A real-time monitoring and early warning method and system for mountain engineering slopes based on digital twins

Abstract

The present invention relates to the field of information data processing, and in particular to a real-time monitoring and early warning method and system for mountain engineering slopes based on digital twins. The method comprises the following steps: obtaining original real-time slope monitoring data and performing standardization processing to obtain standardized real-time slope monitoring data; constructing a digital twin model using the standardized real-time slope monitoring data to obtain a preliminary digital twin model of the slope; and dynamically updating the preliminary digital twin model of the slope using the standardized real-time slope monitoring data and historical multi-source monitoring data of the slope to obtain a real-time updated digital twin model of the slope. By updating the digital twin model in real time, the present invention combines multi-physics field coupling analysis with nonlinear stability prediction to ensure that dynamic monitoring, stability assessment, risk prediction, and emergency response of the slope can promptly reflect the actual situation, thereby improving the scientific nature and timeliness of disaster prevention and control.

Description

Technical Field

[0001] The present invention relates to the field of information data processing technology, and in particular to a real-time monitoring and early warning method and system for mountain engineering slopes based on digital twins. Background Art

[0002] Many existing slope monitoring systems rely on modeling methods based on static data. These models lack the ability to dynamically update real-time monitoring data, making it difficult to reflect real-time changes in slope conditions. Once established, static models tend to deviate from reality over the long term, especially when slopes undergo dynamic adjustments due to changing geological conditions, rainfall, or human activity. This deviation can lead to serious misjudgments, thus compromising the reliability and timeliness of decision-making. Existing slope monitoring systems often lack the ability to coordinate with emergency response measures, making it impossible to achieve real-time linkage between early warning information and emergency decision-making. Once a slope anomaly occurs, existing systems struggle to quickly generate effective emergency response plans, severely impacting the timeliness and scientific nature of disaster prevention and control. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide a real-time monitoring and early warning method and system for mountain engineering slopes based on digital twins to solve at least one of the above technical problems.

[0004] To achieve the above objectives, a real-time monitoring and early warning method for mountain engineering slopes based on digital twins is proposed, which includes the following steps:

[0005] Step S1: Obtain original real-time slope monitoring data and perform standardization processing to obtain standardized real-time slope monitoring data; use the standardized real-time slope monitoring data to construct a digital twin model to obtain a preliminary digital twin model of the slope;

[0006] Step S2: Obtain historical multi-source monitoring data of the slope, and dynamically update the preliminary slope digital twin model using the standardized real-time monitoring data of the slope and the historical multi-source monitoring data of the slope to obtain a real-time updated slope digital twin model;

[0007] Step S3: Perform multi-physics field coupling analysis on the real-time updated slope digital twin model, and perform slope stability assessment based on the multi-physics field coupling analysis results to obtain slope stability analysis data;

[0008] Step S4: performing nonlinear analysis on the slope stability analysis data, and using the nonlinear analysis results to predict slope stability, thereby obtaining slope stability prediction data;

[0009] Step S5: performing slope risk assessment based on the slope stability prediction data, and performing risk warning according to the risk assessment results to obtain slope risk warning information data;

[0010] Step S6: Emergency response measures are formulated based on the slope risk warning information data, and the slope risk warning information data and emergency response measures are updated in real time using the real-time updated slope digital twin model to obtain real-time updated emergency response measures data.

[0011] By acquiring the original real-time monitoring data of the slope and performing standardized processing, the present invention can ensure the uniformity and comparability of the data, providing an accurate basis for the subsequent construction of the digital twin model. After establishing the preliminary digital twin model of the slope, the model is dynamically updated by combining the standardized real-time monitoring data with the historical multi-source monitoring data of the slope, thereby ensuring that the digital twin model can reflect the current actual condition of the slope and avoiding the deviations in the long-term operation of the traditional static model. This real-time update capability improves the reliability and timeliness of the model and can reflect in real time the dynamic adjustments of the slope caused by geological changes, rainfall or human activities. Multi-physics field coupling analysis plays a key role in slope stability assessment. It can comprehensively consider multiple influencing factors and provide a basis for accurately assessing the stability of the slope. Further stability prediction is performed through nonlinear analysis, which can identify the potential instability risk of the slope and issue an early warning. On this basis, slope risk assessment is carried out and risk warnings are triggered, so that the risk management system can respond in time to prevent disasters. Ultimately, emergency response measures are formulated based on risk warning information, and warning and emergency measures are dynamically adjusted through real-time updated digital twin models to ensure that emergency responses in emergency situations can closely match actual conditions, thereby improving the scientific nature and timeliness of disaster prevention and control.

[0012] Preferably, the present invention further provides a real-time monitoring and early warning system for mountain engineering slopes based on digital twins, which is used to execute the above-mentioned real-time monitoring and early warning method for mountain engineering slopes based on digital twins. The real-time monitoring and early warning system for mountain engineering slopes based on digital twins includes:

[0013] The slope real-time monitoring and digital twin initial construction module is used to obtain the original slope real-time monitoring data and perform standardization processing to obtain standardized slope real-time monitoring data; the standardized slope real-time monitoring data is used to construct a digital twin model to obtain a preliminary slope digital twin model;

[0014] The slope historical data fusion and model dynamic update module is used to obtain historical multi-source monitoring data of the slope, and dynamically update the preliminary slope digital twin model using the standardized real-time monitoring data of the slope and the historical multi-source monitoring data of the slope to obtain a real-time updated slope digital twin model;

[0015] The slope multi-physics coupling analysis and stability assessment module is used to perform multi-physics coupling analysis on the real-time updated slope digital twin model, and to assess slope stability based on the multi-physics coupling analysis results to obtain slope stability analysis data;

[0016] The slope nonlinear analysis and stability prediction module is used to perform nonlinear analysis on the slope stability analysis data and use the nonlinear analysis results to predict the slope stability and obtain the slope stability prediction data;

[0017] The slope risk assessment and early warning module is used to conduct slope risk assessment based on slope stability prediction data, and to issue risk early warnings based on the risk assessment results to obtain slope risk early warning information data;

[0018] The emergency response measures formulation and dynamic update module is used to formulate emergency response measures based on slope risk warning information data, and use the real-time updated slope digital twin model to update the slope risk warning information data and emergency response measures in real time to obtain real-time updated emergency response measures data.

[0019] The present invention acquires and standardizes real-time slope monitoring data to construct a preliminary digital twin model of the slope, providing an accurate data basis for subsequent slope status analysis and prediction. The digital twin model is dynamically updated by combining historical data with real-time monitoring data to ensure that the model always reflects the actual status of the slope, improving the accuracy and reliability of the prediction. A comprehensive assessment of the slope through multi-physics field coupling analysis can accurately analyze the stability of the slope and provide a scientific basis for emergency decision-making. Through nonlinear analysis and stability prediction, the instability trend of the slope can be identified, providing early warning for disaster prevention and mitigation. Risk assessment is carried out based on stability prediction data, and risk warnings are issued in a timely manner to ensure the efficiency and preventive nature of slope risk management. Emergency response measures are dynamically formulated and adjusted based on risk warning information to ensure real-time synchronization of emergency treatment plans with the actual risk status of the slope, thereby improving the flexibility and timeliness of emergency responses. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0021] Figure 1 This is a flowchart of the steps of the real-time monitoring and early warning method for mountain engineering slopes based on digital twins of the present invention;

[0022] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0023] Figure 3 for Figure 1Detailed step flow chart of step S2 in FIG. DETAILED DESCRIPTION

[0024] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0025] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0026] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0027] To achieve this, please refer to Figures 1 to 3 The present invention provides a real-time monitoring and early warning method for mountain engineering slopes based on digital twins, the method comprising the following steps:

[0028] Step S1: Obtain original real-time slope monitoring data and perform standardization processing to obtain standardized real-time slope monitoring data; use the standardized real-time slope monitoring data to construct a digital twin model to obtain a preliminary digital twin model of the slope;

[0029] Step S2: Obtain historical multi-source monitoring data of the slope, and dynamically update the preliminary slope digital twin model using the standardized real-time monitoring data of the slope and the historical multi-source monitoring data of the slope to obtain a real-time updated slope digital twin model;

[0030] Step S3: Perform multi-physics field coupling analysis on the real-time updated slope digital twin model, and perform slope stability assessment based on the multi-physics field coupling analysis results to obtain slope stability analysis data;

[0031] Step S4: performing nonlinear analysis on the slope stability analysis data, and using the nonlinear analysis results to predict slope stability, thereby obtaining slope stability prediction data;

[0032] Step S5: performing slope risk assessment based on the slope stability prediction data, and performing risk warning according to the risk assessment results to obtain slope risk warning information data;

[0033] Step S6: Emergency response measures are formulated based on the slope risk warning information data, and the slope risk warning information data and emergency response measures are updated in real time using the real-time updated slope digital twin model to obtain real-time updated emergency response measures data.

[0034] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a flow chart of a method for real-time monitoring and early warning of mountain engineering slopes based on digital twins according to the present invention. In this example, the method for real-time monitoring and early warning of mountain engineering slopes based on digital twins includes the following steps:

[0035] Step S1: Obtain original real-time slope monitoring data and perform standardization processing to obtain standardized real-time slope monitoring data; use the standardized real-time slope monitoring data to construct a digital twin model to obtain a preliminary digital twin model of the slope;

[0036] The embodiment of the present invention obtains real-time monitoring data of the original slope by deploying a high-precision sensor network on the engineering slopes in mountainous areas. The sensor network includes but is not limited to tilt sensors, displacement sensors, strain sensors, pore water pressure sensors and rainfall monitoring equipment. Each sensor is reasonably deployed according to the geometric characteristics of the slope, the distribution of potential risk areas and environmental conditions to ensure the comprehensiveness and accuracy of the monitoring data. The raw data collected by all sensors are transmitted to the data processing center through optical fiber communication or wireless communication technology. The data processing center uses a data cleaning algorithm to process the received raw monitoring data, including removing abnormal data, filling missing data and eliminating data noise. The cleaned data is then standardized, and the data range of each monitoring variable is unified to [0,1] through normalization to eliminate the influence of different dimensions and orders of magnitude on subsequent analysis, thus obtaining standardized real-time slope monitoring data. The standardized real-time slope monitoring data is used to construct a digital twin model. The specific implementation method is to establish a digital twin slope virtual model based on the slope's geometric shape, geological conditions and engineering characteristics, combined with the monitoring data. The slope structure is reconstructed using a three-dimensional modeling tool, and the virtual model is dynamically updated by real-time monitoring data, ultimately generating a preliminary digital twin model of the slope corresponding to the physical entity of the slope.

