Settlement deformation prediction method and system for reinforced soil slope building structure

Abstract

The invention relates to the technical field of slope engineering, in particular to a settlement deformation prediction method and system for a reinforced soil slope building structure. The method comprises the following steps: acquiring a soil body infiltration line position parameter of a reinforced soil slope, an interface friction coefficient of a reinforced material and a soil body, a soil body grain grading curve and environment temperature and humidity duration information, and obtaining initial characteristic data of a slope system state; constructing a heat-force-water multi-field coupling monitoring network in the soil-reinforced material interface area according to the initial characteristic data of the slope system state, and obtaining the interface shear stress, the interface normal strain, the pore water pressure gradient and the temperature gradient of each monitoring point to obtain interface action characteristic data. According to the method, the environment disturbance probability density model based on the temperature fluctuation and the sudden change of the water content is constructed, the influence of a complex environment on the slope stability can be quantitatively described, and the robustness of the model is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of slope engineering, and particularly relates to a method and system for predicting the settlement and deformation of a reinforced soil slope building structure. Background Art

[0002] A reinforced soil slope is a structural form that improves the slope stability and bearing capacity by adding tensile reinforcement materials (such as geogrids, geotextiles, steel tensile reinforcements, etc.) to the soil mass. It is widely used in engineering fields such as roads, railways, dams, building foundations, etc. It is an economic, efficient and reliable support technology. Its core principle is to utilize the friction and anchoring forces between the tensile reinforcement materials and the soil mass to enhance the shear strength and overall stability of the soil mass. The tensile reinforcement materials are usually high-strength and durable geosynthetics (such as geogrids, geotextiles) or metal materials (such as steel tensile reinforcements). They are horizontally laid in the soil mass to form a stress-bearing system. The stress is transmitted between the tensile reinforcement materials and the soil mass through friction and anchoring forces, restricting the lateral deformation of the soil mass, thereby improving the slope stability. The settlement and deformation of the reinforced soil slope building structure refer to the displacement and deformation that occur in the vertical and horizontal directions of the slope structure during the construction and use processes due to the influence of factors such as the self-weight of the soil mass, external loads, the interaction between the soil mass and the tensile reinforcement materials, and environmental factors (such as groundwater, climate, etc.). This kind of settlement and deformation is a common phenomenon in slope engineering, and its magnitude and distribution are directly related to the slope stability and service function. However, in actual engineering, the interaction between the soil mass and the reinforcement materials is affected by various factors, including the physical and mechanical properties of the soil mass, the surface roughness of the reinforcement materials, environmental temperature, water content change, etc. This interaction process is dynamically evolving and has significant spatio-temporal effects. Existing theoretical models often overly simplify this complex interaction relationship and are difficult to accurately reflect the interface stress transfer mechanism and deformation characteristics, resulting in a deviation between the prediction results and the actual situation. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide a method and system for predicting the settlement and deformation of a reinforced soil slope building structure to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for predicting the settlement and deformation of a reinforced soil slope building structure includes the following steps:

[0005] Step S1: Obtain the position parameter of the soil phreatic line of the reinforced soil slope, the interface friction coefficient between the reinforcement material and the soil, the soil particle size distribution curve, and the environmental temperature and humidity duration information to obtain the initial characteristic data of the slope system state;

[0006] Step S2: Construct a thermal-hydro-mechanical multi-field coupling monitoring network in the soil-reinforcement interface region based on the initial characteristic data of the slope system state, and obtain the interface shear stress, interface normal strain, pore water pressure gradient, and temperature gradient at each monitoring point to obtain the interface action characteristic data;

[0007] Step S3: Calculate the interface shear power consumption rate, heat conduction power, and moisture migration power for the interface action characteristic data, and conduct a non-equilibrium statistical analysis of the influence of interface roughness on energy dissipation to obtain the non-equilibrium entropy evolution characteristic data;

[0008] Step S4: Construct an environmental perturbation probability density model based on temperature fluctuations and sudden changes in water content according to the non-equilibrium entropy evolution characteristic data, and conduct a system fluctuation characteristic analysis to obtain the interface kinetic evolution prediction data including the critical drift coefficient and the non-linear diffusion coefficient;

[0009] Step S5: Iteratively solve the local minimum entropy production rate for the interface kinetic evolution prediction data, and conduct stress balance correction based on the creep effect of the reinforcement material to obtain the stress-strain distribution data of the entire slope; perform adaptive numerical integration on the displacement field of the key control section of the slope according to the stress-strain distribution data to obtain the long-term settlement deformation prediction data of the slope.

[0010] The present invention comprehensively grasps the initial state characteristics of the slope system by obtaining the position parameters of the soil phreatic line, the interface friction coefficient, the soil particle size distribution curve, and the environmental temperature and humidity duration information. These data provide a solid foundation for subsequent analysis, ensuring the accuracy and reliability of the prediction model. By monitoring the position of the soil phreatic line, the influence of groundwater level changes on slope stability can be understood; obtaining the interface friction coefficient helps to clarify the interaction mechanism between the reinforcement material and the soil; the soil particle size distribution curve reflects the physical properties of the soil and provides an important basis for analyzing the mechanical behavior of the slope; while the environmental temperature and humidity duration information considers the influence of external environmental factors on the slope, making the prediction model closer to the actual situation. Based on the initial characteristic data, a thermo-hydro-mechanical multi-field coupling monitoring network is constructed to obtain key parameters such as interface shear stress, interface normal strain, pore water pressure gradient, and temperature gradient in real time. The establishment of this multi-field coupling monitoring network fully considers the complex mechanical environment and physical processes faced by the slope under actual working conditions. By monitoring these parameters, the mechanical behavior and physical state changes in the soil-reinforcement material interface region can be dynamically grasped, providing rich data support for subsequent analysis. This multi-field coupling monitoring method can more comprehensively reflect the internal change laws of the slope, improving the understanding and prediction ability of slope behavior. Through in-depth analysis of the interface interaction characteristic data, the interface shear power consumption rate, heat conduction power, and moisture migration power are calculated, and non-equilibrium statistical analysis is carried out in combination with interface roughness to obtain non-equilibrium entropy evolution characteristic data. This process not only reveals the energy conversion and dissipation mechanism in the interface region but also further quantifies the evolution trend and stability characteristics of the system through non-equilibrium statistical analysis. Through this analysis, the dynamic response characteristics of the slope under complex environments can be more clearly understood, providing an important theoretical basis for the construction of subsequent prediction models. This in-depth study of the energy conversion and dissipation mechanism enables the prediction model to better reflect the behavior laws of the slope under actual working conditions. Based on the non-equilibrium entropy evolution characteristic data, an environmental perturbation probability density model is constructed, and the system fluctuation characteristics are analyzed to obtain interface dynamics evolution prediction data including the critical drift coefficient and the nonlinear diffusion coefficient. This process fully considers the random perturbation influence of environmental factors on the slope system. Through the probability density model and fluctuation characteristic analysis, the dynamic evolution process of the slope under complex environments can be more accurately predicted. This modeling and analysis of environmental perturbations enable the prediction model to better adapt to the complex external conditions in actual engineering, improving the accuracy and reliability of the prediction. By solving the critical drift coefficient and the nonlinear diffusion coefficient, the dynamic behavior of the slope system can be more accurately described, providing important parameter support for subsequent settlement deformation prediction. Through iterative solution of the local minimum entropy production rate for the interface dynamics evolution prediction data and stress balance correction in combination with the creep effect of the reinforcement material, the stress-strain distribution data of the entire slope field are obtained.Through adaptive numerical integration, the displacement field of the key control section of the slope and the prediction data of long-term settlement deformation are further obtained. This process not only considers the non-equilibrium characteristics of the system, but also ensures the accuracy and reliability of the prediction results through creep effect correction and adaptive numerical integration. By solving the local minimum entropy production rate, the evolution trend and stability characteristics of the system can be more accurately described; while the stress balance correction based on the creep effect considers the long-term mechanical behavior of the reinforced material, enabling the prediction model to better reflect the settlement deformation law of the slope under actual working conditions. Through this comprehensive analysis and correction, the finally obtained prediction data of the long-term settlement deformation of the slope can provide strong support for engineering design and safety assessment, ensuring the stability and safety of the slope structure.

[0011] The present invention also provides a settlement deformation prediction system for a reinforced soil slope building structure, which is used to execute the settlement deformation prediction method for the reinforced soil slope building structure described above. The settlement deformation prediction system for the reinforced soil slope building structure includes:

[0012] An initial feature acquisition module, which is used to acquire the position parameter of the soil infiltration line of the reinforced soil slope, the interface friction coefficient between the reinforced material and the soil, the soil particle size distribution curve, and the environmental temperature and humidity history information, so as to obtain the initial feature data of the slope system state;

[0013] A multi-field coupling monitoring module, which is used to construct a thermal-mechanical-water multi-field coupling monitoring network in the soil-reinforced material interface area according to the initial feature data of the slope system state, and acquire the interface shear stress, interface normal strain, pore water pressure gradient and temperature gradient of each monitoring point, so as to obtain the interface action feature data;

[0014] An energy dissipation analysis module, which is used to calculate the interface shear power consumption rate, heat conduction power and moisture migration power of the interface action feature data, and conduct a non-equilibrium statistical analysis of the influence of interface roughness on energy dissipation, so as to obtain the non-equilibrium entropy evolution feature data;

[0015] An environmental disturbance modeling module, which is used to construct an environmental disturbance probability density model based on temperature fluctuations and water content mutations according to the non-equilibrium entropy evolution feature data, and conduct a system fluctuation characteristic analysis, so as to obtain the interface dynamics evolution prediction data including the critical drift coefficient and the non-linear diffusion coefficient;

[0016] A settlement prediction correction module, which is used to iteratively solve the local minimum entropy production rate of the interface dynamics evolution prediction data, and conduct stress balance correction based on the creep effect of the reinforced material, so as to obtain the stress-strain distribution data of the entire slope; according to the stress-strain distribution data, perform adaptive numerical integration on the displacement field of the key control section of the slope, so as to obtain the prediction data of the long-term settlement deformation of the slope.

[0017] In the settlement deformation prediction system of the reinforced soil slope construction structure, through the collaborative action of the initial feature acquisition module, multi-field coupling monitoring module, energy dissipation analysis module, environmental disturbance modeling module, and settlement prediction correction module, the comprehensive monitoring, accurate analysis, and reliable prediction of the settlement deformation of the slope system are realized. The initial feature acquisition module provides a comprehensive and accurate data basis for the initial description of the slope system state by collecting the position parameters of the soil phreatic line, interface friction coefficient, soil particle size distribution curve, and environmental temperature and humidity duration information. This module not only captures the initial mechanical and physical characteristics of the slope system but also provides key inputs for subsequent multi-field coupling analysis and settlement deformation prediction. The acquisition of the soil phreatic line position parameters can reflect the influence of groundwater on slope stability, while the interface friction coefficient and particle size distribution curve provide micro and macro-level bases for understanding the mechanical behavior of the slope. The introduction of environmental temperature and humidity duration information further considers the long-term influence of the external environment on the slope, enabling the prediction model to be closer to the actual working conditions. The multi-field coupling monitoring module constructs a thermal-mechanical-hydraulic multi-field coupling monitoring network based on the initial feature data, capable of real-time obtaining key parameters such as interface shear stress, interface normal strain, pore water pressure gradient, and temperature gradient. Through multi-field coupling monitoring, this module comprehensively captures the dynamic response of the slope system under complex working conditions. The establishment of the multi-field coupling monitoring network not only improves the richness and representativeness of the data but also provides high-quality input data for subsequent energy dissipation analysis and environmental disturbance modeling. Through real-time monitoring and data acquisition, this module can dynamically reflect the changes in the mechanical and physical states of the slope system, providing strong support for the long-term stability monitoring of the slope. The energy dissipation analysis module deeply analyzes the interface action characteristic data, calculates the interface shear power consumption rate, heat conduction power, and moisture migration power, and conducts non-equilibrium statistical analysis based on interface roughness. By quantifying the energy conversion and dissipation mechanisms, this module reveals the dynamic behavior of the slope system under complex working conditions. The acquisition of non-equilibrium entropy evolution characteristic data enables the model to evaluate the stability and evolution trend of the system from a thermodynamic perspective, further enhancing the understanding of the behavior of the slope system. Through energy dissipation analysis, this module not only provides a theoretical basis for subsequent environmental disturbance modeling but also provides an important energetic basis for slope stability assessment. The environmental disturbance modeling module constructs an environmental disturbance probability density model considering temperature fluctuations and sudden changes in water content based on non-equilibrium entropy evolution characteristic data, and conducts system fluctuation characteristic analysis. By probability modeling and fluctuation characteristic analysis, this module quantifies the random disturbance influence of environmental factors on the slope system. The acquisition of the critical drift coefficient and non-linear diffusion coefficient enables the model to more accurately describe the dynamic evolution law of the slope system under complex environments. Through environmental disturbance modeling, this module provides important dynamic support for the long-term settlement deformation prediction of the slope system, further enhancing the adaptability and prediction ability of the model to complex working conditions.The settlement prediction correction module iteratively solves the local minimum entropy production rate for the predicted data of interface kinetic evolution, and combines the creep effect of the reinforcement material to correct the stress balance. Finally, the stress-strain distribution data of the entire slope and the long-term settlement deformation prediction data are obtained. This module not only considers the non-equilibrium characteristics of the system, but also ensures the accuracy and reliability of the prediction results through creep correction and adaptive numerical integration. The stress balance correction and the displacement field analysis of the key control section enable the model to more accurately reflect the long-term behavior of the slope system, providing a scientific basis for the design and stability monitoring of slope engineering. Through the synergistic effect of the above modules, the settlement deformation prediction system of the reinforced soil slope building structure can comprehensively and accurately monitor and predict the dynamic behavior of the slope system. This system not only fully considers the complex mechanical characteristics of the slope system and the influence of the external environment, but also provides strong technical support for the safe design and stability assessment of slope engineering through multi-field coupling analysis, energy dissipation evaluation and environmental disturbance modeling. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments read in conjunction with the accompanying drawings:

