Regional slope pre-reinforcement range determination method

Abstract

The invention discloses a method for determining a regional slope pre-reinforcement range, particularly relates to the technical field of stability analysis of geotechnical engineering by a digital twinning technology, and is used for solving the problem of insufficient calculation efficiency caused by over-high model complexity of an existing slope analysis method based on digital twinning. The method comprises the following steps: constructing a regional slope digital twinborn model, identifying a potential instability region in a geologic structural plane network, analyzing a stability evolution path of the potential instability region under external disturbance, evaluating association strength of a stress-displacement response mode and a potential sliding plane, simulating a chained activation effect of a main control structural plane, and determining a key influence region. And finally, a precise pre-reinforcement scheme is generated by taking the blocking chain effect as a target to realize balanced optimization of analysis precision and calculation efficiency.

Description

Technical Field

[0001] This invention relates to the field of digital twin technology in geotechnical engineering stability analysis, and more specifically, to a method for determining the range of regional slope pre-reinforcement. Background Technology

[0002] In the fields of geotechnical engineering and infrastructure construction, digital twin technology, as an emerging method, is gradually being applied to construct virtual models corresponding to physical slopes for stability evaluation and pre-reinforcement design of regional slopes. The aim is to assist engineers in assessing slope conditions and identifying potential pre-reinforcement areas by integrating geological, environmental, and monitoring data to conduct simulation analysis in digital space. Existing technologies typically rely on constructing high-precision regional-scale numerical models to perform such analyses in order to obtain simulation results that closely resemble reality.

[0003] However, in practice, existing methods face significant contradictions: the computational resource consumption of the highly complex numerical models built to achieve analytical accuracy increases positively with the size of the region and the accuracy of the model. This makes it difficult to complete simulation analysis of large areas within the acceptable time frame for engineering decisions under conventional computing resource conditions. This results in an efficiency bottleneck in the process of determining the pre-hardening range based on digital twins, which may affect the timely generation and optimization of engineering design schemes and limit the effectiveness and practicality in engineering scenarios that require rapid response or large-scale application. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a method for determining the scope of regional slope pre-reinforcement to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A method for determining the scope of regional slope pre-reinforcement includes the following steps:

[0007] S1. Obtain a digital twin model of the regional slope;

[0008] S2. Identify the network of geological structural surfaces in the digital twin model, and identify the intersection of structural surfaces with both continuity and obstruction as potential instability areas by analyzing stress redistribution characteristics;

[0009] S3. Analyze the stability evolution path of potential unstable regions under typical external disturbance sequences;

[0010] S4. Evaluate the correlation strength between the internal stress-displacement response mode and the formation of the potential sliding surface in the stability evolution path;

[0011] S5. Based on the correlation strength simulation, the chain effect in which the main control structural surfaces involved in the potential sliding surface are activated sequentially under external disturbances will identify the structural surface regions that play a transmission and pivotal role in the chain effect as key influence regions.

[0012] S6. Based on the key affected areas, generate regional slope pre-reinforcement range schemes with the goal of breaking the chain effect.

[0013] Furthermore, a digital twin model of the regional slope is obtained, including:

[0014] A three-dimensional geological structure model of the regional slope was constructed based on geological survey data;

[0015] An initial numerical model is generated by integrating topographic survey data with a three-dimensional geological structure model.

[0016] The construction of the digital twin model is completed by assigning parameters to the soil and rock mass and setting boundary conditions;

[0017] The geological survey data includes data on the distribution of soil and rock layers and data on the occurrence of geological structural surfaces.

[0018] Furthermore, the network of geological structural surfaces in the digital twin model is identified, and by analyzing stress redistribution characteristics, the intersection areas of structural surfaces with both continuity and barrier properties are identified as potential instability regions, including:

[0019] Extracting the geological structural surface network based on a digital twin model;

[0020] Simulate the stress field and analyze stress redistribution characteristics in a digital twin model;

[0021] Identify the intersection of geological structural surfaces with both continuity and obstruction in the stress release path based on stress redistribution characteristics;

[0022] The intersection of geological structural surfaces where continuity and obstruction coexist are marked as potential instability areas.