[0037] Step S2: Obtain historical multi-source monitoring data of the slope, and dynamically update the preliminary slope digital twin model using the standardized real-time monitoring data of the slope and the historical multi-source monitoring data of the slope to obtain a real-time updated slope digital twin model;

[0038] First, the embodiment of the present invention obtains historical multi-source monitoring data of the slope, which includes geological data, environmental data, engineering data and historical disaster records accumulated in slope monitoring over a long period of time, wherein the geological data includes the physical and mechanical parameters of the slope rock and soil, the environmental data includes regional meteorological information and groundwater level change information, and the engineering data includes the design parameters and construction records of the slope support structure; the database management system is used to organize the historical multi-source monitoring data of the slope into a unified data format, and the historical data and the standardized real-time monitoring data of the slope are processed for consistency through data fusion technology to solve the problem of inconsistent data time and space resolution and dimension; the processed historical data are combined with the real-time monitoring data of the slope to form a unified data format, and the historical data are processed for consistency with the standardized real-time monitoring data of the slope to solve the problem of inconsistent data time and space resolution and dimension; the processed historical data are combined with the real-time monitoring data of the slope to form a unified data format, and the historical data are processed for consistency with the real-time monitoring data of the slope to form a unified data format, and the historical data are processed for consistency with the real-time monitoring data of the slope to form a unified data format, and the historical data are processed for consistency with the real-time monitoring data of the slope to form a unified data format, and the historical data are processed and combined with the real-time monitoring data of the slope to form a unified data format; the historical data are processed and combined ... Standardized slope real-time monitoring data is input into the data processing platform. Based on time series analysis technology, the correlation between real-time data and historical data is established. The weights of historical data and real-time data are assigned through a dynamic weighted fusion algorithm, thereby realizing the effective use of historical data. The preliminary slope digital twin model is updated using a dynamic update algorithm combined with the processed data. The specific operation is to add a historical data driving module to the preliminary digital twin model, integrate the latest data features into the model structure through an incremental data update strategy, and adjust the physical parameters and state parameters in the virtual model in real time, so that the digital twin model can more accurately reflect the actual state of the slope, and finally obtain a real-time updated slope digital twin model.

[0039] Step S3: Perform multi-physics field coupling analysis on the real-time updated slope digital twin model, and perform slope stability assessment based on the multi-physics field coupling analysis results to obtain slope stability analysis data;

[0040] The embodiment of the present invention first integrates and processes the slope structure, geological conditions, environmental factors and historical record data in the real-time updated slope digital twin model to ensure the integrity and consistency of the input data; uses a high-performance computing platform to perform multi-physical field coupling analysis on the real-time updated slope digital twin model, introduces multiple physical field parameters such as mechanical field, thermal field, hydraulic field and power field, adopts finite element calculation method and multi-field coupling algorithm to establish multi-physical field coupling equation, and accurately simulates the response characteristics of the slope under different stress states and environmental changes; uses geological condition data to determine the physical and mechanical properties of the slope material, and calculates the slope under its own weight and external load through mechanical field analysis. The stress distribution and strain state under load are analyzed; environmental data are introduced to simulate the thermal field and the impact of thermal stress caused by temperature gradient on slope stability is analyzed; the pore water pressure distribution and permeability changes of the slope under rainfall and groundwater seepage are evaluated through hydraulic field calculation; the dynamic response and cumulative deformation characteristics caused by earthquake or vibration loads are determined in combination with dynamic field analysis; the above analysis results are fused, and the interaction between each physical field is comprehensively evaluated using the multi-field coupling algorithm to extract the factors affecting the stability of the slope under complex conditions and their changing laws; finally, the multi-physical field coupling analysis results are output as slope stability analysis data in numerical and visual forms.

[0041] Step S4: performing nonlinear analysis on the slope stability analysis data, and using the nonlinear analysis results to predict slope stability, thereby obtaining slope stability prediction data;

[0042] The embodiment of the present invention first uses a nonlinear calculation method to conduct in-depth analysis based on slope stability analysis data, and conducts detailed research on the stress state and deformation characteristics of the slope under different working conditions; by analyzing the stress response of the slope under complex stress conditions, combined with the nonlinear constitutive relationship of the slope material, a high-performance computing platform is used to perform nonlinear mechanical analysis, and the stress distribution, plastic deformation area and crack propagation characteristics of the slope are calculated; in the further analysis process, based on the geomechanical characteristic data of the slope and combined with the structural characteristics of the actual slope, a nonlinear solution method is used to iteratively calculate the strain change law of the slope under the action of external load; after the mechanical analysis is completed, the nonlinear characteristics of the slope deformation behavior are extracted, and the deformation curve and instability trend of the slope under multiple working conditions are verified by comparative analysis of the slope monitoring data; at the same time, the nonlinear change characteristics of different slope structural areas are summarized, and the key nodes and dangerous areas of the slope stress state change are clarified; finally, the above analysis results are digitized and visualized, and output as slope stability prediction data.

[0043] Step S5: performing slope risk assessment based on the slope stability prediction data, and performing risk warning according to the risk assessment results to obtain slope risk warning information data;

[0044] The present invention first conducts a slope risk assessment based on slope stability prediction data, combined with historical and real-time monitoring data, and the geomechanical properties of the slope. This process first compares and analyzes the stability prediction data under different slope conditions to identify key areas and time periods for disaster risk. Secondly, statistical analysis methods, such as regression analysis and cluster analysis, are used to quantitatively analyze the stability trends at each monitoring point on the slope, assessing the risk level of the slope under different environmental conditions. Based on this, a risk matrix method is used to classify the risk level of the slope. Combined with slope deformation monitoring data, the risk level of potentially hazardous areas is further determined. The assessment process specifically considers the impact of the slope's slip surface, crack propagation, and hydrological and meteorological factors, and a comprehensive analysis is conducted using a combination of geomechanical models and risk assessment methods. By setting multiple risk level thresholds, slope stability is quantitatively classified to generate slope risk assessment data. Finally, the assessment results are compared with historical and real-time monitoring data. Combined with the actual risk thresholds, the slope's risk level and potential risk warning are output, forming slope risk warning information data.

[0045] Step S6: Emergency response measures are formulated based on the slope risk warning information data, and the slope risk warning information data and emergency response measures are updated in real time using the real-time updated slope digital twin model to obtain real-time updated emergency response measures data.

[0046] This embodiment of the present invention first formulates emergency response measures based on slope risk warning information data, combined with data from a real-time updated digital twin model of the slope. This involves comprehensively analyzing the slope's current state, historical data, stability prediction data, and risk assessment data to identify potential risk areas and high-risk periods. During this process, real-time slope monitoring data is used to analyze each monitoring point, focusing specifically on areas at risk. Then, based on the slope stability analysis and risk assessment data, combined with relevant geological conditions, meteorological changes, and soil moisture variations, targeted emergency response measures are formulated. These measures include, but are not limited to, slope reinforcement, drainage system improvements, and the establishment of temporary shelters. During the formulation process, the real-time updated digital twin model is used to dynamically simulate the effects of different emergency measures to further optimize the emergency response plan. Furthermore, the digital twin model is used to update emergency response measure data in real time, ensuring that response measures can be adjusted at any time to address sudden slope changes or changes in external factors. Finally, all emergency response measures are updated based on feedback from the real-time monitoring system, generating real-time updated emergency response measure data.

[0047] By acquiring the original real-time monitoring data of the slope and performing standardized processing, the present invention can ensure the uniformity and comparability of the data, providing an accurate basis for the subsequent construction of the digital twin model. After establishing the preliminary digital twin model of the slope, the model is dynamically updated by combining the standardized real-time monitoring data with the historical multi-source monitoring data of the slope, thereby ensuring that the digital twin model can reflect the current actual condition of the slope and avoiding the deviations in the long-term operation of the traditional static model. This real-time update capability improves the reliability and timeliness of the model and can reflect in real time the dynamic adjustments of the slope caused by geological changes, rainfall or human activities. Multi-physics field coupling analysis plays a key role in slope stability assessment. It can comprehensively consider multiple influencing factors and provide a basis for accurately assessing the stability of the slope. Further stability prediction is performed through nonlinear analysis, which can identify the potential instability risk of the slope and issue an early warning. On this basis, slope risk assessment is carried out and risk warnings are triggered, so that the risk management system can respond in time to prevent disasters. Ultimately, emergency response measures are formulated based on risk warning information, and warning and emergency measures are dynamically adjusted through real-time updated digital twin models to ensure that emergency responses in emergency situations can closely match actual conditions, thereby improving the scientific nature and timeliness of disaster prevention and control.

[0048] Preferably, step S1 includes the following steps:

[0049] Step S11: acquiring real-time slope status data and slope environmental parameter data of a mountain engineering slope, and recording the real-time slope status data and slope environmental parameter data as original slope real-time monitoring data;

[0050] Step S12: performing data preprocessing and standardization on the original real-time slope monitoring data to obtain standardized real-time slope monitoring data;

[0051] Step S13: performing slope geometry modeling based on the standardized slope real-time monitoring data to obtain a slope geometry model;

[0052] Step S14: performing slope physical property analysis on the standardized slope real-time monitoring data to obtain slope physical property data;

[0053] Step S15: performing slope mechanical behavior modeling based on the standardized slope real-time monitoring data and slope physical property data to obtain a slope mechanical model;

[0054] Step S16: simulating slope environmental impact factors based on the standardized real-time slope monitoring data to obtain a slope environmental impact model;

[0055] Step S17: The slope geometric model, the slope mechanical model and the slope environmental impact model are integrated according to the slope physical characteristic data to obtain a preliminary slope digital twin model.

