A method and system for simulating and predicting the water content of the peripheral soil of a desert area hole

Abstract

The application discloses a kind of desert area hole leading peripheral soil moisture content simulation prediction method and system, it is related to engineering soil moisture content simulation technical field.The method is first collected model building required model input data and model output data and model running required prediction section model input data, then for each desert area hole leading peripheral soil moisture content simulation model, application model input data and model output data are carried out moisture content simulation, check and adjust corresponding model parameters, and obtain corresponding model index for selection, and select some model with optimal model index for selection, finally, prediction section model input data is imported into the model, and the moisture content time series estimation data of desert area hole leading peripheral soil in prediction section is output, and is output to show, so it can accurately simulate the change of moisture content in desert area hole leading peripheral soil, and guide engineering construction, improve engineering quality, reduce engineering cost.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of simulation of water content of engineering soil, and particularly relates to a method and system for simulating and predicting water content of peripheral soil of a hole in a desert area. BACKGROUND

[0002] The desert area has good sunshine conditions, so that a large number of photovoltaic projects are built. Photovoltaic supports are generally supported by pile foundations, and a hole needs to be drilled in advance before piling in the desert. Because the cohesion of the desert soil is low, the peripheral soil is prone to collapse after the hole is drilled. Therefore, in engineering practice, a process of water injection before hole drilling is often used to maintain the self-stability height in the soil hole by using the additional cohesion generated by the capillary water between the soil after the hole is drilled, by taking advantage of the characteristics that the cohesion of the silt and silty sand in the desert increases from 0 to the shrinkage limit. However, when the construction interval time (i.e. the interval time from hole drilling to pile pressing construction) is too long, the capillary water in the soil will gradually flow away due to the effects of infiltration and evaporation, resulting in the disappearance of cohesion, and even causing the collapse of the soil hole.

[0003] The existing patent CN118392553A discloses a method and system for predicting redistribution of water in the vadose zone under mining subsidence disturbance. The method determines the boundary range of the mining subsidence area by using the probability integral method, obtains soil samples at different sampling points and different depths within the determined boundary range, and measures the initial water content of the soil. Based on the measured values of the deformation and movement of the mining soil subsidence, the stress path of the mining subsidence disturbance is determined. Based on the obtained stress path of the mining subsidence disturbance, a fault scanning experiment of the sample under the set stress state is performed. The experimental data are processed to obtain the fractal dimension and pore ratio in the mining collapse process. Based on the experimental test, the initial air intake value of the soil sample is obtained. The obtained initial air intake value, initial water content, fractal dimension and pore ratio are combined with the soil-water characteristic curve under stress conditions to establish a mathematical model. The spatial and temporal vadose zone water content of different sampling points is obtained by solving the mathematical model, and is converted into a contour map.

[0004] The existing patent CN107402175A, a fractal prediction method of soil-water characteristic curve under deformation condition, discloses a calculation scheme for predicting soil-water characteristic curve under deformation condition based on fractal theory, which takes the soil-water characteristic curve of initial void ratio e0 as the reference state, determines the fractal dimension D and the air entry value after deformation, thereby predicting the soil-water characteristic curve under any void ratio e condition after deformation, and specifically, based on fractal theory, the relationship between moisture content and fractal dimension is obtained through the pore size distribution density function, thereby deriving a fractal model, and based on the fractal model, it is considered that the soil-water characteristic curves of different initial void ratios are mainly controlled by the air entry value, and the fractal dimension is almost unchanged, then a fractal dimension calculation method based on soil-water characteristic curve test data is given based on fractal theory, and finally a prediction method of air entry value under different initial void ratios is established.

[0005] However, due to the complex environmental change factors in desert areas, the traditional Darcy permeation law, Richards permeation equation and the above-mentioned existing patent technology are difficult to accurately describe the moisture content change of the soil body around the lead hole. Therefore, how to provide a moisture content simulation prediction scheme suitable for the soil body around the lead hole in the desert area, so as to help the engineering personnel accurately simulate the moisture content change in the soil body around the lead hole in the desert area, and guide the engineering construction, improve the engineering quality, and reduce the engineering cost, is a research topic for those skilled in the art. SUMMARY

[0006] The purpose of the present application is to provide a moisture content simulation prediction method, system, computer device, computer readable storage medium and computer program product for the soil body around the lead hole in the desert area, to solve the problem that the prior art scheme cannot accurately describe the moisture content change of the soil body around the lead hole in the desert area.