[0019] Figure 1 It is a schematic flow chart of the steps of the settlement deformation prediction method for the reinforced soil slope building structure of the present invention;

[0020] Figure 2 For Figure 1 it is a detailed schematic flow chart of step S1;

[0021] Figure 3 For Figure 1 it is a detailed schematic flow chart of step S2. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] The technical method of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0023] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0024] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0025] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for predicting the settlement deformation of a reinforced soil slope building structure, the method comprising the following steps:

[0026] Step S1: obtaining the soil infiltration line position parameters of the reinforced soil slope, the interface friction coefficient between the reinforcement material and the soil, the soil particle gradation curve, and the environmental temperature and humidity history information, and obtaining the initial characteristic data of the slope system state;

[0027] When obtaining the soil infiltration line position parameters of the reinforced soil slope, the embodiment of the present invention first pre-buries an automatic water level monitoring pipeline inside the slope, and sets a multi-point water level sensor (such as a buoy or conductivity sensor) in the pipeline. The sensor spacing is 1 meter, and the horizontal spacing is set to 3 to 5 meters according to the width of the slope to ensure that the entire slope key area is covered. Through the data acquisition terminal, the water level changes at different depths are collected in real time and the infiltration line distribution curve is drawn. When obtaining the interface friction coefficient between the reinforced material and the soil, the reinforced material and the original soil sample of the slope are selected, and the test is carried out through the indoor large-scale direct shear test equipment. The normal pressure is set to 50, 100, and 150 kPa, and the loading condition of the shear displacement rate of 0.5 mm / min is applied respectively. The shear stress and displacement relationship curve is recorded, and the friction coefficient average value is calculated. For the soil particle grading curve, the particles with a particle size greater than 0.075 mm are separated by wet screening, and the fine particles less than 0.075 mm are tested by a laser particle size analyzer, and the grading curve is generated by combining the two parts of data. The temporal information of ambient temperature and humidity is obtained through a network of temperature and humidity sensors arranged in the slope area. The sensor spacing is set to 10 meters, and the data is collected and stored at intervals of 10 minutes. Finally, the above information is integrated to generate an initial characteristic data file of the slope system state containing time, spatial position and parameter values, which is output in CSV format for subsequent calls.

[0028] Step S2: construct a thermal-mechanical-water multi-field coupling monitoring network in the soil-reinforcement material interface area according to the initial characteristic data of the slope system state, and obtain the interface shear stress, interface normal strain, pore water pressure gradient and temperature gradient of each monitoring point to obtain the interface action characteristic data;

[0029] In the embodiment of the present invention, according to the initial characteristic data of the slope system state obtained in step S1, a multi-field coupling monitoring network of heat-mechanics-water is arranged in the soil-reinforcement material interface area. In specific implementation, multifunctional monitoring sensors are selected to be buried in the interface area, including: ① a micro shear stress sensor (range 200 kPa, accuracy 0.1 kPa), arranged along the interface direction, with 1 sensor arranged every 0.5 meters; ② a strain gauge (range 0.5%, accuracy 0.001%), pasted on the surface of the reinforcement material, with 1 sensor arranged every 1 meter; ③ a micro pore water pressure sensor (range 100 kPa, accuracy 0.01 kPa), buried in the soil pores, with 1 sensor arranged every 0.5 meters; ④ a temperature gradient probe (range -2080 °C, accuracy 0.1 °C), arranged perpendicular to the reinforcement material, with 1 sensor arranged every 1 meter. The sensors are connected to the data acquisition terminal through an optical fiber transmission module, the acquisition frequency is set to 1 Hz, and the data is uniformly transmitted to the slope monitoring system. Through the eigenvalue extraction algorithm, a time series characteristic data matrix of interface shear stress, interface normal strain, pore water pressure gradient, and temperature gradient is obtained and saved in the form of a two-dimensional time-space matrix.

[0030] Step S3: Calculate the interface shear power consumption rate, heat conduction power, and moisture migration power for the interface action characteristic data, and perform non-equilibrium statistical analysis on the influence of interface roughness on energy dissipation to obtain non-equilibrium entropy evolution characteristic data;

[0031] In the embodiment of the present invention, the interface action characteristic data obtained in step S2 is analyzed. First, the shear power consumption rate of each sensor point is calculated using the formula shear power consumption rate = τ·v (τ is the shear stress, v is the interface displacement rate), and the power consumption rate distribution map is generated by interpolation; the heat conduction power is calculated by the formula (k is the soil thermal conductivity, is the temperature gradient), and the soil thermal conductivity is taken as 1.2 W / (m·K); the moisture migration power is based on Darcy's law (k w is the permeability coefficient, is the pore water pressure gradient), the permeability coefficient is obtained from the in-situ permeability test and the value is 1×10 ―5 cm / s. When obtaining the interface surface roughness data, a three-dimensional laser scanner (accuracy 0.01 mm) is used to scan the interface surface to generate a roughness point cloud data model. Combining the roughness parameter analysis, the influence of energy dissipation is calculated using non-equilibrium statistical theory, and further non-equilibrium entropy evolution characteristic data is obtained, which is represented in the form of a time-space change curve and a characteristic parameter table.

[0032] Step S4: Based on the non-equilibrium entropy evolution characteristic data, construct an environmental perturbation probability density model based on temperature fluctuations and water content mutations, and conduct an analysis of the system fluctuation characteristics to obtain interface kinetic evolution prediction data including the critical drift coefficient and the non-linear diffusion coefficient;

[0033] In the embodiment of the present invention, according to the non-equilibrium entropy evolution characteristic data obtained in Step S3, a joint probability density model of temperature fluctuations and water content mutations is constructed by using the probability statistics method. Based on the data of the slope monitoring points, the temperature change signals at different scales are decomposed by using the time series extraction algorithm of temperature fluctuations (based on discrete wavelet transform); the water content mutation is calculated by the change rate of the pore water pressure gradient. The extracted characteristic data is input into the MATLAB probability density fitting toolbox to construct a perturbation probability density model, and the Kalman filtering method is used to analyze the fluctuation characteristics of temperature and water content, and the critical drift coefficient and the non-linear diffusion coefficient are extracted as the key parameters of the interface kinetic evolution. The above analysis results are output in the form of data reports and prediction models, providing input for the subsequent steps.

[0034] Step S5: Iteratively solve the local minimum entropy production rate for the interface kinetic evolution prediction data, and conduct stress balance correction based on the creep effect of the reinforcing material to obtain the stress-strain distribution data of the entire slope; perform adaptive numerical integration on the displacement field of the key control section of the slope according to the stress-strain distribution data to obtain the long-term settlement deformation prediction data of the slope.

[0035] In the embodiment of the present invention, according to the interface kinetic evolution prediction data obtained in Step S4, a three-dimensional finite element model (using software such as ABAQUS or ANSYS) is constructed to simulate the stress-strain distribution of the slope. The model inputs include the slope topography, material parameters (elastic modulus, Poisson's ratio, permeability coefficient, etc.), characteristics of the reinforcing material (creep characteristic curve), and external load conditions. First, based on the principle of minimum entropy production rate, the stress field distribution is optimized by using a variable step size iterative algorithm; subsequently, the time effect of the reinforcing material is corrected by combining long-term creep test data, and a time correlation parameter is introduced in the calculation. After the calculation is completed, adaptive numerical integration is performed on the displacement field of the key section of the slope, and the step size is dynamically adjusted during the integration process to improve the calculation accuracy. Finally, the stress-strain distribution map of the entire slope and the long-term settlement deformation prediction data are output, and compared with the actual settlement monitoring data of the slope, and the model parameters are adjusted until the prediction accuracy meets the engineering requirements (the error is controlled within 5%).

[0036] Preferably, Step S1 includes the following steps:

[0037] Step S11: Obtain the continuous 72-hour monitoring data of the soil body phreatic line position sensor, and conduct segmented processing and statistical analysis on the data at 6-hour intervals to obtain the soil body phreatic line position parameters;

[0038] The embodiment of the present invention arranges three infiltration line monitoring sensors along the depth direction in the slope area. Each sensor contains 10 high-precision pressure water level gauges (range 0-10m, accuracy 0.01m), and the burial depths are 1m, 2m, 3m, etc. to 10m, and the horizontal spacing is set to 5m. The sensor is connected to the monitoring system through a wireless data acquisition module, and the sampling frequency is set to once per minute. The data is exported after 72 hours of continuous monitoring. The Python data analysis tool is used to process the data in 6-hour segments, and the mean, extreme value and fluctuation amplitude of the infiltration line position in each time period are calculated. At the same time, the sliding window method is used to smooth the abnormal fluctuation data, and finally a time series distribution diagram of the soil infiltration line position and a statistical parameter file are generated, including the position mean, fluctuation range and change trend data, to provide basic parameters for subsequent experiments.

[0039] Step S12: performing a soil-reinforcement material interface shear test using a direct shear apparatus according to the soil infiltration line position parameters, thereby obtaining shear test data;

[0040] According to the soil infiltration line position parameters obtained in step S11, the embodiment of the present invention selects representative sample points (such as 1 to 2 points where the infiltration line position changes dramatically and 1 to 2 stable points), takes samples on site and then transports them back to the laboratory for soil-reinforcement material interface shear test. The experiment uses a large direct shear apparatus (equipment model: TSZ30-100), the thickness of the soil sample installed in the shear box is 5 cm, and the size of the reinforcement material is 10 cm×10 cm. The normal pressure of the test loading is 50, 100, and 150 kPa, and the shear rate is set to 0.5 mm / min. During the test, the shear force and shear displacement data are recorded in real time, and at least 3 parallel tests are performed respectively, and the average value is taken as the final data. After the shear test is completed, the original data file containing the shear stress-shear displacement relationship is exported for the next step of analysis.

[0041] Step S13: fitting the stress-displacement curve of the shear test data to obtain the evolution data of the interface friction coefficient as the moisture content changes;

[0042] The embodiment of the present invention uses a nonlinear fitting method to obtain a stress-displacement curve for the shear test data generated in step S12, and the fitting formula uses a hyperbolic model: τ = τ f (1-e -k·δ ), where τ f is the peak shear stress, δ is the shear displacement, and k is the slope of the curve. The fitting calculation was completed using the MATLAB curve fitting tool, and the goodness of fit (R 2> 0.98). The fitting parameters are correlated with the shear test data under different water content conditions to analyze the influence trend of water content change on the interface friction coefficient, and the evolution curve of the interface friction coefficient varying with water content is generated. The output results include the characteristic values of the friction coefficient evolution, the trend diagram, and the model parameter file, providing support for the interface performance evaluation.

[0043] Step S14: Take samples of the slope soil body, obtain the particle weight distribution of soil samples at different depths through the standard sieve analysis test, and conduct a comparative analysis of the Fuller grading curve to obtain the data of the soil particle gradation characteristic curve.

[0044] In the embodiment of the present invention, soil samples are taken from different depths of the slope (sampling depths: 0 - 2m, 2 - 5m, 5 - 10m), at least 2 samples are collected for each layer, and the weight of each sample is not less than 500g. Using the standard sieve analysis test device (sieve hole specifications: 0.075mm, 0.25mm, 0.5mm, 1mm, 2mm, etc., a total of 10 kinds), the sieve analysis test is carried out in accordance with GB / T 50123 - 2019, record the particle weight distribution under different sieve holes, and calculate the cumulative passing rate of particles. Compare the particle distribution data of each layer of soil samples with the ideal Fuller grading curve (formula: P = 100·(d / D) n , n = 0.5) for comparative analysis, evaluate the gradation uniformity and stability of the soil body, generate the data of the particle gradation characteristic curve and the deviation analysis report, and provide parameter support for the slope stability evaluation.