[0056] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S1 in the embodiment of the present invention, step S1 includes the following steps:

[0057] Step S11: acquiring real-time slope status data and slope environmental parameter data of a mountain engineering slope, and recording the real-time slope status data and slope environmental parameter data as original slope real-time monitoring data;

[0058] The embodiment of the present invention obtains real-time slope status data and slope environmental parameter data through high-precision sensor equipment installed on the slopes of mountain projects. The real-time slope status data includes slope surface displacement, inclination angle, and dynamic change data of crack width, and the slope environmental parameter data includes rainfall, wind speed, temperature and other external environmental data. Specifically, the sensor equipment includes a total station, an inclinometer, a laser rangefinder, a rain gauge, an anemometer, and a temperature and humidity sensor. The real-time collected data is uploaded to the slope monitoring data processing center through a wireless transmission module. The data processing center stores the received sensor raw data in a dedicated database at predetermined time intervals through a data acquisition and transmission protocol. Before the data is stored, the data is associated with the timestamp and sensor number, and the working status of the sensor is verified in real time to ensure the integrity and reliability of the data. During this process, the system integrates the real-time acquired slope status data and slope environmental parameter data into unified original slope real-time monitoring data according to established signal acquisition rules.

[0059] Step S12: performing data preprocessing and standardization on the original real-time slope monitoring data to obtain standardized real-time slope monitoring data;

[0060] The embodiment of the present invention firstly introduces a wavelet transform method to perform denoising on the noise data in the original real-time slope monitoring data, and uses a decomposition-reconstruction process to remove the high-frequency noise components in the signal; secondly, the missing values in the monitoring data are supplemented by a Lagrange interpolation method to ensure the continuity of the data; then, the outliers in the original real-time slope monitoring data are eliminated by an anomaly detection method based on the IQR (interquartile range), which specifically includes calculating the first quartile and the third quartile of the data, determining the outlier range according to the 1.5 times interquartile range rule, eliminating the data out of the range, and replacing it with a local weighted regression method; then, the processed data is reordered according to the timestamp to ensure the time series consistency of the data; finally, the different types of data are standardized by the maximum and minimum normalization method, and the standardized data are integrated into standardized real-time slope monitoring data.

[0061] Step S13: performing slope geometry modeling based on the standardized slope real-time monitoring data to obtain a slope geometry model;

[0062] Based on standardized real-time slope monitoring data, the present invention constructs a slope geometry model using 3D laser scanning technology and terrain point cloud data processing methods. First, a 3D laser scanner is used to scan the slope area from multiple angles to obtain high-precision point cloud data of the complete slope surface. A point cloud data preprocessing module then removes noise and filters redundant points from the collected point cloud data. A statistical analysis-based filtering algorithm is used to remove discrete points with large errors, and dense point cloud data is processed in blocks to reduce computational complexity. A point cloud data registration algorithm is then used to align the coordinates of the point cloud data from different scanning angles. The ICP (Iterative Closest Point) algorithm is then used to precisely match overlapping areas and generate a complete point cloud set in a unified coordinate system. The processed point cloud data is then reconstructed using the Delaunay triangulation method, forming a 3D triangulated mesh by connecting each point point, generating a refined 3D structure of the slope geometry. Finally, the reconstructed 3D structure undergoes data format conversion and feature parameter extraction. The extracted parameters include slope angle, slope height, slope curvature, and geometric eigenvalues of key crack locations, completing the slope geometry modeling.

[0063] Step S14: performing slope physical property analysis on the standardized slope real-time monitoring data to obtain slope physical property data;

[0064] In an embodiment of the present invention, the finite element method is first used to numerically calculate the stress distribution inside the slope, and the displacement data and inclination data reflecting the slope state in the standardized real-time slope monitoring data are input into the calculation system as boundary conditions. The stress and strain fields in the region are solved using the Gauss integral method. Secondly, the seepage field characteristics inside the slope are calculated using Darcy's law in combination with the permeability test results and the moisture content monitoring data. Then, based on the mechanical property test results of the slope material, including the compressive strength, shear strength and elastic modulus of the rock or soil, the shear strength characteristics and failure conditions of the slope are analyzed using the Mohr-Coulomb criterion. Next, the density, porosity and permeability coefficient physical properties of the slope are verified by combining laboratory testing and field monitoring, and a high-precision electronic densitometer and a multi-point porosity tester are used to supplement and calibrate the data. Finally, the stress distribution data, seepage field characteristic parameters and material physical property parameters obtained by analysis are integrated into the slope physical property data.

[0065] Step S15: performing slope mechanical behavior modeling based on the standardized slope real-time monitoring data and slope physical property data to obtain a slope mechanical model;

[0066] The embodiment of the present invention first calculates the elastic deformation characteristics of the slope based on the displacement and strain data in the standardized slope real-time monitoring data, combined with the elastic modulus and Poisson's ratio of the rock and soil in the slope physical property data, using the generalized Hooke's law; secondly, based on the theory of plastic mechanics, using the slope shear strength parameters and shear stress data, combined with the Mohr-Coulomb failure criterion, the shear failure risk of the slope is analyzed; then, through the data field coupling technology, the dynamic load information in the standardized slope real-time monitoring data is introduced into the calculation process, and the finite difference method is used to solve the stress-strain distribution law of the slope under the action of dynamic load, and specifically calculates the cumulative displacement and critical instability condition caused by the dynamic load; then, combined with the seepage field characteristics, the seepage-stress coupling analysis method is used to simulate the mechanical response characteristics of the slope in saturated and unsaturated states, focusing on analyzing the influence of seepage pressure on the slope stability; finally, the mechanical analysis results are data fused, and the critical stability coefficient and potential sliding surface parameters of the slope are output to complete the slope mechanical behavior modeling and obtain the slope mechanical model.

[0067] Step S16: simulating slope environmental impact factors based on the standardized real-time slope monitoring data to obtain a slope environmental impact model;

[0068] The embodiment of the present invention first uses meteorological parameters in standardized real-time slope monitoring data, such as rainfall intensity, rainfall duration, temperature change and wind speed, combined with data collected by environmental monitoring equipment, and adopts a time series analysis method to extract the variation pattern of environmental factors; secondly, based on rainfall infiltration theory and hydrological model, the Green-Ampt model is used to calculate the slope infiltration depth and saturated layer formation rate under different rainfall conditions, and the water migration pattern inside the slope is calculated by the permeability coefficient; then, in view of the influence of air temperature and freeze-thaw cycles, the dynamic influence of temperature change on the physical properties of geotechnical materials is analyzed using material mechanical properties test data, and the thermal expansion and contraction effect inside the slope is evaluated using a thermal stress calculation method; then, based on wind speed and slope particle size data, the weakening effect of wind erosion on the slope surface stability is calculated in combination with the wind erosion equation; finally, the above calculation results are data fused, and the overall environmental impact assessment is established using the environmental impact factor superposition calculation method, and the potential instability risk parameters and dynamic change trends of the slope under the influence of the environment are output to obtain a slope environmental impact model.

[0069] Step S17: The slope geometric model, the slope mechanical model and the slope environmental impact model are integrated according to the slope physical characteristic data to obtain a preliminary slope digital twin model.

[0070] In the embodiment of the present invention, first, the spatial topological structure of the slope geometric model is converted into a discrete point set form that can be used for data fusion through the grid segmentation technology of the geometric model data, and the coordinate system of the geometric model is unified with other models by using the spatial coordinate registration algorithm; secondly, the stress distribution and deformation characteristics in the slope mechanical model are associated with the grid points of the geometric model one by one, and the mechanical response information is mapped to the spatial framework of the geometric model through the finite element grid coupling technology; then, the rainfall, temperature, and wind erosion dynamic factor simulation data in the environmental impact model are used to match the time series data of the environmental impact with the dynamic response of the geometric model and the mechanical model, and a multi-field coupling method is used to realize spatiotemporal joint analysis; then, the weighted average method in the data fusion algorithm is used to assign weights to the data output by different models to ensure that the various types of data maintain proportional coordination during the fusion process, and the global optimization algorithm is used to adjust the overall consistency of the fusion results; finally, a complete preliminary slope digital twin model is generated based on the fused model.

[0071] By acquiring real-time slope status data and environmental parameter data, the present invention can comprehensively reflect the current status of the slope and provide real-time and accurate data support for subsequent analysis. The original data is preprocessed and standardized to ensure data consistency and comparability, providing a high-quality data foundation for subsequent modeling. Through slope geometric modeling, the shape and structural characteristics of the slope can be accurately reproduced, providing detailed geometric information for further analysis. Analysis of the physical characteristics of the slope reveals the physical properties of the slope, which helps to assess its stability and the degree to which it is affected by the external environment. Through mechanical behavior modeling, the response of the slope under different loads can be simulated, and potential mechanical changes and risks can be predicted. The simulation of slope environmental impact factors can take into account changes in environmental factors and enhance the model's ability to respond to external environmental influences. The geometric model, mechanical model and environmental impact model are integrated to obtain a preliminary digital twin model of the slope, which lays the foundation for real-time monitoring and risk prediction.

[0072] Preferably, step S2 includes the following steps:

[0073] Step S21: Obtain historical multi-source monitoring data of the slope, and compare and analyze the standardized real-time monitoring data of the slope with the historical multi-source monitoring data of the slope to obtain slope state difference analysis data;

[0074] Step S22: adjusting parameters of the preliminary slope digital twin model based on the standardized slope real-time monitoring data and the slope state difference analysis data to obtain a revised slope digital twin model;

[0075] Step S23: performing conformity verification on the modified slope digital twin model to obtain model verification result data;

[0076] Step S24: Dynamically update the modified slope digital twin model using the model verification result data to obtain a real-time updated slope digital twin model.

[0077] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 1 Detailed step flow diagram of step S2 in the embodiment of the present invention, step S2 includes the following steps:

[0078] Step S21: Obtain historical multi-source monitoring data of the slope, and compare and analyze the standardized real-time monitoring data of the slope with the historical multi-source monitoring data of the slope to obtain slope state difference analysis data;

[0079] The embodiment of the present invention first obtains historical data of the slope, including monitoring data, geological exploration data, rainfall data, and meteorological data from previous years. These historical data can provide detailed information on the stability changes of the slope at different time nodes and the influence of external factors. Then, the standardized real-time monitoring data of the slope is compared and analyzed with the acquired historical multi-source monitoring data of the slope. The specific operation includes: by comparing the various parameters in the real-time monitoring data with the corresponding values of the historical data, the difference between the slope during the real-time monitoring period and the historical data is identified. Using a difference analysis method, such as the normalized difference index method or the data fitting method, the difference between the real-time monitoring data and the historical data is quantitatively calculated to obtain the slope state difference analysis data.

