Data processing method and device for predicting ice content of soil body
By constructing a soil ice content prediction model based on wave velocity and ambient temperature, the problems of complexity and high cost of existing frozen soil ice content detection methods are solved, and high-precision frozen soil ice content prediction is achieved.
Patent Information
- Application Number
- CN202511069263.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-31
- Publication Date
- 2025-11-21
AI Technical Summary
Existing methods for detecting ice content in frozen soil are complex to operate, costly, or difficult to apply in the field, and they also affect the soil condition, making it impossible to achieve high-precision continuous measurement.
By acquiring sample wave velocity characteristic data and soil characteristic data, a soil ice content prediction model is constructed. The prediction model is established using wave velocity and ambient temperature to achieve high-precision prediction of soil ice content.
High-precision continuous measurement of ice content in frozen soil was achieved without altering the soil condition or being constrained by the testing environment.
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Figure CN120995043A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of frozen soil testing, and more specifically, to a data processing method and apparatus for predicting the ice content of soil. Background Technology
[0002] In cold-region engineering construction, the freezing and thawing process of soil directly affects its engineering properties, such as strength, deformation, and stability. Ice content, as a crucial parameter influencing soil mechanical behavior, is essential for the accurate detection of infrastructure design, construction, and maintenance in permafrost regions. Existing methods, such as the drying method, thermal pulse method, and nuclear magnetic resonance (NMR) method, can determine ice content, but they suffer from drawbacks such as complex operation, high cost, or difficulty in field application. The drying method is limited by the testing environment, requiring laboratory testing only, and can easily damage the original structure of the permafrost and cannot measure the ice content in unsaturated soil. The thermal pulse method is more suitable for testing the ice content of low-temperature permafrost, but when the soil temperature is close to the freezing point, the heat input by the probe can significantly disturb the soil state, affecting the measurement results. While the NMR method offers high accuracy, the analyzer is bulky and expensive, and the installation and use process is very complex, limiting its widespread application.
[0003] Therefore, finding a method for measuring ice content that can achieve high-precision continuous measurement without changing the soil state, and that is simple, easy to implement, and not restricted by the testing environment, has become an urgent problem to be solved. Summary of the Invention
[0004] The main objective of this application is to provide a data processing method and apparatus for predicting the ice content of soil, so as to solve at least one of the technical problems existing in the background art.
[0005] To achieve the above objectives, the first aspect of this application proposes a data processing method for predicting the ice content of soil, comprising:
[0006] Obtain sample data to be processed, wherein the sample data to be processed includes sample wave velocity feature data and sample soil feature data, wherein the sample wave velocity feature data is feature data used to represent the wave velocity of the sample soil, and the sample soil feature data is data used to represent the ice content feature of the sample soil.
[0007] The sample wave velocity characteristic data and the sample soil characteristic data are processed to form a model for predicting soil ice content.
[0008] Obtain soil data to be predicted, wherein the soil data to be predicted is relevant data used to represent the soil to be predicted;
[0009] The soil data to be predicted is processed based on the soil ice content prediction model to obtain prediction result data, wherein the prediction result data is used to represent the predicted soil ice content data.
[0010] Furthermore, the soil data to be predicted is processed based on the soil ice content prediction model to obtain prediction result data including:
[0011] The soil to be predicted is identified and processed to obtain wave velocity characteristic data and environmental characteristic data of the soil to be predicted.
[0012] The soil ice content prediction model is subjected to model matching processing based on the environmental feature data of the soil to be predicted to obtain a feature soil ice content prediction model, wherein the feature soil ice content prediction model is the soil ice content prediction model corresponding to the environmental feature data of the soil to be predicted.
[0013] The wave velocity characteristic data of the soil to be predicted is processed based on the ice content prediction model to obtain the prediction result data.
[0014] Furthermore, the soil ice content prediction model is subjected to model matching processing based on the environmental characteristic data of the soil to be predicted, resulting in a characteristic soil ice content prediction model including:
[0015] The environmental feature data of the soil to be predicted is processed based on environmental features to obtain the first environmental feature data of the soil to be predicted and the second environmental feature data of the soil to be predicted. The first environmental feature data of the soil to be predicted is environmental feature data used to represent the initial moisture content of the soil to be predicted, and the second environmental feature data of the soil to be predicted is environmental feature data used to represent the ambient temperature of the soil to be predicted.