[0045] Step S15: Collect historical temperature and relative humidity monitoring information through the weather station and conduct time series analysis to obtain the environmental temperature and humidity duration data.

[0046] In the embodiment of the present invention, the temperature and relative humidity data for 72 hours are collected through the weather station at the slope site. The accuracy of the weather station equipment is ±0.1°C for temperature and ±2% for humidity respectively. The sampling interval is set to 10 minutes, and the data is stored in the built - in memory card of the equipment and exported as a CSV - format file. Use the time series analysis tool to conduct segmented statistics on the data, calculate the average value, extreme value, and change range per hour, and at the same time conduct volatility analysis on the variation laws of temperature and humidity in different time periods (such as day and night). Further use the fast Fourier transform (FFT) method to extract the periodic fluctuation characteristics, generate the environmental temperature and humidity duration characteristic curve and key statistical parameters, including the daily temperature difference, humidity peak value, and fluctuation period, providing a reliable basis for the environmental impact assessment.

[0047] Step S16: Format the soil body phreatic line position parameters, the interface friction coefficient evolution data, the soil particle gradation characteristic curve data, and the environmental temperature and humidity duration data, and establish a coupling action matrix between the parameters to obtain the initial characteristic data of the slope system state.

[0048] In the embodiment of the present invention, various types of data obtained by integrating steps S11 to S15 are uniformly subjected to formatting processing. For example, the position parameters of the soil phreatic line are represented in a matrix form according to two dimensions of time and space; the evolution data of the interface friction coefficient is normalized according to the moisture content interval and the fitting parameters of the friction coefficient; the particle size distribution curve data generates a segmented data table through hierarchical identification (such as depth and particle distribution ratio); the environmental temperature and humidity data is standardized into a normal distribution according to the time series. A multi-parameter coupling matrix is established using the MATLAB matrix operation module, and the matrix form is M = [L ij μ ij G ih T ij , where L ij is the position of the phreatic line, μ ij is the friction coefficient, G ij is the particle size distribution index, and T ij is the temperature and humidity characteristic value. Through parameter correlation analysis, the coupling strength between parameters is evaluated, and finally an initial characteristic data file (JSON format) of the slope system state is output, laying a foundation for subsequent modeling analysis.

[0049] Through a systematic data collection and analysis process, the present invention accurately obtains the initial state characteristics of the slope system, including the dynamic changes of the soil phreatic line, the evolution of the interface friction coefficient, the particle size distribution characteristics of the soil, and the environmental temperature and humidity data. Through long-term high-frequency monitoring, shear test data analysis, and particle size distribution characteristic description, the accuracy of the slope mechanical behavior analysis is ensured. The introduction of environmental temperature and humidity data enhances the adaptability of the model to actual working conditions. This data integration provides a solid foundation for subsequent multi-field coupling analysis and settlement deformation prediction, and provides a reliable guarantee for slope stability assessment.

[0050] Preferably, step S2 includes the following steps:

[0051] Step S21: Determine the stress concentration area of the soil-reinforcement material interface according to the initial characteristic data of the slope system state, and optimize the layout of the monitoring points using the hierarchical grid encryption technology, so as to obtain the spatial layout data of the multi-field coupling monitoring network;

[0052] Based on the initial characteristic data of the slope system state generated in step S16, the present invention embodiment uses a finite element numerical simulation tool (such as ABAQUS) to conduct interface stress analysis of soil - reinforcement materials, determines the stress concentration area through the calculation of interface node stress distribution, and focuses on the areas where shear stress and normal stress change significantly (such as the stress concentration value is more than 1.5 times the global average). In the stress concentration area, the monitoring point layout is optimized by using the hierarchical grid encryption technology. The specific method is as follows: The sensor grid is arranged according to the contour line at the position of high stress gradient change in the area, and the sensor spacing is optimized from the original 10m to 1 - 3m. At the same time, the original spacing is maintained at the position where the stress changes gently. After visualizing the layout scheme, a spatial layout data file of the monitoring network is generated, including the monitoring point coordinates, distribution area description, and grid division information.

[0053] Step S22: According to the spatial layout data, perform system time synchronization calibration processing on the sensors at each monitoring point, so as to obtain the calibration parameter data of the monitoring equipment;

[0054] Based on the spatial layout data generated in step S21, after actually arranging sensors (such as multi - functional earth pressure gauges, displacement gauges, and temperature - humidity sensors), the present invention embodiment calibrates all devices through the system time synchronization module. The specific operation is as follows: Connect all sensors to the same time synchronization server, and use a high - precision time synchronization protocol (such as PTP with an accuracy better than 1ms) to uniformly correct the timestamp deviation of each sensor. At the same time, use a reference device (such as a reference strain gauge with an accuracy of ±0.001) to compare the measured values of the sensors point by point, and readjust and calibrate the devices with deviations exceeding the allowable range (±0.02 kPa or ±0.01 mm) to ensure the synchronization consistency of data acquisition of all sensors. Finally, a calibration parameter file of the monitoring equipment is generated, including device numbers, calibration times, synchronization deviations, and calibration parameters.

[0055] Step S23: Continuously and real - time monitor the interface area according to the calibration parameter data of the monitoring equipment, and use the sliding time window method to denoise the original signal and remove outliers, so as to obtain the time - series evolution data of the interface shear stress and the interface normal strain;

[0056] According to the calibration parameter data obtained in step S22, the embodiment of the present invention starts the slope monitoring system and continuously monitors the interface area in real time. The monitoring period is set to 1 minute, and the original signals of shear stress and normal strain are recorded. The collected raw data are imported into MATLAB for signal processing, and the sliding time window method (the window length is set to 10 minutes and the sliding interval is 5 minutes) is used for signal noise reduction. The noise reduction algorithm uses a threshold filtering method based on wavelet transform; at the same time, the three sigma criterion is used to remove outliers in the data (such as points exceeding 3 times the standard deviation of the mean) and interpolate and repair, and generate high-quality interface shear stress and normal strain time series evolution data files, including time, stress / strain values ​​and fluctuation characteristics. Description.

[0057] Step S24: Calculate the spatial gradient distribution of the pore water pressure field and the temperature field based on the finite difference method through the time series evolution data of the interface shear stress and the interface normal strain, and perform interpolation fitting between the monitoring points to obtain field quantity distribution data;

[0058] The embodiment of the present invention uses the time series evolution data generated in step S23 to calculate the spatial gradient of the pore water pressure field and the temperature field by the finite difference method. Using the monitoring point data as input, a two-dimensional spatial grid model is established, and the grid size is set to 1m×1m. The spatial variation of pore water pressure and temperature is calculated by the difference formula: and Calculation. The gradient data between monitoring points are interpolated and fitted, and the field quantity distribution map is generated using the Kriging interpolation method (Kriging). The spatial distribution data files of the pore water pressure field and temperature field are output, including the field quantity gradient, interpolation fitting accuracy and distribution graphic data.

[0059] Step S25: Perform multi-field coupling effect analysis on the time series evolution data and field quantity distribution data, establish an interaction relationship matrix between physical quantities, and thus obtain interface action characteristic data.

[0060] The embodiment of the present invention integrates the time-series evolution data of the interface shear stress and interface normal strain generated in steps S23 and S24, as well as the pore water pressure field and temperature field distribution data, and adopts a multi-field coupling analysis method to analyze the interaction relationship between physical quantities. An interaction model between shear stress, normal strain, pore water pressure and temperature is established based on the Pearson correlation coefficient and multivariate linear regression, and a relationship matrix is ​​constructed: where r ij is the correlation coefficient between physical quantities. The coupling relationship strength between physical quantities is analyzed based on matrix data, and the coupling effect is visualized with a heat map. Finally, an interface action characteristic data file containing a correlation matrix, key coupling parameters, and action characteristic description is generated for slope safety assessment and protection design optimization.

[0061] Through the construction of a refined monitoring network and data processing, the present invention provides high-quality data support for the multi-field coupling analysis of the slope system. First, by analyzing the initial characteristic data of the slope system state, the stress concentration area at the soil-reinforcement material interface is determined, and the layout of monitoring points is optimized using hierarchical grid encryption technology to ensure that the monitoring network accurately covers the key areas, improving the representativeness and pertinence of the monitoring data and providing a solid foundation for subsequent analysis. On this basis, the system synchronizes and calibrates the monitoring equipment in time, eliminating the time deviation between different sensors and ensuring the synchrony and consistency of the monitoring data in the time series, providing an accurate time reference for the multi-field coupling analysis. Next, the original signal is denoised and outliers are removed by the sliding time window method, effectively removing noise and interference and retaining the true signal characteristics, obtaining the time series evolution data of the interface shear stress and interface normal strain, which reflect the dynamic changes in the mechanical behavior of the interface area. Further, the finite difference method is used to calculate the spatial gradient distribution of the pore water pressure field and temperature field, and the data gaps between monitoring points are filled by interpolation fitting to ensure that the distribution of the physical field is more complete and continuous. Finally, through the multi-field coupling effect analysis of the time series evolution data and field quantity distribution data, an interaction relationship matrix between the mechanical field, hydraulic field, and temperature field is established, and the interface action characteristic data are obtained. This process synthesizes the mutual influences of each physical field and provides a comprehensive and accurate description of the interface action characteristics for settlement deformation prediction. This complete data acquisition and analysis process improves the reliability of slope stability assessment and settlement prediction, ensuring the engineering safety and sustainability.

[0062] Preferably, step S23 includes the following steps:

[0063] Step S231: Configure the sampling frequency and range of the data acquisition system according to the calibration parameter data of the monitoring equipment, and establish a real-time data transmission channel to obtain the original monitoring signal data;

[0064] In the embodiment of the present invention, according to the calibration parameter data of the monitoring equipment, the sampling frequency and range of each sensor are first determined. For example, the sampling frequency of the interface shear stress sensor is set to 10 Hz and the range is 100 kPa, and the sampling frequency of the interface normal strain sensor is set to 20 Hz and the range is 10 mm. After configuration, the sensors are connected to the data acquisition system (such as LabVIEW or RTU data acquisition module), and a real-time data transmission channel is established through the industrial Ethernet protocol (such as Modbus TCP) to ensure that the monitoring signal is transmitted to the data processing terminal with a millisecond-level delay. The data storage is managed by a distributed file system (such as HDFS), and the generated original monitoring signal data includes timestamps, sensor numbers, and the stress and strain values collected in real time.

[0065] Step S232: using the original monitoring signal data to set the sliding time window length based on the signal-to-noise ratio analysis, extracting the statistical features of the data in the window, thereby obtaining signal feature sequence data;

[0066] The embodiment of the present invention uses the original monitoring signal data generated in step S231 to analyze the signal-to-noise ratio. The specific method is: use fast Fourier transform (FFT) to calculate the power spectral density of the signal, and extract the ratio of the effective component power of the signal to the noise power as the signal-to-noise ratio. Set the sliding time window length according to the analysis results. For example, when the signal-to-noise ratio is 20dB, select a window length of 5 seconds. Calculate statistical features for the data in each sliding window, including mean, variance, and skewness coefficient, and serialize the calculation results into signal feature sequence data. The data format is a table or file format of time series and corresponding statistical feature values.

[0067] Step S233: filtering out high-frequency noise from the signal feature sequence data and marking and removing data points that deviate from the mean by more than three times the standard deviation as outliers based on the 3σ criterion, thereby obtaining signal anomaly correction data;

[0068] The embodiment of the present invention performs high-frequency noise filtering on the signal feature sequence data generated in step S232, and uses a low-pass filter (the filter cutoff frequency is set to 1 / 3 of the highest frequency of the signal, for example, the interface shear stress signal is set to 3Hz) to smooth out the high-frequency noise. Then, the 3σ criterion is used to detect and eliminate outliers, that is, the mean and standard deviation of the signal sequence are calculated, and the points that deviate from the mean by more than three times the standard deviation are marked as outliers, and the outliers are repaired by linear interpolation. For example, if the shear stress value of a monitoring point is 80kPa, and the mean is 50kPa and the standard deviation is 10kPa, the value will be marked as abnormal and interpolated to the average value of adjacent time points to generate signal anomaly correction data.

[0069] Step S234: reconstruct the interface shear stress and interface normal strain in the time domain according to the signal anomaly correction data, and establish a continuous change curve through interpolation and smoothing processing, so as to obtain time series evolution data.

[0070] In the embodiment of the present invention, data is corrected according to signal anomalies, and time-domain reconstruction is performed on the interfacial shear stress and interfacial normal strain respectively. The spline interpolation method is used to smoothly connect discrete corrected data points into a continuously varying curve. The specific method is as follows: Using a cubic spline interpolation function, an interpolation curve is generated with the time stamp as the independent variable and the corrected stress or strain value as the dependent variable. At the same time, the interpolation curve is smoothed, for example, by a Savitzky-Golay filter (the filter window width is set to 11 and the fitting order is 3) to reduce curve fluctuations. The finally output time-series evolution data includes continuous curves of the interfacial shear stress and normal strain varying with time, which are used for subsequent multi-field coupling analysis and interfacial characteristic research.