[0080] Step S22: adjusting parameters of the preliminary slope digital twin model based on the standardized slope real-time monitoring data and the slope state difference analysis data to obtain a revised slope digital twin model;

[0081] The embodiment of the present invention first adjusts the parameters of the preliminary slope digital twin model through a numerical optimization method based on the standardized real-time slope monitoring data and the slope state difference analysis data. The process first involves comparing the specific numerical values of the differences between various indicators in the real-time monitoring data and the historical data, and converting these differences into slope state difference analysis data. Then, the difference analysis data is used to identify the key factors that cause changes in slope stability, and the parameters in the preliminary slope digital twin model are corrected based on these factors. During the correction process, the error minimization method is used to ensure that the adjusted parameters can more accurately reflect the current slope state. Specifically, the finite element analysis method is used to calculate the corrected model to verify the rationality of the model parameter adjustment. Through this process, a corrected slope digital twin model is obtained.

[0082] Step S23: performing conformity verification on the modified slope digital twin model to obtain model verification result data;

[0083] The embodiment of the present invention first verifies the conformity of the modified slope digital twin model. The modified slope digital twin model is compared and analyzed with the actual monitoring data, and the error analysis method is used to evaluate the difference between the model's prediction results and the actual situation. To this end, it is necessary to use polynomial regression and least squares mathematical tools to calculate the deviation between the model's predicted value and the actual observed value, and to confirm whether the modified model conforms to the actual slope stability behavior through residual analysis. During the comparison process, focus on the response of the key parts of the slope and calculate its deviation value to ensure that the model can accurately reflect these key factors. Furthermore, by setting a tolerance error range, the deviation value of each parameter is tested. If the error exceeds the set range, certain parameters in the modified model need to be further adjusted. Through these rigorous conformity verification steps, the model verification result data is obtained.

[0084] Step S24: Dynamically update the modified slope digital twin model using the model verification result data to obtain a real-time updated slope digital twin model.

[0085] The embodiment of the present invention first dynamically updates the revised digital twin model of the slope. Based on the model verification result data, the real-time monitoring data of the slope and the state difference analysis data are extracted, and the revised digital twin model of the slope is dynamically adjusted. Using data interpolation technology and time series analysis methods, the new real-time data and historical data are integrated into the model through the weighted average method. To this end, based on the changing trends of different monitoring points on the slope, combined with sensor data and environmental changes, the slope parameters in the revised model are gradually adjusted to ensure that the model can reflect the actual changes of the slope in real time. The model parameters are corrected in real time using a dynamic update algorithm to reflect changes in environmental factors, geological conditions and monitoring data, thereby ensuring the accuracy of the model. By comparing with the actual situation on site, the model is continuously corrected to ensure that it can always accurately predict the stability and risk status of the slope, and finally a real-time updated digital twin model of the slope is obtained.

[0086] By comparing standardized real-time monitoring data with historical data, this method can identify changes in slope conditions and reveal potential risks and trends. Parameter adjustments, combined with differential analysis data, can improve the accuracy of the preliminary model and better reflect the actual slope condition. The revised slope digital twin model is validated to ensure its consistency with the actual slope conditions, thereby ensuring the reliability of the analysis results. Dynamic updates using model validation results ensure that the slope digital twin model continuously reflects real-time changes and provides accurate real-time monitoring data support.

[0087] Preferably, step S3 includes the following steps:

[0088] Step S31: performing geomechanical analysis on the real-time updated slope digital twin model to obtain slope geomechanical analysis data;

[0089] The embodiment of the present invention performs geomechanical analysis on the real-time updated digital twin model of the slope. First, based on the geological data obtained in the real-time updated digital twin model of the slope, the rock and soil layer, geological structure, soil type, and groundwater flow information of the area where the slope is located are extracted. Through mechanical analysis methods, the finite element method is used to evaluate the stability of the slope, and the stress, strain, and friction parameters of the slope are input to perform static and dynamic load analysis to determine the response of various geological factors under different loads. Combined with the data from actual on-site monitoring, the geomechanical theory is used for analysis to calculate the safety factor of the slope, the potential landslide area, and the deformation mode that occurs, and then the geomechanical analysis data of the slope is obtained.

[0090] Step S32: performing thermodynamic behavior analysis on the real-time updated slope digital twin model to obtain slope thermodynamic analysis data;

[0091] The embodiment of the present invention performs thermodynamic behavior analysis on the real-time updated digital twin model of the slope. First, the thermal parameters of the area where the slope is located are obtained based on the real-time updated digital twin model of the slope. On this basis, the thermodynamic analysis method is used, combined with the heat conduction and convection models, to simulate the heat exchange inside and outside the slope, especially considering the thermal response of the slope material to seasonal changes, precipitation, and day and night temperature differences. Furthermore, thermal stress analysis is performed, and the thermal expansion, thermal stress changes of the slope under different temperature gradients and their impact on the stability of the slope are calculated through numerical simulation. The impact of thermal stress on the crack propagation and deformation behavior of the slope rock and soil layer is analyzed in detail to obtain the thermodynamic analysis data of the slope.

[0092] Step S33: simulating various hydrological and meteorological conditions based on the real-time updated slope digital twin model to obtain slope hydrological and meteorological simulation data;

[0093] The embodiment of the present invention simulates various hydrological and meteorological conditions based on a real-time updated digital twin model of the slope. First, meteorological data of the area where the slope is located is collected. Then, based on the real-time updated digital twin model of the slope, a hydrological model is used to simulate the precipitation distribution, evaporation process, and surface water flow in the area, and the infiltration, flow, and accumulation of surface water are further calculated by taking into account soil permeability, vegetation cover, and slope terrain factors. In addition, combined with the meteorological simulation results, the impact of rainwater on slope soil moisture, as well as the effects of water saturation and leakage on slope stability are analyzed. By linking the meteorological model with the hydrological model, slope hydrometeorological simulation data is obtained.

[0094] Step S34: performing slope dynamic response simulation based on the real-time updated slope digital twin model to obtain slope dynamic response data;

[0095] The embodiment of the present invention simulates the dynamic response of the slope based on the real-time updating of the digital twin model of the slope. First, according to the geometric shape of the slope, the material properties and the stress distribution within the slope, appropriate dynamic boundary conditions are set, and the influence of seismic loads, blasting loads or other dynamic loads on the stability of the slope is considered. By calculating the response parameters of the slope under different dynamic loads, the dynamic response process of the slope is simulated. The dynamic behavior of the slope, such as vibration propagation, rupture and its potential impact on stability, is analyzed using the finite element method or discrete element method numerical calculation method. In addition, the nonlinear behavior of the soil and rock in the slope, friction, and viscoelastic factors must also be considered in the simulation process to ensure the accuracy of the simulation results. After the simulation is completed, the dynamic response data of the slope is obtained.

[0096] Step S35: performing multi-physics field coupling analysis on the slope geomechanical analysis data, the slope thermodynamic analysis data, the slope hydrometeorological simulation data, and the slope dynamic response data to obtain slope multi-physics field coupling analysis data;

[0097] The embodiment of the present invention performs multi-physics field coupling analysis based on slope geomechanical analysis data, slope thermodynamic analysis data, slope hydrometeorological simulation data and slope dynamic response data. First, the stress and strain data in the geomechanical analysis results are combined with the temperature and heat flow data in the thermodynamic analysis data to form a complete temperature field and stress field coupling model. Then, the precipitation, temperature and humidity factors under different meteorological conditions in the hydrometeorological simulation data are coupled with the geomechanical and thermodynamic characteristics of the slope. By simulating the effects of water infiltration and temperature changes on the slope, the mechanical behavior analysis of the slope is further optimized. On this basis, the slope dynamic response data is integrated with the simulation results of geomechanics and thermodynamics coupling, and the finite element method or multi-physics field coupling analysis method is used to comprehensively consider the stability performance of the slope under various complex environments to obtain the slope multi-physics field coupling analysis data.

[0098] Step S36: Perform slope stability assessment based on the slope multi-physics field coupling analysis data to obtain slope stability analysis data.

[0099] The embodiment of the present invention evaluates the stability of the slope based on the multi-physics field coupling analysis data of the slope. First, the deformation behavior of the slope is analyzed by a mechanical model based on the stress field, temperature field, humidity field, and dynamic response data in the multi-physics field coupling analysis results. Then, the slope stability is evaluated by combining the stress, strain, and yield criterion in the geomechanical analysis data, the thermal conductivity coefficient and temperature variation range in the thermodynamic analysis data, and the precipitation and humidity conditions in the hydrometeorological simulation data. The displacement and vibration response in the dynamic response data are combined with the above data to analyze the stability performance of the slope under dynamic loads, and the potential instability risk of the slope is judged by the stress-strain curve. In this process, the finite element analysis method is used to integrate the various data, calculate the slope stability coefficient, and evaluate its stability by judging whether the slope meets the safety standard. Finally, the slope stability analysis data is obtained through the analysis results.

[0100] Through geomechanical analysis, the present invention can provide an in-depth understanding of the mechanical behavior of the slope and provide basic data support for evaluating the stability of the slope. Through thermodynamic behavior analysis, the physical characteristics of the slope under temperature changes and heat conduction can be revealed, providing important information for stability prediction. Through hydrometeorological condition simulation, the impact of rainfall, temperature and humidity environmental factors on slope stability can be considered to improve the accuracy of early warning. Through dynamic response simulation, the reaction of the slope under different external influences can be evaluated, providing a dynamic reference for judging its safety. Through multi-physical field coupling analysis, geomechanical, thermodynamic, hydrometeorological and dynamic data are comprehensively analyzed to improve the comprehensiveness and accuracy of slope stability assessment. Slope stability assessment based on the results of multi-physical field coupling analysis provides a reliable basis for timely discovery of potential risks and taking corresponding measures.

[0101] Preferably, step S36 includes the following steps:

[0102] Step S361: identifying slope stress concentration areas based on the slope multi-physics field coupling analysis data to obtain slope stress field distribution data;

[0103] The embodiment of the present invention identifies the stress concentration areas of the slope based on the multi-physics field coupling analysis data of the slope. First, the stress field in the multi-physics field coupling analysis data is analyzed and the stress distribution data is used to identify the areas in the slope where the stress is relatively concentrated. The method for identifying the stress concentration areas is usually based on numerical simulation and mechanical analysis. The stress field data in the slope model is used to apply the stress gradient analysis method or the equivalent stress calculation method to conduct a detailed analysis of the slope to find the areas where the stress increases significantly. These stress concentration areas are usually related to the potential unstable areas of the slope. Therefore, during the calculation process, the stress field is discretized using the finite element method or the slope stability analysis model, and the stress concentration areas are marked using visualization tools to obtain the slope stress field distribution data.