[0016] The soil ice content prediction model is subjected to model matching processing based on the environmental characteristic data of the first soil to be predicted to obtain the first characteristic soil ice content prediction model. The first characteristic soil ice content prediction model is a model for predicting soil ice content based on the initial water content and wave velocity of the soil to be predicted.
[0017] The soil ice content prediction model is subjected to model matching processing based on the second soil environmental feature data to obtain a second feature soil ice content prediction model. The second feature soil ice content prediction model is a model for predicting soil ice content based on the soil environmental temperature and the soil wave velocity.
[0018] Furthermore, obtaining the sample data to be processed includes:
[0019] After conducting physical property analysis on the frozen soil to be tested, multiple soil samples were prepared, with different initial moisture contents among the multiple soil samples.
[0020] The freezing characteristics of multiple soil samples were analyzed and processed to obtain the freezing characteristic data of multiple soil samples.
[0021] Based on the frozen characteristic data of the soil samples, wave velocity measurement was performed on the soil samples to obtain wave velocity characteristic data of the soil samples.
[0022] The sample data to be processed is obtained based on the freezing characteristic data and wave velocity characteristic data of the soil samples.
[0023] Furthermore, the freezing characteristics of multiple soil samples were analyzed and processed separately, resulting in the following data on the freezing characteristics of the soil samples:
[0024] The ice content of the first soil sample was analyzed based on different temperatures, and multiple ice content data of the first soil sample were obtained. The multiple ice content data of the first soil sample corresponded to different temperatures.
[0025] Freezing characteristic analysis was performed on the ice content data and corresponding different temperatures of the multiple first soil samples to obtain the freezing characteristic data of the first soil samples, wherein the first soil sample is the soil sample with the first initial moisture content prepared.
[0026] The freezing characteristics of the soil samples were analyzed and processed to obtain the freezing characteristic data of the soil samples.
[0027] Furthermore, the sample wave velocity characteristic data and the sample soil characteristic data are processed to form a model for predicting soil ice content, including:
[0028] A prediction model for the ice content of the first characteristic soil mass is constructed based on the initial water content of the sample wave velocity characteristic data and the sample soil characteristic data. The formula is: V = (aw0) b )θ i 2 +V0
[0029] Where V is the wave velocity of the soil sample, θ i denoted as , where is the ice content of the soil sample, V0 is the wave velocity when the ice content of the soil sample is zero, w0 is the initial water content of the soil sample, and a and b are fitting parameters.
[0030] A second soil ice content prediction model based on ambient temperature is constructed using the sample wave velocity characteristic data and the sample soil characteristic data. The formula is: V=(cT+d)θ i e
[0031] Where V is the wave velocity of the soil sample, θ i denoted as the ice content of the soil sample, T as the ambient temperature of the soil sample, and c, d, and e as fitting parameters.
[0032] According to a second aspect of this application, a data processing apparatus for predicting the ice content of soil is proposed, comprising:
[0033] The sample data acquisition module is used to acquire sample data to be processed, wherein the sample data to be processed includes sample wave velocity feature data and sample soil feature data, wherein the sample wave velocity feature data is feature data used to represent the wave velocity of the sample soil, and the sample soil feature data is data used to represent the ice content feature of the sample soil.
[0034] The prediction model building module is used to process the sample wave velocity feature data and the sample soil feature data to obtain a soil ice content prediction model.
[0035] The test data acquisition module is used to acquire the soil data to be predicted, wherein the soil data to be predicted is relevant data used to represent the soil to be predicted;
[0036] The prediction result module is used to perform prediction processing on the soil data to be predicted based on the soil ice content prediction model to obtain prediction result data, wherein the prediction result data is used to represent the predicted soil ice content data.
[0037] According to a third aspect of this application, a prediction system for predicting the ice content of soil is proposed, comprising:
[0038] The power module is used to provide a stable power supply to the test system.
[0039] A waveform excitation and receiving device, comprising a waveform generator for generating excitation wave signals and a receiving device including transducers for converting and receiving excitation wave signals, is arranged at both ends of the soil to be measured position.
[0040] The data processing and analysis unit executes the data processing method described above for predicting the ice content of soil, so as to achieve the prediction of the ice content of the soil to be tested.
[0041] According to a fourth aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing the computer to perform the above-described data processing method for predicting the ice content of soil.
[0042] According to a fifth aspect of this application, an electronic device is proposed, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to cause the at least one processor to perform the above-described data processing method for predicting soil ice content.