[0023] Furthermore, based on the stress redistribution characteristics, the identification of the intersection area of ​​geological structural surfaces in the stress release path where continuity and obstruction coexist includes: extracting the distribution characteristics of the maximum shear stress contour lines in the digital twin model to determine the spatial distribution of the stress release path; combining the principal stress direction field analysis to identify obstructive geological structural surfaces in the stress transmission process; and judging the continuity of stress release based on the degree of density variation of stress contour lines in the intersection area of ​​structural surfaces.

[0024] Furthermore, the stability evolution path of potential unstable regions under typical external disturbance sequences is analyzed, including:

[0025] Construct a typical external disturbance sequence that includes rainfall infiltration process and seismic dynamics;

[0026] Simulate the dynamic response of a potentially unstable region under typical external disturbance sequences in a digital twin model;

[0027] Extract the dynamic expansion mode of the plastic zone and the cooperative change characteristics of displacement vectors during the dynamic response process;

[0028] The stability evolution path is determined based on the dynamic expansion mode of the plastic zone and the cooperative change characteristics of the displacement vector.

[0029] Furthermore, the correlation strength between the internal stress-displacement response mode and the formation of the potential sliding surface in the stability evolution path is evaluated, including:

[0030] Extracting internal stress-displacement response modes based on stability evolution paths;

[0031] Analyze the principal stress direction deflection characteristics and displacement vector field divergence characteristics in the internal stress-displacement response mode;

[0032] Spatial coupling analysis was performed on the principal stress direction deflection characteristics and displacement vector field divergence characteristics with the potential sliding surface development location.

[0033] The correlation strength between the internal stress-displacement response mode and the potential sliding surface is quantified using the grey relational analysis method.

[0034] Furthermore, the spatial coupling analysis of the principal stress direction deflection characteristics and displacement vector field divergence characteristics with the potential sliding surface development location includes: establishing a spatial distribution matrix of the principal stress direction deflection angle and displacement vector field divergence value; performing spatial superposition analysis of the spatial coordinate information of the potential sliding surface development location with the distribution matrix; and calculating the principal stress direction deflection amplitude and displacement vector field divergence value at each potential sliding surface location.

[0035] Furthermore, based on the chain effect of sequential activation of the main controlling structural surfaces involved in the potential sliding surface under external disturbances, simulation of correlation strength reveals that the structural surface regions that play a transmitting and pivotal role in the chain effect are identified as key influence regions, including:

[0036] Based on the correlation strength, the key control structure surfaces that need to be analyzed are selected.

[0037] Simulate the dynamic response process of the master control structure surface under the action of typical external disturbance sequences in a digital twin model;

[0038] The stress redistribution characteristics and plastic zone continuity characteristics of the main control structural surfaces during the dynamic response process are analyzed.

[0039] Identify the chain effect of sequential activation of the main controlling structural surfaces based on stress redistribution characteristics and plastic zone continuity characteristics;

[0040] The structural surface regions that bear the functions of stress transmission and plastic zone connection in the chain effect are marked as key influence areas.

[0041] Furthermore, the analysis of stress redistribution characteristics and plastic zone connectivity characteristics of the master control structural surfaces during the dynamic response process includes: extracting stress tensor change data and plastic strain distribution data around each master control structural surface according to the time step, analyzing the temporal variation law of stress state between adjacent master control structural surfaces to determine the stress redistribution propagation path, and tracing the expansion and connectivity process of the plastic zone between each master control structural surface.

