Multi-parameter integrated detection method, device, terminal and storage medium for frozen soil

Abstract

The present invention provides a multi-parameter integrated detection method, device, terminal, and storage medium for frozen soil, belonging to the technical field of frozen soil detection. The method comprises: obtaining soil temperature data of the area to be detected; wherein the soil temperature data includes three-dimensional spatial position, time, and corresponding spatiotemporal temperature; inputting the soil temperature data into a pre-trained multi-parameter detection model to obtain the soil moisture content, soil ice content, soil thermal conductivity, and soil volumetric heat capacity of the area to be detected; wherein the multi-parameter detection model is obtained by training a PINN model based on a pre-constructed dataset. The training process of the multi-parameter detection model includes: constructing a one-dimensional non-steady-state heat conduction equation based on the soil temperature data of the frozen soil sample; constructing a loss function based on the one-dimensional non-steady-state heat conduction equation; and when the loss function reaches convergence, the multi-parameter detection model is trained. The present invention can simultaneously detect multiple parameters in frozen soil.

Description

Technical Field

[0001] The present invention relates to the technical field of frozen soil detection, and in particular to a multi-parameter integrated detection method, device, terminal and storage medium for frozen soil. Background Art

[0002] Ice-rich permafrost is widely distributed at high latitudes and altitudes, and its physical and thermal parameters are of great significance for engineering construction, environmental monitoring, and climate research. Accurately measuring parameters such as temperature, moisture content, ice content, thermal conductivity, and volumetric heat capacity of ice-rich permafrost is crucial for predicting permafrost changes, assessing engineering stability, and understanding climate change.

[0003] In existing technologies, temperature measurement usually uses buried thermocouples or thermistors, which can obtain soil temperature but cannot provide other physical parameters. Water content measurement uses time domain reflectometry (TDR) or frequency domain reflectometry (FDR) to infer soil moisture content by measuring the dielectric constant, but the accuracy decreases in frozen soil. Ice content measurement often uses methods such as sampling analysis or nuclear magnetic resonance, which are complicated and cannot be monitored in real time. Thermal conductivity and volumetric heat capacity measurements use steady-state or non-steady-state thermal conductivity methods, such as the hot plate method or the linear heat source method, which require complex experimental conditions and are difficult to apply in the field. The above methods are independent of each other and cannot achieve simultaneous measurement of multiple parameters. They are also limited in application in ice-rich frozen soil environments. Therefore, there is an urgent need for a method that can simultaneously detect multiple parameters in frozen soil. Summary of the Invention

[0004] The embodiments of the present invention provide a multi-parameter integrated detection method, device, terminal and storage medium for frozen soil to solve the problem that multiple parameters in frozen soil cannot be detected simultaneously.

[0005] In a first aspect, an embodiment of the present invention provides a multi-parameter integrated detection method for frozen soil, comprising:

[0006] Acquire soil temperature data of the area to be detected; wherein the soil temperature data includes three-dimensional spatial position, time and corresponding spatiotemporal temperature;

[0007] The soil temperature data is input into a pre-trained multi-parameter detection model to obtain the soil water content, soil ice content, soil thermal conductivity, and soil volumetric heat capacity of the area to be detected. The multi-parameter detection model is trained on a PINN model based on a pre-constructed dataset, which includes soil temperature data of frozen soil samples, water content, ice content, thermal conductivity, and volumetric heat capacity of frozen soil samples.

[0008] The training process of the multi-parameter detection model includes:

[0009] The soil temperature data of the frozen soil samples are used as the input of the PINN model, and the water content, ice content, thermal conductivity and volumetric heat capacity of the frozen soil samples are used as the output of the PINN model to train the model. A one-dimensional unsteady-state heat conduction equation is constructed based on the soil temperature data of the frozen soil samples, and a loss function is constructed based on the one-dimensional unsteady-state heat conduction equation. When the loss function reaches convergence, the multi-parameter detection model is obtained through training.

[0010] In one possible implementation, the loss function includes a data loss function and a physical loss function;

[0011] The loss function is constructed based on the one-dimensional unsteady heat conduction equation, including:

[0012] Construct data loss function and physical loss function based on one-dimensional unsteady heat conduction equation;

[0013] Construct the loss function based on the data loss function and the physical loss function.