[0043] The technical solutions provided by the embodiments of this application may include the following beneficial effects:
[0044] In this application, sample data to be processed is obtained, including sample wave velocity feature data and sample soil feature data. The sample wave velocity feature data is feature data representing the wave velocity of the sample soil, and the sample soil feature data is data representing the ice content characteristics of the sample soil. A model construction process is performed on the sample wave velocity feature data and the sample soil feature data to obtain a soil ice content prediction model. Soil data to be predicted is obtained, including relevant data representing the soil to be predicted. Prediction processing based on the soil ice content prediction model is performed on the soil data to be predicted to obtain prediction result data, which represents the predicted soil ice content. By constructing an ice content prediction model based on soil wave velocity and soil ice content, and predicting the ice content of the soil to be tested based on the wave velocity of the soil to be tested combined with the prediction model, the prediction of the ice content of the soil to be tested is achieved without changing the soil state and without being constrained by the test environment. Attached Figure Description
[0045] The accompanying drawings, which form part of this application, are used to provide a further understanding of the application and to make other features, objects, and advantages of the application more apparent. The illustrative embodiments and descriptions of this application are used to explain the application and do not constitute an undue limitation of the application. In the drawings:
[0046] Figure 1 A flowchart of a data processing method for predicting the ice content of soil provided in this application;
[0047] Figure 2 A flowchart of a data processing method for predicting the ice content of soil provided in this application;
[0048] Figure 3 A schematic diagram of a data processing device for predicting the ice content of soil provided in this application;
[0049] Figure 4 This is a schematic diagram of a prediction system for predicting the ice content of soil, provided in an embodiment of this application. Detailed Implementation
[0050] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.
[0051] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0052] In this application, the terms "upper," "lower," "left," "right," "front," "rear," "top," "bottom," "inner," "outer," "middle," "vertical," "horizontal," "lateral," and "longitudinal" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are primarily for the purpose of better describing this application and its embodiments, and are not intended to limit the indicated device, element, or component to having a specific orientation, or to be constructed and operated in a specific orientation.
[0053] Furthermore, in addition to indicating location or positional relationship, some of the aforementioned terms may also have other meanings. For example, the term "above" may also be used in some cases to indicate a certain dependency or connection relationship. Those skilled in the art can understand the specific meaning of these terms in this application based on the specific circumstances.
[0054] Furthermore, the terms "installation," "setup," "equipped with," "connection," "linked," and "socketing" should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral structure; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or an internal connection between two devices, components, or parts. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.
[0055] Terminology Explanation:
[0056] Saturated air-drying method: The saturated air-drying method is a test method commonly used to prepare soil samples with different initial moisture contents. The basic steps are to first add water to the soil sample until it is completely saturated (to the maximum pore water filling), and then let it air dry naturally under ventilation conditions at room temperature, gradually evaporating some of the water to obtain samples with different moisture contents.
[0057] Ice content in frozen soil: Ice content in frozen soil refers to the mass or volume percentage of water (i.e., ice) existing in solid form in the soil when it is frozen.
[0058] Unfrozen water content: Unfrozen water content refers to the percentage of water in soil that is still in liquid state at temperatures below freezing point.
[0059] Freezing characteristic curve: The freezing characteristic curve describes the relationship between the unfrozen water content of soil and the ambient temperature. It typically shows that the unfrozen water content gradually decreases as the temperature decreases. This curve reflects the water-ice phase transition behavior of the soil and is fundamental data for studying frozen soil mechanics, water migration, and ice content prediction.
[0060] Excitation wave: An excitation wave refers to a wave signal generated artificially (such as a waveform generator) to excite a response within a medium. In this invention, the excitation wave is a single-cycle sinusoidal pulse wave, which is transmitted to the soil through a transducer.
[0061] Transducer: A transducer is a device that converts one form of energy into another. In wave velocity measurement, it often refers to a piezoelectric transducer, which can convert electrical signals into mechanical waves (such as ultrasonic waves), and can also convert received mechanical waves back into electrical signals. The transmitting end converts the electrical excitation signal into a wave signal and transmits it to the soil; the receiving end receives the wave after it passes through the soil and converts it back into an electrical signal for analysis.
[0062] Wave speed: Wave speed refers to the speed at which an elastic wave propagates in soil.
[0063] In some optional embodiments of this application, a data processing method for predicting the ice content of soil is proposed. Figure 1 A flowchart of a data processing method for predicting soil ice content provided in this application is shown below. Figure 1 As shown, the method includes the following steps:
[0064] S101: Obtain the sample data to be processed, which includes sample wave velocity characteristic data and sample soil characteristic data;
[0065] The sample wave velocity characteristic data is the characteristic data used to represent the wave velocity of the sample soil, and the sample soil characteristic data is the data used to represent the ice content of the sample soil.