[0104] Step S362: determining the position of the slope slip surface based on the slope stress distribution data to obtain slope slip surface analysis data;

[0105] The embodiment of the present invention determines the position of the slope slip surface based on the slope stress distribution data. By performing a detailed analysis of the stress field data obtained in the previous step, the area where slip occurs in the slope is identified. First, using the stress distribution data, by setting a stress threshold, areas of stress concentration are identified. These areas are usually potential slip surfaces. Next, a slip surface determination method is adopted, combined with the stress gradient and stress concentration degree information in the stress distribution data, and further analysis is performed through a mechanical model to determine the position of the slip surface. Specifically, numerical calculation methods, such as finite element analysis, can be used to combine the mechanical properties of the material and the geometric characteristics of the slope to simulate the behavior of the slope under different stresses and determine the position of the slip surface. In this process, the slip surface can also be calibrated with the help of visualization technology and compared with the actual terrain data to ensure the accuracy of the determination. Ultimately, the slope slip surface analysis data is obtained.

[0106] Step S363: performing displacement field calculation on the slope multi-physics field coupling analysis and performing slope deformation trend analysis to obtain slope displacement field analysis data;

[0107] The embodiment of the present invention calculates the displacement field based on the multi-physics field coupling analysis data of the slope and performs slope deformation trend analysis. First, the stress, mechanics, and temperature physical field data related to the slope are extracted from the multi-physics field coupling analysis results, and the displacement field is calculated based on these data. In this process, the slope is simulated using the finite element method or the slope mechanics model to calculate the displacement response of the slope under different loading conditions, especially focusing on the displacement changes of the potential slip surface and its surrounding areas. Then, based on the calculation results of the displacement field, the deformation trend of the slope is analyzed to determine the deformation trend of the slope in various directions, and by analyzing the displacement data of different time periods, the areas with more obvious deformation are identified. In order to accurately predict the deformation path of the slope, it is also necessary to consider the influencing factors of geological structure and soil characteristics. In specific operations, numerical analysis tools such as the finite difference method or the finite element method are used to accurately simulate the dynamic response of the slope and obtain the slope displacement field analysis data.

[0108] Step S364: quantifying the slope instability probability based on the slope slip surface analysis data and the slope displacement field analysis data to obtain slope instability probability assessment data;

[0109] The embodiment of the present invention quantifies the probability of slope instability based on the slope slip surface analysis data and the slope displacement field analysis data. First, the potential slip surface position of the slope is determined through the slope slip surface analysis data, and combined with the slope displacement field analysis data, the areas with more obvious deformation are identified, especially those locations that are greatly affected by stress concentration. On this basis, the probability of slope instability is quantitatively evaluated using the probability analysis method using the stress field distribution, displacement field data and slip surface analysis results of the slope. Specifically, by establishing a slope stability probability model, taking displacement, stress, and geological characteristics as input parameters, and combining historical monitoring data, using statistical methods such as Monte Carlo simulation or limit equilibrium analysis, the instability risk of the slope under different hydrological and meteorological conditions is calculated to obtain slope instability probability evaluation data.

[0110] Step S365: using the slope multi-physics field coupling analysis data to classify the slope instability probability assessment data into stability grades to obtain slope stability grade data;

[0111] The embodiment of the present invention utilizes the slope multi-physics field coupling analysis data to classify the slope instability probability assessment data into stability levels. First, based on the slope instability probability assessment data, combined with the geomechanical properties, stress field, displacement field and thermodynamic factors of the slope, multiple stability levels are set. By analyzing the specific numerical range of the instability probability, different stability level intervals are divided. For example, by calculating the instability probability interval (such as less than 10% is stable, 10% to 30% is slightly unstable, 30% to 50% is moderately unstable, and more than 50% is high-risk instability), and setting a corresponding level identifier for each interval. Then, combined with the slip surface analysis data and displacement field data of the slope, the stability of the slope area at each moment is evaluated one by one, and the stability level is assigned according to the numerical value of the instability probability to obtain the slope stability level data.

[0112] Step S366: Integrate the slope instability probability assessment data and the slope stability grade data to obtain slope stability analysis data.

[0113] The embodiment of the present invention first integrates the slope instability probability assessment data and the slope stability grade data. At the beginning of this process, key indicators such as the instability probability value, stability grade, stress concentration areas of various parts of the slope, and displacement trend information are extracted from the slope slip surface analysis data, displacement field analysis data, and slope instability probability assessment data. Then, this information is comprehensively analyzed by weighted average or direct summation to obtain an overall slope stability analysis data. In this process, for areas with different stability grades, the corresponding stability assessment data are merged and grouped according to certain thresholds or risk levels to form a complete stability assessment system. Finally, based on the geological environment, stress distribution, displacement trend, and thermodynamic change multi-physics field data of the slope, the integrated slope stability analysis data is output.

[0114] The present invention can effectively determine the key weak areas of the slope by identifying stress concentration areas, providing an important basis for subsequent stability assessments. By determining the position of the slip surface, the sliding surface of the slope can be clearly identified, providing scientific support for the assessment of potential unstable areas. By calculating the displacement field and analyzing the deformation trend, the deformation trend of the slope can be comprehensively assessed and potential risks can be predicted in advance. By quantifying the probability of slope instability, the instability risk of the slope can be accurately assessed, providing reliable data support for the formulation of prevention and control measures. By classifying the stability levels, the slope risks can be divided into different levels, helping decision makers to take timely countermeasures. Through data integration, the instability probability assessment and stability level information can be combined to improve the comprehensiveness and accuracy of the slope stability analysis.

[0115] Preferably, step S4 includes the following steps:

[0116] Step S41: using the slope stability analysis data to perform nonlinear mechanical modeling, and performing nonlinear change analysis of the slope stress state to obtain nonlinear mechanical analysis data of the slope;

[0117] The embodiment of the present invention first uses key parameters from slope stability analysis data, such as soil mechanical properties, geological structure, stress-strain distribution, and slope deformation, to perform nonlinear mechanical modeling and construct a nonlinear mechanical model of the slope. This process uses the finite element analysis method (FEM) to discretize the slope, dividing it into multiple finite elements and applying known slope environmental and load conditions to each element. During the modeling process, the nonlinear material behavior of the slope is analyzed, particularly the yield, hardening, and softening properties of the material, as well as the effects of nonlinear contact interfaces and friction, to ensure that the model accurately reflects the behavior of the slope under different stress conditions. Next, based on the nonlinear mechanical model, the nonlinear variation of the slope's stress state is analyzed. Elastoplastic mechanics theory is used to analyze the deformation, stress changes, and failure process of the slope under external forces. Combined with stress-strain curves, the nonlinear response of the slope material under different stress conditions is analyzed, and the slope's safety factor is calculated to assess the slope's stability under different working conditions. Finally, nonlinear mechanical analysis data for the slope is obtained.

[0118] Step S42: performing nonlinear analysis of deformation behavior of each slope under stress conditions based on the slope multi-physics field coupling analysis data to obtain slope nonlinear deformation analysis data;

[0119] This embodiment of the present invention first utilizes multi-physics field analysis technology based on slope multi-field coupled analysis data to perform a nonlinear analysis of the slope's deformation behavior under various load conditions. Specifically, the finite element analysis (FEM) method is used to discretize the slope into multiple small cells, ensuring that stress, temperature, and humidity physical field factors within each cell are fully accounted for. Within each cell, a mathematical model for the coupled physical fields is established by combining soil mechanics, rock mechanics, and hydrological parameters. This model considers the effects of water infiltration and temperature changes on the soil, as well as the impact of environmental changes such as precipitation and evaporation on the slope's stability. Next, a dynamic nonlinear analysis is performed on the slope's load conditions under different external forces. The deformation process of the slope under the coupled physical fields is tracked, and strain and displacement indicators are calculated. These data are then used to analyze the nonlinear deformation behavior of the slope under varying loads. Finally, the slope's nonlinear deformation analysis data is obtained by solving the coupled model for the slope's stress and deformation characteristics.

[0120] Step S43: performing nonlinear time series analysis of the slope key risk area on the slope stability analysis data to obtain slope nonlinear time series analysis data;

[0121] The embodiment of the present invention first selects the key risk areas of the slope for detailed analysis based on the slope stability analysis data. The specific operation includes: using the time series data analysis method, combining the historical slope stability data, real-time monitoring data and the geological characteristics of the slope to identify the key areas where instability occurs. By establishing a nonlinear time series model, the stress changes and deformation behaviors of these areas are analyzed, focusing on the dynamic change characteristics of stress fluctuations and displacement changes in the time series, and further predicting the instability moment of the risk area. Then, based on the time series data of the key risk areas of the slope, the slope stability analysis model is applied to simulate the time series evolution process, and combined with the historical evolution data of the slope, the time series change law of the slope stress and deformation is calculated to obtain the nonlinear time series analysis data of the slope.

[0122] Step S44: performing slope instability prediction based on the slope nonlinear mechanical analysis data, the slope nonlinear deformation analysis data, and the slope nonlinear time series analysis data to obtain slope stability prediction data.

[0123] First, an embodiment of the present invention predicts slope instability based on slope nonlinear mechanical analysis data, slope nonlinear deformation analysis data, and slope nonlinear time series analysis data. The slope nonlinear mechanical analysis data, slope nonlinear deformation analysis data, and slope nonlinear time series analysis data are integrated, and an instability prediction model is constructed by combining the stability analysis results of various slope regions. Based on historical stability data, real-time monitoring data, and various nonlinear analysis data, this model comprehensively considers the mechanical behavior, deformation state, and time-series evolution of the slope's forces to assess the stability of different slope regions. Numerical calculations and simulation methods are used to further calculate the likelihood and critical point of slope instability. For each key region, a critical instability judgment is made based on the force state and nonlinear deformation obtained from the slope nonlinear mechanical analysis data, combined with the time-series variation pattern, to obtain slope stability prediction data. Ultimately, based on this series of data analyses, slope stability prediction data is generated.

[0124] Through nonlinear mechanics analysis of slopes, this invention can provide a deep understanding of the changing patterns of slope stress states, providing an accurate basis for predicting slope stability. Through nonlinear analysis of slope deformation behavior, it can comprehensively assess the deformation characteristics of slopes under different stress conditions, providing key data for slope risk prediction. Through nonlinear time-series analysis of key risk areas on slopes, it can identify potential moments of instability, providing a scientific basis for early warning. By integrating nonlinear mechanics, deformation, and time-series analysis data, it can accurately predict the possibility of slope instability, providing forward-looking guidance for taking emergency measures.