[0042] Furthermore, based on the key impact areas, regional slope pre-reinforcement schemes are generated with the goal of breaking the chain effect, including:

[0043] Analyze the stress transfer path and plastic zone connectivity in key affected areas during the chain effect;

[0044] Determine the support structure layout scheme that can cut off the stress transmission path and block the through channel of the plastic zone;

[0045] Generate a regional slope pre-reinforcement range scheme that includes the support structure type and spatial layout parameters based on the support structure layout scheme.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] 1. By constructing a digital twin model and systematically analyzing the network characteristics of geological structural surfaces, the precise identification of potential instability areas was achieved. Firstly, based on stress redistribution characteristics, the intersection areas of structural surfaces with both continuity and obstruction were identified as potential instability areas. This identification method effectively captures key nodes of stress transmission within the slope, avoiding the computational burden of uniform analysis of the entire area in traditional methods. Subsequently, by analyzing the stability evolution path under typical external disturbance sequences, the dynamic response law of the slope under complex loads was systematically revealed. The technical path from static feature analysis to dynamic evolution process tracking allows the understanding of slope instability mechanisms to deepen from the superficial appearance to the intrinsic mechanism level, providing a scientific basis for subsequent targeted treatment.

[0048] 2. A correlation strength assessment system from stress-displacement response mode to potential sliding surface formation was established. Based on this system, the chain effect of the main control structural surface being activated sequentially under external disturbance was simulated. This system can accurately capture the key transmission path in the slope instability process, thereby identifying the structural surface area that plays a pivotal role as the key influence area. Finally, a pre-reinforcement range scheme is generated with the goal of blocking the chain effect. This allows the reinforcement measures to be precisely applied to the key parts controlling slope stability. While ensuring the accuracy of the analysis, the system effectively optimizes the allocation of computational resources, realizing the transformation from comprehensive calculation to focused analysis. This provides a solution for the pre-reinforcement design of large-scale regional slopes that is both reliable and engineering-practical. Attached Figure Description

[0049] Figure 1 This is a flowchart of a method for determining the range of regional slope pre-reinforcement according to the present invention. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0051] Example: Figure 1 This invention provides a method for determining the scope of regional slope pre-reinforcement, which includes the following steps:

[0052] S1. Obtain a digital twin model of the regional slope;

[0053] S2. Identify the network of geological structural surfaces in the digital twin model, and identify the intersection of structural surfaces with both continuity and obstruction as potential instability areas by analyzing stress redistribution characteristics;

[0054] S3. Analyze the stability evolution path of potential unstable regions under typical external disturbance sequences;

[0055] S4. Evaluate the correlation strength between the internal stress-displacement response mode and the formation of the potential sliding surface in the stability evolution path;

[0056] S5. Based on the correlation strength simulation, the chain effect in which the main control structural surfaces involved in the potential sliding surface are activated sequentially under external disturbances will identify the structural surface regions that play a transmission and pivotal role in the chain effect as key influence regions.

[0057] S6. Based on the key affected areas, generate regional slope pre-reinforcement range schemes with the goal of breaking the chain effect.

[0058] S1. Obtain the digital twin model of the regional slope, implemented as follows:

[0059] In acquiring a digital twin model of the regional slope, a three-dimensional geological structure model of the regional slope is first constructed based on geological survey data. This geological survey data includes soil and rock layer distribution data and geological structural surface attitude data. Soil and rock layer distribution data is obtained through field borehole sampling and ground-penetrating radar detection, specifically including the type, thickness, and spatial location information of soil and rock layers at different depths. Geological structural surface attitude data is obtained through geological compass measurement and three-dimensional laser scanning, specifically including the dip, angle, and strike parameters of faults, joints, and fissures. When constructing the three-dimensional geological structure model, borehole data is used as control points, and spatial interpolation methods such as Kriging interpolation are employed to generate the soil and rock structure model. For soil interface surfaces, the Kriging interpolation method determines the weights by calculating the semivariograms of adjacent borehole data points. The weights are set based on the distance and spatial correlation between data points, with closer data points having higher weights. Then, the interpolation results are corrected by combining geological profile data to ensure the continuity of the rock interface and geological rationality. Finally, a three-dimensional mesh model is generated in professional geological modeling software. This model represents the spatial distribution of the soil and rock mass in the form of nodes and elements. The element size is set according to the geological complexity. For example, a smaller element size, such as 1 meter, is used in areas with dense structural surfaces, while a larger element size, such as 5 meters, is used in uniform areas to balance calculation accuracy and efficiency.