[0014] In one possible implementation, the one-dimensional unsteady heat conduction equation is:

[0015]

[0016] Where, T ( x,t )for x Location t The soil temperature at the moment, ρ 、 c 、 k are the density, volumetric heat capacity and thermal conductivity of the soil, respectively. Q ( x , t ) is the heat source term;

[0017] The loss function is:

[0018]

[0019] Where λ data is the weight coefficient, is the data loss function, λ physics is the weight coefficient, is the physical loss function;

[0020] The data loss function is:

[0021]

[0022] Where, To predict temperature, T obs To observe the temperature, Nis the number of data points;

[0023] The physical loss function is:

[0024]

[0025] Where, To predict temperature, M is the number of sampling points, ρ 、 c 、 k are the density, volumetric heat capacity and thermal conductivity of the soil, respectively. Q is the heat source term.

[0026] In one possible implementation, obtaining soil temperature data of the area to be detected includes:

[0027] The sensor is implanted into the frozen soil of the area to be tested. The sensor includes three parallel probes, the middle probe can heat and measure temperature, and the probes on both sides can measure temperature.

[0028] Make the middle probe apply pulses to the surrounding soil;

[0029] The temperature data of the three probes that change with spatial position and time are collected at the same time to obtain soil temperature data.

[0030] In one possible implementation, before inputting soil temperature data into a pre-trained multi-parameter detection model, the following steps are further included:

[0031] The soil temperature data of the area to be detected is filtered, noise is removed from the filtered soil temperature data, and the noise-removed soil temperature data is input into a pre-trained multi-parameter detection model.

[0032] In one possible implementation, filtering the soil temperature data of the area to be detected includes:

[0033] Filtering the soil temperature data using a filter to obtain filtered soil temperature data;

[0034] Remove noise from the filtered soil temperature data, including:

[0035] The soil temperature data after filtering is subjected to noise removal by using wavelet transform or median filtering to obtain the soil temperature data after noise removal.

[0036] In one possible implementation, the method further includes:

[0037] Acquire a data set; the data set includes soil temperature data of the frozen soil sample, water content of the frozen soil sample, ice content of the frozen soil sample, thermal conductivity of the frozen soil sample, and volumetric heat capacity of the frozen soil sample;

[0038] Use a preset proportion of data in the dataset as the training set and the remaining data as the test set;

[0039] The PINN model is trained according to the training set and tested according to the test set to obtain a multi-parameter detection model.

[0040] In a second aspect, an embodiment of the present invention provides a multi-parameter integrated detection device, comprising:

[0041] A data acquisition module is used to acquire soil temperature data of the area to be detected; wherein the soil temperature data includes three-dimensional spatial position, time and corresponding spatiotemporal temperature;

[0042] A parameter detection module is used to input soil temperature data into a pre-trained multi-parameter detection model to obtain the water content, ice content, thermal conductivity, and volumetric heat capacity of the soil in the area to be detected. The multi-parameter detection model is obtained by training a PINN model based on a pre-constructed data set, which includes soil temperature data, water content, ice content, thermal conductivity, and volumetric heat capacity of frozen soil samples.

[0043] The training process of the multi-parameter detection model includes:

[0044] The soil temperature data of the frozen soil samples are used as the input of the PINN model, and the water content, ice content, thermal conductivity and volumetric heat capacity of the frozen soil samples are used as the output of the PINN model to train the model. A one-dimensional unsteady-state heat conduction equation is constructed based on the soil temperature data of the frozen soil samples, and a loss function is constructed based on the one-dimensional unsteady-state heat conduction equation. When the loss function reaches convergence, the multi-parameter detection model is obtained through training.

[0045] In a third aspect, an embodiment of the present invention provides a terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method described in the first aspect or any possible implementation of the first aspect are implemented.

[0046] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the method described in the first aspect or any possible implementation of the first aspect.

[0047] Embodiments of the present invention provide a multi-parameter integrated detection method, device, terminal, and storage medium for frozen soil. The method obtains soil temperature data of the area to be detected, wherein the soil temperature data includes three-dimensional spatial position, time, and corresponding spatiotemporal temperature, and serves as the basis for subsequent detection. The soil temperature data is input into a pre-trained multi-parameter detection model. The multi-parameter detection model is obtained by training a PINN model based on a pre-constructed dataset, which includes soil temperature data of frozen soil samples, water content, ice content, thermal conductivity, and volumetric heat capacity of the frozen soil samples. The training process of the multi-parameter detection model includes: using the soil temperature data of the frozen soil samples as input to the PINN model, and using the water content, ice content, thermal conductivity, and volumetric heat capacity of the frozen soil samples as outputs to train the PINN model; constructing a one-dimensional unsteady-state heat conduction equation based on the soil temperature data of the frozen soil samples; constructing a loss function based on the one-dimensional unsteady-state heat conduction equation; and evaluating the difference between the model's predicted value and the true value based on the loss function to optimize the model parameters. When the loss function reaches convergence, a multi-parameter detection model is trained to facilitate the simultaneous detection of multiple parameters such as the water content, ice content, thermal conductivity and volumetric heat capacity of frozen soil. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0049] Figure 1 This is a flowchart of the implementation of the multi-parameter integrated detection method for frozen soil provided by an embodiment of the present invention;

