Real-time monitoring and early warning method and system for mountainous area engineering slope based on digital twinning

Abstract

The invention relates to the technical field of information data processing, in particular to a mountain area engineering slope real-time monitoring and early warning method and system based on digital twinning. The method comprises the following steps: acquiring original slope real-time monitoring data, and carrying out standardization processing to obtain standardized slope real-time monitoring data; the method comprises the following steps: constructing a digital twinborn model by using standardized slope real-time monitoring data to obtain a preliminary slope digital twinborn model; and dynamically updating the preliminary side slope digital twinborn model by using the standardized side slope real-time monitoring data and the side slope historical multi-source monitoring data to obtain a real-time updated side slope digital twinborn model. According to the method, the digital twinborn model is updated in real time, multi-physics coupling analysis and nonlinear stability prediction are combined, it is ensured that dynamic monitoring, stability evaluation, risk prediction and emergency response of the slope can reflect the actual condition in time, and therefore the scientificity and timeliness of disaster prevention and control are improved.

Description

Technical Field

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

[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 and are difficult to reflect the real-time changes in slope states. Once a static model is established, it will gradually deviate from the actual situation during long-term operation. Especially when the slope undergoes dynamic adjustments due to geological condition changes, rainfall, or human activities, this deviation will lead to serious judgment errors, thereby affecting the reliability and timeliness of decision-making. Existing slope monitoring systems usually lack the collaborative ability with emergency response measures and cannot achieve real-time linkage between early warning information and emergency decision-making. Once an abnormality occurs on the slope, it is very difficult for existing systems to quickly generate effective emergency response plans, resulting in serious impacts on 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 object, a real-time monitoring and early warning method for mountain engineering slopes based on digital twins includes the following steps: Step S1: Obtain the original real-time monitoring data of the slope and perform standardization processing to obtain the standardized real-time monitoring data of the slope; use the standardized real-time monitoring data of the slope to construct a digital twin model to obtain a preliminary digital twin model of the slope; Step S2: Obtain the historical multi-source monitoring data of the slope, and use the standardized real-time monitoring data of the slope and the historical multi-source monitoring data of the slope to dynamically update the preliminary digital twin model of the slope to obtain a real-time updated digital twin model of the slope; Step S3: Perform multi-physical field coupling analysis on the real-time updated digital twin model of the slope, and perform slope stability assessment based on the results of the multi-physical field coupling analysis to obtain slope stability analysis data; Step S4: Perform non-linear analysis on the slope stability analysis data, and use the results of the non-linear analysis to predict the slope stability to obtain slope stability prediction data; Step S5: Perform slope risk assessment based on the slope stability prediction data, and perform risk early warning according to the results of the risk assessment to obtain slope risk early warning information data; Step S6: Formulate emergency response measures based on the slope risk warning information data, and use the real-time updated slope digital twin model to update the slope risk warning information data and the emergency response measures in real time to obtain real-time updated emergency response measure data.

[0005] By obtaining the original real-time slope monitoring data and performing standardized processing, the present invention can ensure the unity and comparability of the data, providing an accurate basis for the subsequent construction of the digital twin model. After establishing the preliminary slope digital twin model, the model is dynamically updated by combining the standardized real-time monitoring data and the historical multi-source monitoring data of the slope, so as to ensure that the digital twin model can reflect the current actual situation of the slope and avoid the deviation in the long-term operation of the traditional static model. This real-time update ability improves the reliability and timeliness of the model and can reflect the dynamic adjustment of the slope caused by geological changes, rainfall or human activities in real time. The multi-physical field coupling analysis plays a key role in the slope stability assessment, which can comprehensively consider various influencing factors and provide a basis for accurately assessing the slope stability. Through non-linear analysis, the stability prediction is further carried out to identify the potential instability risks of the slope and give early warnings. On this basis, the slope risk assessment is carried out and the risk warning is triggered, so that the risk management system can respond in time to prevent disasters from occurring. Finally, the emergency response measures are formulated based on the risk warning information, and the warning and emergency measures are dynamically adjusted through the real-time updated digital twin model to ensure that the emergency response can closely match the actual situation in case of emergencies, improving the scientificity and timeliness of disaster prevention and control.

[0006] Preferably, the present invention also provides a real-time monitoring and warning system for mountain engineering slopes based on digital twins, which is used to execute the above-mentioned real-time monitoring and warning method for mountain engineering slopes based on digital twins. The real-time monitoring and warning system for mountain engineering slopes based on digital twins includes: A slope real-time monitoring and digital twin initial construction module, which is used to obtain the original slope real-time monitoring data, perform standardized processing to obtain standardized slope real-time monitoring data, and use the standardized slope real-time monitoring data to construct a digital twin model to obtain a preliminary slope digital twin model; A slope historical data fusion and model dynamic update module, which is used to obtain the historical multi-source monitoring data of the slope, and use the standardized slope real-time monitoring data and the historical multi-source monitoring data of the slope to dynamically update the preliminary slope digital twin model to obtain a real-time updated slope digital twin model; A slope multi-physical field coupling analysis and stability assessment module, which is used to perform multi-physical field coupling analysis on the real-time updated slope digital twin model, and perform slope stability assessment based on the results of the multi-physical field coupling analysis to obtain slope stability analysis data; The slope non - linear analysis and stability prediction module is used to perform non - linear analysis on slope stability analysis data, and use the non - linear analysis results to predict slope stability, obtaining 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 perform risk early warning according to the risk assessment results, obtaining slope risk early warning information data; The emergency response measure formulation and dynamic update module is used to formulate emergency response measures based on slope risk early warning information data, and use the real - time updated slope digital twin model to real - time update the slope risk early warning information data and emergency response measures, obtaining real - time updated emergency response measure data.

[0007] The present invention obtains and standardizes slope real - time monitoring data, constructs a preliminary slope digital twin model, providing an accurate data basis for subsequent slope state analysis and prediction. Dynamically updates the digital twin model by combining historical data and real - time monitoring data to ensure that the model always reflects the actual state of the slope, improving the accuracy and reliability of prediction. Conducts a comprehensive assessment of the slope through multi - physical - field coupling analysis, which can accurately analyze the slope stability and provide a scientific basis for emergency decision - making. Through non - linear analysis and stability prediction, it can identify the instability trend of the slope, providing early warning for disaster prevention and mitigation. Conducts risk assessment based on stability prediction data and issues risk early warning in a timely manner to ensure the efficiency and preventive nature of slope risk management. Dynamically formulates and adjusts emergency response measures according to risk early warning information to ensure the real - time synchronization of the emergency response plan with the actual risk state of the slope, improving the flexibility and timeliness of emergency response. Brief Description of the Drawings

[0008] By reading the detailed description of the non - restrictive embodiments with reference to the following drawings, other features, objectives, and advantages of the present invention will become more apparent: Figure 1 It is a schematic diagram of the step - by - step process of the real - time monitoring and early warning method for mountain engineering slopes based on digital twin of the present invention; Figure 2 For Figure 1 the detailed step - by - step process schematic diagram of step S1 in; Figure 3 For Figure 1 the detailed step - by - step process schematic diagram of step S2 in. Detailed Embodiment

[0009] The following clearly and completely describes the technical method of the present invention with reference to the drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0010] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0011] It should be understood that although terms such as "first" and "second" may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0012] To achieve the above object, 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, and the method includes the following steps: Step S1: Obtain the original real-time monitoring data of the slope, and perform standardization processing to obtain the standardized real-time monitoring data of the slope; use the standardized real-time monitoring data of the slope to construct a digital twin model to obtain a preliminary slope digital twin model; Step S2: Obtain the historical multi-source monitoring data of the slope, and use the standardized real-time monitoring data of the slope and the historical multi-source monitoring data of the slope to dynamically update the preliminary slope digital twin model to obtain a real-time updated slope digital twin model; Step S3: Perform multi-physical field coupling analysis on the real-time updated slope digital twin model, and perform slope stability evaluation based on the results of the multi-physical field coupling analysis to obtain slope stability analysis data; Step S4: Perform non-linear analysis on the slope stability analysis data, and use the results of the non-linear analysis to predict the slope stability to obtain slope stability prediction data; Step S5: Perform slope risk assessment based on the slope stability prediction data, and perform risk early warning according to the results of the risk assessment to obtain slope risk early warning information data; Step S6: Develop emergency response measures based on the slope risk early warning information data, and use the real-time updated slope digital twin model to real-time update the slope risk early warning information data and the emergency response measures to obtain real-time updated emergency response measure data.

[0013] In the embodiment of the present invention, with reference to Figure 1 As shown, it is a schematic diagram of the step flow of a real-time monitoring and early warning method for mountain engineering slopes based on digital twins according to the present invention. In this example, the real-time monitoring and early warning method for mountain engineering slopes based on digital twins includes the following steps: Step S1: Obtain the original slope real-time monitoring data, perform standardization processing to obtain the standardized slope real-time monitoring data; use the standardized slope real-time monitoring data to construct a digital twin model to obtain a preliminary slope digital twin model; In the embodiment of the present invention, a high-precision sensor network is deployed on the mountain engineering slope to obtain the original slope real-time monitoring data. The sensor network includes, but is not limited to, inclination sensors, displacement sensors, strain sensors, pore water pressure sensors, and rainfall monitoring equipment. Each sensor is reasonably arranged according to the slope geometric characteristics, potential risk area distribution, and environmental conditions to ensure the comprehensiveness and accuracy of the monitoring data; the original data collected by all sensors is 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 original monitoring data, including removing abnormal data, filling in missing data, and eliminating data noise to ensure the authenticity and integrity of the data; subsequently, the cleaned data is subjected to standardization processing, and the data ranges of each monitoring variable are unified to [0,1] through a normalization method to eliminate the influence of different dimensions and orders of magnitude on subsequent analysis, obtaining the standardized slope real-time monitoring data; use this standardized slope real-time monitoring data to construct a digital twin model. The specific implementation method is to establish a virtual model of the digital twin of the slope based on the geometric shape, geological conditions, and engineering characteristics of the slope, combined with the monitoring data, reconstruct the slope structure using a three-dimensional modeling tool, and drive the dynamic update of the virtual model through real-time monitoring data, and finally generate a preliminary slope digital twin model corresponding to the slope physical entity.