[0007] In order to achieve the above-mentioned purpose, the following technical scheme is adopted in the present application:

[0008] In a first aspect, a moisture content simulation prediction method for the soil body around the lead hole in the desert area is provided, comprising:

[0009] Collecting model input data and model output data required for model building and prediction segment model input data required for model running, wherein the model input data contains test segment model input data and calibration segment model input data whose parameter types are the same as the prediction segment model input data, and the model output data contains test segment moisture content time series data of the soil body around the lead hole in the desert area corresponding to the test segment model input data and calibration segment moisture content time series data of the soil body around the lead hole in the desert area corresponding to the calibration segment model input data;

[0010] A plurality of desert area well peripheral soil water content simulation models with different sub-models are built, then for each model in the plurality of desert area well peripheral soil water content simulation models, the test section model input data and the test section water content time series data are applied to perform corresponding water content preliminary simulation, so as to determine corresponding model parameters, then for each model, the calibration section model input data and the calibration section water content time series data are applied to perform corresponding water content secondary simulation, so as to verify and readjust the corresponding model parameters, and obtain corresponding selection model indicators, finally, according to the selection model indicators of each model, a certain model with the optimal selection model indicator is selected from the plurality of desert area well peripheral soil water content simulation models, wherein the desert area well peripheral soil water content simulation model includes a water injection initial infiltration process sub-model and a permeation sub-model, an evaporation sub-model and a soil water gas characteristic coupling sub-model in the infiltration and evaporation process;

[0011] The prediction section model input data is imported into the certain model, and water content time series estimation data of the desert area well peripheral soil in the prediction section is outputted and obtained;

[0012] The water content time series estimation data is outputted and displayed.

[0013] Based on the above invention content, a water content simulation and prediction scheme suitable for desert area well peripheral soil is provided, that is, model input data and model output data required for model building and prediction section model input data required for model running are collected first, then for each desert area well peripheral soil water content simulation model, model input data and model output data are applied to simulate water content, verify and readjust corresponding model parameters, and obtain corresponding selection model indicators, and a certain model with the optimal selection model indicator is selected, finally, the prediction section model input data is imported into the model, and water content time series estimation data of the desert area well peripheral soil in the prediction section is outputted and obtained, and is outputted and displayed, so that by collecting environmental data, coupling each model and combining engineering practice data, the change of water content in the desert area well peripheral soil can be accurately simulated, engineering construction can be guided, engineering quality can be improved, engineering cost can be reduced, and the scheme is convenient for practical application and popularization.

[0014] In a possible design, the prediction section model input data, the test section model input data or the calibration section model input data includes soil parameters of the desert area well peripheral soil and meteorological parameters of the area where the soil is located, wherein the soil parameters include initial water content of the soil, permeability coefficient of the soil and / or particle size gradation of the soil, and the meteorological parameters include net radiation, air temperature, air humidity and / or wind speed.

[0015] In a possible design, the soil property parameters are obtained based on geology exploration data of a nearby area.

[0016] In a possible design, the meteorological parameters in the test section model input data or the verification section model input data are obtained based on meteorological data reanalysis of the CFSR (Climate Forecast System Reanalysis) of the United States or the ECMWF (European Centre for Medium-Range Weather Forecasts), and the meteorological parameters in the prediction section model input data are obtained based on meteorological forecast data.

[0017] In a possible design, the prediction section model input data required for model running are collected, including:

[0018] For each prediction section, one or two typical desert area injection holes are selected, and soil property parameters of soil bodies surrounding the typical desert area injection holes and meteorological parameters of areas where the soil bodies are located are collected as corresponding prediction section model input data required for model running, where the soil property parameters include initial water content of the soil bodies, permeability coefficients of the soil bodies, and / or particle size gradations of the soil bodies, and the meteorological parameters include net radiation, air temperature, air humidity, and / or wind speed.

[0019] In a possible design, the water injection initial infiltration process submodel is constructed based on Darcy's law;

[0020] and / or, the permeation submodel is constructed based on the Richard equation or the coupled heat and moisture Philip & De Vries equation;

[0021] and / or, the evaporation submodel is constructed based on the Penman equation or the Penman-Wilson equation;

[0022] and / or, the soil water gas characteristic coupling submodel is constructed based on the Brooks-Corey equation, the Van Genuchten equation, or the Fredlund and Xing equation.

[0023] In a possible design, the model index for comparison and selection includes an output error condition index and a calculation required time length index.

[0024] In a possible design, the model output data further include soil hole collapse time lengths of desert area injection holes corresponding to the test section model input data and soil hole collapse time lengths of desert area injection holes corresponding to the verification section model input data;

[0025] After the prediction section model input data are input into the certain model, an estimated soil hole collapse time length of a desert area injection hole in a prediction section is further output;

[0026] The method further comprises outputting the estimated duration of soil hole collapse of the desert area lead hole in the prediction section.

[0027] In one possible design, after outputting the estimated duration of soil hole collapse of the desert area lead hole in the prediction section, the method further comprises:

[0028] According to the estimated duration of soil hole collapse of the desert area lead hole in the prediction section, the optimal water injection time and / or the latest pile driving construction time of the desert area lead hole are determined.

[0029] The optimal water injection time and / or the latest pile driving construction time are outputted.

[0030] In a second aspect, a system for simulating and predicting the water content of the peripheral soil of a desert area lead hole is provided, which comprises a data collection layer, a model building layer, a model running layer and an output visualization layer connected in sequence.