[0050] Figure 2 1 is a schematic structural diagram of a multi-parameter integrated detection device for frozen soil provided by an embodiment of the present invention;

[0051] Figure 3 is a schematic diagram of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] In the following description, specific details such as particular system structures and techniques are provided for purposes of illustration, not limitation, to facilitate a thorough understanding of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

[0053] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below with reference to the accompanying drawings.

[0054] See also Figure 1 , which shows a flowchart of the implementation of the multi-parameter integrated detection method for frozen soil provided by an embodiment of the present invention, and is described in detail as follows:

[0055] Step 101: Acquire soil temperature data of the area to be detected; wherein the soil temperature data includes three-dimensional spatial position, time and corresponding spatiotemporal temperature.

[0056] In this embodiment, the three-dimensional spatial location refers to a coordinate point in a three-dimensional coordinate system (typically the x, y, and z axes). This helps understand the distribution of soil temperature at different depths and horizontal positions, as soil temperature may vary with depth and position. For example, in permafrost, the soil temperature near the surface may be directly affected by atmospheric temperature fluctuations, while the temperature of the deeper soil layers is relatively stable. By recording the three-dimensional spatial location, a more accurate spatial distribution map of soil temperature can be depicted.

[0057] Soil temperature also fluctuates over time, particularly with seasonal changes, the alternation of day and night, or due to external heat sources such as solar radiation and human activity. Recording temporal information can capture these dynamic changes and analyze the temporal evolution of soil temperature. For example, throughout the day, soil surface temperature may rise and fall as the sun rises and sets. By recording temperature data at different times, this diurnal variation can be studied.

[0058] Furthermore, spatiotemporal temperature is a temperature data set that integrates spatial and temporal dimensions, describing how soil temperature varies at different locations and time points. Soil temperature values measured at specific three-dimensional spatial locations and time points are the most direct temperature data, reflecting the actual soil temperature state at that location and time. By combining temperature values with spatial location and time, a complete spatiotemporal evolution model of soil temperature can be constructed, providing detailed data support for subsequent parameter inversion and frozen soil property analysis.

[0059] In one possible implementation, soil temperature data of the area to be inspected is obtained by implanting a sensor into the frozen soil of the area to be inspected; the sensor includes three parallel probes, the middle probe can heat and measure temperature, and the probes on both sides can measure temperature; the middle probe applies a pulse to the surrounding soil; and the temperature data of the three probes that change with spatial position and time are simultaneously collected to obtain soil temperature data.

[0060] In this embodiment, the sensor is implanted in the frozen soil of the area to be tested, ensuring close contact between the probe and the soil to reduce contact thermal resistance so as to accurately measure the soil temperature. The middle probe can both heat the soil and measure temperature. The heating function is used to apply transient heat pulses, and the temperature measurement function is used to record temperature changes. The probes on both sides are only used to measure temperature and record temperature changes in the surrounding soil. The sensor applies the same amount of energy to the soil as in the data calibration process during the previous training model, and collects the spatiotemporal evolution data of the soil heat read by the probe. A transient heat pulse is applied to the surrounding soil through the middle probe to stimulate the transient thermal response of the soil. This transient thermal response can provide information on the thermophysical properties of the soil (such as thermal conductivity, volumetric heat capacity, etc.). The pulse duration is usually 10 to 20 seconds to avoid damage to the soil structure.

[0061] Furthermore, probes can be made of materials with varying thermal conductivity to accommodate varying temperature ranges and soil conditions. Heating methods can include electrical, laser, or pulsed heating to further enhance the controllability of transient thermal responses. By increasing the number of probes, a multi-point measurement array can be formed to obtain more comprehensive spatial distribution information.