[0014] Step S2: Obtain the historical multi-source monitoring data of the slope, and use the standardized slope real-time monitoring data and the historical multi-source monitoring data of the slope to dynamically update the preliminary slope digital twin model to obtain a real-time updated slope digital twin model; In the embodiments of the present invention, first, historical multi-source monitoring data of the slope is obtained. The historical multi-source monitoring data of the slope includes geological data, environmental data, engineering data, and historical disaster records accumulated in slope monitoring over a long time. The geological data includes the physical and mechanical parameters of the slope rock and soil mass. The environmental data includes regional meteorological information and underground water level change information. 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 subjected to consistency processing through data fusion technology to solve the problems of inconsistent data spatio-temporal resolution and dimension. The processed historical data and the standardized real-time monitoring data of the slope are input into the data processing platform. Based on time series analysis technology, the correlation relationship between the real-time data and the historical data is established, and the weights of the historical data and the real-time data are allocated through a dynamic weighted fusion algorithm, so as to realize the effective utilization of the historical data. The dynamic update algorithm is used to update the preliminary slope digital twin model in combination with the processed data. The specific operation is to add a historical data-driven 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.

[0015] Step S3: Perform multi-physical field coupling analysis on the real-time updated slope digital twin model, and perform slope stability assessment based on the results of the multi-physical field coupling analysis to obtain slope stability analysis data; In the embodiments of the present invention, first, the slope structure, geological conditions, environmental factors, and historical record data in the real-time updated slope digital twin model are integrated and processed to ensure the integrity and consistency of the input data. The multi-physical field coupling analysis of the real-time updated slope digital twin model is carried out using a high-performance computing platform. By introducing various physical field parameters such as the mechanical field, thermal field, hydraulic field, and dynamic field, a multi-physical field coupling equation is established using the finite element calculation method and the multi-field coupling algorithm to accurately simulate the response characteristics of the slope under different stress states and environmental changes. The physical and mechanical properties of the slope material are determined using the geological condition data, and the stress distribution and strain state of the slope under self-weight and external loads are calculated through mechanical field analysis. The environmental data is introduced to simulate the thermal field, and the influence of thermal stress caused by temperature gradient on the slope stability is analyzed. The pore water pressure distribution and seepage force change of the slope under rainfall and groundwater seepage are evaluated through hydraulic field calculation. Combining the dynamic field analysis, the dynamic response and cumulative deformation characteristics caused by earthquake or vibration loads are determined. The above analysis results are data-fused, and the multi-field coupling algorithm is used to comprehensively evaluate the interaction between physical fields, extract the stability influencing factors of the slope under complex conditions and their changing rules. Finally, the multi-physical field coupling analysis results are output as slope stability analysis data in numerical and visual forms.

[0016] Step S4: Perform a nonlinear analysis on the slope stability analysis data, and use the nonlinear analysis results to predict the slope stability to obtain slope stability prediction data; In the embodiments of the present invention, first, based on the slope stability analysis data, in-depth analysis is carried out using a nonlinear calculation method, and the stress state and deformation characteristics of the slope under different working conditions are studied in detail. By analyzing the stress response of the slope under complex stress conditions, combined with the nonlinear constitutive relationship of the slope material, a nonlinear mechanical analysis is performed using a high-performance computing platform to calculate the stress distribution, plastic deformation region, and crack propagation characteristics of the slope. During the further analysis process, based on the slope geomechanical characteristic data 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 external loads. After the mechanical analysis is completed, the nonlinear characteristics of the slope deformation behavior are extracted, and through the comparative analysis of the slope monitoring data, the deformation curve and instability trend of the slope under multiple working conditions are verified. At the same time, the nonlinear change characteristics of different slope structure regions are summarized to clarify the key nodes and dangerous areas of the change in the slope stress state. Finally, the above analysis results are numerically and visually processed and output as slope stability prediction data.

[0017] Step S5: Perform a slope risk assessment based on the slope stability prediction data, and issue a risk warning according to the risk assessment result to obtain slope risk warning information data; In the embodiments of the present invention, first, based on the slope stability prediction data, combined with the slope historical monitoring data, real-time monitoring data, and slope geomechanical characteristics, slope risk assessment is carried out. In this process, first, by comparing and analyzing the stability prediction data of the slope under different working conditions, the key areas and periods of disasters are identified. Secondly, statistical analysis methods, such as regression analysis and clustering analysis, are used to quantitatively analyze the stability change trends of each monitoring point of the slope, and the risk levels of the slope under different environmental conditions are evaluated. On this basis, the risk matrix method is used to classify the risk degree of the slope, and combined with the deformation monitoring data of the slope, the risk levels of potential dangerous areas are further confirmed. During the assessment process, the influence of the slip surface, crack expansion, and hydro-meteorological factors of the slope is particularly considered, and a comprehensive analysis is carried out by combining the geomechanical model and the risk assessment method. By setting multiple risk level thresholds, the slope stability is quantitatively classified to obtain slope risk assessment data. Finally, the evaluation results are compared with the historical data and real-time monitoring data, and combined with the actual risk threshold, the risk level and potential risk warning of the slope are output to form slope risk warning information data.

[0018] Step S6: Based on the slope risk warning information data, formulate emergency response measures, and use the real-time updated slope digital twin model to real-time update the slope risk warning information data and the emergency response measures to obtain real-time updated emergency response measure data.

[0019] In the embodiments of the present invention, first, based on the slope risk warning information data, combined with the data of the real-time updated slope digital twin model, formulate emergency response measures. The specific operations include comprehensively analyzing the current state, historical data, stability prediction data, and risk assessment data of the slope to identify potential risk areas and high-risk periods. During this process, the real-time monitoring data of the slope is used to analyze each monitoring point, especially paying attention to those areas in the critical dangerous state. Then, based on the slope stability analysis and risk assessment data, combined with the corresponding geological conditions, meteorological changes, and soil moisture change factors, formulate targeted emergency response measures. These measures include, but are not limited to, slope reinforcement, improvement of the drainage system, and setting up temporary refuge areas. During the process of formulating the measures, the real-time updated digital twin model is used to further optimize the emergency response plan by dynamically simulating the effects of different emergency measures. At the same time, the digital twin model is used to real-time update the emergency response measure data to ensure that the response measures can be adjusted at any time to cope with sudden slope changes or changes in external factors. Finally, through the feedback of the real-time monitoring system, all emergency response measures are updated to form real-time updated emergency response measure data.

[0020] By acquiring the real-time monitoring data of the original slope and performing standardized processing, the present invention can ensure the unity and comparability of the data, providing an accurate basis for the construction of the subsequent digital twin model. After establishing the preliminary slope digital twin model, the model is dynamically updated by combining the standardized real-time monitoring data with the historical multi-source monitoring data of the slope, so as to ensure that the digital twin model can reflect the current actual situation of the slope and avoid the deviation in the long-term operation of the traditional static model. This real-time update ability improves the reliability and timeliness of the model and can reflect the dynamic adjustment of the slope caused by geological changes, rainfall or human activities in real time. The multi-physical field coupling analysis plays a key role in the slope stability assessment, which can comprehensively consider various influencing factors and provide a basis for accurately evaluating the slope stability. Through non-linear analysis, further stability prediction is carried out to identify the potential instability risks of the slope and give early warnings. On this basis, slope risk assessment is carried out and risk warnings are triggered, so that the risk management system can respond in a timely manner to prevent disasters from occurring. Finally, based on the risk warning information, emergency response measures are formulated, and the warnings and emergency measures are dynamically adjusted through the real-time updated digital twin model to ensure that the emergency response can closely match the actual situation in case of emergencies, improving the scientificity and timeliness of disaster prevention and control.

[0021] Preferably, step S1 includes the following steps: Step S11: Acquire the real-time slope state data and slope environment parameter data of the mountainous engineering slope, and record the real-time slope state data and slope environment parameter data as the original slope real-time monitoring data; Step S12: Perform data preprocessing and standardized processing on the original slope real-time monitoring data to obtain standardized slope real-time monitoring data; Step S13: Based on the standardized slope real-time monitoring data, perform slope geometric modeling to obtain a slope geometric model; Step S14: Perform slope physical property analysis on the standardized slope real-time monitoring data to obtain slope physical property data; Step S15: Based on the standardized slope real-time monitoring data and slope physical property data, perform slope mechanical behavior modeling to obtain a slope mechanical model; Step S16: Based on the standardized slope real-time monitoring data, perform simulation of slope environmental impact factors to obtain a slope environmental impact model; Step S17: According to the slope physical property data, fuse the slope geometric model, slope mechanical model and slope environmental impact model to obtain a preliminary slope digital twin model.