[0031] The data collection layer is configured to collect model input data and model output data required for model building, and prediction section model input data required for model running, wherein the model input data comprises test section model input data and calibration section model input data of the same parameter type as the prediction section model input data, and the model output data comprises test section water content time series data of the peripheral soil of the desert area lead hole corresponding to the test section model input data and calibration section water content time series data of the peripheral soil of the desert area lead hole corresponding to the calibration section model input data.

[0032] The model building layer is configured to build a plurality of desert area lead hole peripheral soil water content simulation models with different sub-models, then for each model in the plurality of desert area lead hole peripheral soil water content simulation models, apply the test section model input data and the test section water content time series data to perform corresponding preliminary water content simulation, so as to determine corresponding model parameters, then for each model, apply the calibration section model input data and the calibration section water content time series data to perform corresponding secondary water content simulation, so as to verify and adjust the corresponding model parameters, and obtain corresponding selection model indicators, and finally according to the selection model indicators of each model, select a certain model with the optimal selection model indicator from the plurality of desert area lead hole peripheral soil water content simulation models, wherein the desert area lead hole peripheral soil water content simulation model comprises a water injection initial infiltration process sub-model, and a permeation sub-model, an evaporation sub-model and a soil water gas characteristic coupling sub-model in the infiltration and evaporation process.

[0033] The model running layer is configured to input the prediction section model input data into the certain model, and output time series estimation data of the water content of the desert region peripheral soil body of the desert region.

[0034] The output visual layer is configured to output the time series estimation data of the water content.

[0035] In a third aspect, the present application provides a computer device, comprising a memory, a processor and a transceiver connected in sequence, wherein the memory is configured to store a computer program, the transceiver is configured to receive and send messages, and the processor is configured to read the computer program and execute the water content simulation and prediction method according to any possible design of the first aspect.

[0036] In a fourth aspect, the present application provides a computer readable storage medium, wherein instructions are stored on the computer readable storage medium, and when the instructions are executed on a computer, the water content simulation and prediction method according to any possible design of the first aspect is executed.

[0037] In a fifth aspect, the present application provides a computer program product, comprising a computer program or instructions, and when the computer program or the instructions are executed on a computer, the water content simulation and prediction method according to any possible design of the first aspect is realized.

[0038] The above-mentioned scheme has the following beneficial effects:

[0039] (1) The present application provides a water content simulation and prediction scheme for desert region peripheral soil bodies of a well, that is, model input data and model output data required for model building and prediction section model input data required for model running are collected, then for each desert region peripheral soil body water content simulation model, model input data and model output data are applied to simulate, verify and adjust the corresponding model parameters, and the corresponding comparison and selection model indicators are obtained, and a certain model with the optimal comparison and selection model indicator is selected, finally, the prediction section model input data is input into the model, and time series estimation data of the water content of the desert region peripheral soil body of the desert region in the prediction section is output, and the output is displayed, so that through the collection of environmental data, the coupling of each model and the combination of engineering practice data, the change of the water content in the desert region peripheral soil body of the well can be accurately simulated, the engineering construction can be guided, the engineering quality can be improved, the engineering cost can be reduced, and the actual application and popularization are facilitated.

[0040] (2) The present application fully utilizes various data to build models, part of the environmental data is obtained through an open source public database, and a complex soil-water-air permeation and evaporation model is built at a low cost.

[0041] (3) The scheme is closely combined with engineering practice and has high application value, that is, by reasonably simulating the physical processes of water injection initial infiltration and infiltration evaporation after pile hole drilling, the model provides a theoretical reference for the change of water content of the surrounding soil of the pile hole;

[0042] (4) The scheme can help the engineering personnel to determine the construction interval by predicting the change of water content, thereby providing support for engineering decision-making;

[0043] (5) The scheme can provide theoretical support for the pile hole drilling and pressing construction in the desert, and provide a reference for avoiding the collapse of the pile hole to cause rework in the construction arrangement and scheduling, and has good practical value. BRIEF DESCRIPTION OF DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0045] Figure 1 The flowchart of the water content simulation and prediction method of the surrounding soil of the pile hole in the desert area provided by the embodiments of the present application is shown.

[0046] Figure 2 The water injection and infiltration example diagram of the surrounding soil of the pile hole in the desert area provided by the embodiments of the present application during the water injection initial infiltration period is shown.

[0047] Figure 3 The water infiltration and evaporation example diagram of the surrounding soil of the pile hole in the desert area provided by the embodiments of the present application during the infiltration evaporation period is shown.

[0048] Figure 4 The structure diagram of the water content simulation and prediction system of the surrounding soil of the pile hole in the desert area provided by the embodiments of the present application is shown.

[0049] Figure 5 The structure diagram of the computer device provided by the embodiments of the present application is shown. DETAILED DESCRIPTION

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings and the description of the embodiments or the prior art to the present application, and obviously, the following description of the drawings structure only constitutes some embodiments of the present application, and for those skilled in the art, other embodiments can also be obtained without creative labor on the basis of these embodiments. It should be noted that the description of these embodiment modes is used to help understand the present application, but does not constitute a limitation on the present application.