[0062] Step 102: Input the soil temperature data into a pre-trained multi-parameter detection model to obtain the soil moisture content, soil ice content, soil thermal conductivity, and soil volumetric heat capacity of the area to be detected; wherein the multi-parameter detection model is obtained by training a PINN model based on a pre-constructed data set, and the data set includes soil temperature data of frozen soil samples, moisture content of frozen soil samples, ice content of frozen soil samples, thermal conductivity of frozen soil samples, and volumetric heat capacity of frozen soil samples; the training process of the multi-parameter detection model is as follows: the soil temperature data of the frozen soil samples is used as the input of the PINN model, and the moisture content, ice content, thermal conductivity, and volumetric heat capacity of the frozen soil samples are used as the output of the PINN model to train the PINN model; construct a one-dimensional unsteady-state heat conduction equation based on the soil temperature data of the frozen soil samples, and construct a loss function based on the one-dimensional unsteady-state heat conduction equation. When the loss function reaches convergence, the multi-parameter detection model is obtained through training.

[0063] In this embodiment, by inputting soil temperature data into the PINN model, the water content, ice content, thermal conductivity, and volumetric heat capacity of the soil in the area to be tested can be obtained. These parameters are crucial for understanding the physical properties and thermal behavior of the soil.

[0064] In this example, when constructing the dataset, sensors were vertically implanted into prefabricated frozen soil samples of known ice content, ensuring close contact between the probe and the soil and reducing contact thermal resistance. A central, heatable probe applied a transient heat pulse to the surrounding soil, stimulating a thermal response. The duration and power of the heating process were controllable to avoid damage to the soil structure. For soils of varying properties, the heat input per unit line heat source has no fixed value. The key control principle is: the duration of the pulsed heat source is between 10 and 20 seconds to prevent excessive power from causing thawing. After energy input, if the heating curves for soils with different ice contents do not intersect, the input power is considered suitable for measurement. During calibration, once the input heat and power are determined, the measurement process must strictly adhere to these values. During data acquisition, the temperature changes of the three probes are simultaneously recorded to obtain a complete heating curve. This data was obtained through calibration experiments under laboratory conditions, ensuring its accuracy and reliability. The water content of the frozen soil sample, the ice content of the frozen soil sample, the thermal conductivity of the frozen soil sample and the volumetric heat capacity of the frozen soil sample are measured through experiments or are known calibration values.

[0065] In this example, a one-dimensional unsteady-state heat conduction equation was constructed based on soil temperature data from frozen soil samples. This equation describes the physical process of soil temperature variation over time and space. A loss function is used to measure the difference between the model's predicted values and the actual values. By optimizing the loss function, the model's predicted values are brought as close to the actual values as possible. When the loss function converges, the difference between the model's predicted values and the actual values is sufficiently small that the model can accurately predict various soil physical parameters.

[0066] In one possible implementation, the loss function includes a data loss function and a physical loss function; the loss function is constructed based on the one-dimensional non-steady-state heat conduction equation, and the specific processing is as follows: the data loss function and the physical loss function are constructed based on the one-dimensional non-steady-state heat conduction equation; the loss function is constructed based on the data loss function and the physical loss function.

[0067] In this embodiment, the data loss function measures the difference between the model's predicted value and the actual observed value. The physical loss function measures whether the model's predicted value satisfies the residual of a physical equation (such as the one-dimensional unsteady-state heat conduction equation). Combining the data loss function and the physical loss function creates a comprehensive loss function that is used for model training and optimization, comprehensively measuring the model's predictive performance.

[0068] In one possible implementation, the one-dimensional unsteady heat conduction equation is:

[0069]

[0070] Where, T ( x,t )forx Location t The soil temperature at the moment, ρ 、 c 、 k are the density, volumetric heat capacity and thermal conductivity of the soil, respectively. Q ( x , t ) is the heat source term;

[0071] The loss function is:

[0072]

[0073] Where λ data is the weight coefficient, is the data loss function, λ physics is the weight coefficient, is the physical loss function;

[0074] The data loss function is:

[0075]

[0076] Where, To predict temperature, T obs To observe the temperature, N is the number of data points;

[0077] The physical loss function is:

[0078]

[0079] Where, To predict temperature, M is the number of sampling points, ρ 、 c 、 k are the density, volumetric heat capacity and thermal conductivity of the soil, respectively. Q is the heat source term.

[0080] In this embodiment, when training the PINN model, the network parameters and the physical parameters to be inverted (ρ, c, k) are first randomly initialized. In the forward propagation, the position x, time t, and temperature T(x, t) are input to calculate the predicted water content, ice content, volumetric heat capacity, and thermal conductivity. When calculating the loss, according to the loss function Calculate the total loss. Calculate the gradient of the loss with respect to the network and physical parameters during backpropagation. Update the network and physical parameters using an optimization algorithm (Adam optimizer) during parameter updates. Repeat the above process until the loss function converges.

