and power are crucial for accurately simulating heat conduction in frozen soil. The pulse of heat power used in this study is Q = 15 W / m, and the heating time is t = 10 s.

[0075] Table 1 Thermal physical parameters of materials in the numerical model

[0076]

[0077] The following equation is used as the control equation for heat conduction simulation:

[0078]

[0079] where T is the temperature; t is the time; u is the velocity; and Q is the heat source.

[0080] The area between the three probes is the key area for simulating transient heat conduction. The cylindrical area with a radius of 15 mm in the middle of the frozen soil is refined, with grid cell sizes ranging from 0.022 mm to 2.2 mm. The outer area of the cylinder is coarsened, with grid cell sizes ranging from 1.1 mm to 8.8 mm, as shown in Figure 1 (b). This treatment has the advantage of reducing the computational load of a single simulation while ensuring the accuracy of the TRC simulation.

[0081] Using the above numerical simulation model, after inputting the six parameters, the transient heat conduction in frozen soil can be simulated, and the TRC curve of the heating needle can be obtained. After each simulation, the TRC time range derived from the model is 0 to 60 seconds, with a sampling frequency of 1 Hz.

[0082] Monte Carlo sampling of the six parameters includes:

[0083] Six parameters (θi, θu, ρs, λs, Cs, Gs) are needed to be input to get a simulated TRC in the numerical model. Among the six parameters, (λs, Cs, Gs, ρs) are the parameters related to soil texture; (θi, θu, ρs) are the parameters related to the volume fractions of the three phases of frozen soil. The six parameters (λs, Cs, Gs, θi, θu, ρs) are sampled by Monte Carlo method.

[0084] Due to the limitation of soil porosity, the following boundary conditions are set:

[0085] ρ s / G s +θ i +θ w ≤1 (6)

[0086] Considering the numerical range of frozen soil parameters in nature, the value range of λs is 1-5 W / m·K, the value range of Cs is 600-1500 J / kg·K, and the value range of Gs is 2.6-2.8. The sampling times of the three parameters are all 5. The value range of ρs is 0.6-1.9 g / cm 3 , and the value range of θu is 0-50%, and the sampling times of both are more than 10. The sampling ranges of the six parameters are shown in Table 2. Finally, the Monte Carlo sampling results of (θ i , θu, ρ s , λ s , C s , G s ) are shown in Table 3. Figure 2

[0087] Table 2 Value ranges of dry density, ice content, and unfrozen water content of three soils

[0088]

[0089]

[0090] Therefore, by Monte Carlo sampling of the six parameters, a total of tens of thousands of parameter combinations of (θi, θu, ρs, λs, Cs, Gs) with different values are established. Based on the tens of thousands of parameter combinations (θi, θu, ρs, λs, Cs, Gs) in Section 3.1, (λf, Cf, ρf) can be calculated by equations (1)-(4) and input into the numerical model. After tens of thousands of simulations, tens of thousands of TRCs with (θi, θu, ρs, λs, Cs, Gs) and (λf, Cf, ρf) as labels can be obtained.

[0091] It should be noted that the TRC derived in the numerical model only includes temperature data within 60 seconds before heating, i.e. T0, T1, T2…T 60 ​(Acquired at a frequency of 1 second, a total of 61 data points).

[0092] In this embodiment, constructing a machine learning model includes: establishing a numerical model based on (θ i ,θ u ,ρ s ,λ s ,C s ,G s ) six parameters as labels of the TRC database. Then, the BP model is used to i ,θu) to perform inversion analysis and establish the following mapping relationship:

[0093]

[0094] Among them, [θ i ,θ u ],TRC=[T f0 ,T f1 ,T f2 …T f60 ],s=[ρ s ,λ s ,C s ].T fi This is the temperature response data of frozen soil within 60 seconds, with a measurement frequency of 1 second.

[0095] This embodiment uses the commonly used and classic machine learning algorithm - Back Propagation (BP), and the construction steps are as follows: Figure 3 The constructed model consists of two hidden layers, each with 100 neurons. The input feature dimensions range from 61 to 127, and the output dimension is 1 or 2. An MLPRegressor is used as the feedforward neural network, wrapped with a MultiOutputRegressor. Early stopping is used to stop training when performance on the validation set no longer improves to prevent overfitting. Other parameters used in the BP model construction are shown in Table 3.