[0051] It should be understood that, although the terms first and second, etc. can be used herein to describe various objects, the objects should not be limited by these terms. The terms are only used to distinguish one object from another. For example, a first object can be termed a second object, and, similarly, a second object can be termed a first object, without departing from the scope of example embodiments of the present application.

[0052] It should be understood that, for the term “and / or” which can occur in the present document, it merely describes an association relationship of associated objects, which means that three relationships can exist, for example, A and / or B can mean that A exists alone, B exists alone, or A and B exist simultaneously, and the like; for example, A, B and / or C can mean that any one of A, B and C exists or any combination thereof; for the term “ / and” which can occur in the present document, it describes another association relationship of another associated object, which means that two relationships can exist, for example, A / and B can mean that A exists alone or A and B exist simultaneously; in addition, for the character “ / ” which can occur in the present document, it generally means that the associated objects before and after are in an “or” relationship.

[0053] Embodiments

[0054] As shown in Figures 1-3 , the first aspect of the present embodiment provides a method for simulating and predicting the water content of the peripheral soil of a desert area leading hole, which can be but is not limited to executed by a computer device with certain computing resources, such as a cloud server, a personal computer (PC, which refers to a multi-purpose computer suitable for personal use in size, price and performance; desktop computers, notebook computers to small notebook computers and tablet computers, and ultrabooks, etc.), a smart phone, a personal digital assistant (PDA), or a wearable device, etc. As shown in Figure 1 , the method for simulating and predicting the water content, which can be but is not limited to include the following steps S1-S4.

[0055] S1. Collecting model input data and model output data required for model building and prediction segment model input data required for model running, wherein the model input data contains but is not limited to test segment model input data and calibration segment model input data of the same type as the prediction segment model input data, and the model output data contains but is not limited to test segment water content time series data of the peripheral soil of a desert area leading hole corresponding to the test segment model input data and calibration segment water content time series data of the peripheral soil of a desert area leading hole corresponding to the calibration segment model input data, etc.

[0056] In the step S1, the test section model input data and the test section water content time series data are used as one of the basic data for model building, which need to be complete and high-quality data and need to be based on the boundary conditions that meet the model requirements. The data can be obtained by conventional extraction based on a complete set of environmental data, in-situ test data, high-credibility laboratory data and / or related literature data, etc. The environmental data includes but is not limited to soil property parameters such as initial water content of soil, soil permeability coefficient and soil particle size gradation, etc. The in-situ test data includes but is not limited to water content sampling detection results of the peripheral soil of the hole in the desert area at different time periods and soil hole collapse time, etc. The laboratory data or the related literature data includes but is not limited to evaporation-permeation laboratory test results of similar soil, etc. Specifically, the test section model input data includes but is not limited to soil property parameters of the peripheral soil of the hole in the desert area and meteorological parameters of the area where the soil is located, etc. The soil property parameters include but are not limited to initial water content of soil, soil permeability coefficient and / or soil particle size gradation, etc. The meteorological parameters include but are not limited to net radiation, air temperature, air humidity and / or wind speed, etc. Since the soil property parameters change little over time, the soil property parameters can be further obtained based on the nearest geological exploration data. The meteorological parameters in the test section model input data can be obtained based on open-source databases, i.e. the meteorological parameters in the test section model input data can be further obtained based on the meteorological data of the Climate Forecast System Reanalysis (CFSR) of the U.S. Weather Center or the European Centre for Medium-Range Weather Forecasts (ECMWF), which need to be compared with conventional values before use in order to avoid using abnormal data as much as possible. In addition, the time unit of the test section water content time series data can be but is not limited to hour. The test section model input data can be divided into dynamic data and static data. The dynamic data includes the meteorological parameters (which are time series data, and the time unit can be the same as that of the test section water content time series data). The static data includes the soil property parameters, etc.

[0057] In the step S1, the check section model input data and the check section water content time series data are used as two of the basic data for model building, which are more and allow reasonable speculation to be filled in when part of the data is missing, and need to meet the boundary conditions required by the model. Similarly, the check section model input data includes but is not limited to the soil parameters of the soil outside the desert area and the meteorological parameters of the area where the soil is located, etc., wherein the soil parameters include but are not limited to the initial water content of the soil, the permeability coefficient of the soil and / or the particle size gradation of the soil, etc., and the meteorological parameters include but are not limited to the net radiation, air temperature, air humidity and / or wind speed, etc.; and the meteorological parameters in the check section model input data can also be further based on the meteorological data reanalysis of the U.S. Weather Center CFSR or the European Weather Center ECMWF. In addition, the time unit of the check section water content time series data can be but is not limited to hours, and the check section model input data can be divided into dynamic data and static data, for example, the dynamic data is the meteorological parameters (which are time series data, the time unit can be the same as the check section water content time series data), and the static data is the soil parameters, etc.