[0081] Specifically, the thermal conductivity of the soil as a whole is kThe correlation between the thermal conductivity of each component (used to solve multiple parameters such as water content, ice content, thermal conductivity and volumetric heat capacity) is:

[0082]

[0083] Where, k dry 、 k w 、 k i are the thermal conductivity of dry soil, the thermal conductivity of water, and the thermal conductivity of ice respectively; θ w , θ i are the volumetric water content and volumetric ice content of the soil, respectively.

[0084] The embodiment of the present invention obtains soil temperature data of the area to be inspected; the soil temperature data includes three-dimensional spatial position, time, and corresponding spatiotemporal temperature, which serves as the basis for subsequent inspection. The soil temperature data is input into a pre-trained multi-parameter inspection model. The multi-parameter inspection model is trained using a PINN model based on a pre-constructed dataset, including soil temperature data, water content, ice content, thermal conductivity, and volumetric heat capacity of frozen soil samples. The training process of the multi-parameter inspection model includes: using the soil temperature data of the frozen soil samples as input to the PINN model; using the water content, ice content, thermal conductivity, and volumetric heat capacity of the frozen soil samples as outputs; and constructing a one-dimensional unsteady-state heat conduction equation based on the soil temperature data of the frozen soil samples. A loss function is then constructed based on the one-dimensional unsteady-state heat conduction equation. The loss function is used to evaluate the difference between the model's predicted values and the true values, thereby optimizing the model parameters. When the loss function reaches convergence, a multi-parameter detection model is trained to facilitate the simultaneous detection of multiple parameters such as the water content, ice content, thermal conductivity and volumetric heat capacity of frozen soil.

[0085] In one possible implementation, before inputting the soil temperature data into a pre-trained multi-parameter detection model, the following processing may be performed: filtering the soil temperature data of the area to be detected, removing noise from the filtered soil temperature data, and inputting the soil temperature data after the noise removal into the pre-trained multi-parameter detection model.

[0086] In this embodiment, filtering can smooth the data, reduce high-frequency noise and unnecessary fluctuations in the data, and make the data more stable and reliable. Denoising can further clean up the data and remove random errors and interference that may affect the accuracy of the model prediction.

[0087] In one possible implementation, the soil temperature data of the area to be detected is filtered. Specifically, the process is as follows: the soil temperature data is filtered using a filter to obtain filtered soil temperature data; and the filtered soil temperature data is de-noised. Specifically, the process is as follows: the filtered soil temperature data is de-noised using a wavelet transform or a median filter to obtain de-noised soil temperature data.

[0088] In this embodiment, based on the sampling frequency and signal characteristics, a Butterworth filter or a Chebyshev filter is selected to filter the raw temperature data to obtain a smooth temperature-time curve. The filter transfer function is:

[0089]

[0090] f c is the cutoff frequency, n is the filter order.

[0091] Filtered temperature signal:

[0092]

[0093] F and F -1 denote the Fourier transform and inverse Fourier transform, respectively. Represents the raw temperature signal.

[0094] In a possible implementation, the method can also perform the following processing: obtaining a data set; the data set includes soil temperature data of the frozen soil sample, water content of the frozen soil sample, ice content of the frozen soil sample, thermal conductivity of the frozen soil sample, and volumetric heat capacity of the frozen soil sample; using a preset proportion of data in the data set as a training set, and using the remaining data as a test set; training the PINN model based on the training set, and testing the PINN model based on the test set to obtain a multi-parameter detection model.

[0095] In this embodiment, a preset proportion (e.g., 80%) of the data set is used as the training set. This training set is used to train the model, enabling it to learn patterns and relationships within the data. The remaining data (e.g., 20%) is used as the test set. This test set is used to verify the model's performance and assess its performance on unseen data. The performance of the model is verified through evaluation on the test set. If the model performs well on the test set, it indicates that the model has good generalization ability and can be used in practical multi-parameter detection.

[0096] In other possible approaches, one or more stages in the PINN model and sensor may adopt other approaches. This application mainly uses the PINN model and sensor processing as an example for explanation.

[0097] This method utilizes a closely coupled three-probe sensor to measure transient thermal response. The parameters and measurement processes involved enable integrated, multi-parameter sensing of ice-rich frozen soils. Specifically, this includes high-precision, simultaneous measurement of physical parameters such as temperature, water content, ice content, thermal conductivity, and volumetric heat capacity. By precisely controlling the heating process and data processing, the accuracy and reliability of the measurements can be significantly improved.