[0071] Through a refined data acquisition and processing process, the present invention ensures the high quality and reliability of the monitoring data. First, the sampling frequency and range of the data acquisition system are configured by calibrating the parameter data of the monitoring device, and a real-time data transmission channel is established, enabling efficient acquisition of the original monitoring signal data. This process not only ensures the accuracy and timeliness of data acquisition but also provides a stable and reliable data source for subsequent data processing. Reasonable settings of the sampling frequency and range can ensure the integrity and accuracy of the monitoring signal, avoiding information loss or distortion caused by insufficient or excessive sampling. After obtaining the original monitoring signal data, the sliding time window length is set using signal-to-noise ratio analysis, and the statistical features of the data within the window are extracted. This process can effectively capture the short-term change characteristics of the signal by dynamically adjusting the size of the time window while suppressing the influence of noise on the signal. The extraction of statistical features further quantifies the key information of the signal, providing a basis for subsequent noise filtering and outlier processing. Subsequently, high-frequency noise filtering is performed on the signal feature sequence data, and outliers deviating from the mean by more than three standard deviations are removed based on the 3σ criterion. This processing effectively removes random noise and extreme outliers in the signal, retaining the true characteristics of the signal. High-frequency noise filtering can smooth the short-term fluctuations of the signal, while outlier removal avoids the interference of extreme data on the overall analysis, making the signal more stable and reliable. Finally, time-domain reconstruction is performed on the interfacial shear stress and interfacial normal strain according to signal anomalies, and a continuously varying curve is established through interpolation smoothing. This process not only restores the integrity and continuity of the signal but also further optimizes the signal quality through interpolation smoothing. Time-domain reconstruction clearly presents the dynamic changes of the interfacial shear stress and normal strain, and the continuously varying curve provides high-quality input data for subsequent multi-field coupling analysis. Through the synergistic effect of these steps, the entire data acquisition and processing process realizes the efficient conversion from the acquisition of the original signal to high-quality time-series evolution data, providing a solid data foundation for slope settlement deformation prediction.

[0072] Preferably, step S25 includes the following steps:

[0073] Step S251: Conduct a correlation analysis on the interfacial shear stress and interfacial normal strain in the time-series evolution data, determine the time-delay characteristics of the stress-strain response, and establish a stress-strain coupling matrix for the time-delay effect, thereby obtaining mechanical coupling characteristic data;

[0074] In the embodiment of the present invention, a correlation analysis is conducted on the interfacial shear stress and interfacial normal strain in the time-series evolution data. By combining the Pearson correlation coefficient and the cross-correlation function, the correlation index of the two sets of data is calculated first to determine the degree of linear correlation between them; then the cross-correlation function is used to analyze the phase shift of the stress and strain signals in time to identify the time-delay characteristics. For example, a sliding time window (window length is 1 minute) is selected, and the time deviation corresponding to the peak points of the time-series signals of the shear stress and normal strain is calculated respectively within each window. The average time-delay is 0.2 seconds. Based on this, a stress-strain coupling matrix is established using MATLAB. The matrix elements are the correlation coefficients and time-delay parameters of stress-strain within different time windows, and finally, mechanical coupling characteristic data is generated.

[0075] Step S252: Use the pore water pressure gradient and temperature gradient in the field quantity distribution data to conduct a thermal-hydraulic coupling effect analysis, and evaluate the thermal-hydraulic coupling coefficient that describes the influence of the temperature field on the pore water pressure distribution based on the thermo-osmosis theory, thereby obtaining thermal-hydraulic coupling characteristic data;

[0076] In the embodiment of the present invention, the pore water pressure gradient and temperature gradient in the field quantity distribution data are used, and a numerical analysis method based on the thermo-osmosis theory is adopted to evaluate the thermal-hydraulic coupling effect. First, the pore water pressure field gradient is calculated through Darcy's law, and the spatial distribution of the temperature field gradient is calculated in combination with Fourier's heat conduction law; subsequently, the thermo-osmosis coupling coefficient (defined as the influence of unit temperature change on the pore water pressure distribution, with the unit of kPa / ℃) is introduced, and finite element simulation is carried out through COMSOL Multiphysics to simulate the dynamic influence of the temperature field change on the water pressure field. For example, when the temperature gradient is set to 1℃ / m and the pore water pressure gradient is set to 10 kPa / m, the thermal-hydraulic coupling coefficient is obtained as 0.8 kPa / ℃, and finally, thermal-hydraulic coupling characteristic data is generated.

[0077] Step S253: Construct a three-field coupling equation set that describes the interaction between the interfacial shear stress, pore water pressure, and temperature according to the mechanical coupling characteristic data and the thermal-hydraulic coupling characteristic data, and perform numerical solution using the Petrov-Galerkin method, thereby obtaining multi-field coupling effect evolution data;

[0078] In the embodiment of the present invention, a three-field coupling equation set is constructed according to the mechanical coupling characteristic data and the thermal-hydraulic coupling characteristic data. The three-field coupling equations include the equilibrium equation of the mechanical field, the seepage equation of the hydraulic field, and the heat conduction equation of the thermal field, and are in the following forms: Mechanical field: Hydraulic field: Thermal field: The equations are discretized in weak form by the Petrov-Galerkin method and numerically solved using COMSOL or ABAQUS to calculate the dynamic evolution process of the interfacial shear stress, pore water pressure, and temperature, and finally the evolution data of the multi-field coupling effect is obtained.

[0079] Step S254: Feature extraction based on principal component analysis is performed on the evolution data of the multi-field coupling effect to determine the dominant and secondary relationships between the physical fields, and a coupling effect strength matrix considering weight coefficients is established, thereby obtaining the coupling effect level data;

[0080] In the embodiment of the present invention, principal component analysis (PCA) is performed on the evolution data of the multi-field coupling effect to extract the coupling characteristics between the physical fields. First, the evolution data matrix is centered and standardized, then the data covariance matrix is calculated and its eigenvalue decomposition is performed, and the top 3 principal components with the largest contribution rates of the principal components are extracted. For example, the principal component analysis results show that the contribution rate of the shear stress to the coupling effect is 55%, the temperature field is 30%, and the pore water pressure is 15%. A coupling effect strength matrix is constructed according to the contribution rates of the principal components, and the dominant relationship (shear stress-temperature field) and the secondary relationship (temperature field-pore water pressure) are distinguished to generate the coupling effect level data.

[0081] Step S255: An interaction relationship matrix including the mechanical field, the hydraulic field, and the temperature field is constructed according to the coupling effect level data, and normalization processing of the matrix elements is performed. The tensor analysis method is used to determine the coupling effect strength and the action direction between the physical quantities, thereby generating the interface action characteristic data.

[0082] In the embodiment of the present invention, an interaction relationship matrix including the mechanical field, the hydraulic field, and the temperature field is constructed according to the coupling effect level data, and the elements of the matrix are the coupling effect strength indexes between the physical fields. For example, the action strength of the mechanical field on the hydraulic field is 0.6, and the action strength of the temperature field on the mechanical field is 0.3. The matrix elements are normalized so that the total strength is 1. Subsequently, using the tensor analysis method and combining with the MATLAB Tensor Toolbox, the matrix is decomposed into a third-order tensor to analyze the coupling effect strength and the action direction of each physical field. The results show that the mechanical field has the strongest dominance, and its coupling direction is consistent with the temperature field. Finally, the interface action characteristic data is generated to describe the multi-field coupling effect and its dynamic evolution law of the slope interface.

[0083] Through systematic multi-field coupling analysis, the present invention deeply reveals the complex interaction relationships among the mechanical, hydraulic, and thermal fields in the slope system, providing a scientific basis for slope stability analysis and settlement deformation prediction. By performing a correlation analysis on the interface shear stress and interface normal strain in the time-series evolution data, the time-delay characteristics of the stress-strain response are determined, and a stress-strain coupling matrix for the time-delay effect is established. This process not only quantifies the dynamic characteristics of the mechanical response but also reveals the internal relationship between stress and strain, laying a solid mechanical foundation for subsequent multi-field coupling analysis. The introduction of the time-delay effect enables the model to more accurately describe the mechanical behavior of the slope under actual working conditions, further improving the prediction accuracy. Subsequently, the thermal-hydraulic coupling effect is analyzed using the pore water pressure gradient and temperature gradient in the field quantity distribution data, and the thermal-hydraulic coupling coefficient is evaluated based on the theory of thermo-osmosis. This analysis process reveals the influence mechanism of the temperature field on the pore water pressure distribution and clarifies the interaction relationship between the thermal field and the hydraulic field. By evaluating the thermal-hydraulic coupling coefficient, the influence of temperature changes on the pore water pressure can be quantitatively described, providing a key parameter for the construction of the multi-field coupling model. Further, combining the mechanical coupling characteristic data and the thermal-hydraulic coupling characteristic data, a three-field coupling equation set describing the interaction among the interface shear stress, pore water pressure, and temperature is constructed, and the Petrov-Galerkin method is used for numerical solution. This process realizes the comprehensive coupling of the mechanical field, hydraulic field, and temperature field, enabling the model to comprehensively consider the influence of multi-field factors on the slope behavior. The multi-field coupling effect evolution data obtained through numerical solution can more comprehensively reflect the dynamic change law of the slope system under complex working conditions. Based on the multi-field coupling effect evolution data, features are extracted through principal component analysis to determine the dominant and secondary relationship among the physical fields, and a coupling effect intensity matrix considering the weight coefficient is established. This process further optimizes the structure of the multi-field coupling model and clarifies the relative importance of each physical field in the coupling process. The introduction of the weight coefficient enables the model to more accurately reflect the actual influence of different physical fields on the slope behavior, improving the adaptability and reliability of the model. Finally, an interaction relationship matrix including the mechanical field, hydraulic field, and temperature field is constructed based on the coupling effect level data, and the coupling effect intensity and direction among the physical quantities are determined through normalization processing and tensor analysis methods. This process not only quantifies the complex relationship of multi-field coupling but also further clarifies the directionality of the coupling effect through tensor analysis methods. The generated interface action characteristic data provides comprehensive and accurate input for the settlement deformation prediction of the slope, enabling the prediction model to more accurately reflect the behavior law of the slope under multi-field coupling conditions.

[0084] Preferably, step S3 includes the following steps:

[0085] Step S31: Establish an energy decomposition model considering interface friction work and interface deformation work based on the interface action characteristic data, and quantitatively characterize the energy conversion mechanism during the shearing process, thereby obtaining the interface shear energy consumption characteristic data;

[0086] In the embodiment of the present invention, an energy decomposition model is established according to the interface action characteristic data, and the interface friction work and interface deformation work are calculated respectively. The friction work is calculated by the formula W f =∫F t dx, where F t is the interface friction force, and dx is the shear displacement increment, and the specific values are collected in real time by a force sensor and a displacement sensor. For example, when the friction force is set to 150 N and the shear displacement is 0.01 m, the single-shear friction work is 1.5 J. The deformation work is calculated by the formula W d =∫σ·ε·dV, where σ is the normal stress, ε is the deformation strain, and dV is the volume element; the stress-strain field in the interface region is simulated by finite element analysis, and the total deformation work is calculated to be 2.8 J. After decomposing and summarizing the friction work and the deformation work, the conversion mechanism of energy from friction dissipation to deformation storage during the shearing process is quantitatively characterized, and finally the interface shear energy consumption characteristic data is generated.

[0087] Step S32: Calculate the heat conduction power in the interface region by using the temperature gradient information in the interface action characteristic data based on Fourier's law of heat conduction, and establish a heat conduction energy balance equation according to the heat accumulation and dissipation effects caused by temperature fluctuations, thereby obtaining the heat conduction energy consumption characteristic data;

[0088] In the embodiment of the present invention, the heat conduction power is calculated by using the temperature gradient information in the interface action characteristic data based on Fourier's law of heat conduction. The heat conduction power formula is where P is in watts (W), representing the heat conducted through the interface region per unit time; k is the thermal conductivity, in watts per meter per kelvin (W / (m·K)), reflecting the ability of the material to conduct heat energy. For example, metals have a relatively high thermal conductivity, while non-metallic materials have a lower thermal conductivity; A is the heat transfer area, in square meters (m 2 ), representing the effective heat transfer area of the interface region, is the temperature gradient, in kelvin per meter (K / m), representing the temperature change per unit length. The temperature gradient is usually measured by a thermistor or an infrared thermal imager. When the thermal conductivity is set to 0.8 W / (m·K), the heat transfer area is 0.01 m 2 , and the temperature gradient is 50 K / m, the calculated heat conduction power is 0.4 W. Then, according to the heat accumulation and dissipation effects caused by temperature fluctuations, a heat conduction energy balance equation is established: Q in ―Q out =ΔU, where Q inis the input heat, in joules (J), which is the thermal energy provided by a heat source or the external environment; Q out is the dissipated heat, in joules (J), which is the thermal energy lost through heat conduction or heat dissipation; ΔU is the change in heat at the interface, in joules (J), representing the net change in heat within the interface region. Simulation calculations show that the heat accumulation part accounts for 35% of the total input heat, and the dissipated part accounts for 65%, finally generating characteristic data of heat conduction energy consumption.