[0096] Table 3 Other parameters of BP model

[0097]

[0098]

[0099] In this embodiment, the specific application in the field of real-time measurement of TRC using pt100 temperature sensor, pulse heat source parameters for (Q = 15W / m, t = 10s). Linear power supply and time control switch is used to ensure the duration and power of heat pulse. Data collector (DT85) is used to collect the data of temperature sensor. In order to reduce the influence of randomness, three consecutive measurements should be carried out, and the interval time is more than 12 hours. The average value of three measurement results is taken, and the median filter algorithm is used to denoise the curve. The denoised curve is regarded as the final measured TRC.

[0100] The frozen soil in the field is sampled and transported to the laboratory for measurement of other parameters. For dry density, wax sealing method or drying method should be used for measurement.

[0101] The thermal conductivity of soil particles is determined by the following method. The soil transported back is made into a sample with dry density p0 and dried. At this time, the soil body is composed of soil and air two phases. The test of the thermophysical parameters of dry soil sample can obtain the thermophysical parameters of soil particles by inversion:

[0102]

[0103] C s =C0 (8)

[0104] Where s, a, 0 represent the thermophysical parameters of soil particles, air and soil body respectively. Gs is the specific gravity of soil particles, and the recommended value is 2.7; p0 is the dry density.

[0105] The (p s, l s, C s) measured in the laboratory and the TRC measured in the field in one minute are input into the machine learning model in real time, so that (q i, q u) at different times can be obtained in real time.

[0106] Embodiment 3

[0107] This embodiment 3 provides a non-transitory computer readable storage medium for storing computer instructions, which are executed by a processor to realize the method for simultaneously measuring the ice content and unfrozen water content of frozen soil as described above, which comprises:

[0108] Obtaining the temperature response curve of frozen soil under pulse heat source and specific frozen soil parameters; the specific frozen soil parameters include frozen soil particle density, frozen soil particle thermal conductivity, frozen soil particle specific heat capacity and frozen soil particle specific gravity;

[0109] The obtained temperature response curve and specific frozen soil parameters are processed using a pre-trained inversion analysis model to obtain the frozen soil ice content and unfrozen water content; wherein, the inversion analysis model is trained using a training set, and the training set includes multiple groups of data, each group of data includes a temperature response curve, and each temperature response curve is labeled with the frozen soil ice content, the frozen soil unfrozen water content, the frozen soil particle density, the frozen soil particle thermal conductivity, the frozen soil particle specific heat capacity and the frozen soil particle specific gravity; a machine learning algorithm is used, with the temperature response curve and different frozen soil parameters as input, and the frozen soil ice content and the frozen soil unfrozen water content as output, to train the training set and extract features, compare the prediction effects when different frozen soil parameters are used as input, and obtain the optimal inversion analysis model.

[0110] Example 4

[0111] This embodiment 4 provides a computer device, including a memory and a processor, wherein the processor and the memory communicate with each other, the memory stores program instructions executable by the processor, and the processor invokes the program instructions to execute the method for simultaneously measuring the pore ice content and unfrozen water content of frozen soil as described above, the method comprising:

[0112] Obtaining a temperature response curve of frozen soil under a pulse heat source and specific frozen soil parameters; the specific frozen soil parameters include frozen soil particle density, frozen soil particle thermal conductivity, frozen soil particle specific heat capacity, and frozen soil particle specific gravity;

[0113] The obtained temperature response curve and specific frozen soil parameters are processed using a pre-trained inversion analysis model to obtain the frozen soil ice content and unfrozen water content; wherein, the inversion analysis model is trained using a training set, and the training set includes multiple groups of data, each group of data includes a temperature response curve, and each temperature response curve is labeled with the frozen soil ice content, the frozen soil unfrozen water content, the frozen soil particle density, the frozen soil particle thermal conductivity, the frozen soil particle specific heat capacity and the frozen soil particle specific gravity; a machine learning algorithm is used, with the temperature response curve and different frozen soil parameters as input, and the frozen soil ice content and the frozen soil unfrozen water content as output, to train the training set and extract features, compare the prediction effects when different frozen soil parameters are used as input, and obtain the optimal inversion analysis model.