[0022] As an embodiment of the present invention, refer to Figure 2 as shown, for Figure 1Schematic diagram of the detailed step flow of step S1. In the embodiment of the present invention, step S1 includes the following steps: Step S11: Obtain the real-time slope state data and slope environment parameter data of the mountainous engineering slope, and record the real-time slope state data and slope environment parameter data as the original slope real-time monitoring data; In the embodiment of the present invention, the real-time slope state data and slope environment parameter data are obtained through high-precision sensor devices installed on the mountainous engineering slope. Among them, the real-time slope state data includes the dynamic change data of the surface displacement, inclination angle, and crack width of the slope, and the slope environment parameter data includes the external environment data such as rainfall, wind speed, and temperature; specifically, the sensor devices include total stations, inclinometers, laser rangefinders, as well as rain gauges, anemometers, and temperature and humidity sensors. 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 into a dedicated database at a predetermined time interval through a data acquisition and transmission protocol; before data storage, the data is associated through timestamp marking and sensor numbering, and the working state 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 obtained slope state data and slope environment parameter data into unified original slope real-time monitoring data according to the established signal acquisition rules.

[0023] Step S12: Perform data preprocessing and standardization processing on the original slope real-time monitoring data to obtain standardized slope real-time monitoring data; In the embodiment of the present invention, first, for the noise data in the original slope real-time monitoring data, wavelet transform method is introduced for denoising, and the high-frequency noise components in the signal are removed by the decomposition-reconstruction process; second, for the missing values in the monitoring data, Lagrange interpolation method is used for filling to ensure the continuity of the data; then, for the outliers in the original slope real-time monitoring data, outlier detection method based on IQR (interquartile range) is used for elimination, specifically including calculating the first quartile and the third quartile of the data, and determining the outlier range according to the 1.5 times interquartile range rule, eliminating the data outside the range and using local weighted regression method for replacement; then, the processed data is re-sorted according to the timestamp to ensure the time series consistency of the data; finally, different types of data are standardized through the maximum-minimum normalization method, and the standardized processed data is integrated into standardized slope real-time monitoring data.

[0024] Step S13: Based on the standardized slope real-time monitoring data, perform slope geometric shape modeling to obtain a slope geometric model; In the embodiment of the present invention, based on the real-time monitoring data of the standardized slope, a slope geometric shape model is constructed through 3D laser scanning technology and terrain point cloud data processing methods. First, a 3D laser scanner is used to perform multi-angle scanning and acquisition on the slope area to obtain complete high-precision point cloud data of the slope surface; then, the collected point cloud data is subjected to noise removal and redundant point filtering by the point cloud data preprocessing module. A filtering algorithm based on statistical analysis is used to remove discrete points with large errors, and the dense point cloud data is block-processed to reduce the calculation amount; next, a point cloud data registration algorithm is used to align the coordinates of the point cloud data under different scanning perspectives, and the ICP (Iterative Closest Point) algorithm is used to accurately match the overlapping areas and generate a complete point cloud set under a unified coordinate system; subsequently, the Delaunay triangulation method is used to perform surface reconstruction on the processed point cloud data, and a 3D triangular mesh is formed by connecting points one by one to generate a refined 3D structure of the slope geometric shape; finally, the reconstructed 3D structure is subjected to data format conversion and feature parameter extraction. The extracted parameters include slope surface angle, slope height, slope surface curvature, and geometric feature values of the key crack positions, completing the slope geometric shape modeling.

[0025] Step S14: Perform slope physical property analysis on the real-time monitoring data of the standardized slope to obtain slope physical property data; In the embodiment of the present invention, first, the finite element method is used to perform numerical calculation on the internal stress distribution of the slope. The displacement data and inclination data reflecting the slope state in the real-time monitoring data of the standardized slope are used as boundary conditions and input into the calculation system, and the Gauss integration method is used to solve the stress and strain fields in the region; secondly, combined with the results of permeability tests and moisture content monitoring data, the characteristics of the seepage field inside the slope are calculated through Darcy's law; then, based on the test results of the mechanical properties of the slope materials, including the compressive strength, shear strength, and elastic modulus of rocks or soils, the shear strength characteristics and failure conditions of the slope are analyzed through the Mohr-Coulomb criterion; next, a combination of laboratory tests and on-site monitoring is used to verify the physical properties of the slope such as density, porosity, and permeability coefficient, and a high-precision electronic density meter and a multi-point porosity tester are used for data supplementation and calibration; finally, the stress distribution data, seepage field characteristic parameters, and material physical property parameters obtained from the analysis are integrated into slope physical property data.

[0026] Step S15: Perform slope mechanical behavior modeling based on the real-time monitoring data of the standardized slope and the slope physical property data to obtain a slope mechanical model; In the embodiments of the present invention, first, based on the displacement and strain data in the standardized real-time monitoring data of the slope, and in combination with the elastic modulus and Poisson's ratio of the rock and soil mass in the physical property data of the slope, the generalized Hooke's law is used to calculate the elastic deformation characteristics of the slope; second, based on the plastic mechanics theory, using the shear strength parameters and shear stress data of the slope, and in combination 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 real-time monitoring data of the slope 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 loads, specifically calculating the cumulative displacement and critical instability conditions caused by dynamic loads; then, in combination with the characteristics of the seepage field, the seepage-stress coupling analysis method is used to simulate the mechanical response characteristics of the slope in the saturated state and the unsaturated state, and the influence of seepage pressure on the slope stability is analyzed emphatically; finally, the mechanical analysis results are fused, and the critical stability coefficient and potential slip surface parameters of the slope are output to complete the modeling of the mechanical behavior of the slope and obtain the mechanical model of the slope.

[0027] Step S16: Simulate the slope environmental impact factors based on the standardized real-time monitoring data of the slope to obtain the slope environmental impact model. In the embodiments of the present invention, first, the meteorological parameters in the standardized real-time monitoring data of the slope, such as rainfall intensity, rainfall duration, temperature change, and wind speed, are used, and in combination with the data collected by the environmental monitoring equipment, the time series analysis method is used to extract the change rules of environmental factors; second, based on the rainfall infiltration theory and the hydrological model, the Green-Ampt model is used to calculate the slope infiltration depth and the formation speed of the saturated layer under different rainfall conditions, and at the same time, the moisture migration law inside the slope body is calculated through the permeability coefficient; then, for the influence of temperature and freeze-thaw cycles, the dynamic influence of temperature change on the physical properties of rock and soil materials is analyzed using the material mechanics performance test data, and the thermal stress calculation method is used to evaluate the thermal expansion and contraction effect inside the slope body; then, based on the wind speed and slope body particle size data, the weakening effect of wind erosion on the surface stability of the slope is calculated in combination with the wind erosion equation; finally, the above calculation results are 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 action of the environment are output to obtain the slope environmental impact model.

[0028] Step S17: Integrate the slope geometric model, the slope mechanical model, and the slope environmental impact model according to the slope physical property data to obtain the preliminary slope digital twin model.

[0029] In the embodiments of the present invention, first, through the grid segmentation technology of geometric model data, the spatial topological structure of the slope geometric model is transformed into a discrete point set form that can be used for data fusion, and the coordinate system of the geometric model and other models is unified using a spatial coordinate registration algorithm; second, 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 into the spatial framework of the geometric model through the finite element grid coupling technology; then, using the simulation data of rainfall, temperature, and wind erosion dynamic factors in the environmental impact model, the time series data of the environmental impact is matched with the dynamic responses of the geometric model and the mechanical model, and spatio-temporal joint analysis is realized through the multi-field coupling method; next, the weighted average method in the data fusion algorithm is used to assign weights to the data output by different models to ensure that 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 result; finally, a complete preliminary slope digital twin model is generated based on the fused model.

[0030] By obtaining real-time slope state data and environmental parameter data, the present invention can comprehensively reflect the current state 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 basis for subsequent modeling. Through slope geometric shape modeling, the shape and structural characteristics of the slope can be accurately reproduced, providing detailed geometric information for further analysis. Analyzing the physical properties of the slope reveals the physical properties of the slope, which helps to evaluate its stability and the degree of influence by the external environment. Through mechanical behavior modeling, the response of the slope under different loads can be simulated, predicting potential mechanical changes and risks. The simulation of slope environmental impact factors can consider changes in environmental factors, enhancing the model's response ability to external environmental impacts. Fusing the geometric model, mechanical model, and environmental impact model to obtain a preliminary slope digital twin model lays a foundation for real-time monitoring and risk prediction.

[0031] Preferably, 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: Adjust the parameters of the preliminary slope digital twin model based on the standardized real-time monitoring data of the slope and the slope state difference analysis data to obtain a corrected slope digital twin model; Step S23: Verify the compliance of the corrected slope digital twin model to obtain model verification result data; Step S24: Dynamically update the corrected slope digital twin model using the model verification result data to obtain a real-time updated slope digital twin model.

[0032] As an embodiment of the present invention, with reference to Figure 3 shown in Figure 1 is a detailed step - by - step schematic diagram of step S2 in Step S21: Obtain the 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 the slope state difference analysis data; In the embodiment of the present invention, first, historical data of the slope is obtained, including annual monitoring data, geological exploration data, rainfall data, and meteorological data. 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 obtained historical multi - source monitoring data of the slope. The specific operations include: by comparing the parameters in the real - time monitoring data with the corresponding values of the historical data, the differences between the slope during the real - time monitoring period and the historical data are identified. Using difference analysis methods, such as the normalized difference index method or data fitting method, the differences between the real - time monitoring data and the historical data are quantitatively calculated to obtain the slope state difference analysis data.

[0033] Step S22: Based on the standardized real - time monitoring data of the slope and the slope state difference analysis data, adjust the parameters of the preliminary slope digital twin model to obtain a corrected slope digital twin model; In the embodiment of the present invention, first, based on the standardized real - time monitoring data of the slope and the slope state difference analysis data, the parameters of the preliminary slope digital twin model are adjusted by a numerical optimization method. This process first involves comparing the specific values of the differences between the indicators in the real - time monitoring data and the historical data, and converting these differences into slope state difference analysis data. Then, the key factors causing changes in the slope stability are identified using the difference analysis data, and the parameters in the preliminary slope digital twin model are corrected according to these factors. During the correction process, a method of minimizing errors is adopted to ensure that the adjusted parameters can more accurately reflect the current slope state. Specifically, a 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.