[0066] In some optional embodiments of this application, a data processing method for predicting the ice content of soil is proposed, including:
[0067] After performing physical property analysis on the frozen soil to be tested, multiple soil samples were prepared. Physical property analysis was then conducted on the soil samples to obtain its state information. Based on this state information, soil samples were prepared. Untouched soil samples were taken from the test location and subjected to physical property analysis, including particle size distribution, liquid limit, plastic limit, specific gravity, and natural moisture content. Based on the optimum moisture content and specific gravity conversion results, multiple initial volumetric moisture content samples were prepared, each with a different initial moisture content. The freezing characteristics of each soil sample were analyzed and processed to obtain freezing characteristic data. Wave velocity measurements were then performed on the multiple soil samples based on their freezing characteristic data to obtain wave velocity characteristic data. Finally, the processed sample data was obtained from both the freezing characteristic data and the wave velocity characteristic data.
[0068] In some optional embodiments of this application, a data processing method for predicting the ice content of soil is proposed, including:
[0069] The freezing characteristic analysis of multiple soil samples was performed separately to obtain freezing characteristic data of multiple samples, including: analyzing the ice content of the first soil sample based on different temperatures to obtain ice content data of multiple first soil samples, with each ice content data corresponding to a different temperature; performing freezing characteristic analysis on the ice content data of multiple first soil samples and their corresponding different temperatures to obtain freezing characteristic data of the sample soil, where the first soil sample is the soil sample with the first initial moisture content; and performing freezing characteristic analysis of multiple soil samples separately to obtain freezing characteristic data of multiple samples soil.
[0070] In some optional embodiments of this application, five soil samples with different initial moisture contents—soil sample 1, soil sample 2, soil sample 3, soil sample 4, and soil sample 5—are prepared by saturated air-drying. These soil samples correspond to different initial moisture contents, and their moisture contents cover the possible ice content range of the soil. For example, the initial moisture contents of the soil samples are 8.4%, 16.8%, 25.2%, 33.6%, and 39.1%, respectively. The soil samples are then frozen to a first ambient temperature. The unfrozen water content of each soil sample at this ambient temperature is measured using nuclear magnetic resonance (NMR) to calculate the ice content of the soil. The soil samples are then raised to a second ambient temperature, and the unfrozen water content of each soil sample at this second ambient temperature (T2) is measured using NMR to calculate the ice content of the soil. For example, ice content can be measured by performing nuclear magnetic resonance measurements at ambient temperatures of -20, -15, -10, -5, -3, -1, 0, 10, and 20°C to obtain the unfrozen water content of soil samples with different initial moisture contents at different temperatures. Then, the ice content of the soil at that temperature can be calculated. Based on the above calculation data, the soil freezing characteristic 1 corresponding to soil sample 1, the soil freezing characteristic 2 corresponding to soil sample 2, the soil freezing characteristic 3 corresponding to soil sample 3, the soil freezing characteristic 4 corresponding to soil sample 4, and the soil freezing characteristic 5 corresponding to soil sample 5 can be obtained. For example, based on the unfrozen water content and ice content of soil sample 1 at ambient temperatures of -20, -15, -10, -5, -3, -1, 0, 10, and 20°C, the soil freezing characteristic data of soil sample 1 can be obtained.
[0071] Wave velocity measurements were performed on multiple soil samples at different ambient temperatures to obtain different wave velocities corresponding to the initial moisture content of the soil samples under different environments. The wave velocity was calculated from the time it took for the excited wave to pass through the soil sample and the length of the soil sample. For example, wave velocity measurements were performed on soil sample 1 at ambient temperatures of -20, -15, -10, -5, -3, -1, 0, 10, and 20℃ to obtain wave velocities v1, v2, v3, v4, v5, v6, v7, v8, and v9, respectively. Wave velocity measurements were performed on multiple soil samples, and a model was constructed based on the different wave velocities corresponding to samples with different initial moisture contents at different temperatures to obtain a soil ice content prediction model.