[0089] Step S33: Calculate the moisture migration power in the interface region based on Darcy's seepage theory according to the pore water pressure gradient information in the interface action characteristic data, and establish a moisture transport energy balance equation based on the coupling effect of capillary action and osmosis, so as to obtain characteristic data of moisture migration energy consumption;

[0090] In the embodiment of the present invention, according to the pore water pressure gradient information in the interface action characteristic data, Darcy's seepage theory is used to calculate the moisture migration power in the interface region. The seepage power formula is where P is the moisture migration power, in watts (W), representing the moisture migration power caused by the pore water pressure gradient per unit time; k h is the permeability coefficient, in meters per second (m / s), representing the ability of a liquid to penetrate a material, which is affected by the porosity of the material and the viscosity of the liquid; A is the seepage area, in square meters (m 2 ), representing the effective channel area for liquid migration; is the pore water pressure gradient, in pascals per meter (Pa / m), representing the change in pore water pressure per unit length, which can be measured through permeability experiments or monitoring systems. Set the permeability coefficient to 1.2×10 -5 m / s, the seepage area to 0.005 m 2 , and the pore water pressure gradient to 200 kPa / m. The calculated moisture migration power is 1.2×10-3 W. According to the coupling effect of capillary action and osmosis, establish a moisture transport energy balance equation: Q capillary +Q permeation =Q total , where Q capillary is the energy caused by capillary action, in joules (J), reflecting the energy consumption of moisture migrating through the capillary channels in the interface region; Q permeation is the energy caused by osmosis, in joules (J), reflecting the energy consumption of moisture migrating due to osmotic pressure; Q total is the total moisture migration energy, in joules (J), which is the sum of the energies of capillary action and osmosis. Calculations show that the energy consumption of capillary action accounts for 45%, and the energy consumption of osmosis accounts for 55%, finally generating characteristic data of moisture migration energy consumption.

[0091] Step S34: Establish an energy dissipation correction model based on interface roughness through interface shear energy consumption characteristic data, heat conduction energy consumption characteristic data, and moisture migration energy consumption characteristic data, so as to obtain corrected energy consumption characteristic data including the influence of interface morphology characteristics on energy conversion efficiency;

[0092] In an embodiment of the present invention, an energy dissipation correction model based on interface roughness is established through interface shear energy consumption characteristic data, heat conduction energy consumption characteristic data, and moisture migration energy consumption characteristic data. The interface roughness is measured by a three-dimensional scanner to obtain the roughness parameter R a = 5 μm. The correction model formula is: E adjusted = E original ·(1 + R a ·α), where E adjusted is the corrected energy, in joules (J), the energy value after considering the influence of interface roughness on energy dissipation; E original is the original energy, in joules (J), the energy value calculated without considering interface roughness; R a is the interface roughness, in micrometers (μm), measured by three-dimensional scanning or a surface profiler, representing the average height of the interface surface undulation; α is the roughness influence coefficient, dimensionless, reflecting the influence degree of roughness on energy dissipation efficiency, determined by experimental fitting, with a value of 0.02. According to the formula calculation, the correction amplitude of interface roughness on energy conversion efficiency is 10%. Finally, integrate each energy consumption characteristic data to generate corrected energy consumption characteristic data considering interface morphology characteristics.

[0093] Step S35: Perform non-equilibrium statistical mechanics analysis using the corrected energy consumption characteristic data, so as to obtain entropy production characteristic data, where the non-equilibrium statistical mechanics analysis is specifically to establish an entropy production rate equation describing the degree of the system away from the equilibrium state, and use the Fokker-Planck equation to perform a probability description of the fluctuation characteristics of the system;

[0094] In an embodiment of the present invention, non-equilibrium statistical mechanics analysis is performed using the corrected energy consumption characteristic data to establish an entropy production rate equation describing the degree of the system away from the equilibrium state: where is the entropy production rate, in joules per kelvin per second (J / (K·s)), representing the entropy change rate per unit time when the system is away from the equilibrium state; is the energy dissipation rate, in watts (W), representing the energy dissipated by the system per unit time; T is the temperature, in kelvin (K), representing the absolute temperature of the interface region in the system; dA is the interface unit area, in square meters (m 2 ), representing the area of each small region in the entropy production analysis. Set the energy dissipation rate to 0.5 W, the temperature to 300 K, and the interface unit area to 0.01 m2 , the calculated entropy production rate is 1.67×10 -3 J / (K·s). Combining with the Fokker-Planck equation, a probabilistic description of the fluctuation characteristics of the system energy dissipation is carried out. The results show that the peak probability corresponding time of the system entropy increase process is 5 s, and finally the entropy production characteristic data is generated.

[0095] Step S36: Construct the non-equilibrium entropy evolution equation of the system according to the entropy production characteristic data, and quantitatively evaluate the stability and evolution trend of the system by solving the variation law of the entropy production rate with time, so as to obtain the non-equilibrium entropy evolution characteristic data.

[0096] In the embodiment of the present invention, according to the entropy production characteristic data, the non-equilibrium entropy evolution equation of the system is constructed: where is the entropy change rate, with the unit of joule per kelvin per second (J / (K·s)), representing the dynamic change trend of entropy with time; is the entropy production rate, with the unit of joule per kelvin per second (J / (K·s)), reflecting the rate of entropy increase when the system is far from the equilibrium state; is the entropy dissipation rate, with the unit of joule per kelvin per second (J / (K·s)), representing the rate of entropy reduction due to external actions. By numerically solving the variation law of the entropy production rate with time, the system stability and evolution trend are obtained. For example, when the interfacial shear rate increases to 1.5 times, the entropy production rate increases by 30%, but the dissipation rate increases by 45%, indicating that the system tends to be stable. Finally, the non-equilibrium entropy evolution characteristic data of the system is calculated and used to evaluate the dynamic stability of the interfacial region.

[0097] In the present invention, an energy decomposition model considering interface friction work and interface deformation work is established to quantitatively characterize the energy conversion mechanism during the shearing process, and characteristic data of interface shear energy consumption are obtained. This process not only clarifies the distribution and conversion laws of energy during the interface shearing process, but also provides basic data for subsequent energy analysis. The acquisition of the characteristic data of interface shear energy consumption enables the model to more accurately describe the energy consumption during the interface shearing process, providing an important mechanical basis for slope stability analysis. Based on Fourier's law of heat conduction, the heat conduction power of the interface region is calculated using the temperature gradient information in the interface action characteristic data, and a heat conduction energy balance equation is established. This analysis process reveals the heat accumulation and dissipation effects caused by temperature fluctuations, and characteristic data of heat conduction energy consumption are obtained. Through the characteristic data of heat conduction energy consumption, the model can more comprehensively consider the influence of temperature changes on the stability of the slope system, further improving the accuracy and reliability of prediction. According to the pore water pressure gradient information in the interface action characteristic data, the moisture migration power of the interface region is calculated based on Darcy's seepage theory, and a moisture migration energy balance equation is established. This process clarifies the coupling effect of capillary action and osmosis, and characteristic data of moisture migration energy consumption are obtained. The acquisition of the characteristic data of moisture migration energy consumption enables the model to more accurately describe the influence of the hydraulic field on slope stability, providing an important hydraulic basis for slope settlement deformation prediction. By integrating the characteristic data of interface shear energy consumption, heat conduction energy consumption, and moisture migration energy consumption, an energy dissipation correction model based on interface roughness is established. This model considers the influence of interface morphology characteristics on the energy conversion efficiency, and corrected energy consumption characteristic data are obtained. The introduction of interface roughness enables the model to be closer to the actual working conditions, further improving the accuracy and reliability of energy analysis. Subsequently, non-equilibrium statistical mechanics analysis is performed using the corrected energy consumption characteristic data to obtain entropy production characteristic data. By establishing an entropy production rate equation and using the Fokker-Planck equation to probabilistically describe the fluctuation characteristics of the system, the model can more comprehensively reflect the degree to which the system is far from the equilibrium state. This analysis process not only reveals the internal dynamic characteristics of the system, but also provides an important theoretical basis for subsequent stability assessment. Finally, based on the entropy production characteristic data, a non-equilibrium entropy evolution equation of the system is constructed, and by solving the variation law of the entropy production rate with time, the stability and evolution trend of the system are quantitatively evaluated. The acquisition of the non-equilibrium entropy evolution characteristic data enables the model to more accurately predict the dynamic behavior of the slope system, providing a scientific basis for slope settlement deformation prediction. The synergistic effect of this series of steps enables the model to comprehensively analyze the complex behavior of the slope system from an energy perspective, providing strong support for the safety design and stability assessment of slope engineering.

[0098] Preferably, step S4 includes the following steps:

[0099] Step S41: Construct an environmental perturbation probability density model based on temperature fluctuations and moisture content mutations according to the non-equilibrium entropy evolution characteristic data, so as to obtain an environmental perturbation dynamics model;

[0100] In the embodiment of the present invention, when constructing the environmental perturbation dynamics model, first, long-term sampling of temperature fluctuations and moisture content changes in the research area is carried out through monitoring equipment, and the time resolution of the collected data is set to 1 hour to ensure sufficient accuracy and dynamic range. The collected data is denoised by wavelet transform, and the change trend of the temperature fluctuation amplitude and the mutation point information of the moisture content are extracted therefrom. Subsequently, according to the non-equilibrium entropy evolution theory, the temperature and moisture content data are normalized, converted into dimensionless perturbation intensity indicators, and the kernel density estimation method is used to construct a joint probability density distribution model of temperature fluctuations and moisture content mutations. This model reflects the state transition probability characteristics of the system under environmental perturbations, and the output results include the probability density curve and the distribution information of the key perturbation regions. In a specific agricultural scenario, for example, when used for soil environment monitoring, the synchronous acquisition of temperature and moisture content data can be achieved by configuring a thermocouple temperature sensor and a soil humidity sensor, and the sensor acquisition range is set to 100°C and 50% humidity.

[0101] Step S42: Solve the C-K equation for the environmental perturbation dynamics model, and calculate the critical drift coefficient of the system by numerical iteration method, so as to obtain the system drift characteristic data;

[0102] In the embodiment of the present invention, based on the environmental perturbation dynamics model, the Chapman-Kolmogorov equation (abbreviated as C-K equation) is used to describe the system state evolution. By collecting the time series evolution data of the probability density model, a state transition matrix describing the interaction between temperature fluctuations and moisture content mutations is constructed. Subsequently, the C-K equation is discretized by the finite difference method, and the numerical solution of the equation is iteratively solved. During the calculation process, a discrete time series with a time step of 0.1 hour is introduced, and the spatial resolution is set to an accuracy of 0.5°C for temperature and 0.1% for humidity to ensure the solution accuracy. In the solution, by calculating the change trend of the perturbation state, the critical drift coefficient of the system is extracted, and this coefficient is used to measure the rate of evolution of the system from a stable state to an unstable state. In an industrial cold chain storage and transportation scenario, such as temperature fluctuation monitoring, the critical drift coefficient can be calculated using the actual observed data of temperature fluctuations in the refrigerated truck, so as to predict the environmental perturbation risk during the storage and transportation process.

[0103] Step S43: Solve the nonlinear diffusion equation by the variational iteration method based on the system drift characteristic data, and obtain the nonlinear diffusion coefficient by minimizing the system free energy functional, so as to obtain the system diffusion characteristic data;

[0104] In the embodiments of the present invention, based on the system drift characteristic data, the variational iteration method is used to solve the nonlinear diffusion equation to obtain the system diffusion characteristic data. The specific operations include: First, based on the drift coefficient data, the initial diffusion coefficient distribution is constructed by using the high-order interpolation method; Second, by defining the free energy functional of the system and combining the constraint conditions of the change in non-equilibrium entropy, an optimization objective function is established; Then, the variational iteration method is used to solve the objective function, continuously minimizing the system free energy functional until it converges to the global optimal solution. In the soil moisture diffusion experiment, the moisture diffusion process can be simulated by setting the initial conditions (for example, the initial soil moisture gradient is 10% / m) and the boundary conditions (for example, the evaporation rate is 1% / h), so as to obtain the specific change law of the nonlinear diffusion coefficient.