[0098] This invention overcomes the limitation of traditional methods that can only measure a single parameter, significantly improving measurement efficiency. Utilizing transient thermal response and advanced data processing algorithms, it significantly enhances measurement accuracy. The sensor's simple structure makes it easy to deploy in the field and suitable for a variety of complex ice-rich frozen soil environments. For the first time, it combines closely coupled three-probe technology with machine learning to achieve integrated multi-parameter sensing of ice-rich frozen soil.

[0099] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0100] The following are device embodiments of the present invention. For details not fully described therein, reference may be made to the corresponding method embodiments described above.

[0101] Figure 2 The following is a schematic diagram of the structure of a multi-parameter integrated detection device for frozen soil provided by an embodiment of the present invention. For ease of explanation, only the parts related to the embodiment of the present invention are shown, which are detailed as follows:

[0102] like Figure 2 As shown, the multi-parameter integrated detection device 2 for frozen soil includes:

[0103] The data acquisition module 21 is used to acquire soil temperature data of the area to be detected; wherein the soil temperature data includes a three-dimensional spatial position, time, and corresponding spatiotemporal temperature;

[0104] a parameter detection module 22 for inputting soil temperature data into a pre-trained multi-parameter detection model to obtain the water content, ice content, thermal conductivity, and volumetric heat capacity of the soil in the area to be detected; wherein the multi-parameter detection model is obtained by training a PINN model based on a pre-constructed data set, which includes soil temperature data, water content, ice content, thermal conductivity, and volumetric heat capacity of frozen soil samples;

[0105] The training process of the multi-parameter detection model includes:

[0106] The soil temperature data of the frozen soil samples are used as the input of the PINN model, and the water content, ice content, thermal conductivity and volumetric heat capacity of the frozen soil samples are used as the output of the PINN model to train the model. A one-dimensional unsteady-state heat conduction equation is constructed based on the soil temperature data of the frozen soil samples, and a loss function is constructed based on the one-dimensional unsteady-state heat conduction equation. When the loss function reaches convergence, the multi-parameter detection model is obtained through training.

[0107] In one possible implementation, the loss function includes a data loss function and a physical loss function, and the parameter detection module 22 may be used to:

[0108] Construct data loss function and physical loss function based on one-dimensional unsteady heat conduction equation;

[0109] Construct the loss function based on the data loss function and the physical loss function.

[0110] In a possible implementation, the data acquisition module 21 may be used to:

[0111] The sensor is implanted into the frozen soil of the area to be tested. The sensor includes three parallel probes, the middle probe can heat and measure temperature, and the probes on both sides can measure temperature.

[0112] Make the middle probe apply pulses to the surrounding soil;

[0113] The temperature data of the three probes that change with spatial position and time are collected at the same time to obtain soil temperature data.

[0114] In a possible implementation, the data acquisition module 21 may be used to:

[0115] The soil temperature data of the area to be detected is filtered, noise is removed from the filtered soil temperature data, and the noise-removed soil temperature data is input into a pre-trained multi-parameter detection model.

[0116] In a possible implementation, the data acquisition module 21 may be used to:

[0117] Filtering the soil temperature data using a filter to obtain filtered soil temperature data;

[0118] Remove noise from the filtered soil temperature data, including:

[0119] The soil temperature data after filtering is subjected to noise removal by using wavelet transform or median filtering to obtain the soil temperature data after noise removal.

[0120] In a possible implementation, the parameter detection module 22 may be used to:

[0121] Acquire a data set; the data set includes soil temperature data of the frozen soil sample, water content of the frozen soil sample, ice content of the frozen soil sample, thermal conductivity of the frozen soil sample, and volumetric heat capacity of the frozen soil sample;

[0122] Use a preset proportion of data in the dataset as the training set and the remaining data as the test set;

[0123] The PINN model is trained according to the training set and tested according to the test set to obtain a multi-parameter detection model.