[0034] Step S23: Verify the compliance of the corrected slope digital twin model to obtain model verification result data; In the embodiments of the present invention, the compliance verification of the corrected digital twin model of the slope is first carried out. The corrected digital twin model of the slope is compared and analyzed with the actual monitoring data, and the error analysis method is used to evaluate the difference between the prediction results of the model and the actual situation. For this purpose, mathematical tools such as polynomial regression and least squares method are needed to calculate the deviation between the model prediction value and the actual observation value, and residual analysis is used to confirm whether the corrected model conforms to the actual slope stability behavior. During the comparison process, the response of the key parts of the slope is focused on, and its deviation value is calculated to ensure that the model can accurately reflect these key factors. Further, by setting a tolerance error range, the deviation value of each parameter is tested. If the error exceeds the set range, some parameters in the corrected model need to be further adjusted. Through these strict compliance verification steps, the model verification result data is obtained.

[0035] Step S24: Dynamically update the corrected digital twin model of the slope by using the model verification result data to obtain a real-time updated digital twin model of the slope.

[0036] In the embodiments of the present invention, the corrected digital twin model of the slope is first dynamically updated. According to the model verification result data, the real-time monitoring data of the slope and the state difference analysis data are extracted, and the corrected digital twin model of the slope is dynamically adjusted. The data interpolation technology and time series analysis method are used to integrate the new real-time data and historical data into the model by the weighted average method. For this purpose, based on the change trend of different monitoring points of the slope, combined with the sensor data and environmental changes, the slope parameters in the corrected model are gradually adjusted to ensure that the model can reflect the actual changes of the slope in real time. The dynamic update algorithm is used to correct the model parameters in real time to reflect the changes of environmental factors, geological conditions and monitoring data, so as to ensure 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.

[0037] By comparing the standardized real-time monitoring data with the historical data, the present invention can identify the changes in the slope state, reveal potential risks and trends. Combining the difference analysis data for parameter adjustment can improve the accuracy of the preliminary model and better reflect the actual state of the slope. Verifying the corrected digital twin model of the slope ensures the consistency between the model and the actual slope condition, thus ensuring the reliability of the analysis results. Using the model verification results for dynamic update ensures that the digital twin model of the slope can continuously reflect the real-time changes and provide accurate real-time monitoring data support.

[0038] Preferably, step S3 includes the following steps: Step S31: Conduct geomechanical analysis on the real-time updated digital twin model of the slope to obtain slope geomechanical analysis data; In the embodiments of the present invention, geomechanical analysis is performed on the real-time updated slope digital twin model. First, according to the geological data obtained from the real-time updated slope digital twin model, information such as the rock and soil layers, geological structures, soil types, and groundwater flow conditions in the area where the slope is located is extracted. Through mechanical analysis methods, the finite element method is used to evaluate the stability of the slope. The stress, strain, and friction parameters of the slope are input for static and dynamic load analysis to determine the response of each geological factor under different loads. Combining the data of on-site actual monitoring, geomechanical theory is used for analysis to calculate the safety factor, potential landslide area, and deformation mode of the slope, and then the geomechanical analysis data of the slope is obtained.

[0039] Step S32: Perform thermodynamic behavior analysis on the real-time updated slope digital twin model to obtain slope thermodynamic analysis data; In the embodiments of the present invention, thermodynamic behavior analysis is performed on the real-time updated slope digital twin model. First, based on the real-time updated slope digital twin model, the thermal parameters in the area where the slope is located are obtained. On this basis, using thermodynamic analysis methods and combining heat conduction and convection models, the heat exchange inside and outside the slope is simulated, especially considering the thermal response of slope materials due to seasonal changes, precipitation, and day-night temperature differences. Further, thermal stress analysis is carried out, and through numerical simulation, the thermal expansion, thermal stress changes of the slope under different temperature gradients and their effects on the slope stability are calculated. The focus is on analyzing the influence of thermal stress on the crack propagation and deformation behavior of the slope rock and soil layers to obtain slope thermodynamic analysis data.

[0040] Step S33: Perform simulations of various hydro-meteorological conditions based on the real-time updated slope digital twin model to obtain slope hydro-meteorological simulation data; In the embodiments of the present invention, simulations of various hydro-meteorological conditions are performed based on the real-time updated slope digital twin model. First, the meteorological data in the area where the slope is located is collected. Then, according to the real-time updated slope digital twin model, a hydrological model is used to simulate the precipitation distribution, evaporation process, and surface water flow in the area, and factors such as soil permeability, vegetation cover, and slope terrain are considered to further calculate the infiltration, flow, and water accumulation of surface water. In addition, combining the meteorological simulation results, the influence of rainwater on the soil moisture of the slope and the effect of water saturation and leakage on the slope stability are analyzed. Through the linkage of the meteorological model and the hydrological model, slope hydro-meteorological simulation data is obtained.

[0041] Step S34: Perform slope dynamic response simulation based on the real-time updated slope digital twin model to obtain slope dynamic response data; The embodiments of the present invention simulate the slope dynamic response based on a real-time updated slope digital twin model. First, appropriate dynamic boundary conditions are set according to the geometric shape, material properties of the slope, and the stress distribution within the slope, and the influence of seismic loads, blasting loads, or other dynamic loads on the slope stability is considered. By calculating the response parameters of the slope under different dynamic loads, the dynamic response process of the slope is simulated. Numerical calculation methods such as the finite element method or the discrete element method are used to analyze the dynamic behavior of the slope, such as vibration propagation, rupture, and its potential impact on stability. In addition, the non-linear behavior, friction, and viscoelastic factors of the soil and rock within the slope need to be considered during the simulation to ensure the accuracy of the simulation results. After the simulation is completed, the slope dynamic response data is obtained.

[0042] Step S35: Perform multi-physical field coupling analysis on the slope geomechanical analysis data, slope thermodynamic analysis data, slope hydro-meteorological simulation data, and slope dynamic response data to obtain slope multi-physical field coupling analysis data; The embodiments of the present invention perform multi-physical field coupling analysis based on the slope geomechanical analysis data, slope thermodynamic analysis data, slope hydro-meteorological 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 flux 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 hydro-meteorological simulation data are coupled with the geomechanical and thermodynamic characteristics of the slope. By simulating the influence 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 the geomechanical and thermodynamic coupling, and the finite element method or multi-physical field coupling analysis method is used to comprehensively consider the stability performance of the slope under various complex environments to obtain the slope multi-physical field coupling analysis data.

[0043] Step S36: Evaluate the slope stability based on the slope multi-physical field coupling analysis data to obtain slope stability analysis data.

[0044] In the embodiments of the present invention, based on the data of slope multi-physical field coupling analysis, the slope stability is evaluated. First, according to the stress field, temperature field, humidity field, and dynamic response data in the multi-physical field coupling analysis results, the deformation behavior of the slope is analyzed through a mechanical model. Then, combining the stress, strain, and yield criterion in the geomechanics analysis data, the thermal conductivity and temperature change range in the thermodynamics analysis data, and the precipitation and humidity conditions in the hydro-meteorological simulation data, the slope stability analysis method is used to evaluate the slope stability. Combining the displacement and vibration response in the dynamic response data with the above data, the stability performance of the slope under dynamic loads is analyzed, and the potential instability risk of the slope is judged through the stress-strain curve. In this process, the finite element analysis method is used to integrate the 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.

[0045] Through geomechanics analysis, the present invention can deeply understand the mechanical behavior of the slope and provide basic data support for evaluating the slope stability. Through thermodynamics behavior analysis, it can reveal the physical characteristics of the slope under temperature change and heat conduction and provide important information for stability prediction. Through hydro-meteorological condition simulation, it can consider the influence of rainfall, temperature, and humidity environmental factors on the slope stability and improve the accuracy of early warning. Through dynamic response simulation, it can evaluate the response of the slope under different external influences and provide a dynamic reference for judging its safety. Through multi-physical field coupling analysis, the geomechanics, thermodynamics, hydro-meteorology, and dynamics data are comprehensively analyzed, thereby improving the comprehensiveness and accuracy of slope stability evaluation. Evaluating the slope stability based on the multi-physical field coupling analysis results provides a reliable basis for timely discovering potential risks and taking corresponding measures.

[0046] Preferably, step S36 includes the following steps: Step S361: Identify the slope stress concentration area from the slope multi-physical field coupling analysis data to obtain the slope stress field distribution data; In the embodiments of the present invention, the slope stress concentration area is identified from the slope multi-physical field coupling analysis data. First, by analyzing the stress field in the multi-physical field coupling analysis data, the stress distribution data is used to identify the areas where the stress in the slope is relatively concentrated. The identification method of the stress concentration area is usually based on numerical simulation and mechanical analysis. Through the stress field data in the slope model, the stress gradient analysis method or the equivalent stress calculation method is applied 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 instability areas of the slope. Therefore, during the calculation process, the finite element method or the slope stability analysis model is used to discretize the stress field, and the stress concentration areas are marked through a visualization tool to obtain the slope stress field distribution data.

[0047] Step S362: Determine the position of the slope slip surface based on the slope stress distribution data to obtain slope slip surface analysis data; In the embodiment of the present invention, the position of the slope slip surface is determined based on the slope stress distribution data. By analyzing the stress field data obtained in the previous step in detail, the area where the slope slips is identified. First, using the stress distribution data, by setting a stress threshold, the areas with stress concentration are identified, and these areas are usually potential slip surfaces. Next, a slip surface determination method is adopted, combining the stress gradient and stress concentration degree information in the stress distribution data, and further analyzed through a mechanical model to determine the position of the slip surface. Specifically, numerical calculation methods such as finite element analysis can be used, combined with the mechanical properties of the material and the geometric characteristics of the slope, to simulate the behavior of the slope under different stress actions and determine the position of the slip surface. During this process, visualization technology can also be used to calibrate the slip surface and compare it with the actual terrain data to ensure the accuracy of the determination. Finally, slope slip surface analysis data is obtained.