[0105] Step S44: Perform coupling analysis based on the system drift characteristic data and the system diffusion characteristic data, establish a kinetic equation set including the critical drift coefficient and the nonlinear diffusion coefficient, and perform numerical solution by using the fourth-order Runge-Kutta method, so as to obtain the interface kinetic evolution prediction data.

[0106] In the embodiments of the present invention, the system drift characteristic data and the diffusion characteristic data are combined to perform coupling analysis and establish a kinetic equation set including the critical drift coefficient and the nonlinear diffusion coefficient. The specific method is: First, construct the coupling function form according to the characteristic data of the first two steps to describe the interaction between drift and diffusion in different states; Second, perform numerical solution of the kinetic equation set by using the fourth-order Runge-Kutta method. During the solution process, set the time step to 0.01 second, and use the adaptive grid adjustment method to optimize the calculation efficiency and accuracy. The calculation result outputs the interface kinetic evolution prediction data, including the evolution trajectory of the interface morphology over time and the stability analysis report. In the scenario of predicting the evolution of the soil-water interface, applying this method can model the dynamic change of the water distribution during agricultural irrigation, set the initial conditions (for example, the irrigation volume is 50L / m 2 ) and the boundary conditions (for example, the evaporation rate is 0.5L / m 2 ·h), and finally predict the lateral diffusion and deep infiltration behavior of the water after irrigation.

[0107] The present invention constructs an environmental perturbation probability density model based on non-equilibrium entropy evolution characteristic data, fully considering the effects of temperature fluctuations and sudden changes in water content on the slope system. This process quantifies the randomness of environmental factors and establishes a dynamic model of environmental perturbation for the system, providing a theoretical framework in a dynamic environment for subsequent analysis. The establishment of the model enables the stability analysis of the slope system to be closer to the actual working conditions and effectively captures the potential impact of environmental changes on the slope behavior. Solve the C-K equation for the dynamic model of environmental perturbation, and calculate the critical drift coefficient of the system through a numerical iteration method. This process not only reveals the drift characteristics of the system under environmental perturbation but also provides a quantitative description for understanding the dynamic response of the slope system. The calculation of the critical drift coefficient enables the model to more accurately capture the trend changes of the system in a complex environment and provides important parameters for subsequent dynamic analysis. Based on the system drift characteristic data, use the variational iteration method to solve the non-linear diffusion equation, and obtain the non-linear diffusion coefficient by minimizing the system free energy functional. This process clarifies the diffusion characteristics of the system under environmental perturbation and enables the model to more comprehensively describe the dynamic behavior of the slope system under complex conditions. The acquisition of the non-linear diffusion coefficient further improves the parameter system of the dynamic model and enhances the prediction ability of the model for slope behavior. Combine the system drift characteristic data and diffusion characteristic data to establish a dynamic equation set containing the critical drift coefficient and the non-linear diffusion coefficient, and perform numerical solution using the fourth-order Runge-Kutta method. This process not only achieves an accurate prediction of the dynamic evolution of the slope system but also ensures the efficiency and accuracy of the solution through numerical methods. The acquisition of the interface dynamic evolution prediction data provides a scientific basis for the long-term prediction of slope settlement deformation, enabling the model to more accurately reflect the dynamic change law of the slope under complex environments. Through the synergistic effect of this series of steps, the settlement deformation prediction model of the slope system can more comprehensively consider the random perturbation of environmental factors and more accurately describe the dynamic behavior of the system under complex conditions. This not only improves the prediction accuracy of the model but also provides strong theoretical support for the safety design and stability assessment of slope engineering.

[0108] Preferably, step S41 includes the following steps:

[0109] Step S411: Perform wavelet transform analysis on the temperature field information in the non-equilibrium entropy evolution characteristic data to identify the main frequency components and amplitude characteristics of the temperature fluctuations, thereby obtaining the temperature fluctuation characteristic data;

[0110] When the embodiment of the present invention performs wavelet transform analysis on the temperature field information in the non-equilibrium entropy evolution characteristic data, first, the collected temperature data is preprocessed to remove noise and ensure data uniformity. Then, the discrete wavelet transform (DWT) is used to decompose the temperature data. A suitable wavelet basis (such as the Daubechies wavelet) is selected, and the decomposition level is set to 5. The decomposed coefficients correspond to different frequency bands. By calculating the amplitude spectra of each frequency band, the main frequency components in the temperature fluctuations are identified, and the amplitude characteristics of each frequency component are analyzed. In a specific application scenario, such as an agricultural irrigation system, the temperature sensor collects temperature data once an hour, and the data range is 0 to 50 °C. After analysis, the main period and variation amplitude of the temperature fluctuations are obtained, such as the fluctuation period is 24 hours, and the amplitude is ±5 °C. These characteristics can be used for subsequent perturbation analysis.

[0111] Step S412: Perform mutation point detection on the water content information in the non-equilibrium entropy evolution characteristic data based on wavelet singular value decomposition, and determine the time position and amplitude of the water content mutation, so as to obtain the water content mutation characteristic data;

[0112] When the embodiment of the present invention performs mutation point detection on the water content information in the non-equilibrium entropy evolution characteristic data based on wavelet singular value decomposition (SVD), first, the long-term trend is removed from the time-series data collected by the water content sensor and denoising processing is performed. Then, the wavelet singular value decomposition method is applied to decompose the water content data into multiple scale components, highlighting the local change characteristics of the data at each scale. By analyzing the decomposition results, the mutation points in the data, that is, the time position and amplitude of the water content mutation, are identified. A threshold method is used to mark significant mutation points. For example, when the data change amplitude is greater than a certain set threshold (such as 10%), it is regarded as a mutation event. In practical applications, such as soil moisture monitoring, if the soil water content suddenly changes from 25% to 35%, it will be marked as a mutation event, and this information will be used for the construction of the environmental perturbation model.

[0113] Step S413: Establish a time series analysis model based on the temperature fluctuation characteristic data and the water content mutation characteristic data, and use the autoregressive moving average method to evaluate the random change law of environmental factors, so as to obtain the environmental perturbation time series data;

[0114] When establishing a time series analysis model based on temperature fluctuation characteristic data and water content mutation characteristic data in an embodiment of the present invention, first, the temperature fluctuation data and the water content mutation data are combined into a bivariate time series. Then, the autoregressive moving average method (ARMA) is used for model fitting, setting the autoregressive order to 2 and the moving average order to 2 to ensure the stability and accuracy of the model. The least squares method is used to estimate the model parameters, and the optimal model is selected through AIC (Akaike Information Criterion). This model is used to evaluate the random variation laws of temperature and water content under environmental disturbances, and disturbance simulation is carried out through the fitted model to obtain disturbance prediction data for future periods. In the agricultural greenhouse environment, this model can be used to predict the temperature and water content fluctuations within the next week, so as to optimize the regulation of the irrigation system.

[0115] Step S414: Perform a normality test on the environmental disturbance time series data, and judge the skewness and kurtosis characteristics of the data distribution through the Shapiro-Wilk test method, so as to obtain the data distribution characteristic data;

[0116] When performing a normality test on the environmental disturbance time series data in an embodiment of the present invention, first, the Shapiro-Wilk test is performed on the collected temperature and water content disturbance data to judge the skewness and kurtosis characteristics of the data. The specific method is to calculate the Shapiro-Wilk statistic and the corresponding p-value of the data. When the p-value is less than 0.05, it indicates that the data does not conform to the normal distribution, and further data transformation or non-parametric methods need to be used. The test results will provide information about the data distribution type to guide subsequent modeling and analysis. In the greenhouse temperature fluctuation data, if the test results show that the temperature fluctuation data presents obvious positive skewness, logarithmic transformation or other appropriate transformation methods may need to be adopted to make the data close to the normal distribution.

[0117] Step S415: Perform kernel density estimation based on the data distribution characteristic data, construct the probability density function of temperature fluctuation, so as to obtain the temperature fluctuation probability distribution data;

[0118] When performing kernel density estimation based on the data distribution characteristic data in an embodiment of the present invention, first, a suitable distribution type is selected according to the results of the Shapiro-Wilk test, and kernel density estimation is performed on the temperature fluctuation data. The commonly used Gaussian kernel function is selected, and the bandwidth parameter is set to 0.5. Kernel density estimation is used to calculate the probability density function (PDF) of temperature fluctuation, generate a continuous probability distribution curve, and reflect the occurrence probability within different temperature fluctuation ranges. In specific applications, for example, in the greenhouse environment, kernel density estimation may reveal that the temperature fluctuations are mainly concentrated in the range of 10-20 °C, and the maximum occurrence probability is 70%. This information can help predict the intensity and frequency of future temperature fluctuations and provide data support for the regulation of the temperature control system.

[0119] Step S416: Construct a probability density function for the moisture content mutation based on the data distribution characteristic data, and conduct an analysis of the asymmetry characteristics of the mutation process, so as to obtain the moisture content mutation probability distribution data;

[0120] When constructing the probability density function for the moisture content mutation according to the data distribution characteristic data in the embodiment of the present invention, first perform a clustering analysis on the data of the moisture content mutation event to determine the amplitude range and occurrence frequency of the mutation event. Then, use the kernel density estimation method to construct the probability density function (PDF) of the moisture content mutation, and study the asymmetry characteristics of the mutation process by analyzing the skewness and kurtosis of the data, especially the distribution of the mutation amplitude. This analysis helps to understand the distribution characteristics of the mutation event. For example, the mutation amplitude is usually greater than 10%, and most mutations occur in the interval of 30-40% of the moisture content. In applications, these mutation data help to predict the mutation changes of moisture, so as to optimize the irrigation strategy and avoid excessive water loss.

[0121] Step S417: Establish a Langevin dynamics model in the form of a stochastic differential equation based on the temperature fluctuation probability distribution data and the moisture content mutation probability distribution data, and conduct a comparative analysis of the evolution characteristics of the system under double environmental perturbations, so as to obtain the environmental perturbation dynamics model.

[0122] When establishing the Langevin dynamics model in the form of a stochastic differential equation based on the temperature fluctuation probability distribution data and the moisture content mutation probability distribution data in the embodiment of the present invention, first use the probability density functions of temperature fluctuation and moisture content mutation as input data, and combine thermodynamics and seepage theory to construct a stochastic differential equation describing the evolution of the system under double environmental perturbations. This equation describes the stochastic change of the system state and the coupling effect of environmental perturbations. By solving this Langevin equation numerically, the evolution characteristics of the system under environmental perturbations are obtained, such as the influence of temperature and moisture content fluctuations on the soil moisture dynamics. In the agricultural field, this model can help predict the influence of temperature and moisture perturbations on the change of soil humidity, and then guide the precise irrigation decision-making to ensure the stability of the crop growth environment.

[0123] The present invention analyzes the temperature field information in the non-equilibrium entropy evolution characteristic data through wavelet transform, can identify the main frequency components and amplitude characteristics of temperature fluctuations, and thus obtains the temperature fluctuation characteristic data. This process not only captures the dynamic characteristics of temperature changes, but also provides key inputs for subsequent environmental perturbation analysis. The high-resolution characteristic of wavelet transform enables the model to accurately identify the details of temperature fluctuations, providing an important basis for understanding the response of the slope system in the thermal environment. Conduct mutation point detection based on wavelet singular value decomposition for the water content information, determine the time position and amplitude size of the water content mutation, and obtain the water content mutation characteristic data. This analysis process can accurately locate the mutation events of the water content and reveal its impact on slope stability. The acquisition of the water content mutation characteristic data provides a quantitative description of the hydrological environment change for the model, further enriching the characterization of environmental factors. Based on the temperature fluctuation and water content mutation characteristic data, establish a time series analysis model, and use the autoregressive moving average method to evaluate the stochastic change law of environmental factors, obtaining the environmental perturbation time series data. This process quantifies the dynamic change law of environmental factors through time series analysis, providing data support for subsequent probability modeling. The application of the autoregressive moving average method enables the model to effectively capture the randomness and regularity of environmental perturbations, improving the adaptability of the model to environmental changes. Conduct a normality test on the environmental perturbation time series data, judge the skewness and kurtosis characteristics of the data distribution through the Shapiro-Wilk test method, and obtain the data distribution characteristic data. This test process provides a scientific basis for the subsequent construction of the probability density function, ensuring that the description of the model for environmental perturbations is closer to the actual situation. Based on the data distribution characteristic data, conduct kernel density estimation, construct the probability density function of temperature fluctuations, and obtain the temperature fluctuation probability distribution data. This process not only quantifies the uncertainty of temperature fluctuations, but also provides a probability description of temperature changes for the model, further improving the characterization ability of the model for thermal environment perturbations. Construct the probability density function for the water content mutation, and analyze the asymmetry characteristics of the mutation process, obtaining the water content mutation probability distribution data. This analysis process provides the probability characteristics of water content changes for the model, enabling the model to more accurately describe the mutation impact of the hydrological environment. Based on the probability distribution data of temperature fluctuations and water content mutations, establish a Langevin dynamics model in the form of a stochastic differential equation, and conduct a comparative analysis of the evolution characteristics of the system under dual environmental perturbations, obtaining the environmental perturbation dynamics model. This process incorporates the random perturbations of temperature and water content into a unified framework through the construction of a stochastic dynamics model, providing a comprehensive and accurate description of the dynamic evolution of the slope system. The application of the Langevin dynamics model enables the model to more accurately predict the behavior of the slope under complex environmental perturbations, providing strong support for settlement deformation prediction and stability assessment.