[0058] In step S1, the prediction segment model input data serves as the foundational data for model operation. This data can be specifically collected based on actual engineering needs and must meet the boundary conditions required by the model. Similarly, the prediction segment model input data includes, but is not limited to, soil properties of the soil surrounding the borehole in desert areas and meteorological parameters of the area. The soil properties include, but are not limited to, initial soil moisture content, soil permeability coefficient, and / or soil particle size distribution. The meteorological parameters include, but are not limited to, net radiation, air temperature, air humidity, and / or wind speed. Furthermore, the meteorological parameters in the prediction segment model input data can be further specifically derived from weather forecast data. To improve computational efficiency during model operation, preferably, the model input data for the prediction segment required for model operation is collected, including but not limited to the following steps: For each prediction segment (which can be understood as a section where the soil moisture content of the soil surrounding the borehole in the desert region needs to be simulated and predicted; similarly, the aforementioned test segment can be understood as a test section, and the aforementioned verification segment can be understood as a verification section), one or two typical boreholes in the desert region are selected, and the soil properties of the soil surrounding the typical boreholes in the desert region and the meteorological parameters of the region where the soil is located are collected as the corresponding model input data for the prediction segment required for model operation. The soil properties include, but are not limited to, the initial soil moisture content, soil permeability coefficient and / or soil particle size distribution, etc., and the meteorological parameters include, but are not limited to, net radiation, air temperature, air humidity and / or wind speed, etc. Furthermore, the model input data for the prediction segment can also be divided into dynamic data and static data. For example, the dynamic data includes the meteorological parameters (which are time-series data, and the time unit can be the same as the time-series data of the water content in the verification segment and the model input data in the test segment), and the static data includes the soil properties, etc.

[0059] S2. Construct multiple simulation models of soil moisture content around the borehole in desert areas with different sub-models. Then, for each model in the multiple simulation models of soil moisture content around the borehole in desert areas, apply the input data of the test section model and the time series data of the test section moisture content to perform a preliminary simulation of the corresponding moisture content in order to determine the corresponding model parameters. Then, for each model, apply the input data of the verification section model and the time series data of the verification section moisture content to perform a secondary simulation of the corresponding moisture content in order to verify and readjust the corresponding model parameters and obtain the corresponding comparative selection model index. Finally, based on the comparative selection model index of each model, select a model with the optimal comparative selection model index from the multiple simulation models of soil moisture content around the borehole in desert areas. The simulation models of soil moisture content around the borehole in desert areas include, but are not limited to, sub-models of the initial infiltration process, as well as infiltration sub-models, evaporation sub-models, and soil-water-gas characteristic coupling sub-models during the infiltration-evaporation process.

[0060] In the step S2, as shown in the figure, Figures 2-3 the simulation process from water injection to pile pressing construction is divided into water injection initial infiltration period and infiltration evaporation period, and in the infiltration evaporation period, the physical processes such as water infiltration in unsaturated soil, liquid water evaporation and gaseous water diffusion also need to be considered. Therefore, specifically, the desert area borehole peripheral soil water content simulation model will include but not limited to water injection initial infiltration process sub-model and infiltration sub-model, evaporation sub-model and soil water gas characteristic coupling sub-model in the infiltration evaporation process, etc., wherein the infiltration sub-model is used to describe the infiltration process by converting the water content into the matric potential (also called matric suction), which can be but not limited to constructed by Richard equation or thermal-hygroscopic coupling Philip & De Vries equation, etc.; the evaporation sub-model is used to simulate the liquid water evaporation and gaseous water diffusion of unsaturated soil, which can be but not limited to constructed by Penman equation or Penman-Wilson equation, etc.; the soil water gas characteristic coupling sub-model is used to describe the relationship between the volume water content and the matric suction of unsaturated soil, which can be but not limited to constructed by Brooks-Corey equation, Van Genuchten equation or Fredlund and Xing equation, etc. The aforementioned Richard equation, thermal-hygroscopic coupling Philip & De Vries equation, Penman equation, Penman-Wilson equation, Brooks-Corey equation, Van Genuchten equation and Fredlund and Xing equation, etc. are all existing equations, for example, the expression of the Richard equation is as follows:

[0061]

[0062] In the formula, P represents the matric potential, λ represents the slope of the soil water characteristic curve, k x represents the permeability coefficient in the x-axis direction, k y represents the permeability coefficient in the y-axis direction, Q represents the boundary flow, y represents the position water head, ρ represents the water density, g represents the gravity acceleration, and t represents the time; for another example, the expression of the Penman-Wilson equation is as follows:

[0063]

[0064] In the formula, E represents the free water surface evaporation amount, Γ represents the slope of the saturated vapor pressure and temperature relationship curve, Q' represents the equivalent net radiation amount of the soil surface (unit: mm / d), A represents the reciprocal of the soil gas relative humidity, η represents the humidity constant, E aa correction parameter representing wind speed and humidity; for example, the Fredlund and Xing equation is expressed as follows:

[0065]

[0066] wherein θ represents the volumetric water content, a, m and n represent model fitting parameters, e represents the base of the natural logarithm, ψ represents the negative pore water pressure, C(ψ) represents the matric suction correction parameter, θ S represents the saturated volumetric water content. In addition, since the water injection amount is constant during the water injection initial infiltration period, the evaporation amount is small and can be ignored, and it is an engineering of stable seepage, so the Darcy permeation law can be directly used for calculation, that is, the water injection initial infiltration process sub-model is constructed by using the Darcy permeation law.