[0125] Preferably, step S41 includes the following steps:

[0126] Step S411: using the slope stability analysis data to perform stress-strain relationship modeling to obtain slope nonlinear stress-strain model data;

[0127] The embodiment of the present invention first constructs a stress-strain relationship model of the slope based on the slope stability analysis data. The stress and strain data of different areas of the slope are obtained from the real-time monitoring system, and the relationship between the stress and strain of the slope material under different external forces is determined through experiments or existing physical formulas based on these data. Then, using these data and the results of mechanical experiments, the finite element analysis calculation tool is used to model the stress-strain characteristics of the slope. At this time, it is necessary to conduct a detailed analysis of the different slope soil, rock properties and terrain conditions to ensure that the stress-strain model can accurately reflect the deformation behavior and stress characteristics of the slope under various loads. The specific steps include setting the mechanical parameters of the slope area, adjusting the different physical properties of the surface and deep layers of the slope, and obtaining the nonlinear stress-strain model data of the slope through the slope stress state analysis.

[0128] Step S412: Calculating the mechanical properties of the slope under various stress conditions using the slope nonlinear stress-strain model data to obtain slope stress characteristic analysis data;

[0129] In the embodiment of the present invention, first, the nonlinear stress-strain model data of the slope is used as input, and the mechanical characteristics of the slope under different stress conditions are calculated based on the model. In the specific operation, the stress and strain of different areas of the slope are simulated using the finite element analysis method, taking into account various stress factors including external loads, groundwater flow, and soil moisture. According to the different stress conditions of the slope, the slope is divided into multiple stress areas, and each area is mechanically analyzed according to its soil layer structure, rock properties and stress environment. During the calculation process, by establishing stress fields and strain fields, the mechanical response data of the slope under different working conditions are obtained, and then the slope stress characteristic analysis data is obtained.

[0130] Step S413: performing a stability evaluation on the slope stress characteristic analysis data under various mechanical conditions to obtain slope nonlinear stability analysis data;

[0131] After obtaining the analysis data of the force characteristics of the slope, the embodiment of the present invention evaluates the stability of the slope based on these data. First, by applying the limit equilibrium analysis method or the slip surface analysis method, combined with the geological structure characteristics of the slope, the landslide surface or fracture surface of the slope is identified. In the calculation process, the soil strength parameters of each area are first accurately measured. Taking into account the important parameters of the internal friction angle, cohesion, and elastic modulus of the rock and soil, a static equilibrium equation is constructed to analyze whether the slope can remain stable under the current force conditions. Then, using the slope stability analysis data, the safety factor of the slope under different force conditions is calculated to evaluate the stability of each area of the slope. In order to further verify the evaluation results, dynamic adjustments and corrections are performed in combination with the existing on-site monitoring data to ensure that the stability analysis conforms to the actual situation. Finally, the stability analysis results of each evaluation area are summarized to obtain the overall nonlinear stability analysis data of the slope.

[0132] Step S414: Modeling the time series change of the slope stress state based on the slope nonlinear stability analysis data to obtain the time series data of the nonlinear change of the slope stress state;

[0133] After obtaining nonlinear slope stability analysis data, the embodiments of the present invention model the time-series variation of the slope's stress state based on this data. First, stability data of the slope at different time points is collected, including monitoring data and analysis data. Based on this data, a time series model of the slope's stress state variation is constructed. The time series model uses common time series analysis methods, such as the Autoregressive Integrated Moving Average (ARIMA) model, to model the time-series variation of the slope's stress state, simulating the stress conditions and variation trends of the slope over different time periods. Using the slope's stress and displacement data, combined with information on the slope's geological conditions, climate conditions, and external loads, a detailed analysis of the stress state at each time node is performed to capture the nonlinear variation of the stress state over time. During the modeling process, data must be continuously updated and corrected to ensure the accuracy of the model. Ultimately, the time-series variation model is used to obtain time-series data on the nonlinear variation of the slope's stress state.

[0134] Step S415: identifying key change points of the nonlinear change time series data of the slope stress state, and analyzing the change law based on the key change point identification results to obtain key change law data of the slope stress state;

[0135] After obtaining time series data on nonlinear changes in the slope stress state, an embodiment of the present invention identifies key change points. First, the changing trend of the slope stress state in the time series data is analyzed, and an appropriate algorithm is selected for change point detection. Statistical change point detection methods, such as the CUSUM (Cumulative Sum Chart) method or the Pelt (Pruned Exact Linear Time) algorithm, can be used to identify significant change points in the time series data. During change point identification, it is first necessary to determine the stable interval and change interval of the time series, and identify the sudden change points or gradual change stages of the stress state. Next, based on the identified key change points, the changing patterns of the slope stress state are analyzed. Change pattern analysis can further clarify the changing characteristics of the slope stress state by extracting the trend changes, fluctuation amplitudes, and correlations with external factors between the change points. Based on these change patterns, key change pattern data for the slope stress state are obtained.

[0136] Step S416: Integrate the slope nonlinear stress-strain model data and the key change law data of the slope stress state to obtain the slope nonlinear mechanical analysis data.

[0137] The embodiment of the present invention first integrates the nonlinear stress-strain model data of the slope and the key change law data of the force state of the slope. During the data integration process, the two data sets are first normalized to ensure that the data format and dimension are consistent. Then, by setting specific weight coefficients, different data sets are weightedly fused. The determination of the weight coefficient is based on the actual needs of the slope stability analysis. For example, different weights are set for the stress-strain model data and the force state change law data according to their contribution to the prediction of slope instability. Then, after the data fusion, the weighted average method or the principal component analysis method is used to synthesize the data to obtain the nonlinear mechanical analysis data of the slope.

[0138] By establishing a nonlinear stress-strain model of the slope, the present invention can deeply analyze the stress-strain relationship of the slope and provide basic data for further stability analysis. By calculating the mechanical properties under different stress conditions, key mechanical property data is provided for slope stability assessment, which helps to accurately judge the safety status of the slope. Through stable state assessment, the stability of the slope under different mechanical conditions can be judged, providing data support for preventing potential instability risks in advance. By analyzing the changes in the stress state of the slope through time series modeling, the stress characteristics of the slope at different periods can be fully understood, which is helpful for dynamic monitoring of the stability of the slope. By identifying key change points and analyzing change laws, important turning points in the stress state of the slope can be accurately captured, providing a reliable basis for timely warning and emergency response. By integrating nonlinear stress-strain model data and key change law data, comprehensive and systematic data support is provided for the nonlinear mechanical analysis of the slope, which helps to deeply understand the mechanical behavior of the slope.

[0139] Preferably, step S5 includes the following steps:

[0140] Step S51: identifying key stability factors for the slope stability prediction data and classifying risk factors to obtain slope risk factor classification data;

[0141] The embodiment of the present invention first conducts a comprehensive analysis of the slope stability prediction data to identify the key factors that affect the stability of the slope. These factors include but are not limited to the geological structure, topography, soil properties, hydrological conditions, meteorological conditions, and dynamic and static load factors of the slope. Each factor is classified in detail according to the degree of its influence on the stability of the slope. For example, hydro-meteorological conditions and geomechanical conditions are divided into different categories, and their specific influence range on the stability of the slope is marked. Next, these risk factors are systematically classified. For example, "hydrological factors" are classified separately and further subdivided into rainfall and groundwater level; "geological factors" can be classified into rock structure and soil type, and the different degrees of influence of each type of factor on the slope stability are determined. Through this classification, slope risk factor classification data is obtained.

[0142] Step S52: performing risk factor weight analysis based on the slope risk factor classification data to obtain slope risk weight analysis data;

[0143] Based on slope risk factor classification data, the present invention first performs a weight analysis on each risk factor. This analysis quantifies the relative importance of each risk factor on slope stability, using analytical methods such as the Analytic Hierarchy Process (AHP) or fuzzy comprehensive evaluation. By assigning weights to each risk factor, the impact of factors such as rainfall, soil moisture, slope, and geotechnical structure on slope stability is considered, and a weight value is assigned to each factor. For example, if a risk factor has a greater impact on slope stability, it is assigned a higher weight, and vice versa. During the weight analysis process, the weight coefficient of each factor is calculated by analyzing historical data, real-time monitoring data, and existing theoretical models. Ultimately, slope risk weight analysis data is obtained.

[0144] Step S53: using the slope stability prediction data and the slope risk weight analysis data to perform slope risk level assessment to obtain slope risk level assessment data;

[0145] An embodiment of the present invention utilizes slope stability prediction data and slope risk weight analysis data to assess slope risk levels. First, various prediction results from the slope stability prediction data, such as stress field, displacement field, and slip surface analysis results based on historical data, are combined with slope risk factor weight analysis data to assign corresponding weights to each risk factor. Using this data, the impact of each risk factor is quantified through a weighted summation method to calculate the slope risk level. During this process, multiple risk levels are established, such as low risk, medium risk, and high risk, and slope risk is divided into different levels based on specific numerical ranges. For example, if the slope stress field data indicates a significant stress concentration in a local area, and the risk factor weight for that area is high, the assessment result will assign that area a high risk level. The entire assessment process considers the correlation between various data types to ensure that the assessment results accurately reflect the current stability state of the slope, thereby obtaining slope risk level assessment data.

[0146] Step S54: using the standardized real-time slope monitoring data and the historical multi-source slope monitoring data to identify the slope instability area of the slope risk level assessment data, and obtaining slope instability area analysis data;

[0147] This embodiment of the present invention uses standardized real-time slope monitoring data and historical multi-source slope monitoring data to identify unstable slope areas using slope risk assessment data. First, the standardized real-time slope monitoring data includes various types of current slope monitoring data. Standardization ensures that the data is analyzed on a uniform scale. Next, data comparison and trend analysis are performed in conjunction with historical multi-source slope monitoring data to identify areas with potential instability risk. Using the slope risk assessment data as a reference, the stability of each area is further analyzed, and higher-risk areas are identified as unstable by setting thresholds. To identify unstable areas, Geographic Information System (GIS) technology and spatial analysis methods are used, combined with slope topographic and geological data, to identify areas most affected by risk factors. Specifically, if a region's risk level exceeds a set threshold and its monitoring data exhibits an abnormal trend, the region is identified as unstable. Ultimately, analysis data for slope instability areas is generated.