[0114] Example 5

[0115] This embodiment 5 provides an electronic device, including: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory to cause the electronic device to execute instructions for implementing the method for simultaneously measuring the pore ice content and unfrozen water content of frozen soil as described above, the method including:

[0116] Obtaining a temperature response curve of frozen soil under a pulse heat source and specific frozen soil parameters; the specific frozen soil parameters include frozen soil particle density, frozen soil particle thermal conductivity, frozen soil particle specific heat capacity, and frozen soil particle specific gravity;

[0117] The obtained temperature response curve and specific frozen soil parameters are processed using a pre-trained inversion analysis model to obtain the frozen soil ice content and unfrozen water content; wherein, the inversion analysis model is trained using a training set, and the training set includes multiple groups of data, each group of data includes a temperature response curve, and each temperature response curve is labeled with the frozen soil ice content, the frozen soil unfrozen water content, the frozen soil particle density, the frozen soil particle thermal conductivity, the frozen soil particle specific heat capacity and the frozen soil particle specific gravity; a machine learning algorithm is used, with the temperature response curve and different frozen soil parameters as input, and the frozen soil ice content and the frozen soil unfrozen water content as output, to train the training set and extract features, compare the prediction effects when different frozen soil parameters are used as input, and obtain the optimal inversion analysis model.

[0118] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0119] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0120] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0121] These computer program instructions can also be loaded into computer or other programmable data processing devices, to cause a series of operational steps to be performed on the computer or other programmable devices, so that the computer implemented processes are generated, and the instructions executed on the computer or other programmable devices provide steps for implementing the functions specified in the flowchart Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0122] The above description of the specific embodiments of the present application is made with reference to the accompanying drawings, but is not a limitation on the scope of protection of the present application. Those skilled in the art should understand that various modifications or changes made on the basis of the disclosed technical solutions of the present application without inventive labor should be covered within the scope of protection of the present application.

Claims

1. A method for simultaneously measuring the ice content and unfrozen water content in frozen soil pores, characterized in that: include: Obtaining a temperature response curve of frozen soil under a pulse heat source and specific frozen soil parameters; the specific frozen soil parameters include frozen soil particle density, frozen soil particle thermal conductivity, frozen soil particle specific heat capacity, and frozen soil particle specific gravity; The obtained temperature response curve and specific frozen soil parameters are processed using a pre-trained inversion analysis model to obtain the frozen soil ice content and unfrozen water content; wherein, the inversion analysis model is trained using a training set, wherein the training set includes multiple groups of data, each group of data includes a temperature response curve and a label corresponding to the temperature response curve, wherein the frozen soil ice content, the frozen soil unfrozen water content, the frozen soil particle density, the frozen soil particle thermal conductivity, the frozen soil particle specific heat capacity and the frozen soil particle specific gravity are used as labels; a machine learning algorithm is used, with the temperature response curve and different frozen soil parameters as input and the frozen soil ice content and the frozen soil unfrozen water content as output, to train the training set and extract features, compare the prediction effects when different frozen soil parameters are used as input, and obtain the optimal inversion analysis model; Among them, a set of parameters including frozen soil ice content, frozen soil unfrozen water content, frozen soil particle density, frozen soil particle thermal conductivity, frozen soil particle specific heat capacity and frozen soil particle specific gravity are set, and a set of frozen soil thermal conductivity and frozen soil specific heat capacity are calculated according to the set parameters; based on the transient heat conduction numerical simulation model of frozen soil, the thermal conductivity and frozen soil specific heat capacity under heat pulse are set to obtain the corresponding temperature response curve; Monte Carlo sampling is performed on the set frozen soil ice content, frozen soil unfrozen water content, frozen soil particle density, frozen soil particle thermal conductivity, frozen soil particle specific heat capacity and frozen soil particle specific gravity, and multiple sets of frozen soil thermal conductivity and frozen soil specific heat capacity are calculated. The obtained multiple sets of frozen soil thermal conductivity and frozen soil specific heat capacity are input into the transient heat conduction numerical simulation model to obtain corresponding multiple sets of temperature response curves.