[0048] Step S363: Calculate the displacement field for the slope multi-physical field coupling analysis and analyze the slope deformation trend to obtain slope displacement field analysis data; In the embodiment of the present invention, the displacement field is calculated based on the slope multi-physical field coupling analysis data and the slope deformation trend is analyzed. First, various stress, mechanical, and temperature physical field data related to the slope are extracted from the results of the multi-physical field coupling analysis, and the displacement field is calculated through these data. During this process, the finite element method or a slope mechanical model is used to simulate the slope, calculate the displacement response of the slope under different loading conditions, especially paying attention to the displacement changes in the potential slip surface and its surrounding areas. Then, based on the calculation results of the displacement field, the slope deformation trend is analyzed to determine the deformation trend of the slope in each direction, and by analyzing the displacement data at different time periods, the areas with obvious deformation are identified. In order to accurately predict the deformation path of the slope, the influencing factors of geological structure and soil properties also need to be considered. 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 to obtain slope displacement field analysis data.

[0049] Step S364: Quantify 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; The embodiments of the present invention quantify the slope instability probability based on the slope slip surface analysis data and the slope displacement field analysis data. First, through the slope slip surface analysis data, the potential slip surface position of the slope is determined. Combining with the slope displacement field analysis data, the regions with obvious deformation are identified, especially those positions greatly affected by stress concentration. On this basis, using the stress field distribution, displacement field data and slip surface analysis results of the slope, a probability analysis method is adopted to quantitatively evaluate the slope instability probability. Specifically, by establishing a slope stability probability model, taking displacement, stress, and geological characteristics factors as input parameters, and combining historical monitoring data, statistical methods such as Monte Carlo simulation or limit equilibrium analysis method are used to calculate the instability risk of the slope under different hydro-meteorological conditions, and the slope instability probability evaluation data is obtained.

[0050] Step S365: Use the slope multi-physical field coupling analysis data to divide the stability levels of the slope instability probability evaluation data to obtain the slope stability level data; The embodiments of the present invention use the slope multi-physical field coupling analysis data to divide the stability levels of the slope instability probability evaluation data. First, according to the slope instability probability evaluation 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 intervals (such as less than 10% is stable, 10% to 30% is slightly unstable, 30% to 50% is moderately unstable, and more than 50% is highly risk unstable), and corresponding level identifiers are set for each interval. Then, combining the slope slip surface analysis data and the displacement field data, the stability of each slope region at each moment is evaluated one by one, and according to the value of the instability probability, the stability level is assigned to obtain the slope stability level data.

[0051] Step S366: Integrate the slope instability probability evaluation data and the slope stability level data to obtain the slope stability analysis data.

[0052] In the embodiments of the present invention, the slope instability probability assessment data and the slope stability level data are first integrated. At the beginning of this process, key indicators are extracted from the slope slip surface analysis data, displacement field analysis data, and slope instability probability assessment data, such as the instability probability value, stability level, stress concentration areas of each part of the slope, and displacement trend information. Then, these information are comprehensively analyzed through weighted average or direct summation to obtain an overall slope stability analysis data. In this process, for different stability level regions, the corresponding stability assessment data are merged and grouped according to a certain threshold or risk level to form a complete stability assessment system. Finally, based on the multi-physical field data of the slope's geological environment, stress distribution, displacement trend, and thermodynamic changes, the integrated slope stability analysis data are output.

[0053] Through the identification of stress concentration areas, the present invention can effectively determine the key weak areas of the slope, providing an important basis for subsequent stability assessment. Through the determination of the slip surface position, the sliding surface of the slope can be clarified, providing scientific support for evaluating potential instability areas. Through the calculation of the displacement field and the analysis of the deformation trend, the deformation trend of the slope can be comprehensively evaluated, and potential risks can be predicted in advance. Through the quantification of the slope instability probability, the instability risk of the slope can be accurately evaluated, providing reliable data support for formulating prevention and control measures. Through the division of stability levels, the slope risks can be divided into different levels, helping decision-makers take corresponding measures in a timely manner. Through data integration, the instability probability assessment and stability level information can be combined, improving the comprehensiveness and accuracy of slope stability analysis.

[0054] Preferably, step S4 includes the following steps: Step S41: Use the slope stability analysis data to perform non-linear mechanical modeling and analyze the non-linear change law of the slope stress state to obtain the slope non-linear mechanical analysis data; In the embodiments of the present invention, first, using the key parameters in the slope stability analysis data, such as soil mechanical properties, geological structure, stress-strain distribution, and slope deformation conditions, a non-linear mechanical model is constructed by performing non-linear mechanical modeling on the slope. In this process, the finite element analysis method (FEM) is used to discretize the slope, dividing the slope into multiple finite elements, and applying known slope environmental conditions and load conditions to each element. During the modeling process, the non-linear material behavior of the slope is analyzed with a focus on the yield, hardening, and softening characteristics of the material, as well as the effects of non-linear contact interfaces and friction forces, ensuring that the model can truly reflect the behavior of the slope under different loading conditions. Next, based on the non-linear mechanical model, an analysis of the non-linear variation law of the slope stress state is carried out. The elastic-plastic mechanics theory is used to analyze the deformation, stress changes, and failure process of the slope under external forces. Combining with the stress-strain curve, the non-linear response of the slope material under different loading conditions is analyzed, and the safety factor of the slope is calculated to evaluate the stability of the slope under different working conditions. Finally, the non-linear mechanical analysis data of the slope is obtained.

[0055] Step S42: Based on the slope multi-physical field coupling analysis data, perform a non-linear analysis of the deformation behavior under each slope loading condition to obtain the slope non-linear deformation analysis data. In the embodiments of the present invention, first, based on the slope multi-physical field coupling analysis data, the non-linear analysis of the deformation behavior under each slope loading condition is carried out using the multi-physical field analysis technology. The specific operations include: using the finite element analysis method (FEM) to discretize the slope, dividing it into multiple small elements to ensure that the physical field factors such as stress, temperature, and humidity in each element are fully considered. In each element, a mathematical model coupling various physical fields is established by combining soil mechanics, rock mechanics, and hydrological parameters, considering the influence of water infiltration and temperature changes on the soil, and the influence of precipitation and evaporation environmental changes on the slope surface on the slope stability. Then, for the loading conditions of the slope under different external forces, a dynamic non-linear analysis is carried out to track the deformation process of the slope under the coupling action of different physical fields, calculate the strain and displacement indexes, and use these data to analyze the non-linear deformation behavior of the slope under variable loads. Finally, by solving the slope stress and deformation characteristics of the coupling model, the slope non-linear deformation analysis data is obtained.

[0056] Step S43: Perform a non-linear time series analysis on the key risk areas of the slope based on the slope stability analysis data to obtain the slope non-linear time series analysis data. In the embodiments of the present invention, first, based on the slope stability analysis data, key risk areas of the slope are selected for detailed analysis. The specific operations include: adopting the time series data analysis method, combining historical slope stability data, real-time monitoring data, and slope geological characteristics to identify the key areas prone to instability. By establishing a non-linear time series model, the stress changes and deformation behaviors in 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 areas. Then, based on the time series data of the key risk areas of the slope, a 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 rules of the slope stress and deformation are calculated to obtain the non-linear time series analysis data of the slope.

[0057] Step S44: Based on the slope non-linear mechanical analysis data, slope non-linear deformation analysis data, and slope non-linear time series analysis data, predict slope instability to obtain slope stability prediction data.

[0058] In the embodiments of the present invention, first, based on the slope non-linear mechanical analysis data, slope non-linear deformation analysis data, and slope non-linear time series analysis data, predict slope instability. Integrate the slope non-linear mechanical analysis data, slope non-linear deformation analysis data, and slope non-linear time series analysis data, and construct an instability prediction model by combining the stability analysis results of each area of the slope. This model is based on historical stability data, real-time monitoring data, and various non-linear analysis data, comprehensively considering the mechanical behavior, deformation state, and stress time series evolution law of the slope, and evaluating the stability status of different areas of the slope. Through numerical calculation and simulation methods, further calculate the possibility and critical point of slope instability. For each key area, according to the stress state and non-linear deformation obtained from the slope non-linear mechanical analysis data, combined with the time series change rule, conduct critical instability judgment to obtain slope stability prediction data. Finally, based on this series of data analysis, generate the prediction data of slope stability.

[0059] Through the non-linear mechanical analysis of the slope, the present invention can deeply understand the change rule of the slope stress state, providing an accurate basis for predicting the slope stability. Through the non-linear analysis of the slope deformation behavior, it can comprehensively evaluate the deformation characteristics of the slope under different stress conditions, providing key data for slope risk prediction. Through the non-linear time series analysis of the key risk areas of the slope, it can identify potential instability times, providing a scientific basis for early warning. By comprehensively considering the non-linear mechanical, deformation, and time series analysis data, it can accurately predict the possibility of slope instability, providing forward-looking guidance for taking emergency measures.