[0148] Step S55: performing time series prediction on the slope instability area analysis data to obtain slope time series risk prediction data;

[0149] This embodiment of the present invention performs time-series prediction of unstable slope areas based on analysis data of unstable slope areas. First, the analysis data of identified unstable slope areas is combined with historical monitoring data. Time series analysis methods are used to analyze the changing trends of the monitoring data for each unstable area to perform time-series prediction. This process uses the ARIMA (Autoregressive Integrated Moving Average) model, a long short-term memory (LSTM) network, or other prediction methods suitable for time-series data based on the fluctuations, changing trends, and seasonality factors in the historical data to establish a time-series prediction model for unstable areas. This model predicts future risk changes in unstable areas based on the temporal relationships of the historical data, thereby generating time-series slope risk prediction data. This process uses real-time monitoring data and historical data of the slope as input, and the prediction results calculated by the model are output. During model training, by comparing the predicted results with the actual monitoring data, model parameters are adjusted to improve prediction accuracy.

[0150] Step S56: Integrate the slope risk level assessment data, the slope instability area analysis data, and the slope time series risk prediction data to obtain slope risk warning information data.

[0151] The embodiment of the present invention first integrates the slope risk level assessment data, the slope instability area analysis data and the slope time series risk prediction data, aligns the various types of data in chronological order, and standardizes them according to the source and type of different data. The consistency of the data is ensured by unifying the format and unit. On this basis, combined with multi-dimensional data analysis, the various types of data are comprehensively evaluated using weighted average, decision tree analysis or principal component analysis data fusion technology to obtain the overall risk warning information of the slope. Specifically, the slope risk level assessment data provides an overall level of risk, while the slope instability area analysis data highlights the risk situation in a specific area, and the time series risk prediction data further predicts the risk changes in the future. Through weighted fusion, timely slope risk warning information data is obtained.

[0152] The present invention helps to accurately assess the causes of slope risks by identifying key stability factors and classifying risk factors, facilitating subsequent risk management and emergency response. Through risk factor weight analysis, the degree of influence of each risk factor can be quantified, providing a scientific basis for slope risk assessment. Through slope risk level assessment, accurate emergency response plans can be provided for slopes of different risk levels, improving the scientific nature and effectiveness of risk prevention and control. Through the identification of unstable areas of the slope, it helps to locate potential dangerous areas and take prevention and control measures in a timely manner. Through the time-series risk prediction of the slope, the changing trend of the slope risk can be predicted, providing dynamic support for early warning and decision-making. By integrating the slope risk level, unstable area and time-series prediction data, the real-time risk status of the slope can be fully reflected, providing accurate and comprehensive risk warning information for emergency response and decision-making.

[0153] Preferably, step S6 includes the following steps:

[0154] Step S61: Designing response strategies for each risk level based on the slope risk warning information data to obtain slope risk classification response data;

[0155] The embodiment of the present invention designs response strategies for each risk level based on slope risk warning information data. First, the slope risk warning information data is analyzed to identify the specific circumstances of each risk level, including the level of risk, scope of impact, and duration. Different response strategies are designed based on the risk level. For high-risk areas, emergency response strategies are designed; for medium-risk areas, medium-emergency response measures are designed; and for low-risk areas, conventional monitoring and warning strategies are designed. In this process, detailed slope risk grading response data is formulated using slope stability prediction data, real-time monitoring data, and historical data as a basis, combined with risk assessment standards in the field of geological engineering.

[0156] Step S62: performing emergency treatment demand analysis on the slope risk classification response data based on the real-time updated slope digital twin model to obtain slope emergency treatment demand data;

[0157] The embodiment of the present invention uses a real-time updated digital twin model of the slope to analyze the emergency treatment needs of the slope risk classification response data. First, the current state of the slope is comprehensively analyzed using the real-time geological data, slope stress and displacement field data, historical monitoring data, and environmental factor data reflected in the slope digital twin model. The slope risk classification response data is used as input to identify the potential emergency treatment needs corresponding to each risk level, including slope reinforcement, enhanced monitoring, or personnel evacuation needs. For high-risk areas, the slope instability trend, deformation rate, and soil moisture change factors reflected in the analysis model are used to accurately assess the type and quantity of emergency resource requirements. For medium and low-risk areas, the treatment needs are refined item by item based on the predicted slope stability. During this process, the actual changes in the slope are continuously tracked through the real-time update of the slope digital twin model, ensuring that the emergency demand analysis is always based on the latest data information, and ultimately generating slope emergency treatment demand data.

[0158] Step S63: Optimizing the emergency strategies for each risk scenario on the slope emergency treatment demand data to obtain slope emergency response plan data;

[0159] The embodiment of the present invention optimizes the emergency strategy for each risk scenario based on the slope emergency treatment demand data. First, by analyzing the slope emergency treatment demand data, the emergency response strategies required for areas with different risk levels are extracted. In high-risk areas, the possibility of slope instability is first analyzed, and targeted emergency measures are designed in combination with real-time slope displacement, stress changes, and rainfall factors. Medium-risk areas focus on setting up local monitoring equipment to carry out emergency response and personnel evacuation preparations in a timely manner. In low-risk areas, conventional monitoring and early warning response strategies are adopted to ensure that timely responses can be made in the event of an abnormality. For each risk scenario, different emergency response plans are simulated and optimized to select the optimal emergency response plan. In this process, the historical multi-source monitoring data and real-time monitoring data of the slope are combined, and the weights and implementation steps of each emergency strategy are adjusted in real time through the analysis of the slope digital twin model, and finally the optimized slope emergency response plan data is obtained.

[0160] Step S64: adjusting the slope emergency response plan data in real time based on the real-time updated slope digital twin model to obtain real-time updated slope risk warning information data;

[0161] An embodiment of the present invention adjusts slope emergency response plan data in real time based on a real-time updated slope digital twin model. First, the slope stability data collected by the real-time monitoring system is combined with historical data and model predictions to update the various parameters in the slope digital twin model to ensure that it reflects the current state of the slope. Based on the updated model and combined with slope risk warning information data, the slope risk level is reassessed, and the emergency response plan data is adjusted accordingly. For example, in the event of heavy rainfall, the model can calculate the impact of precipitation on the slope in real time and determine whether immediate reinforcement, water diversion, or other intervention measures are needed. For high-risk areas, the model calculates the trend of slope displacement changes and determines whether additional monitoring equipment or strengthened protective measures in surrounding areas are needed. During this process, combined with real-time feedback from the slope digital twin model, the emergency response plan can be flexibly adjusted to ensure real-time response to changes in slope risk and timely warning or emergency response. Through this process, real-time updated slope risk warning information data can be generated.

[0162] Step S65: Dynamically integrate the real-time updated slope risk warning information data with the slope emergency response plan data to obtain the real-time updated emergency response measure data.

[0163] An embodiment of the present invention dynamically fuses real-time updated slope risk warning information data with slope emergency response plan data. First, the slope status data obtained by the real-time monitoring system is combined with the updated slope risk warning information data to analyze the current slope risk level and determine emergency response measures for different risk levels. During this process, the real-time updated slope digital twin model is used to reflect the current condition of the slope, ensuring the accuracy and timeliness of the data. By fusing the slope risk warning information data with the emergency response plan data, dynamic emergency response measures based on the current state of the slope are formed. For example, when abnormal displacement is detected in a certain area of the slope, slope protection measures, including reinforcement, water diversion, or other emergency treatment measures, are adjusted in real time to address the risk of slope instability. During the dynamic fusion process, relevant parameters need to be monitored and updated in real time to ensure that all emergency response measures can be adjusted in a timely manner according to the actual situation of the slope changes, thereby providing effective support for real-time emergency decision-making and ultimately generating real-time updated emergency response measures data.

[0164] By designing response strategies for each risk level, the present invention can formulate effective emergency plans for different risk levels and improve the accuracy and pertinence of emergency responses. By performing emergency treatment demand analysis on slope risk classification response data, the specific emergency measures required for each risk level can be clearly identified, ensuring the accuracy and effectiveness of response needs. By optimizing the emergency strategies for each risk scenario, it can be ensured that emergency response measures minimize the harm to people and property caused by slope disasters, and improve the scientific nature and adaptability of emergency plans. By adjusting the slope emergency response plan in real time, emergency measures can be dynamically optimized according to real-time monitoring data, ensuring that the emergency response is always consistent with the actual state of the slope, and improving the flexibility and timeliness of the emergency response. By dynamically fusing the real-time updated slope risk warning information with the emergency response plan data, it is possible to achieve seamless connection between the slope state and the emergency measures, ensuring the real-time and comprehensiveness of the emergency response.

[0165] Preferably, the present invention further provides a real-time monitoring and early warning system for mountain engineering slopes based on digital twins, which is used to execute the above-mentioned real-time monitoring and early warning method for mountain engineering slopes based on digital twins. The real-time monitoring and early warning system for mountain engineering slopes based on digital twins includes:

[0166] The slope real-time monitoring and digital twin initial construction module is used to obtain the original slope real-time monitoring data and perform standardization processing to obtain standardized slope real-time monitoring data; the standardized slope real-time monitoring data is used to construct a digital twin model to obtain a preliminary slope digital twin model;

[0167] The slope historical data fusion and model dynamic update module is used to obtain historical multi-source monitoring data of the slope, and dynamically update the preliminary slope digital twin model using the standardized real-time monitoring data of the slope and the historical multi-source monitoring data of the slope to obtain a real-time updated slope digital twin model;

[0168] The slope multi-physics coupling analysis and stability assessment module is used to perform multi-physics coupling analysis on the real-time updated slope digital twin model, and to assess slope stability based on the multi-physics coupling analysis results to obtain slope stability analysis data;

[0169] The slope nonlinear analysis and stability prediction module is used to perform nonlinear analysis on the slope stability analysis data and use the nonlinear analysis results to predict the slope stability and obtain the slope stability prediction data;

[0170] The slope risk assessment and early warning module is used to conduct slope risk assessment based on slope stability prediction data, and to issue risk early warnings based on the risk assessment results to obtain slope risk early warning information data;

[0171] The emergency response measures formulation and dynamic update module is used to formulate emergency response measures based on slope risk warning information data, and use the real-time updated slope digital twin model to update the slope risk warning information data and emergency response measures in real time to obtain real-time updated emergency response measures data.