[0124] The embodiment of the present invention obtains soil temperature data of the area to be inspected; the soil temperature data includes three-dimensional spatial position, time, and corresponding spatiotemporal temperature, which serves as the basis for subsequent inspection. The soil temperature data is input into a pre-trained multi-parameter inspection model. The multi-parameter inspection model is trained using a PINN model based on a pre-constructed dataset, including soil temperature data, water content, ice content, thermal conductivity, and volumetric heat capacity of frozen soil samples. The training process of the multi-parameter inspection model includes: using the soil temperature data of the frozen soil samples as input to the PINN model; using the water content, ice content, thermal conductivity, and volumetric heat capacity of the frozen soil samples as outputs; and constructing a one-dimensional unsteady-state heat conduction equation based on the soil temperature data of the frozen soil samples. A loss function is then constructed based on the one-dimensional unsteady-state heat conduction equation. The loss function is used to evaluate the difference between the model's predicted values and the true values, thereby optimizing the model parameters. When the loss function reaches convergence, a multi-parameter detection model is trained to facilitate the simultaneous detection of multiple parameters such as the water content, ice content, thermal conductivity and volumetric heat capacity of frozen soil.

[0125] Figure 3 Schematic diagram of a terminal provided by an embodiment of the present invention. Figure 3 As shown, the terminal 3 of this embodiment includes: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30. When the processor 30 executes the computer program 32, the steps in the above-mentioned embodiments of the multi-parameter integrated detection method for frozen soil are implemented, for example Figure 1 Alternatively, when the processor 30 executes the computer program 32, the functions of the modules in the above-mentioned device embodiments are realized, for example Figure 2 The functions of each module are shown.

[0126] Exemplarily, the computer program 32 may be divided into one or more modules, which are stored in the memory 31 and executed by the processor 30 to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program 32 in the terminal 3. For example, the computer program 32 may be divided into Figure 2 The modules shown.

[0127] The terminal 3 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal 3 may include, but is not limited to, a processor 30 and a memory 31. Those skilled in the art will understand that Figure 3 It is only an example of terminal 3 and does not constitute a limitation on terminal 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal may also include input and output devices, network access devices, buses, etc.

[0128] The processor 30 may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0129] The memory 31 can be an internal storage unit of the terminal 3, such as a hard drive or memory of the terminal 3. The memory 31 can also be an external storage device of the terminal 3, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. equipped with the terminal 3. Furthermore, the memory 31 can include both the internal storage unit of the terminal 3 and an external storage device. The memory 31 is used to store the computer program and other programs and data required by the terminal. The memory 31 can also be used to temporarily store data that has been output or is about to be output.

[0130] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0131] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0132] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0133] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For example, the division of the modules or units is merely a logical functional division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of devices or units, and can be electrical, mechanical, or other forms.

[0134] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0135] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0136] If the integrated module is implemented as a software functional unit and sold or used as a standalone product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention can also implement all or part of the process steps in the above-mentioned method embodiments by using a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned multi-parameter integrated detection method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include any entity or device capable of carrying the computer program code, recording medium, USB flash drive, removable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practices in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practices, computer-readable media does not include electrical carrier signals and telecommunication signals.

[0137] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A multi-parameter integrated detection method for frozen soil, characterized in that: include: Acquire soil temperature data of the area to be detected; wherein the soil temperature data includes three-dimensional spatial position, time and corresponding spatiotemporal temperature; Inputting the soil temperature data into a pre-trained multi-parameter detection model to obtain the water content, ice content, thermal conductivity and volumetric heat capacity of the soil in the area to be detected; wherein the multi-parameter detection model is obtained by training a PINN model based on a pre-constructed data set, and the data set includes soil temperature data of frozen soil samples, water content, ice content, thermal conductivity and volumetric heat capacity of frozen soil samples; The training process of the multi-parameter detection model includes: The soil temperature data of the frozen soil sample is used as the input of the PINN model, and the water content, ice content, thermal conductivity and volumetric heat capacity of the frozen soil sample are used as the output of the PINN model to train the PINN model; a one-dimensional unsteady-state heat conduction equation is constructed based on the soil temperature data of the frozen soil sample, and a loss function is constructed based on the one-dimensional unsteady-state heat conduction equation. When the loss function reaches convergence, the multi-parameter detection model is obtained through training; The one-dimensional unsteady heat conduction equation is: Where, T ( x,t )for x Location t The soil temperature of the frozen soil sample at the time, ρ 、 c 、 k are the density of the soil of the frozen soil sample, the volumetric heat capacity of the frozen soil sample and the thermal conductivity of the frozen soil sample, Q ( x , t ) is the heat source term; Thermal conductivity of frozen soil samples k The correlation between the thermal conductivity of each component is: Where, k dry 、 k w 、 k i are the thermal conductivity of dry soil, the thermal conductivity of water, and the thermal conductivity of ice respectively; θ w , θ i are the water content and ice content of the frozen soil sample, respectively.