[0067] In the step S2, in detail, the model range and part of the parameters of the desert area hole leading peripheral soil water content simulation model can be selected as follows: according to the hole diameter of 60 cm and the hole depth of 1.1 m, the model range is taken as the soil hole as the center, the diameter is taken as 5 m and the depth is taken as 4 m; wherein the time length of the water injection initial infiltration period is generally set to 1 hour, and the water injection is performed to the extent that the soil body fracture surface above is saturated with water content, so as to ensure the longest construction interval, and the model calculation period is considered as 30 hours (because in the actual engineering, the water injection is usually performed first, the hole is led after 12 hours, the pile is pressed after 12 hours after the hole is led, and 30 hours can cover the process).

[0068] In the step S2, the specific implementation means of the water content primary simulation and the water content secondary simulation can be realized by using existing function fitting means or model training verification means, etc. The specific acquisition method of the selected model index can be obtained after verifying the model parameters, or the selected model index can be obtained by calculating several typical examples after adjusting the model parameters again. Since the model selection mainly considers the error condition and the calculation time, preferably, the selected model index includes but is not limited to the output error condition index and the calculation time index, wherein the output error condition index is based on the measured soil water content and specifically includes but is not limited to the average deviation value, variance and / or average error rate of the overall sample, etc. In this way, in the model selection process, the desert area hole peripheral soil water content simulation model with the minimum output error condition index value and the shortest calculation time index value can be reasonably selected as the certain model with the optimal selected model index according to the engineering demand condition and the calculation power, so as to balance the prediction accuracy and the calculation speed. Generally, according to the literature conclusion and the actual situation, the desert area hole peripheral soil water content simulation model obtained by combining the Philip & De Vries equation and the Penman-Wilson equation will have a good simulation effect, i.e. the model combination is more suitable for describing the heat and moisture coupling non-isothermal flow process of the unsaturated soil with a large temperature gradient on the surface of the atmospheric continuous sunny ground, which is consistent with the high radiation, high temperature difference and fast water evaporation in the desert area. In addition, in addition to considering different model combinations, different soil grid size factors also need to be considered, and for the sake of safety, the influence of evaporation and penetration on the water loss in the desert soil should be fully considered, and the calculation result should consider the most unfavorable change of the water content change.

[0069] S3. The prediction section model input data is input into the certain model, and the water content time series estimation data of the desert area hole peripheral soil in the prediction section is output.

[0070] In the step S3, the water content time series estimation data reflects the change of the water content of the typical hole peripheral soil. In addition, when the model output data also includes the soil hole collapse time of the desert area hole corresponding to the test section model input data and the soil hole collapse time of the desert area hole corresponding to the calibration section model input data, the prediction section model input data is input into the certain model, and the estimated collapse time of the desert area hole in the prediction section can also be output.

[0071] S4. The water content time series estimation data is output.

[0072] In the step S4, the water content time series estimation data can be displayed in a form of a graph, and whether the hole is unstable can be determined according to the water content of the soil and the related analysis of soil mechanics. If the water content at a certain time is lower than a critical value, the construction interval can be shortened, and the engineering department can be reported and advised.

[0073] In the step S4, preferably, after the soil hole collapse estimation time length of the hole in the desert area in the prediction section is obtained, the method further includes but is not limited to the following steps: first, the optimal water injection time and / or the latest pile driving construction time of the hole in the desert area are determined according to the soil hole collapse estimation time length; and then the optimal water injection time and / or the latest pile driving construction time are displayed. The optimal water injection time can be but is not limited to the water injection time corresponding to the longest soil hole collapse estimation time length, and the latest pile driving construction time can be but is not limited to the soil hole collapse estimation time corresponding to the longest soil hole collapse estimation time length. Generally, through actual operation, it can be found that in summer, the temperature is high and the evaporation speed is fast, the pile driving construction needs to be performed within 12 hours after the hole is formed, and in winter, the temperature is low and the evaporation speed is slow, the construction can be performed the next day.

[0074] The water content simulation prediction method based on the foregoing steps S1-S4 provides a water content simulation prediction scheme suitable for the soil surrounding the hole in the desert area, that is, the model input data and the model output data required for model building and the prediction section model input data required for model running are collected, then the model input data and the model output data are applied to each water content simulation model of the soil surrounding the hole in the desert area to simulate, verify, and adjust the corresponding model parameters, obtain the corresponding selected model indicators, select a model with the optimal selected model indicator, finally import the prediction section model input data into the model, output the water content time series estimation data of the soil surrounding the hole in the desert area in the prediction section, and display the data. Thus, by collecting environmental data, coupling each model, and combining engineering practice data, the change of the water content in the soil surrounding the hole in the desert area can be accurately simulated, the engineering construction can be guided, the engineering quality can be improved, the engineering cost can be reduced, and the method is convenient for practical application and promotion.