[0060] Next, the topographic survey data and the 3D geological structure model are integrated to generate an initial numerical model. The topographic survey data is obtained through UAV aerial photography or total station measurement to generate a digital elevation model (DEM). The DEM stores elevation values ​​in raster form, with the raster resolution set according to the terrain undulation. For example, a resolution of 0.5 meters is used in steep slope areas, and a resolution of 2 meters is used in gentle slope areas. The integration process first spatially aligns the DEM and the 3D geological structure model by unifying them to the same coordinate system through coordinate transformation. Then, in the numerical modeling software, the DEM is used as the surface boundary, and the top interface of the 3D geological structure model is adjusted to match the elevation data of the DEM. The adjustment method uses a surface fitting algorithm, such as the least squares method to fit the geological interface and the terrain surface. When generating the initial numerical model, the integrated model is discretized into a finite element mesh. The mesh type is selected according to the analysis requirements, such as using tetrahedral or hexahedral elements. The number of elements is determined based on the area size and accuracy requirements. For example, for an area of ​​1000 meters by 500 meters, approximately 100,000 elements are generated. The initial numerical model contains geometric information and preliminary material groupings, but specific geotechnical parameters have not yet been assigned.

[0061] Then, the digital twin model was constructed by assigning geotechnical parameters and setting boundary conditions. The geotechnical parameters were assigned based on indoor and field test results. The parameters included elastic modulus, Poisson's ratio, cohesion, internal friction angle, and density. The elastic modulus was obtained through uniaxial compression tests, with a value ranging from 50 MPa to 10 GPa, specifically set according to the geotechnical type. For example, the assigned elastic modulus was 5 GPa for soft rock and 100 MPa for clay. Poisson's ratio was calculated using the ratio of transverse strain to axial strain, with a value ranging from 0.2 to 0.4. Cohesion and internal friction angle were obtained through direct shear tests or triaxial tests. The cohesion value ranged from 5 kPa to 500 kPa, and the internal friction angle ranged from 10 degrees to 40 degrees. The density was measured using a hydrometer, with a value ranging from 1500 kg / m³ to 2500 kg / m³. In the numerical model, parameters are assigned to each material group in cubic meters. Boundary conditions include displacement and stress boundaries. Displacement boundaries are applied at the bottom and sides of the model, with the bottom set as a fixed constraint (zero displacement in all directions) and the sides set as a normal constraint, allowing tangential displacement. Stress boundaries are achieved by applying gravity loads with a gravitational acceleration of 9.8 m / s². The influence of groundwater level is also considered, and pore water pressure distribution is simulated. Pore water pressure is calculated based on the hydrostatic pressure assumption, and the water level is obtained through hydrogeological surveys. After all settings are completed, the model convergence is verified in the numerical simulation software, for example, by initial running under the small deformation assumption to ensure reasonable stress distribution. Finally, a digital twin model is output, which contains complete geometric properties, material parameters, and boundary conditions, and can be used for subsequent stability analysis.

[0062] S2. Identify the network of geological structural surfaces in the digital twin model. By analyzing stress redistribution characteristics, identify the intersection areas of structural surfaces where continuity and obstruction coexist as potential instability regions. This is implemented as follows:

[0063] In the process of identifying the geological structural surface network in the digital twin model, the geological structural surface network is first extracted based on the digital twin model. The digital twin model contains complete three-dimensional geological information. Geological interface data in the model is read using professional geological interpretation software. The geological interface data includes the interfaces between different soil and rock layers and geological structural surfaces. The extraction process adopts an automatic identification method based on curvature analysis. Curvature analysis identifies geological structural surfaces by calculating the curvature values ​​of the model surface in three orthogonal directions. Areas with curvature values ​​greater than 0.001 are identified as potential structural surfaces. At the same time, the automatic identification results are corrected by combining human interpretation. The human interpretation judges the continuity and extension direction of the structural surfaces based on the experience of geological engineers. The finally extracted geological structural surface network is represented in the form of a three-dimensional triangular mesh. Each triangular mesh cell contains spatial coordinates and normal vector information. The mesh cell size is set according to the scale of the structural surface. For example, a 10-meter size cell is used for the main structural surface with an extension length of more than 50 meters, and a 2-meter size cell is used for the secondary structural surface with an extension length of less than 10 meters. After extraction, the topological relationship of the structural surface network is established, and the intersection relationship and spatial connectivity between the structural surfaces are recorded.