[0060] Preferably, step S41 includes the following steps: Step S411: Use the slope stability analysis data to model the stress-strain relationship and obtain the slope non-linear stress-strain model data; In the embodiment of the present invention, first, based on the slope stability analysis data, a stress-strain relationship model of the slope is constructed. The stress and strain data of different regions of the slope are obtained from the real-time monitoring system. According to these data, through experiments or existing physical formulas, the relationship between stress and strain of the slope material under different external forces is determined. Then, using these data and the results of mechanical experiments, a finite element analysis method 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 different slope soil types, lithologies, and topographic 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 through the analysis of the stress state of the slope, the non-linear stress-strain model data of the slope are obtained.

[0061] Step S412: Use the slope non-linear stress-strain model data to calculate the mechanical characteristics under various stress conditions of the slope and obtain the slope stress characteristic analysis data; In the embodiment of the present invention, first, the slope non-linear stress-strain model data is used as input, and the mechanical characteristics of the slope under different stress conditions are calculated according to this model. In specific operations, the finite element analysis method is used to simulate the stress and strain of different regions of the slope, considering 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 regions, and each region conducts a mechanical analysis according to its soil layer structure, lithology characteristics, and stress environment. During the calculation process, by establishing a stress field and a strain field, the mechanical response data of the slope under different working conditions are obtained, and then the slope stress characteristic analysis data are obtained.

[0062] Step S413: Evaluate the stability state of the slope stress characteristic analysis data under various mechanical conditions to obtain the slope non-linear stability analysis data; After obtaining the slope stress characteristic analysis data, the embodiments of the present invention evaluate the slope stability state 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 existing in the slope is identified. During the calculation process, the soil layer strength parameters of each region are accurately measured first. Considering important parameters such as the internal friction angle, cohesion, and elastic modulus of the rock and soil, by constructing a static equilibrium equation, it is analyzed whether the slope can maintain stability under the current stress conditions. Then, using the slope stability analysis data, the safety factor of the slope under different stress conditions is calculated to evaluate the stability of each region of the slope. To further verify the evaluation results, dynamic adjustment and correction are carried out 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 region are summarized to obtain the overall non-linear stability analysis data of the slope.

[0063] Step S414: Based on the non-linear stability analysis data of the slope, model the time-series change of the slope stress state to obtain the non-linear change time-series data of the slope stress state; After obtaining the non-linear stability analysis data of the slope, the embodiments of the present invention model the time-series change of the slope stress state based on these data. First, collect the stability data of the slope at different time points, including monitoring data and analysis data. According to these data, construct a time-series model of the change of the slope stress state. The time-series model uses common time-series analysis methods, such as the autoregressive integrated moving average (ARIMA) model, to model the time-series change of the slope stress state and simulate the stress conditions and their change trends of the slope in different time periods. Using the stress and displacement data of the slope, combined with the geological conditions, climate conditions, and external load information of the slope, further detailed analysis of the stress state at each time node is carried out to capture the non-linear change law of the stress state over time. During the modeling process, the data needs to be continuously updated and corrected to ensure the accuracy of the model. Finally, through the time-series change model, the non-linear change time-series data of the slope stress state is obtained.

[0064] Step S415: Identify the key change points of the non-linear change time-series data of the slope stress state, and analyze the change law based on the identification results of the key change points to obtain the key change law data of the slope stress state; After obtaining the time-series data of the non-linear change of the slope stress state in the embodiments of the present invention, key change points are identified. First, the change trend of the slope stress state in the time-series data is analyzed, and a suitable algorithm is selected for change point detection. A change point detection method based on statistics, such as the CUSUM (Cumulative Sum Control Chart) method or the Pelt (Pruned Exact Linear Time) algorithm, can be used to identify significant change points in the time-series data. During the change point identification process, it is first necessary to determine the stable interval and change interval of the time series, and identify the mutation points or gradually changing stages of the stress state. Next, based on the identified key change points, the change law of the slope stress state is analyzed. The change law analysis can further clarify the change characteristics of the slope stress state by extracting the trend changes, fluctuation amplitudes between change points, and the correlation with external factors. According to these change laws, the key change law data of the slope stress state are obtained.

[0065] Step S416: Integrate the slope non-linear stress-strain model data and the key change law data of the slope stress state to obtain slope non-linear mechanical analysis data.

[0066] In the embodiments of the present invention, first, the slope non-linear stress-strain model data and the key change law data of the slope stress state are integrated. During the data integration process, the two data sets are first normalized to ensure that the data formats and dimensions are consistent. Then, by setting specific weight coefficients, weighted fusion of different data sets is performed. The determination of the weight coefficients is based on the actual requirements of slope stability analysis. For example, for the stress-strain model data and the stress state change law data, different weights are set according to their contributions to slope instability prediction. Then, after data fusion, the weighted average method or the principal component analysis method is used for data synthesis to obtain slope non-linear mechanical analysis data.

[0067] By establishing a non-linear stress-strain model of the slope, the present invention can deeply analyze the stress-strain relationship of the slope, providing basic data for further stability analysis. By calculating the mechanical properties under different loading conditions, key mechanical property data for slope stability assessment are provided, helping to accurately judge the safety state of the slope. Through the stability 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 change of the stress state of the slope through time series modeling, the stress characteristics of the slope at different times can be comprehensively understood, which is helpful for dynamically monitoring the slope stability. By identifying key change points and analyzing change rules, important turning points of the stress state of the slope can be accurately captured, providing a reliable basis for timely warning and emergency response. By integrating the non-linear stress-strain model data and key change rule data, comprehensive and systematic data support for the non-linear mechanical analysis of the slope is provided, which is helpful for deeply understanding the mechanical behavior of the slope.

[0068] Preferably, step S5 includes the following steps: Step S51: Identify key stability factors for slope stability prediction data and classify risk factors to obtain slope risk factor classification data; In the embodiment of the present invention, the slope stability prediction data is first comprehensively analyzed to identify key factors affecting slope stability. 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 its influence degree on slope stability. For example, hydrological and meteorological conditions and geomechanical conditions are classified into different categories, and the specific influence range on slope stability 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 stratum structure and soil type, and the different degrees of influence of each type of factor on slope stability are determined. Through this classification, slope risk factor classification data is obtained.

[0069] Step S52: Conduct a weight analysis of risk factors based on the slope risk factor classification data to obtain slope risk weight analysis data; In the embodiments of the present invention, based on the classified data of slope risk factors, weight analysis is first performed on each risk factor. This analysis is carried out by quantifying the relative importance of each risk factor's impact on slope stability, and the analysis methods adopted include the Analytic Hierarchy Process (AHP) or the fuzzy comprehensive evaluation method. By allocating weights to each risk factor, factors such as rainfall, soil moisture, slope, and rock-soil structure that affect slope stability are considered, and a weight value is assigned to each factor. For example, if a certain risk factor has a greater impact on slope stability, a higher weight is given to it, and vice versa. During the weight analysis process, the weight coefficients of each factor are calculated by analyzing the historical data, real-time monitoring data, and existing theoretical models of each factor. Finally, slope risk weight analysis data is obtained.

[0070] Step S53: Use the slope stability prediction data and the slope risk weight analysis data to evaluate the slope risk level, and obtain slope risk level evaluation data; In the embodiments of the present invention, the slope risk level is evaluated using the slope stability prediction data and the slope risk weight analysis data. First, combined with various prediction results in the slope stability prediction data, such as the analysis results of the stress field, displacement field, and slip surface based on historical data, etc., and combined with the slope risk factor weight analysis data, a corresponding weight value is assigned to each risk factor. Using these data, the impact of each risk factor is quantified by weighted summation, and the slope risk level is calculated. During this process, multiple risk levels are set, such as low risk, medium risk, and high risk, and the slope risk is divided into different levels according to specific numerical ranges. For example, if the stress field data of the slope indicates that there is a large stress concentration in a local area, and the weight of the risk factor in this area is large, the risk level of this area is determined to be high risk in the evaluation result. The entire evaluation process will consider the correlation between various data to ensure that the evaluation result accurately reflects the current stability state of the slope, and slope risk level evaluation data is obtained.

[0071] Step S54: Use the standardized slope real-time monitoring data and the slope historical multi-source monitoring data to identify the slope instability area for the slope risk level evaluation data, and obtain slope instability area analysis data; Embodiments of the present invention identify the slope instability area based on the standardized real-time monitoring data of the slope and the historical multi-source monitoring data of the slope. First, the standardized real-time monitoring data of the slope includes various types of monitoring data of the current slope, and through standardized processing, the data is ensured to be analyzed on a unified scale. Then, combined with the historical multi-source monitoring data of the slope, data comparison and trend analysis are carried out to identify the areas with potential instability risks. Using the slope risk level assessment data as a reference, the stability of each area is further analyzed, and the areas with higher risks are marked as instability areas by setting thresholds. During the identification process of the instability area, Geographic Information System (GIS) technology and spatial analysis methods are used, combined with the slope terrain and geological feature data, to calibrate the areas greatly affected by risk factors. Specifically, if the risk level of a certain area exceeds the set critical value and the monitoring data of this area shows an abnormal change trend, then this area is determined as an instability area. Finally, the slope instability area analysis data is obtained.

[0072] Step S55: Perform time series prediction on the slope instability area analysis data to obtain the slope time series risk prediction data; Embodiments of the present invention perform time series prediction on the slope instability area based on the slope instability area analysis data. First, using the identified slope instability area analysis data, it is combined with the historical monitoring data, and for the change trend of the monitoring data of each instability area, time series analysis methods are used for time series prediction. During this process, based on the fluctuations, change trends, and seasonal change factors in the historical data, an autoregressive integrated moving average (ARIMA) model, a long short-term memory network (LSTM), or other prediction methods suitable for time series data are used to establish a time series prediction model for the instability area. This model predicts the future risk changes of the instability area according to the time series relationship of the historical data, and then generates the slope time series risk prediction data. During this process, the real-time monitoring data and historical data of the slope are used as inputs, and the prediction results obtained through model calculation are used as outputs. During the model training process, by comparing the differences between the prediction results and the actual monitoring data, the model parameters are adjusted to improve the prediction accuracy.