[0075] As shown in Figure 4 The second aspect of the embodiment provides a virtual system for implementing the water content simulation prediction method of the first aspect, which includes a data collection layer, a model building layer, a model running layer, and an output visualization layer which are sequentially and communicatively connected.

[0076] The data collection layer is configured to collect model input data and model output data required by model building and prediction section model input data required by model running, wherein the model input data includes test section model input data and calibration section model input data of the same parameter type as the prediction section model input data, and the model output data includes test section water content time series data of the desert area peripheral soil of a borehole and calibration section water content time series data of the desert area peripheral soil of the borehole corresponding to the test section model input data and the calibration section model input data, respectively.

[0077] The model building layer is configured to build a plurality of desert area peripheral soil water content simulation models having different sub-models, and then perform preliminary water content simulation on each model in the plurality of desert area peripheral soil water content simulation models by using the test section model input data and the test section water content time series data to determine corresponding model parameters, and then perform secondary water content simulation on each model by using the calibration section model input data and the calibration section water content time series data to verify and adjust the corresponding model parameters and obtain corresponding selection model indexes, and finally select a model having an optimal selection model index from the plurality of desert area peripheral soil water content simulation models according to the selection model indexes of the models, wherein the desert area peripheral soil water content simulation model includes a water injection initial infiltration process sub-model and a permeation sub-model, an evaporation sub-model and a soil water gas characteristic coupling sub-model in a permeation evaporation process.

[0078] The model running layer is configured to import the prediction section model input data into the model, and output water content time series estimation data of the desert area peripheral soil of the borehole in the prediction section.

[0079] The output visual layer is configured to output and display the water content time series estimation data.

[0080] The working process, working details and technical effects of the foregoing system provided by the second aspect of the embodiment can be referred to the water content simulation prediction method described in the first aspect, which will not be described herein again.

[0081] As Figure 5As shown, the third aspect of the present embodiment provides a computer device for performing the water content simulation prediction method according to the first aspect, which comprises a memory, a processor and a transceiver connected in sequence, wherein the memory is configured to store a computer program, the transceiver is configured to transmit and receive messages, and the processor is configured to read the computer program and perform the water content simulation prediction method according to the first aspect. Specifically, the memory can include, but is not limited to, a random access memory (RAM), a read-only memory (ROM), a flash memory, a first input first output (FIFO) memory and / or a first input last output (FILO) memory, etc.; and the processor can be, but is not limited to, a microprocessor of the STM32F105 series. In addition, the computer device can further include, but is not limited to, a power module, a display screen and other necessary components.

[0082] The working process, working details and technical effects of the aforementioned computer device provided by the third aspect of the present embodiment can be referred to the water content simulation prediction method according to the first aspect, which will not be described here again.

[0083] The fourth aspect of the present embodiment provides a computer readable storage medium storing instructions of the water content simulation prediction method according to the first aspect, i.e., the computer readable storage medium stores instructions, and when the instructions are run on a computer, the water content simulation prediction method according to the first aspect is performed. The computer readable storage medium refers to a carrier for storing data, which can include, but is not limited to, floppy disks, optical disks, hard disks, flash memories, USB flash drives and / or memory sticks, etc. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable devices.

[0084] The working process, working details and technical effects of the aforementioned computer readable storage medium provided by the fourth aspect of the present embodiment can be referred to the water content simulation prediction method according to the first aspect, which will not be described here again.

[0085] The fifth aspect of the present embodiment provides a computer program product comprising a computer program or instructions, which, when executed by a computer, implement the water content simulation prediction method according to the first aspect. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable devices.

[0086] Finally, it should be noted that the above description is only the preferred embodiment of the present application, and is not intended to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for simulating and predicting the water content of the soil surrounding a borehole in a desert region, characterized in that, include: Collect model input and output data required for model building, and prediction segment model input data required for model operation. The model input data package contains test segment model input data and validation segment model input data with parameter types identical to the prediction segment model input data. The model output data package contains time-series data of test segment water content in the desert area surrounding the borehole, corresponding to the test segment model input data; time-series data of validation segment water content in the desert area surrounding the borehole, corresponding to the validation segment model input data; and time-series data of desert area water content in the desert area corresponding to the test segment model input data. The soil collapse time of the pilot hole and the soil collapse time of the pilot hole in the desert area corresponding to the model input data of the verification segment are collected. The model input data of the prediction segment required for model operation includes: for each prediction segment, one or two typical pilot holes in the desert area are selected, and the soil properties of the soil around the pilot hole of the typical pilot hole in the desert area and the meteorological parameters of the area where the soil is located are collected as the corresponding model input data of the prediction segment required for model operation. The soil properties include the initial moisture content of the soil, the permeability coefficient of the soil and / or the particle size distribution of the soil, and the meteorological parameters include net radiation, air temperature, air humidity and / or wind speed. Multiple simulation models of soil moisture content around boreholes in desert areas with different sub-models were constructed. Then, for each model in these simulation models, preliminary water content simulations were performed using the input data of the test section model and the time-series water content data of the test section to determine the corresponding model parameters. Next, for each model, secondary water content simulations were performed using the input data of the verification section model and the time-series water content data of the verification section to verify and readjust the corresponding model parameters and obtain the corresponding comparative selection model index. Finally, based on the comparative selection model index of each model, a model with the optimal comparative selection model index was selected from the multiple simulation models of soil moisture content around boreholes in desert areas. The simulation models of soil moisture content around boreholes in desert areas include a sub-model of the initial infiltration process, and sub-models of infiltration, evaporation, and soil-water-gas coupling characteristics during the infiltration-evaporation process. The comparative selection model index includes an output error index and a calculation time index. The prediction segment model input data is imported into a certain model, and the output is the time series estimation data of water content of the soil around the borehole in the desert area of ​​the prediction segment and the estimated time of soil collapse of the borehole in the desert area of ​​the prediction segment. The output displays the time-series estimated water content and the estimated duration of soil hole collapse.