[0072] In another optional embodiment of this application, sample data to be processed is obtained, and model construction processing is performed based on sample wave velocity characteristic data and sample soil characteristic data in the sample data to be processed. For example,
[0073] Washed and dried silty clay was mixed to achieve an optimum moisture content of 15.5%. Each batch of soil was allowed to stand for 24 hours to ensure uniform moisture content. The soil was then compacted using a sample compactor to prepare cylindrical specimens with dimensions of Φ39.1×80mm. The specimens were placed in a saturation chamber and evacuated for saturation. A vacuum pump was activated to remove air from the specimens. Evacuation continued for 1 hour after the vacuum gauge reached approximately 100 kPa. The vacuum chamber was then slightly opened to allow distilled water to be slowly injected into the chamber through the inlet pipe. The specimens were allowed to stand for 24 hours to fully saturate. The saturated specimens were then air-dried and weighed until they reached the target moisture content. The target volumetric moisture contents were 39.1%, 33.6%, 25.2%, 16.8%, and 8.4%. After air-drying to the target moisture content, the specimens were sealed with plastic wrap and cured for 7 days.
[0074] The samples, air-dried to the target moisture content, were frozen in a cold bath at a constant temperature of -20℃ for 12 hours. Then, they were frozen sequentially at corresponding temperature gradients of -20, -15, -10, -5, -3, -1, 0, 10, and 20℃ for 12 hours each, followed by nuclear magnetic resonance (NMR) measurements. The temperature was controlled within ±0.1℃ of the set temperature, and two parallel experiments were conducted to verify and reduce experimental error.
[0075] NMR technology calculates the volumetric water content of porous media by measuring the transverse relaxation time T2 of hydrogen nuclei. Therefore, the ice content of soil can be calculated by measuring the unfrozen water content in the sample. Standard oil samples are used for calibration. First, the center frequency of the standard oil sample is found using the FID sequence, and then the sample data is collected using the CPMG sequence. NMR signals of the samples are collected at 0, 5, 10, and 20℃, and a paramagnetic regression line is fitted to calculate the unfrozen water content. During testing, the initial water content of the soil samples and the environmental conditions are set sequentially. Each sample needs to be constant at the set temperature for 12 hours before each test.
[0076] First, using soil samples with known moisture content, the nuclear magnetic resonance (NMR) signal curve was calibrated under positive temperature conditions to determine the temperature variation, and the paramagnetic regression line was obtained by fitting the curve. The fitting formula is as follows:
[0077] M(T) = aT + b
[0078] In the formula, M is the NMR signal intensity, T is the absolute temperature, and a and b are two parameters for linear fitting.
[0079] Then, under a negative temperature T1, an NMR test was performed on the sample to obtain its signal intensity. Given a negative temperature T1, if the unfrozen water content of the soil is w0, the NMR signal is calculated based on the above formula. Based on the NMR signal measured at the current temperature T1 as M0, the unfrozen water content can be calculated using the following formula:
[0080]
[0081] In the formula: w u ω represents the unfrozen water content in the soil under condition T1; w0 represents the initial moisture content of the soil.
[0082] The initial moisture content of the sample is a fixed value. Based on the known unfrozen water content and the density change during the ice-water phase transition, the volumetric ice content of the sample at each sub-zero temperature can be calculated using the following formula:
[0083]
[0084] In the formula θ i ρ is the volumetric ice content. w The density of pure water is generally taken as 1.0 g / cm³. 3 , ρ i The density of pure water is generally taken as 0.9 g / cm³. 3 w0 is the initial moisture content of the sample, w u The unfrozen water content of the sample.
[0085] Wave velocity measurements were performed on the above soil samples to obtain wave velocities with different known ice contents. Specifically:
[0086] Wave velocity measurements were performed on soil samples with different initial moisture contents at the aforementioned ambient temperatures. A fixed 100kHz sinusoidal excitation signal with an amplitude of 10V was used in the waveform generator. Vaseline was used as a coupling agent to ensure close contact between the transducer probe and the sample surface. Calculating the elastic wave velocity requires determining the wave's travel length and travel time within the sample. The wave's travel length is determined by measuring the distance between the transmitting and receiving ends, i.e., the sample length. There are many methods for determining the travel time; previous research indicates that the initial arrival wave method is currently the most widely used data processing method. Based on the initial arrival wave method, the first deflection point of the received wave is selected as the initial arrival position of the elastic wave. The travel time of the elastic wave within the sample length is determined based on the excitation wave's starting point and the initial arrival point of the received wave displayed on the oscilloscope.
[0087] The wave velocity is determined by the travel length L of the elastic wave through the sample and the travel time t, as shown in the following formula:
[0088] In the formula, V is the propagation velocity of the elastic wave in the sample, L is the travel length of the elastic wave in the sample, i.e. the sample length, t0 is the inter-transducer delay response time, t1 is the excitation wave initiation time, t2 is the receiving wave initial arrival time, and the difference between the two is the travel time of the elastic wave in the soil.