[0073] 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 the slope risk warning information data.

[0074] In the embodiment of the present invention, first, the slope risk level assessment data, slope instability area analysis data, and slope time - series risk prediction data are integrated. The various types of data are aligned according to the time sequence and standardized according to the sources and types of different data. By unifying the format and unit, the consistency of the data is ensured. On this basis, combined with multi - dimensional data analysis, using data fusion techniques such as weighted average, decision tree analysis, or principal component analysis, the various types of data are comprehensively evaluated to obtain the overall risk warning information of the slope. Specifically, the slope risk level assessment data provides the overall risk level, the slope instability area analysis data highlights the risk situation in specific areas, and the time - series risk prediction data further anticipates the risk changes in a future period. Through weighted fusion, the time - effective slope risk warning information data is obtained.

[0075] The present invention helps to accurately evaluate the causes of slope risks by identifying key stability factors and classifying risk factors, which is convenient for subsequent risk management and emergency response. Through the analysis of the weights of risk factors, the influence degree 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 with different risk levels, improving the scientificity and effectiveness of risk prevention and control. Through the identification of slope instability areas, it helps to locate potential dangerous areas and take prevention and control measures in a timely manner. Through slope time - series risk prediction, the changing trend of slope risks can be predicted, providing dynamic support for early warning and decision - making. By integrating slope risk levels, instability areas, and time - series prediction data, the real - time risk situation of the slope can be comprehensively reflected, providing accurate and comprehensive risk warning information for emergency response and decision - making.

[0076] Preferably, step S6 includes the following steps: Step S61: Design response strategies for each risk level based on the slope risk warning information data to obtain slope risk classification response data; In the embodiment of the present invention, response strategies for each risk level are designed based on the slope risk warning information data. First, the slope risk warning information data is analyzed to identify the specific situations of each risk level, including the high or low of the risk level, the influence range, and the duration. According to the risk level division, different response strategies are designed. For high - risk level areas, an emergency response strategy is designed; for medium - risk level areas, medium - emergency response measures are designed; for low - risk level areas, conventional monitoring and early warning strategies are designed. In this process, the slope stability prediction data, real - time monitoring data, and historical data are used as the basis, and combined with the risk assessment standards in the field of geological engineering, detailed slope risk classification response data is formulated.

[0077] Step S62: Based on the real-time updated slope digital twin model, conduct an analysis of the emergency treatment requirements for slope risk classification response data to obtain slope emergency treatment requirement data; In the embodiment of the present invention, based on the real-time updated slope digital twin model, an analysis of the emergency treatment requirements for slope risk classification response data is carried out. First, 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 are used to comprehensively analyze the current state of the slope. Taking the slope risk classification response data as the input, identify the potential emergency treatment requirements corresponding to each risk level, including slope reinforcement, enhanced monitoring, or personnel evacuation requirements. For high-risk areas, analyze the slope instability trend, deformation rate, and soil moisture change factors reflected in the analysis model, and accurately evaluate the type and quantity of emergency resources required; for medium- and low-risk areas, refine the treatment requirements item by item according to the predicted slope stability. During this process, through the real-time update of the slope digital twin model, continuously track the actual change dynamics of the slope, ensure that the emergency requirement analysis is always based on the latest data information, and finally generate slope emergency treatment requirement data.

[0078] Step S63: Optimize the emergency strategies for each risk scenario for the slope emergency treatment requirement data to obtain slope emergency response plan data; In the embodiment of the present invention, the emergency strategies for each risk scenario are optimized based on the slope emergency treatment requirement data. First, through the analysis of the slope emergency treatment requirement data, extract the emergency response strategies required for different risk level areas. In high-risk areas, first analyze the possibility of slope instability, and combine the real-time slope displacement, stress changes, and rainfall factors to design targeted emergency measures. Medium-risk areas focus on setting up local monitoring equipment and making timely emergency responses and personnel evacuation preparations. In low-risk areas, adopt regular monitoring and early warning response strategies to ensure timely response in case of abnormalities. For each risk scenario, optimize by simulating different emergency response plans and select the optimal emergency response plan. During this process, combined with the historical multi-source monitoring data and real-time monitoring data of the slope, through the analysis of the slope digital twin model, adjust the weights and implementation steps of various emergency strategies in real time, and finally obtain the optimized slope emergency response plan data.

[0079] Step S64: Based on the real-time updated slope digital twin model, make real-time adjustments to the slope emergency response plan data to obtain real-time updated slope risk warning information data; In the embodiments of the present invention, the slope emergency response plan data is adjusted in real time based on the real-time updated slope digital twin model. First, the slope stability data collected by the real-time monitoring system, combined with historical data and model prediction, is used to update the parameters in the slope digital twin model to ensure that it reflects the current state of the slope. Based on the updated model, combined with the slope risk warning information data, the risk level of the slope is re-evaluated, and then the emergency response plan data is adjusted. For example, during heavy rainfall, the model can calculate the impact of precipitation on the slope in real time to determine whether immediate reinforcement, water diversion, or other intervention measures are required. For high-risk areas, by calculating the trend of slope displacement changes, the model needs to add additional monitoring equipment or strengthen the protection measures in the surrounding area. During this process, combined with the real-time feedback information of the slope digital twin model, the emergency response plan is flexibly adjusted to ensure that it can respond to slope risk changes in real time and give early warnings or emergency responses in a timely manner. Through this process, real-time updated slope risk warning information data can be generated.

[0080] Step S65: Dynamically fuse 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.

[0081] In the embodiments of the present invention, the real-time updated slope risk warning information data is dynamically fused with the slope emergency response plan data. First, the slope state data obtained by the real-time monitoring system, combined with the updated slope risk warning information data, is used to analyze the current slope risk level and determine the emergency response measures for different risk levels. During this process, the real-time updated slope digital twin model is used to reflect the current situation of the slope to ensure 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 in a certain area of the slope is detected, the slope protection measures are adjusted in real time, including reinforcement, water diversion, or other emergency treatment measures, to cope with 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, so as to provide effective support for real-time emergency decision-making and finally generate real-time updated emergency response measure data.

[0082] By designing response strategies for each risk level, the present invention can formulate effective emergency plans for different risk levels, improving the accuracy and pertinence of emergency responses. Through the analysis of emergency treatment requirements for 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 requirements. By optimizing the emergency strategies for each risk scenario, the emergency response measures can be ensured to minimize the harm of slope disasters to people and property, improving the scientificity and adaptability of the emergency plan. By adjusting the slope emergency response plan in real time, the emergency measures can be dynamically optimized according to real-time monitoring data, ensuring that the emergency response always remains consistent with the actual state of the slope and improving the flexibility and timeliness of the emergency response. By dynamically integrating the real-time updated slope risk warning information with the emergency response plan data, seamless connection between the slope state and emergency measures can be achieved, ensuring the real-time and comprehensiveness of the emergency response.

[0083] Preferably, the present invention further provides a real-time monitoring and early warning system for mountain engineering slopes based on digital twin, which is used to execute the above-mentioned real-time monitoring and early warning method for mountain engineering slopes based on digital twin. The real-time monitoring and early warning system for mountain engineering slopes based on digital twin includes: A real-time slope monitoring and digital twin initial construction module, which is used to obtain the original real-time slope monitoring data, perform standardized 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 slope digital twin model; A slope historical data fusion and model dynamic update module, which is used to obtain multi-source slope historical monitoring data, and dynamically update the preliminary slope digital twin model by using the standardized real-time slope monitoring data and the multi-source slope historical monitoring data to obtain a real-time updated slope digital twin model; A slope multi-physical field coupling analysis and stability evaluation module, which is used to perform multi-physical field coupling analysis on the real-time updated slope digital twin model, and perform slope stability evaluation based on the results of the multi-physical field coupling analysis to obtain slope stability analysis data; A slope non-linear analysis and stability prediction module, which is used to perform non-linear analysis on the slope stability analysis data, and use the results of the non-linear analysis to perform slope stability prediction to obtain slope stability prediction data; A slope risk assessment and early warning module, which is used to perform slope risk assessment based on the slope stability prediction data, and perform risk early warning according to the results of the risk assessment to obtain slope risk early warning information data; An emergency response measure formulation and dynamic update module, which is used to formulate emergency response measures based on the slope risk early warning information data, and use the real-time updated slope digital twin model to perform real-time updates on the slope risk early warning information data and the emergency response measures to obtain real-time updated emergency response measure data.

[0084] The present invention provides an accurate data basis for subsequent slope state analysis and prediction by acquiring and standardizing real-time slope monitoring data and constructing a preliminary slope digital twin model. 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 state of the slope, thereby improving the accuracy and reliability of prediction. Through multi-physical field coupling analysis, a comprehensive assessment of the slope is carried out, which can accurately analyze the stability of the slope and provide a scientific basis for emergency decision-making. Through non-linear analysis and stability prediction, the instability trend of the slope can be identified, providing early warning for disaster prevention and mitigation. Based on the stability prediction data, risk assessment is carried out, and risk warnings are issued in a timely manner to ensure the efficiency and preventiveness of slope risk management. Emergency response measures are dynamically formulated and adjusted according to the risk warning information to ensure the real-time synchronization of the emergency treatment plan with the actual risk state of the slope, and improve the flexibility and timeliness of emergency response.

[0085] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is not limited by the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be included in the present invention.