2. The multi-parameter integrated detection method for frozen soil according to claim 1, characterized in that: The loss function includes a data loss function and a physical loss function; The constructing of a loss function according to the one-dimensional unsteady-state heat conduction equation includes: Constructing the data loss function and the physical loss function according to the one-dimensional unsteady heat conduction equation; A loss function is constructed according to the data loss function and the physical loss function.

3. The multi-parameter integrated detection method for frozen soil according to claim 2, characterized in that: The loss function is: Where λ data is the weight coefficient, is the data loss function, λ physics is the weight coefficient, is the physical loss function; The data loss function is: Where, To predict temperature, T obs To observe the temperature, N is the number of data points; The physical loss function is: Where, To predict temperature, M is the number of sampling points, ρ 、 c 、 k are the density of the soil of the frozen soil sample, the volumetric heat capacity of the frozen soil sample and the thermal conductivity of the frozen soil sample, Q is the heat source term.

4. The multi-parameter integrated detection method for frozen soil according to claim 1, characterized in that: The step of obtaining soil temperature data of the area to be detected includes: The sensor is implanted into the frozen soil of the area to be tested; wherein the sensor includes three parallel probes, the middle probe can heat and measure temperature, and the probes on both sides can measure temperature; Make the middle probe apply pulses to the surrounding soil; The temperature data of the three probes that change with spatial position and time are collected simultaneously to obtain the soil temperature data.

5. The multi-parameter integrated detection method for frozen soil according to claim 1, characterized in that: Before inputting the soil temperature data into the pre-trained multi-parameter detection model, the method further includes: The soil temperature data of the area to be detected is filtered, noise is removed from the filtered soil temperature data, and the noise-removed soil temperature data is input into a pre-trained multi-parameter detection model.

6. The multi-parameter integrated detection method for frozen soil according to claim 5, characterized in that: The filtering process of the soil temperature data of the area to be detected includes: Filtering the soil temperature data using a filter to obtain filtered soil temperature data; The step of removing noise from the filtered soil temperature data comprises: The soil temperature data after filtering is subjected to noise removal processing by using wavelet transform or median filtering to obtain the soil temperature data after noise removal.

7. The multi-parameter integrated detection method for frozen soil according to claim 1, characterized in that: The method further comprises: Acquire a data set; the data set includes soil temperature data of the frozen soil sample, water content of the frozen soil sample, ice content of the frozen soil sample, thermal conductivity of the frozen soil sample, and volumetric heat capacity of the frozen soil sample; Using a preset proportion of data in the data set as a training set and the remaining data as a test set; The PINN model is trained according to the training set, and the PINN model is tested according to the test set to obtain the multi-parameter detection model.

8. A multi-parameter integrated detection device for frozen soil, characterized in that: include: A data acquisition module is used to acquire soil temperature data of the area to be detected; wherein the soil temperature data includes a three-dimensional spatial position, time, and corresponding spatiotemporal temperature; a parameter detection module, configured to input the soil temperature data into a pre-trained multi-parameter detection model to obtain the water content, ice content, thermal conductivity, and volumetric heat capacity of the soil in the area to be detected; wherein the multi-parameter detection model is obtained by training a PINN model based on a pre-constructed data set, wherein the data set includes soil temperature data, water content, ice content, thermal conductivity, and volumetric heat capacity of frozen soil samples; The training process of the multi-parameter detection model includes: The soil temperature data of the frozen soil sample is used as the input of the PINN model, and the water content, ice content, thermal conductivity and volumetric heat capacity of the frozen soil sample are used as the output of the PINN model to train the PINN model; a one-dimensional unsteady-state heat conduction equation is constructed based on the soil temperature data of the frozen soil sample, and a loss function is constructed based on the one-dimensional unsteady-state heat conduction equation. When the loss function reaches convergence, the multi-parameter detection model is obtained through training; The one-dimensional unsteady heat conduction equation is: Where, T ( x,t )for x Location t The soil temperature of the frozen soil sample at the time, ρ 、 c 、 k are the density of the soil of the frozen soil sample, the volumetric heat capacity of the frozen soil sample and the thermal conductivity of the frozen soil sample, Q ( x , t ) is the heat source term; Thermal conductivity of frozen soil samples k The correlation between the thermal conductivity of each component is: Where, k dry 、 k w 、 k i are the thermal conductivity of dry soil, the thermal conductivity of water, and the thermal conductivity of ice respectively; θ w , θ i are the water content and ice content of the frozen soil sample, respectively.

9. A terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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