[0089] S102: Model construction is performed on the sample wave velocity characteristic data and sample soil characteristic data to obtain the soil ice content prediction model;
[0090] In some optional embodiments of this application, a data processing method for predicting the ice content of soil is proposed, including:
[0091] A prediction model for the ice content of the first characteristic soil mass is constructed based on the initial water content of the sample wave velocity characteristic data and the sample soil characteristic data. The formula is: V = (aw0) b )θ i 2 +V0
[0092] Where V is the wave velocity of the soil sample, θ i denoted as , where is the ice content of the soil sample, V0 is the wave velocity when the ice content of the soil sample is zero, w0 is the initial water content of the soil sample, and a and b are fitting parameters.
[0093] A second soil ice content prediction model based on ambient temperature is constructed using the sample wave velocity characteristic data and the sample soil characteristic data. The formula is: V=(cT+d)θ i e
[0094] Where V is the wave velocity of the soil sample, θ i denoted as the ice content of the soil sample, T as the ambient temperature of the soil sample, and c, d, and e as fitting parameters.
[0095] S103: Obtain soil data to be predicted;
[0096] The soil data to be predicted is the relevant data used to represent the soil to be predicted. The soil data to be predicted includes data on the wave velocity characteristics and environmental characteristics of the soil to be predicted. The environmental characteristics are characteristic data used to represent the environmental state of the soil to be predicted. The environmental characteristics include indoor test characteristics and field engineering measurement characteristics.
[0097] S104: Perform prediction processing on the soil data to be predicted based on the soil ice content prediction model to obtain the prediction result data;
[0098] The prediction results data are used to represent the predicted ice content in the soil.
[0099] In some optional embodiments of this application, a data processing method for predicting the ice content of soil is proposed. Figure 2 A flowchart of a data processing method for predicting soil ice content provided in this application is shown below. Figure 2 As shown, the method includes the following steps:
[0100] S201: Identify and process the soil to be predicted to obtain wave velocity characteristic data and environmental characteristic data of the soil to be predicted.
[0101] S202: Perform model matching processing on the soil ice content prediction model based on the environmental characteristic data of the soil to be predicted to obtain the characteristic soil ice content prediction model.
[0102] The soil ice content prediction model is a soil ice content model corresponding to the environmental characteristic data of the soil to be predicted.
[0103] In some optional embodiments of this application, a data processing method for predicting the ice content of soil is proposed, including:
[0104] Environmental feature identification processing is performed on the soil environmental feature data to be predicted, resulting in first and second soil environmental feature data to be predicted. The first soil environmental feature data represents the initial moisture content of the soil to be predicted, and the second soil environmental feature data represents the ambient temperature of the soil to be predicted. Model matching processing is then performed on the soil ice content prediction model based on the first soil environmental feature data to obtain a first-feature soil ice content prediction model, which predicts the ice content of the soil based on the initial moisture content and wave velocity of the soil to be predicted. Finally, model matching processing is performed on the second soil environmental feature data to obtain a second-feature soil ice content prediction model, which predicts the ice content of the soil based on the ambient temperature and wave velocity of the soil to be predicted.
[0105] In an optional embodiment of this application, in indoor model tests, the soil is prepared with a specific initial moisture content, and a wave velocity ice content model incorporating the initial moisture content is used. In field engineering measurements, the soil is mainly undisturbed, and the ambient temperature is measured using a temperature sensor; a wave velocity ice content model incorporating the ambient temperature is selected. Actual wave velocity measurements are performed on the soil to be tested, and the ice content is predicted based on the corresponding ice content prediction model to obtain the predicted ice content result.
[0106] S203: Perform prediction processing on the characteristic wave velocity data of the soil to be predicted based on the characteristic ice content prediction model to obtain the prediction result data.
[0107] In some optional embodiments of this application, a data processing apparatus for predicting the ice content of soil is proposed. Figure 4 A schematic diagram of a data processing device for predicting soil ice content provided in this application is shown below. Figure 4 As shown, it includes:
[0108] The sample data acquisition module 31 is used to acquire sample data to be processed. The sample data to be processed includes sample wave velocity feature data and sample soil feature data. The sample wave velocity feature data is feature data used to represent the wave velocity of the sample soil, and the sample soil feature data is data used to represent the ice content feature of the sample soil.