[0086] The above description is only a specific implementation manner of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather conform to the widest scope 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, It includes the following steps: Step S1: Obtain the real-time monitoring data of the original slope and perform standardization processing to obtain the standardized real-time monitoring data of the slope; use the standardized real-time monitoring data of the slope to construct a digital twin model to obtain a preliminary digital twin model of the slope; Step S2: Obtain the historical multi-source monitoring data of the slope, and use the standardized real-time monitoring data of the slope and the historical multi-source monitoring data of the slope to dynamically update the preliminary digital twin model of the slope to obtain a real-time updated digital twin model of the slope; Step S3: Perform multi-physical field coupling analysis on the real-time updated digital twin model of the slope, and conduct slope stability assessment based on the results of the multi-physical field coupling analysis to obtain slope stability analysis data; Step S4: Perform non-linear analysis on the slope stability analysis data, and use the results of the non-linear analysis to predict the slope stability to obtain slope stability prediction data; Step S5: Conduct slope risk assessment based on the slope stability prediction data, and issue risk warnings according to the results of the risk assessment to obtain slope risk warning information data; Step S6: Develop emergency response measures based on the slope risk warning information data, and use the real-time updated digital twin model of the slope to real-time update the slope risk warning information data and the emergency response measures to obtain real-time updated emergency response measure data.

2. The real-time monitoring and early warning method for mountain engineering slopes based on digital twins according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Obtain the real-time slope state data and slope environment parameter data of the mountainous engineering slope, and record the real-time slope state data and slope environment parameter data as the original real-time monitoring data of the slope; Step S12: Perform data preprocessing and standardization processing on the original real-time monitoring data of the slope to obtain the standardized real-time monitoring data of the slope; Step S13: Based on the standardized real-time monitoring data of the slope, conduct slope geometric shape modeling to obtain a slope geometric model; Step S14: Analyze the physical properties of the slope for the standardized real-time monitoring data of the slope to obtain slope physical property data; Step S15: Based on the standardized real-time monitoring data of the slope and the slope physical property data, conduct slope mechanical behavior modeling to obtain a slope mechanical model; Step S16: Based on the standardized real-time monitoring data of the slope, simulate the slope environmental impact factors to obtain a slope environmental impact model; Step S17: According to the slope physical property data, fuse the slope geometric model, the slope mechanical model, and the slope environmental impact model to obtain a preliminary digital twin model of the slope.

3. The real-time monitoring and early warning method for mountain engineering slopes based on digital twins according to claim 2, characterized in that, Step S2 includes the following steps: Step S21: Obtain the 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: Based on the standardized real-time monitoring data of the slope and the slope state difference analysis data, adjust the parameters of the preliminary digital twin model of the slope to obtain a corrected digital twin model of the slope; Step S23: Conduct compliance verification on the corrected digital twin model of the slope to obtain model verification result data; Step S24: Use the model verification result data to dynamically update the corrected digital twin model of the slope to obtain a real-time updated digital twin model of the slope.

4. The real-time monitoring and early warning method for mountain engineering slopes based on digital twins according to claim 3, characterized in that, Step S3 includes the following steps: Step S31: Conduct geomechanical analysis on the real-time updated slope digital twin model to obtain slope geomechanical analysis data; Step S32: Conduct thermodynamic behavior analysis on the real-time updated slope digital twin model to obtain slope thermodynamic analysis data; Step S33: Simulate various hydro-meteorological conditions based on the real-time updated slope digital twin model to obtain slope hydro-meteorological simulation data; Step S34: Simulate the slope dynamic response based on the real-time updated slope digital twin model to obtain slope dynamic response data; Step S35: Conduct multi-physics field coupling analysis on the slope geomechanical analysis data, slope thermodynamic analysis data, slope hydro-meteorological simulation data, and slope dynamic response data to obtain slope multi-physics field coupling analysis data; Step S36: Conduct slope stability assessment based on the slope multi-physics field coupling analysis data to obtain slope stability analysis data.

5. The real-time monitoring and early warning method for mountain engineering slopes based on digital twins according to claim 4, characterized in that, Step S36 includes the following steps: Step S361: Identify the slope stress concentration area from the slope multi-physics field coupling analysis data to obtain slope stress field distribution data; Step S362: Determine the position of the slope slip surface based on the slope stress distribution data to obtain slope slip surface analysis data; Step S363: Calculate the displacement field of the slope multi-physics field coupling analysis and analyze the slope deformation trend to obtain slope displacement field analysis data; Step S364: Quantify the slope instability probability based on the slope slip surface analysis data and slope displacement field analysis data to obtain slope instability probability assessment data; Step S365: Use the slope multi-physics field coupling analysis data to classify the stability level of the slope instability probability assessment data to obtain slope stability level data; Step S366: Integrate the slope instability probability assessment data and slope stability level data to obtain slope stability analysis data.

6. The real-time monitoring and early warning method for mountain engineering slopes based on digital twin according to claim 5, characterized in that, Step S4 includes the following steps: Step S41: Use the slope stability analysis data to conduct non-linear mechanics modeling and analyze the non-linear change law of the slope stress state to obtain slope non-linear mechanics analysis data; Step S42: Conduct non-linear analysis of the deformation behavior under various slope loading conditions based on the slope multi-physics field coupling analysis data to obtain slope non-linear deformation analysis data; Step S43: Conduct non-linear time series analysis of the key risk areas of the slope on the slope stability analysis data to obtain slope non-linear time series analysis data; Step S44: Conduct slope instability prediction based on the slope non-linear mechanics analysis data, slope non-linear deformation analysis data, and slope non-linear time series analysis data to obtain slope stability prediction data.

7. The real-time monitoring and early warning method for mountain engineering slopes based on digital twin according to claim 6, characterized in that, Step S41 includes the following steps: Step S411: Use the slope stability analysis data to conduct stress-strain relationship modeling to obtain slope non-linear stress-strain model data; Step S412: Use the slope non-linear stress-strain model data to calculate the mechanical properties of the slope under various loading conditions to obtain slope mechanical property analysis data; Step S413: Conduct stability state assessment of the slope mechanical property analysis data under various mechanical conditions to obtain slope non-linear stability analysis data; Step S414: Based on the data of slope non-linear stability analysis, model the time-series variation of the slope stress state to obtain the non-linear variation time-series data of the slope stress state; Step S415: Identify the key change points of the non-linear variation time-series data of the slope stress state, and analyze the change law based on the identification results of the key change points to obtain the key change law data of the slope stress state; Step S416: Integrate the slope non-linear stress-strain model data and the key change law data of the slope stress state to obtain the slope non-linear mechanical analysis data.

8. The real-time monitoring and early warning method for mountain engineering slopes based on digital twin according to claim 7, characterized in that, Step S5 includes the following steps: Step S51: Identify the key stability factors of the slope stability prediction data and classify the risk factors to obtain the slope risk factor classification data; Step S52: Analyze the weights of the risk factors based on the slope risk factor classification data to obtain the slope risk weight analysis data; Step S53: Use the slope stability prediction data and the slope risk weight analysis data to evaluate the slope risk level and obtain the slope risk level evaluation data; Step S54: Use the standardized slope real-time monitoring data and the slope historical multi-source monitoring data to identify the slope instability area of the slope risk level evaluation data and obtain the slope instability area analysis data; Step S55: Conduct time-series prediction on the slope instability area analysis data to obtain the slope time-series risk prediction data; Step S56: Integrate the slope risk level evaluation data, the slope instability area analysis data and the slope time-series risk prediction data to obtain the slope risk early warning information data.

9. The real-time monitoring and early warning method for mountain engineering slopes based on digital twin according to claim 8, characterized in that, Step S6 includes the following steps: Step S61: Design the response strategies for each risk level based on the slope risk early warning information data to obtain the slope risk classification response data; Step S62: Analyze the emergency treatment requirements of the slope risk classification response data based on the real-time updated slope digital twin model to obtain the slope emergency treatment requirement data; Step S63: Optimize the emergency strategies for each risk scenario of the slope emergency treatment requirement data to obtain the slope emergency response plan data; Step S64: Make real-time adjustments to the slope emergency response plan data based on the real-time updated slope digital twin model to obtain the real-time updated slope risk early warning information data; Step S65: Dynamically fuse the real-time updated slope risk early warning information data and the slope emergency response plan data to obtain the real-time updated emergency response measure data.

10. A real-time monitoring and early warning system for mountain engineering slopes based on digital twin, characterized in that, For implementing the real-time monitoring and early warning method for mountain engineering slopes based on digital twins as described in Claim 1, the real-time monitoring and early warning system for mountain engineering slopes based on digital twins includes: A slope real-time monitoring and digital twin initial construction module, which is used to obtain the original slope real-time monitoring data, perform standardized processing to obtain the standardized slope real-time monitoring data; use the standardized slope real-time monitoring data 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 the slope historical multi-source monitoring data, and dynamically update the preliminary slope digital twin model by using the standardized slope real-time monitoring data and the slope historical multi-source monitoring data, so as to obtain the real-time updated slope digital twin model; The slope multi-physical field coupling analysis and stability evaluation module is used to conduct multi-physical field coupling analysis on the real-time updated slope digital twin model, and conduct slope stability evaluation based on the results of the multi-physical field coupling analysis to obtain slope stability analysis data; The slope non-linear analysis and stability prediction module is used to conduct non-linear analysis on the slope stability analysis data, and use the results of the non-linear analysis to predict the slope stability to obtain slope stability prediction data; The slope risk assessment and early warning module is used to conduct slope risk assessment based on the slope stability prediction data, and conduct risk early warning according to the results of the risk assessment to obtain slope risk early warning information data; The emergency response measure formulation and dynamic update module is used to formulate emergency response measures based on the slope risk early warning information data, and use the real-time updated slope digital twin model to real-time update the slope risk early warning information data and the emergency response measures to obtain real-time updated emergency response measure data.

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