[0124] Preferably, step S5 includes the following steps:

[0125] Step S51: Establish the system entropy production rate functional based on the predicted data of interface kinetic evolution, derive the Euler equation of local minimum entropy production rate using the variational method, and discretize the equation to obtain the discrete entropy production rate equations;

[0126] When establishing the system entropy production rate functional according to the predicted data of interface kinetic evolution in the embodiments of the present invention, first, calculate the total entropy generation of the system using the kinetic evolution data of the slope system. Set the entropy production rate functional of the system as a function of system state variables (such as temperature, pressure, and moisture content, etc.) and their rates of change. By deriving the thermodynamic equations of the system, obtain the relationship between the entropy production rate and local state variables. Next, use the variational method to derive the Euler equation for minimizing the entropy production rate. In actual operation, assume that temperature and moisture content are the main state variables, and assume that the entropy production rate functional includes the coupling effects of heat conduction, seepage, and mechanical deformation. By introducing Lagrange multipliers, transform the Euler equation into an optimization problem to obtain the optimal entropy production rate of the system under the coupling of different physical fields. Discretize the Euler equation to transform the continuous control equation into a discrete equation set for solving on a computer. In slope engineering, the system state variables may include the temperature field, stress field, and moisture field of the soil, etc.

[0127] Step S52: Solve the discrete entropy production rate equations as a non - linear equation set using the Newton - Raphson iteration method, and set the convergence criterion to obtain the numerical solution of the local entropy production rate;

[0128] When solving the discrete entropy production rate equations as a non - linear equation set using the Newton - Raphson iteration method in the embodiments of the present invention, first, transform the discrete entropy production rate equations obtained in Step S51 into the standard form of a non - linear equation set. Then, use the Newton - Raphson iteration method to solve these equations. This method starts from an initial guess value and iteratively updates the approximate value of the solution until the set convergence criterion is met. Specifically, set the convergence criterion as the relative change of the solution being less than 1e - 6 and the residual of the equation being less than 1e - 8. In each iteration, update the solution by calculating the Jacobian matrix (i.e., the derivative matrix of the entropy production rate equation). For the slope system, the initial guess value can be based on prior experience or the known initial state of the slope, and calculate iteratively until the final stable numerical solution of the local entropy production rate is obtained. During this process, the input physical parameters include the permeability, viscosity, temperature change rate of the soil, etc.

[0129] Step S53: Evaluate the stability characteristics of the local equilibrium state of the system for the numerical solution of the local entropy production rate to obtain the local equilibrium state characteristic data;

[0130] When the embodiment of the present invention evaluates the stability characteristics of the local equilibrium state of the numerical solution of the local entropy production rate, first, based on the obtained numerical solution of the local entropy production rate, it is evaluated whether the local equilibrium state of the system is stable. This can be achieved by calculating the time derivative of the entropy change rate (or entropy production rate) of the system in the current state. If the entropy production rate is less than 0, the system is in a stable equilibrium state; if it is greater than 0, the system is in a non-equilibrium state and needs to be further adjusted. To evaluate the stability of the equilibrium state, the eigenvalues of the system (such as Lyapunov exponents or eigenvectors) are calculated, and the stability of the system is judged according to the signs of the eigenvalues. In specific applications, such as the water-soil coupling system of a slope, the equilibrium stability can be evaluated by examining the changes in the entropy production rate at different temperatures and humidities. If there are large non-equilibrium entropy changes in the system, it may mean that there is a potential risk of instability in the slope system, and intervention measures need to be taken.

[0131] Step S54: Based on the local equilibrium state characteristic data, establish a pre-obtained generalized Kelvin creep constitutive model for the reinforcing material, and describe the evolution law of the creep strain with time by the piecewise linear approximation method, so as to obtain the creep characteristic data;

[0132] When the embodiment of the present invention establishes a pre-obtained generalized Kelvin creep constitutive model for the reinforcing material based on the local equilibrium state characteristic data, first, according to the local equilibrium state characteristic data, a suitable creep constitutive model is selected. Usually, the generalized Kelvin model is used to describe the creep behavior of the material. In actual operation, it is assumed that the stress-strain relationship of the reinforcing material can be modeled by a combination of a series of elastic and viscous elements. According to the experimental data and on-site monitoring data, the creep strain data at different stress levels are obtained, and then the change law of the creep strain with time is decomposed into several segments by the piecewise linear approximation method, and the linear regression method is used to fit each segment of data, so as to obtain the creep strain rate of each segment. For the application scenario of reinforced soil, the experimental data can be used to identify the parameters of the creep constitutive model of different types of reinforcing materials (such as steel bars, geogrids, etc.), and the creep characteristic data can be obtained, such as the stress-strain behavior of the material at the initial stage of loading and the change trend of the creep strain rate with time.

[0133] Step S55: Use the creep characteristic data to correct the stress state of the slope system, so as to obtain the stress balance correction data; according to the stress balance correction data, perform mesh division and stress-strain calculation through the isoparametric element technology, so as to obtain the stress-strain distribution data;

[0134] When the stress state of the slope system is corrected using creep characteristic data in the embodiments of the present invention, first, the mechanical response of the reinforcing materials in the slope system is corrected in combination with the creep characteristic data. It is assumed that creep deformation will occur in the reinforced soil mass of the slope during long-term loading, which affects the stress distribution of the slope. By introducing the creep strain characteristic data into the stress calculation model, the original stress state is adjusted. Specifically, the finite element analysis method can be used, with the creep strain data as the input, to correct the original stress distribution. Through the isoparametric element technique, mesh division is carried out according to the stress balance correction data, and the stress and strain in each element are calculated. In slope engineering, assuming that the slope has a slope of 10 - 30° and there is reinforced soil mass, the corrected stress and strain distribution can help evaluate the influence of the reinforcement effect on slope stability. Finally, the corrected stress and strain distribution data are obtained, providing a basis for subsequent settlement prediction and stability analysis.

[0135] Step S56: Perform adaptive numerical integration on the displacement field of the key control section of the slope according to the stress and strain distribution data, so as to obtain the long-term settlement deformation prediction data of the slope, where the key control section of the slope is specifically evaluated by the strain energy density criterion.

[0136] When the embodiments of the present invention perform adaptive numerical integration on the displacement field of the key control section of the slope according to the stress and strain distribution data, first, based on the stress and strain distribution data obtained in step S55, the most critical control section in the slope is determined. This section is usually the area where stress concentration and large deformation occur in the soil mass, and is evaluated by the strain energy density criterion. A typical section is selected, and adaptive numerical integration is carried out according to its stress and strain distribution to analyze its displacement evolution process. Specifically, the adaptive step size method is adopted, the time is divided into multiple stages, and the integration step size is adjusted according to the stress and strain states within each stage, so as to obtain the evolution data of the displacement field. In slope stability analysis, it is assumed that the key control section is located at the lower or upper part of the slope, and adaptive integration is carried out, and finally the displacement prediction data of this section at different time steps are obtained. Through this process, the long-term settlement deformation prediction data of the slope are obtained, providing an important basis for subsequent slope monitoring and stability evaluation.

[0137] The present invention establishes a functional of the system entropy production rate through the prediction data of the interfacial kinetic evolution, and derives the Euler equation of the local minimum entropy production rate by using the variational method. This process combines the dynamic evolution of the system with the entropy theory, and obtains a discrete equation set of the entropy production rate through discretization, providing a theoretical basis for subsequent numerical solutions. This analysis method based on the entropy theory can reveal the stability characteristics of the system from a thermodynamic perspective, providing a more comprehensive understanding of the dynamic behavior of the slope system. The Newton-Raphson iteration method is used to perform nonlinear solution on the discrete equation set of the entropy production rate, and a convergence criterion is set to ensure the accuracy and reliability of the solution. Through this process, a numerical solution of the local entropy production rate is obtained, providing key data for evaluating the stability of the local equilibrium state of the system. The evaluation of the stability characteristics of the local equilibrium state further clarifies the dynamic behavior of the slope system in different regions, providing an important reference basis for subsequent mechanical analysis. Based on the characteristic data of the local equilibrium state, a generalized Kelvin creep constitutive model of the reinforced material is established, and the evolution law of the creep strain with time is described by the piecewise linear approximation method. This process fully considers the long-term mechanical behavior of the reinforced material, enabling the model to more accurately reflect the stress-strain characteristics of the slope system under creep conditions. The acquisition of the creep characteristic data provides a scientific basis for stress state correction, further enhancing the prediction ability of the model for the long-term behavior of the slope system. The stress state of the slope system is corrected by using the creep characteristic data, and mesh division and stress-strain calculation are performed through the isoparametric element technique. This process not only corrects the stress state to adapt to the creep effect, but also obtains stress-strain distribution data through numerical calculation, providing a detailed description of the overall mechanical behavior of the slope system. The application of the isoparametric element technique ensures the efficiency and accuracy of the calculation, enabling the model to more precisely reflect the mechanical characteristics of the slope system. According to the stress-strain distribution data, an adaptive numerical integration is performed on the displacement field of the key control section of the slope to obtain the prediction data of the long-term settlement deformation of the slope. The selection of the key control section is based on the strain energy density criterion, which can effectively identify the key areas in the slope system and ensure the reliability and pertinence of the prediction results. Through this series of steps, the model can not only accurately predict the long-term settlement deformation of the slope, but also provide strong technical support for the design and stability monitoring of slope engineering.

[0138] The present invention also provides a settlement deformation prediction system for a reinforced soil slope building structure, which is used to execute the settlement deformation prediction method for the reinforced soil slope building structure described above. The settlement deformation prediction system for the reinforced soil slope building structure includes:

[0139] An initial feature acquisition module, configured to acquire the position parameter of the soil infiltration line of the reinforced soil slope, the interface friction coefficient between the reinforced material and the soil, the soil particle size distribution curve, and the environmental temperature and humidity duration information, so as to obtain the initial feature data of the slope system state;

[0140] The multi-field coupling monitoring module is used to construct a thermal-mechanical-hydraulic multi-field coupling monitoring network in the soil-reinforced material interface area according to the initial characteristic data of the slope system state, and obtain the interface shear stress, interface normal strain, pore water pressure gradient and temperature gradient of each monitoring point to obtain the interface action characteristic data;

[0141] The energy dissipation analysis module is used to calculate the interface shear power consumption rate, heat conduction power and moisture migration power of the interface action characteristic data, and conduct a non-equilibrium statistical analysis of the influence of interface roughness on energy dissipation to obtain the non-equilibrium entropy evolution characteristic data;

[0142] The environmental disturbance modeling module is used to construct an environmental disturbance probability density model based on temperature fluctuations and water content mutations according to the non-equilibrium entropy evolution characteristic data, and conduct a system fluctuation characteristic analysis to obtain the interface dynamics evolution prediction data including the critical drift coefficient and the nonlinear diffusion coefficient;

[0143] The settlement prediction correction module is used to iteratively solve the local minimum entropy production rate of the interface dynamics evolution prediction data, and conduct stress balance correction based on the creep effect of the reinforced material to obtain the stress and strain distribution data of the entire slope; perform adaptive numerical integration on the displacement field of the key control section of the slope according to the stress and strain distribution data to obtain the long-term settlement deformation prediction data of the slope.

[0144] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to cover all changes within the meaning and scope of the equivalent elements of the application documents within the present invention.

[0145] The above are only the specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for predicting the settlement deformation of a reinforced soil slope building structure, characterized in that: The following steps are involved: Step S1: obtaining the soil infiltration line position parameters of the reinforced soil slope, the interface friction coefficient between the reinforcement material and the soil, the soil particle gradation curve, and the environmental temperature and humidity history information, and obtaining the initial characteristic data of the slope system state; Step S2: construct a thermal-mechanical-water multi-field coupling monitoring network in the soil-reinforcement material interface area according to the initial characteristic data of the slope system state, and obtain the interface shear stress, interface normal strain, pore water pressure gradient and temperature gradient of each monitoring point to obtain the interface action characteristic data; Step S3: Calculate the interface shear power consumption rate, heat conduction power and water migration power of the interface action characteristic data, and perform non-equilibrium statistical analysis based on the effect of interface roughness on energy dissipation to obtain non-equilibrium entropy evolution characteristic data; Step S4: constructing an environmental disturbance probability density model based on temperature fluctuation and water content mutation according to the non-equilibrium entropy evolution characteristic data, and performing system fluctuation characteristic analysis to obtain interface dynamics evolution prediction data including critical drift coefficient and nonlinear diffusion coefficient; Step S5: Iteratively solve the local minimum entropy production rate of the interface dynamic evolution prediction data, and perform stress balance correction based on the creep effect of the reinforced material to obtain the stress-strain distribution data of the entire slope; perform adaptive numerical integration of the displacement field of the key control section of the slope based on the stress-strain distribution data to obtain the long-term settlement and deformation prediction data of the slope.