[0172] The present invention acquires and standardizes real-time slope monitoring data to construct a preliminary digital twin model of the slope, providing an accurate data basis for subsequent slope status analysis and prediction. The digital twin model is dynamically updated by combining historical data with real-time monitoring data to ensure that the model always reflects the actual status of the slope, improving the accuracy and reliability of the prediction. A comprehensive assessment of the slope through multi-physics field coupling analysis can accurately analyze the stability of the slope and provide a scientific basis for emergency decision-making. Through nonlinear analysis and stability prediction, the instability trend of the slope can be identified, providing early warning for disaster prevention and mitigation. Risk assessment is carried out based on stability prediction data, and risk warnings are issued in a timely manner to ensure the efficiency and preventive nature of slope risk management. Emergency response measures are dynamically formulated and adjusted based on risk warning information to ensure real-time synchronization of emergency treatment plans with the actual risk status of the slope, thereby improving the flexibility and timeliness of emergency responses.

[0173] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is not limited by the above description. Therefore, it is intended that all changes that fall within the meaning and scope of the equivalent elements of the application documents are included in the present invention.

[0174] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A real-time monitoring and early warning method for mountain engineering slopes based on digital twins, characterized in that: The following steps are involved: Step S1: Obtain original real-time slope monitoring data and perform standardization processing to obtain standardized real-time slope monitoring data; use the standardized real-time slope monitoring data to construct a digital twin model to obtain a preliminary digital twin model of the slope; Step S2: Obtain historical multi-source monitoring data of the slope, and dynamically update the preliminary slope digital twin model using the standardized real-time monitoring data of the slope and the historical multi-source monitoring data of the slope to obtain a real-time updated slope digital twin model; Step S3: Perform multi-physics field coupling analysis on the real-time updated slope digital twin model, and perform slope stability assessment based on the multi-physics field coupling analysis results to obtain slope stability analysis data; Step S4: performing nonlinear analysis on the slope stability analysis data, and using the nonlinear analysis results to predict slope stability, thereby obtaining slope stability prediction data; Step S5: performing slope risk assessment based on the slope stability prediction data, and performing risk warning according to the risk assessment results to obtain slope risk warning information data; Step S6: formulating emergency response measures based on the slope risk warning information data, and using the real-time updated slope digital twin model to update the slope risk warning information data and emergency response measures in real time to obtain real-time updated emergency response measure data; Step S1 includes the following steps: Step S11: acquiring real-time slope status data and slope environmental parameter data of a mountain engineering slope, and recording the real-time slope status data and slope environmental parameter data as original slope real-time monitoring data; Step S12: performing data preprocessing and standardization on the original real-time slope monitoring data to obtain standardized real-time slope monitoring data; Step S13: performing slope geometry modeling based on the standardized slope real-time monitoring data to obtain a slope geometry model; Step S14: performing slope physical property analysis on the standardized slope real-time monitoring data to obtain slope physical property data; Step S15: performing slope mechanical behavior modeling based on the standardized slope real-time monitoring data and slope physical property data to obtain a slope mechanical model; Step S16: simulating slope environmental impact factors based on the standardized real-time slope monitoring data to obtain a slope environmental impact model; Step S17: fusing the slope geometry model, slope mechanical model, and slope environmental impact model based on the slope physical property data to obtain a preliminary slope digital twin model; Step S2 includes the following steps: Step S21: Obtain historical multi-source monitoring data of the slope, and compare and analyze the standardized real-time monitoring data of the slope with the historical multi-source monitoring data of the slope to obtain slope state difference analysis data; Step S22: adjusting parameters of the preliminary slope digital twin model based on the standardized slope real-time monitoring data and the slope state difference analysis data to obtain a revised slope digital twin model; Step S23: performing conformity verification on the modified slope digital twin model to obtain model verification result data; Step S24: dynamically updating the modified slope digital twin model using the model verification result data to obtain a real-time updated slope digital twin model; Step S3 includes the following steps: Step S31: performing geomechanical analysis on the real-time updated slope digital twin model to obtain slope geomechanical analysis data; Step S32: performing thermodynamic behavior analysis on the real-time updated slope digital twin model to obtain slope thermodynamic analysis data; Step S33: simulating various hydrological and meteorological conditions based on the real-time updated slope digital twin model to obtain slope hydrological and meteorological simulation data; Step S34: performing slope dynamic response simulation based on the real-time updated slope digital twin model to obtain slope dynamic response data; Step S35: performing multi-physics field coupling analysis on the slope geomechanical analysis data, the slope thermodynamic analysis data, the slope hydrometeorological simulation data, and the slope dynamic response data to obtain slope multi-physics field coupling analysis data; Step S36: performing slope stability assessment based on the slope multi-physics field coupling analysis data to obtain slope stability analysis data; Step S36 includes the following steps: Step S361: identifying slope stress concentration areas based on the slope multi-physics field coupling analysis data to obtain slope stress field distribution data; Step S362: determining the position of the slope slip surface based on the slope stress distribution data to obtain slope slip surface analysis data; Step S363: performing displacement field calculation on the slope multi-physics field coupling analysis and performing slope deformation trend analysis to obtain slope displacement field analysis data; Step S364: quantifying the slope instability probability based on the slope slip surface analysis data and the slope displacement field analysis data to obtain slope instability probability assessment data; Step S365: using the slope multi-physics field coupling analysis data to classify the slope instability probability assessment data into stability grades to obtain slope stability grade data; Step S366: Integrate the slope instability probability assessment data and the slope stability grade data to obtain slope stability analysis data; Step S4 includes the following steps: Step S41: using the slope stability analysis data to perform nonlinear mechanical modeling, and performing nonlinear change analysis of the slope stress state to obtain nonlinear mechanical analysis data of the slope; Step S42: performing nonlinear analysis of deformation behavior of each slope under stress conditions based on the slope multi-physics field coupling analysis data to obtain slope nonlinear deformation analysis data; Step S43: performing nonlinear time series analysis of the slope key risk area on the slope stability analysis data to obtain slope nonlinear time series analysis data; Step S44: performing slope instability prediction based on the slope nonlinear mechanical analysis data, the slope nonlinear deformation analysis data, and the slope nonlinear time series analysis data to obtain slope stability prediction data; Step S41 includes the following steps: Step S411: using the slope stability analysis data to perform stress-strain relationship modeling to obtain slope nonlinear stress-strain model data; Step S412: Calculating the mechanical properties of the slope under various stress conditions using the slope nonlinear stress-strain model data to obtain slope stress characteristic analysis data; Step S413: performing a stability evaluation on the slope stress characteristic analysis data under various mechanical conditions to obtain slope nonlinear stability analysis data; Step S414: Modeling the time series change of the slope stress state based on the slope nonlinear stability analysis data to obtain the time series data of the nonlinear change of the slope stress state; Step S415: identifying key change points of the nonlinear change time series data of the slope stress state, and analyzing the change law based on the key change point identification results to obtain key change law data of the slope stress state; Step S416: Integrate the slope nonlinear stress-strain model data and the key change law data of the slope stress state to obtain the slope nonlinear mechanical analysis data.

2. The method for real-time monitoring and early warning of mountain engineering slopes based on digital twins according to claim 1 is characterized in that: Step S5 includes the following steps: Step S51: identifying key stability factors for the slope stability prediction data and classifying risk factors to obtain slope risk factor classification data; Step S52: performing risk factor weight analysis based on the slope risk factor classification data to obtain slope risk weight analysis data; Step S53: using the slope stability prediction data and the slope risk weight analysis data to perform slope risk level assessment to obtain slope risk level assessment data; Step S54: using the standardized real-time slope monitoring data and the historical multi-source slope monitoring data to identify the slope instability area of the slope risk level assessment data, and obtaining slope instability area analysis data; Step S55: performing time series prediction on the slope instability area analysis data to obtain slope time series risk prediction data; Step S56: Integrate the slope risk level assessment data, the slope instability area analysis data, and the slope time series risk prediction data to obtain slope risk warning information data.

3. The real-time monitoring and early warning method for mountain engineering slopes based on digital twins according to claim 2 is characterized in that: Step S6 includes the following steps: Step S61: Designing response strategies for each risk level based on the slope risk warning information data to obtain slope risk classification response data; Step S62: performing emergency treatment demand analysis on the slope risk classification response data based on the real-time updated slope digital twin model to obtain slope emergency treatment demand data; Step S63: Optimizing the emergency strategies for each risk scenario on the slope emergency treatment demand data to obtain slope emergency response plan data; Step S64: adjusting the slope emergency response plan data in real time based on the real-time updated slope digital twin model to obtain real-time updated slope risk warning information data; Step S65: Dynamically integrate the real-time updated slope risk warning information data with the slope emergency response plan data to obtain real-time updated emergency response measure data.

4. A real-time monitoring and early warning system for mountain engineering slopes based on digital twins, characterized by: Used to execute the real-time monitoring and early warning method for mountain engineering slopes based on digital twins according to claim 1, the real-time monitoring and early warning system for mountain engineering slopes based on digital twins comprises: The slope real-time monitoring and digital twin initial construction module is used to obtain the original slope real-time monitoring data and perform standardization processing to obtain standardized slope real-time monitoring data; the standardized slope real-time monitoring data is used to construct a digital twin model to obtain a preliminary slope digital twin model; The slope historical data fusion and model dynamic update module is used to obtain historical multi-source monitoring data of the slope, and dynamically update the preliminary slope digital twin model using the standardized real-time monitoring data of the slope and the historical multi-source monitoring data of the slope to obtain a real-time updated slope digital twin model; The slope multi-physics coupling analysis and stability assessment module is used to perform multi-physics coupling analysis on the real-time updated slope digital twin model, and to assess slope stability based on the multi-physics coupling analysis results to obtain slope stability analysis data; The slope nonlinear analysis and stability prediction module is used to perform nonlinear analysis on the slope stability analysis data and use the nonlinear analysis results to predict the slope stability and obtain the slope stability prediction data; The slope risk assessment and early warning module is used to conduct slope risk assessment based on slope stability prediction data, and to issue risk early warnings based on the risk assessment results to obtain slope risk early warning information data; The emergency response measures formulation and dynamic update module is used to formulate emergency response measures based on slope risk warning information data, and use the real-time updated slope digital twin model to update the slope risk warning information data and emergency response measures in real time to obtain real-time updated emergency response measures data.

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