2. The method for simultaneously measuring the pore ice content and unfrozen water content of frozen soil according to claim 1, characterized in that: The following four equations are used to calculate the thermal conductivity parameters and density of frozen soil: r f =ρ s +0.92θ i +θ w Among them, ρ s is the density of soil particles; θ is the volume fraction; φ is the mass fraction, and Gs is the specific gravity of soil particles.

3. The method for simultaneously measuring the pore ice content and unfrozen water content of frozen soil according to claim 2, characterized in that: In the transient heat conduction numerical simulation model of frozen soil, the following equation is used as the control equation for heat conduction simulation: Where T is temperature, t is time, u is velocity, Q is heat source; ρ f is the density of frozen soil, C f is the specific heat capacity of frozen soil, λ f is the thermal conductivity of frozen soil.

4. The method for simultaneously measuring the pore ice content and unfrozen water content of frozen soil according to claim 1, characterized in that: Using BP model to (θ i ,θ u ) to perform inversion analysis and establish the following mapping relationship: Where, TRC=[T f0 ,T f1 ,T f2 …T f60 ],s=[ρ s ,λ s ,C s ].T fi is the temperature response data of frozen soil at the i-th second; θ i represents the ice content of frozen soil, θ u represents the unfrozen water content of frozen soil; ρ s represents the density of frozen soil particles; s represents the thermal conductivity of frozen soil particles; C s represents the specific heat capacity of frozen soil particles.

5. A system for simultaneously measuring the ice content and unfrozen water content in frozen soil pores, characterized in that: include: An acquisition module is used to obtain a temperature response curve of frozen soil under a pulse heat source and specific frozen soil parameters; the specific frozen soil parameters include frozen soil particle density, frozen soil particle thermal conductivity, frozen soil particle specific heat capacity and frozen soil particle specific gravity; The processing module is used to process the obtained temperature response curve and specific frozen soil parameters using a pre-trained inversion analysis model to obtain the frozen soil ice content and unfrozen water content; wherein, the inversion analysis model is trained using a training set, and the training set includes multiple groups of data, each group of data includes a temperature response curve and a label corresponding to the temperature response curve, wherein the frozen soil ice content, the frozen soil unfrozen water content, the frozen soil particle density, the frozen soil particle thermal conductivity, the frozen soil particle specific heat capacity and the frozen soil particle specific gravity are used as labels; a machine learning algorithm is used, with the temperature response curve and different frozen soil parameters as input, and the frozen soil ice content and the frozen soil unfrozen water content as output, to train the training set and extract features, compare the prediction effects when different frozen soil parameters are used as input, and obtain the optimal inversion analysis. An analysis model is developed; a set of parameters including the ice content of frozen soil, the unfrozen water content of frozen soil, the density of frozen soil particles, the thermal conductivity of frozen soil particles, the specific heat capacity of frozen soil particles and the specific gravity of frozen soil particles are set, and a set of thermal conductivity and specific heat capacity of frozen soil are calculated according to the set parameters; based on the transient heat conduction numerical simulation model of frozen soil, the thermal conductivity and specific heat capacity of frozen soil under heat pulse are set to obtain the corresponding temperature response curve; Monte Carlo sampling is performed on the set frozen soil ice content, unfrozen water content of frozen soil, the density of frozen soil particles, the thermal conductivity of frozen soil particles, the specific heat capacity of frozen soil particles and the specific gravity of frozen soil particles to calculate multiple sets of thermal conductivity and specific heat capacity of frozen soil, and the obtained multiple sets of thermal conductivity and specific heat capacity of frozen soil are input into the transient heat conduction numerical simulation model to obtain the corresponding multiple sets of temperature response curves.

6. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the method for simultaneously measuring the pore ice content and unfrozen water content of frozen soil as described in any one of claims 1 to 4 is implemented.

7. A computer device, characterized in that: The method comprises a memory and a processor, wherein the processor and the memory communicate with each other, the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the method for simultaneously measuring the pore ice content and unfrozen water content of frozen soil as described in any one of claims 1 to 4.

8. An electronic device, characterized in that: include: A processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to execute instructions for implementing the method for simultaneously measuring the pore ice content and unfrozen water content of frozen soil as described in any one of claims 1 to 4.

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