2. The settlement deformation prediction method of reinforced soil slope building structure according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: acquiring 72 hours of continuous monitoring data of the soil infiltration line position sensor, and performing segment processing and statistical analysis on the data at 6-hour intervals, thereby obtaining soil infiltration line position parameters; Step S12: performing a soil-reinforcement material interface shear test using a direct shear apparatus according to the soil infiltration line position parameters, thereby obtaining shear test data; Step S13: fitting the stress-displacement curve of the shear test data to obtain the evolution data of the interface friction coefficient as the moisture content changes; Step S14: sampling the slope soil, obtaining the particle weight distribution of soil samples at different depths through a standard screening test, and performing a Fuller gradation curve comparison analysis to obtain soil particle gradation characteristic curve data; Step S15: collecting historical temperature and relative humidity monitoring information through the weather station and performing time series analysis to obtain environmental temperature and humidity data over time; Step S16: formatting the soil infiltration line position parameters, interface friction coefficient evolution data, soil particle gradation characteristic curve data and ambient temperature and humidity history data, and establishing a coupling matrix between the parameters, thereby obtaining the initial characteristic data of the slope system state.

3. The settlement deformation prediction method of reinforced soil slope building structure according to claim 2 is characterized in that: Step S2 includes the following steps: Step S21: determining the stress concentration area of ​​the soil-reinforcement material interface according to the initial characteristic data of the slope system state, and optimizing the layout of the monitoring points using the layered grid encryption technology, thereby obtaining the spatial layout data of the multi-field coupling monitoring network; Step S22: performing system time synchronization calibration processing on the sensor of each monitoring point according to the spatial layout data, thereby obtaining calibration parameter data of the monitoring equipment; Step S23: Continuously monitor the interface area in real time according to the calibration parameter data of the monitoring equipment, and use the sliding time window method to reduce noise and remove outliers on the original signal, so as to obtain the time series evolution data of the interface shear stress and the interface normal strain; Step S24: Calculate the spatial gradient distribution of the pore water pressure field and the temperature field based on the finite difference method through the time series evolution data of the interface shear stress and the interface normal strain, and perform interpolation fitting between the monitoring points to obtain field quantity distribution data; Step S25: Perform multi-field coupling effect analysis on the time series evolution data and field quantity distribution data, establish an interaction relationship matrix between physical quantities, and thus obtain interface action characteristic data.

4. The settlement deformation prediction method of reinforced soil slope building structure according to claim 3 is characterized in that: Step S23 includes the following steps: Step S231: configuring the sampling frequency and measuring range of the data acquisition system according to the calibration parameter data of the monitoring equipment, and establishing a real-time data transmission channel, thereby obtaining the original monitoring signal data; Step S232: using the original monitoring signal data to set the sliding time window length based on the signal-to-noise ratio analysis, extracting the statistical features of the data in the window, thereby obtaining signal feature sequence data; Step S233: filtering out high-frequency noise from the signal feature sequence data and marking and removing data points that deviate from the mean by more than three times the standard deviation as outliers based on the 3σ criterion, thereby obtaining signal anomaly correction data; Step S234: reconstruct the interface shear stress and interface normal strain in the time domain according to the signal anomaly correction data, and establish a continuous change curve through interpolation and smoothing processing, so as to obtain time series evolution data.

5. The settlement deformation prediction method of reinforced soil slope building structure according to claim 4 is characterized in that: Step S25 includes the following steps: Step S251: performing correlation analysis on the interface shear stress and the interface normal strain in the time-series evolution data, determining the time-lag characteristics of the stress-strain response, and establishing a stress-strain coupling matrix of the time-lag effect, thereby obtaining mechanical coupling characteristic data; Step S252: using the pore water pressure gradient and temperature gradient in the field quantity distribution data to perform a heat-water coupling analysis, and evaluating the heat-water coupling coefficient based on the thermal seepage theory to describe the effect of the temperature field on the pore water pressure distribution, thereby obtaining heat-water coupling characteristic data; Step S253: constructing a three-field coupling equation group describing the interaction between the interface shear stress and the pore water pressure and temperature according to the mechanical coupling characteristic data and the thermal-water coupling characteristic data, and numerically solving it using the Petrov-Galerkin method to obtain the multi-field coupling effect evolution data; Step S254: extracting features based on principal component analysis on the multi-field coupling effect evolution data, determining the dominant and secondary action relationships between the physical fields, and establishing a coupling action intensity matrix taking into account weight coefficients, thereby obtaining coupling action level data; Step S255: Construct an interaction relationship matrix including mechanical field, hydraulic field and temperature field according to the coupling hierarchical data, normalize the matrix elements, and use the tensor analysis method to determine the coupling intensity and direction between the physical quantities, thereby generating interface action characteristic data.

6. The settlement deformation prediction method of reinforced soil slope building structure according to claim 5 is characterized in that: Step S3 includes the following steps: Step S31: establishing an energy decomposition model that takes into account interface friction work and interface deformation work according to the interface action characteristic data, and quantitatively characterizing the energy conversion mechanism in the shearing process, thereby obtaining interface shearing energy consumption characteristic data; Step S32: Calculate the heat conduction power of the interface region based on Fourier's heat conduction law using the temperature gradient information in the interface action characteristic data, and establish a heat conduction energy balance equation based on the heat accumulation and dissipation effects caused by temperature fluctuations, thereby obtaining heat conduction energy consumption characteristic data; Step S33: Calculate the water migration power in the interface region based on Darcy's seepage theory according to the pore water pressure gradient information in the interface action characteristic data, and establish a water migration energy balance equation according to the coupling effect of capillary action and osmotic action, thereby obtaining water migration energy consumption characteristic data; Step S34: establishing an energy dissipation correction model based on interface roughness through interface shear energy consumption characteristic data, heat conduction energy consumption characteristic data and water migration energy consumption characteristic data, thereby obtaining corrected energy consumption characteristic data including the influence of interface morphology characteristics on energy conversion efficiency; Step S35: performing non-equilibrium statistical mechanics analysis using the corrected energy consumption characteristic data, thereby obtaining entropy generation characteristic data, wherein the non-equilibrium statistical mechanics analysis specifically includes establishing an entropy generation rate equation that describes the degree to which the system is far from the equilibrium state, and using the Fokker-Planck equation to probabilistically describe the fluctuation characteristics of the system; Step S36: construct the non-equilibrium entropy evolution equation of the system according to the entropy generation characteristic data, and quantitatively evaluate the stability and evolution trend of the system by solving the change law of the entropy generation rate over time, so as to obtain the non-equilibrium entropy evolution characteristic data.

7. The settlement deformation prediction method of reinforced soil slope building structure according to claim 6 is characterized in that: Step S4 includes the following steps: Step S41: constructing an environmental disturbance probability density model based on temperature fluctuation and water content mutation according to the non-equilibrium entropy evolution characteristic data, thereby obtaining an environmental disturbance dynamics model; Step S42: solving the CK equation for the environmental disturbance dynamics model, and calculating the critical drift coefficient of the system by a numerical iteration method, thereby obtaining system drift characteristic data; Step S43: solving the nonlinear diffusion equation using a variational iteration method based on the system drift characteristic data, and obtaining the nonlinear diffusion coefficient by minimizing the system free energy functional, thereby obtaining the system diffusion characteristic data; Step S44: performing coupling analysis based on the system drift characteristic data and the system diffusion characteristic data, and establishing a group of kinetic equations including a critical drift coefficient and a nonlinear diffusion coefficient, and numerically solving them using the fourth-order Runge-Kutta method to obtain interface kinetic evolution prediction data.

8. The settlement deformation prediction method of reinforced soil slope building structure according to claim 7 is characterized in that: Step S41 includes the following steps: Step S411: performing wavelet transform analysis on the temperature field information in the non-equilibrium entropy evolution characteristic data to identify the main frequency components and amplitude characteristics of the temperature fluctuation, thereby obtaining temperature fluctuation characteristic data; Step S412: performing mutation point detection based on wavelet singular value decomposition on the water content information in the non-equilibrium entropy evolution characteristic data, and determining the time position and amplitude of the water content mutation, thereby obtaining water content mutation characteristic data; Step S413: establishing a time series analysis model based on the temperature fluctuation characteristic data and the water content mutation characteristic data, and using the autoregressive moving average method to evaluate the random change law of environmental factors, thereby obtaining environmental disturbance time series data; Step S414: Perform a normality test on the environmental disturbance time series data, and determine the skewness and peak characteristics of the data distribution by using the Shapiro-Wilk test method, thereby obtaining data distribution characteristic data; Step S415: performing kernel density estimation based on the data distribution characteristic data, constructing a probability density function of temperature fluctuation, and thus obtaining temperature fluctuation probability distribution data; Step S416: constructing a probability density function for the water content mutation according to the data distribution characteristic data, and performing an asymmetric characteristic analysis of the mutation process, thereby obtaining the water content mutation probability distribution data; Step S417: A Langevin kinetic model in the form of a stochastic differential equation is established based on the temperature fluctuation probability distribution data and the water content mutation probability distribution data, and a comparative analysis of the evolution characteristics of the system under dual environmental disturbances is performed to obtain an environmental disturbance kinetic model.

9. The settlement deformation prediction method of reinforced soil slope building structure according to claim 8 is characterized in that: Step S5 includes the following steps: Step S51: establishing a system entropy generation rate functional according to the interface dynamics evolution prediction data, deriving the Euler equation of the local minimum entropy generation rate by using the variational method, and discretizing the equation to obtain a discrete equation group of entropy generation rate; Step S52: using the Newton-Raphson iteration method to solve the entropy generation rate discrete equation group for nonlinear equation group, and setting a convergence criterion, so as to obtain a local entropy generation rate numerical solution; Step S53: evaluating the stability characteristics of the local equilibrium state of the system on the numerical solution of the local entropy generation rate, thereby obtaining local equilibrium state characteristic data; Step S54: establishing a pre-acquired generalized Kelvin creep constitutive model of the reinforcement material based on the local equilibrium state characteristic data, and describing the evolution law of the creep strain with time by a piecewise linear approximation method, thereby obtaining creep characteristic data; Step S55: using creep characteristic data to correct the stress state of the slope system, thereby obtaining stress balance correction data; performing meshing and stress-strain calculation according to the stress balance correction data by isoparametric unit technology, thereby obtaining stress-strain distribution data; Step S56: Adaptively numerically integrate the displacement field of the key control section of the slope according to the stress-strain distribution data, so as to obtain the long-term settlement deformation prediction data of the slope, wherein the key control section of the slope is specifically evaluated by the strain energy density criterion.

10. A settlement deformation prediction system for reinforced soil slope building structure, characterized in that: Used to execute the settlement deformation prediction method of the reinforced soil slope building structure according to claim 1, the settlement deformation prediction system of the reinforced soil slope building structure comprises: The initial feature acquisition module is used to obtain the soil infiltration line position parameters of the reinforced soil slope, the interface friction coefficient between the reinforcement material and the soil, the soil particle grading curve, and the environmental temperature and humidity history information, and obtain the initial characteristic data of the slope system state; The multi-field coupling monitoring module is used to construct a thermal-mechanical-water multi-field coupling monitoring network in the soil-reinforcement material interface area according to the initial characteristic data of the slope system state, and obtain the interface shear stress, interface normal strain, pore water pressure gradient and temperature gradient of each monitoring point to obtain the interface action characteristic data; Energy dissipation analysis module, used to calculate the interface shear power consumption rate, heat conduction power and moisture migration power of the interface action characteristic data, and to perform non-equilibrium statistical analysis based on the effect of interface roughness on energy dissipation, to obtain non-equilibrium entropy evolution characteristic data; Environmental perturbation modeling module, which is used to construct an environmental perturbation probability density model based on temperature fluctuation and water content mutation according to the non-equilibrium entropy evolution characteristic data, and to analyze the system fluctuation characteristics to obtain interface dynamics evolution prediction data including critical drift coefficient and nonlinear diffusion coefficient; The settlement prediction and correction module is used to iteratively solve the local minimum entropy production rate of the interface dynamic evolution prediction data, and perform stress balance correction based on the creep effect of the reinforced material to obtain the stress-strain distribution data of the entire slope; based on the stress-strain distribution data, the displacement field of the key control section of the slope is adaptively numerically integrated to obtain the long-term settlement and deformation prediction data of the slope.

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