[0109] The prediction model building module 32 is used to process the sample wave velocity characteristic data and sample soil characteristic data to obtain the soil ice content prediction model.
[0110] The test data acquisition module 33 is used to acquire the soil data to be predicted, which is the relevant data used to represent the soil to be predicted.
[0111] The prediction result module 34 is used to perform prediction processing on the soil data to be predicted based on the soil ice content prediction model to obtain prediction result data, which is used to represent the predicted soil ice content data.
[0112] In some optional embodiments of this application, a prediction system for predicting the ice content of soil is proposed, comprising: The power module is used to provide a stable power supply to the test system; the power module connects the waveform excitation and receiving device and the data processing and analysis unit, and supplies power to the waveform excitation and receiving device and the data processing and analysis unit. The waveform excitation and receiving device comprises a waveform generator to produce excitation wave signals and a receiving device including transducers for converting and receiving the excitation wave signals. These transducers are positioned at both ends of the soil sample to be measured. The device enables wave velocity measurement of the soil. The waveform generator produces periodic excitation wave signals, with the excitation waveform selected as a single-cycle sine wave at approximately 100kHz. The excitation wave signal is converted into an elastic wave by the transducer and emitted, passes through the soil sample, and is then received by another transducer and converted into an electrical signal. The transducers can be matched with the waveform generator to convert and receive the excitation wave type.
[0115] The data processing and analysis unit executes the data processing method described above for predicting the ice content of soil, so as to achieve the prediction of the ice content of the soil to be tested.
[0116] The specific methods of execution of each unit in the above embodiments have been described in detail in the embodiments of the method, and will not be elaborated here.
[0117] In summary, this application involves obtaining sample data to be processed, including sample wave velocity feature data and sample soil feature data. The sample wave velocity feature data represents the wave velocity of the sample soil, and the sample soil feature data represents the ice content characteristics of the sample soil. A model construction process is performed on the sample wave velocity feature data and the sample soil feature data to obtain a soil ice content prediction model. Soil data to be predicted is obtained, including relevant data representing the soil to be predicted. Prediction processing based on the soil ice content prediction model is then performed on the soil data to be predicted to obtain prediction result data, which represents the predicted soil ice content. By constructing an ice content prediction model based on soil wave velocity and soil ice content, and predicting the ice content of the soil to be tested based on the wave velocity of the soil to be tested combined with the prediction model, the prediction of the ice content of the soil to be tested is achieved without changing the soil state and without being constrained by the test environment.
[0118] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.
[0119] Obviously, those skilled in the art should understand that the various units or steps of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device, or fabricating them separately as individual integrated circuit modules, or fabricating multiple modules or steps into a single integrated circuit module. Thus, this application is not limited to any particular combination of hardware and software.
[0120] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.
Claims
1. A data processing method for predicting the ice content of soil, characterized in that, include: Obtain sample data to be processed, wherein the sample data to be processed includes sample wave velocity feature data and sample soil feature data, wherein the sample wave velocity feature data is feature data used to represent the wave velocity of the sample soil, and the sample soil feature data is data used to represent the ice content feature of the sample soil. The sample wave velocity characteristic data and the sample soil characteristic data are processed to form a model for predicting soil ice content. Obtain soil data to be predicted, wherein the soil data to be predicted is relevant data used to represent the soil to be predicted; The soil data to be predicted is processed based on the soil ice content prediction model to obtain prediction result data, wherein the prediction result data is used to represent the predicted soil ice content data.
2. The data processing method according to claim 1, characterized in that, The soil data to be predicted is processed using the soil ice content prediction model to obtain prediction results, including: The soil to be predicted is identified and processed to obtain wave velocity characteristic data and environmental characteristic data of the soil to be predicted. The soil ice content prediction model is subjected to model matching processing based on the environmental feature data of the soil to be predicted to obtain a feature soil ice content prediction model, wherein the feature soil ice content prediction model is the soil ice content prediction model corresponding to the environmental feature data of the soil to be predicted. The wave velocity characteristic data of the soil to be predicted is processed based on the ice content prediction model to obtain the prediction result data.