2. The water content simulation and prediction method according to claim 1, characterized in that, The prediction segment model input data, the test segment model input data, or the verification segment model input data package contain soil properties of the soil surrounding the borehole in the desert area and meteorological parameters of the area where the soil is located. The soil properties include the initial moisture content of the soil, the soil permeability coefficient, and / or the soil particle size distribution. The meteorological parameters include net radiation, air temperature, air humidity, and / or wind speed.

3. The water content simulation and prediction method according to claim 2, characterized in that, The soil parameters are obtained based on nearby geological survey data.

4. The water content simulation and prediction method according to claim 2, characterized in that, The meteorological parameters in the input data of the test section model or the input data of the verification section model are obtained based on the reanalysis of meteorological data, and the meteorological parameters in the input data of the prediction section model are obtained based on meteorological forecast data.

5. The water content simulation and prediction method according to claim 1, characterized in that, The sub-model of the initial infiltration process of water injection was constructed using Darcy's law of permeability; And / or, the permeation sub-model is constructed using the Richard equation or the thermo-humid coupling Philip & De Vries equation; And / or, the evaporation sub-model is constructed using the Penman equation or the Penman-Wilson equation; And / or, the soil-water-gas characteristic coupling sub-model is constructed using the Brooks-Corey equation, the Van Genuchten equation, or the Fredlund and Xing equation.

6. The water content simulation and prediction method according to claim 1, characterized in that, After outputting the estimated time of borehole collapse in the predicted desert region, the method further includes: Based on the estimated time of soil collapse of the pilot hole in the desert region in the predicted section, determine the optimal water injection time and / or the latest pile driving construction time for the pilot hole in the desert region. The output displays the optimal water injection time and / or the latest pile driving time.

7. A system for simulating and predicting the water content of the soil surrounding a borehole in a desert region, characterized in that, It includes a data collection layer, a model building layer, a model running layer, and an output visualization layer, which are connected in sequence. The data collection layer is used to collect model input and output data required for model building, and prediction segment model input data required for model operation. The model input data package contains test segment model input data and validation segment model input data with parameter types identical to the prediction segment model input data. The model output data package contains time-series data of test segment water content in the desert soil surrounding the borehole, corresponding to the test segment model input data; time-series data of validation segment water content in the desert soil surrounding the borehole, corresponding to the validation segment model input data; and data corresponding to the test segment model input data. The data includes the soil collapse time of the pilot borehole in the desert region and the soil collapse time of the pilot borehole in the desert region corresponding to the model input data of the verification segment. The data collected for the model operation of the prediction segment includes: for each prediction segment, selecting one or two typical pilot boreholes in the desert region, and collecting the soil properties of the soil surrounding the pilot boreholes and the meteorological parameters of the region where the soil is located as the corresponding model input data for the model operation. The soil properties include the initial moisture content of the soil, the permeability coefficient of the soil and / or the particle size distribution of the soil, and the meteorological parameters include net radiation, air temperature, air humidity and / or wind speed. The model building layer is used to build multiple simulation models of soil moisture content around the borehole in desert areas with different sub-models. Then, for each model in the multiple simulation models of soil moisture content around the borehole in desert areas, the corresponding preliminary simulation of water content is performed using the input data of the test section model and the time series data of water content in the test section to determine the corresponding model parameters. Then, for each model, the corresponding secondary simulation of water content is performed using the input data of the verification section model and the time series data of water content in the verification section to verify and readjust the corresponding model parameters and obtain the corresponding comparative selection model index. Finally, based on the comparative selection model index of each model, a model with the optimal comparative selection model index is selected from the multiple simulation models of soil moisture content around the borehole in desert areas. The simulation model of soil moisture content around the borehole in desert areas includes a sub-model of the initial infiltration process and a sub-model of infiltration, evaporation and soil-water-air characteristic coupling during the infiltration and evaporation process. The comparative selection model index includes an output error index and a calculation time index. The model running layer is used to import the model input data of the prediction segment into a certain model and output the time series estimation data of water content of the soil around the borehole in the desert area of ​​the prediction segment and the estimated time of soil collapse of the borehole in the desert area of ​​the prediction segment. The output visualization layer is used to output and display the time-series estimated water content data and the estimated duration of soil hole collapse.

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