3. The data processing method according to claim 2, characterized in that, The soil ice content prediction model is subjected to model matching processing based on the environmental characteristic data of the soil to be predicted, resulting in a characteristic soil ice content prediction model including: The environmental feature data of the soil to be predicted is processed based on environmental features to obtain the first environmental feature data of the soil to be predicted and the second environmental feature data of the soil to be predicted. The first environmental feature data of the soil to be predicted is environmental feature data used to represent the initial moisture content of the soil to be predicted, and the second environmental feature data of the soil to be predicted is environmental feature data used to represent the ambient temperature of the soil to be predicted. The soil ice content prediction model is subjected to model matching processing based on the environmental characteristic data of the first soil to be predicted to obtain the first characteristic soil ice content prediction model. The first characteristic soil ice content prediction model is a model for predicting soil ice content based on the initial water content and wave velocity of the soil to be predicted. The soil ice content prediction model is subjected to model matching processing based on the second soil environmental feature data to obtain a second feature soil ice content prediction model. The second feature soil ice content prediction model is a model for predicting soil ice content based on the soil environmental temperature and the soil wave velocity.
4. The data processing method according to claim 1, characterized in that, Obtaining the sample data to be processed includes: After conducting physical property analysis on the frozen soil to be tested, multiple soil samples were prepared, with different initial moisture contents among the multiple soil samples. The freezing characteristics of multiple soil samples were analyzed and processed to obtain the freezing characteristic data of multiple soil samples. Based on the frozen characteristic data of the soil samples, wave velocity measurement was performed on the soil samples to obtain wave velocity characteristic data of the soil samples. The sample data to be processed is obtained based on the freezing characteristic data and wave velocity characteristic data of the soil samples.
5. The data method according to claim 4, characterized in that, The freezing characteristics of multiple soil samples were analyzed and processed separately, and the freezing characteristic data of the soil samples were obtained as follows: The ice content of the first soil sample was analyzed based on different temperatures, and multiple ice content data of the first soil sample were obtained. The multiple ice content data of the first soil sample corresponded to different temperatures. Freezing characteristic analysis was performed on the ice content data and corresponding different temperatures of the multiple first soil samples to obtain the freezing characteristic data of the first soil samples, wherein the first soil sample is the soil sample with the first initial moisture content prepared. The freezing characteristics of the soil samples were analyzed and processed to obtain the freezing characteristic data of the soil samples.
6. The data processing method according to claim 1, characterized in that, The sample wave velocity characteristic data and the sample soil characteristic data are processed to form a model for predicting soil ice content, which includes: A prediction model for the ice content of the first characteristic soil mass is constructed based on the initial water content of the sample wave velocity characteristic data and the sample soil characteristic data. The formula is: V = (aw0) b )θ i 2 +V0 Where V is the wave velocity of the soil sample, θ i denoted as , where is the ice content of the soil sample, V0 is the wave velocity when the ice content of the soil sample is zero, w0 is the initial water content of the soil sample, and a and b are fitting parameters. A second soil ice content prediction model based on ambient temperature is constructed using the sample wave velocity characteristic data and the sample soil characteristic data. The formula is: V=(cT+d)θ i e Where V is the wave velocity of the soil sample, θ i denoted as the ice content of the soil sample, T as the ambient temperature of the soil sample, and c, d, and e as fitting parameters.
7. A data processing device for predicting the ice content of soil, characterized in that, include: The sample data acquisition module is used to acquire sample data to be processed, wherein the sample data to be processed includes sample wave velocity feature data and sample soil feature data, wherein the sample wave velocity feature data is feature data used to represent the wave velocity of the sample soil, and the sample soil feature data is data used to represent the ice content feature of the sample soil. The prediction model building module is used to process the sample wave velocity feature data and the sample soil feature data to obtain a soil ice content prediction model. The test data acquisition module is used to acquire the soil data to be predicted, wherein the soil data to be predicted is relevant data used to represent the soil to be predicted; The prediction result module is used to perform prediction processing on the soil data to be predicted based on the soil ice content prediction model to obtain prediction result data, wherein the prediction result data is used to represent the predicted soil ice content data.
8. A prediction system for predicting the ice content of soil, characterized in that, include: The power module is used to provide a stable power supply to the test system. A waveform excitation and receiving device, comprising a waveform generator for generating excitation wave signals and a receiving device including transducers for converting and receiving excitation wave signals, is arranged at both ends of the soil to be measured position. The data processing and analysis unit executes the data processing method for predicting the ice content of soil as described in any one of claims 1-9, so as to achieve the prediction of the ice content of the soil to be tested.
9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the data processing method for predicting soil ice content as described in any one of claims 1-6.
10. An electronic device, characterized in that, include: At least one processor; The at least one processor is also connected in communication with a memory, wherein the memory stores a computer program that can be executed by the at least one processor to cause the at least one processor to perform the data processing method for predicting ice content in soil as described in any one of claims 1-6.