[0064] Next, the stress field was simulated and the stress redistribution characteristics were analyzed in the digital twin model. The stress field simulation adopted the finite element analysis method. Gravity load and tectonic stress were applied to the digital twin model. The gravity load was achieved by applying a gravitational acceleration of 9.8 m / s². The tectonic stress was set according to the regional geological data. For example, the direction of the maximum principal stress was set to 60 degrees north of east, and the stress magnitude was 5 MPa. The simulation process adopted an elastoplastic constitutive model. The constitutive model parameters included elastic modulus, Poisson's ratio, cohesion, and internal friction angle. These parameters were obtained from the previous assignment of soil and rock parameters. The solution process adopted the incremental iteration method. The convergence criterion for each load step was that the nodal force residual was less than 0. After completing the stress field simulation, the stress distribution data in the model is extracted, including six stress components and three principal stresses. When analyzing the stress redistribution characteristics, the maximum shear stress value and principal stress direction of each element are calculated. The maximum shear stress is calculated from the three principal stress values, specifically half the difference between the first and third principal stresses. The principal stress directions are obtained through eigenvector decomposition. The stress redistribution characteristics are evaluated by comparing the difference between the initial stress field and the simulated stress field. The difference evaluation indicators include stress deflection angle and stress concentration factor. The stress deflection angle is used to calculate the change in principal stress direction, and the stress concentration factor is used to calculate the ratio of local stress to average stress.

[0065] Then, based on the stress redistribution characteristics, the intersection areas of geological structural surfaces with both continuity and obstruction in the stress release path are identified. Specifically, the spatial distribution of the stress release path is determined by extracting the maximum shear stress contour distribution characteristics from the digital twin model. The maximum shear stress contours are generated using a linear interpolation method, and the contour spacing is set according to the stress gradient. For example, a spacing of 0.1 MPa is used in areas with drastic stress changes, and a spacing of 0.5 MPa is used in areas with gentle stress. Combined with the principal stress direction field analysis, obstructive geological structural surfaces in the stress transmission process are identified. The principal stress direction field is represented by an arrow diagram, where the arrow length represents the magnitude of the principal stress, and the arrow direction represents the direction of the principal stress. When the principal stress direction is significantly deflected at a specific structural surface, with a deflection angle exceeding 30 degrees, the structure is considered... The surface is identified as an obstructive geological structure. The degree of stress release continuity is judged by the density variation of stress contour lines in the intersection area of ​​the structure. Dense stress contour lines indicate a large stress gradient, corresponding to areas where stress release is obstructed, while sparse stress contour lines indicate a small stress gradient, corresponding to areas where stress release is unobstructed. When both dense and sparse contour line areas exist in the intersection area of ​​the same structure, and the length of the transition zone between the dense and sparse areas is less than 20% of the intersection area size, the intersection area is determined to have both continuity and obstruction. In practice, the degree of continuity is quantified by calculating the ratio of contour line spacing. For example, when the ratio of the minimum spacing to the maximum spacing is less than 0.3, it is considered to have obvious obstructive characteristics, and when the ratio is greater than 0.7, it is considered to have obvious continuity characteristics.

[0066] Finally, the intersection of geological structural planes with both connectivity and obstruction is marked as a potential instability zone. This marking process uses different colors in the 3D model to distinguish between them; for example, red represents a potential instability zone and blue represents a stable zone. The marking information includes the spatial extent of the intersection zone, the combination of major structural planes, and stress state characteristics. The spatial extent is represented by a cuboid circumscribed in the intersection zone, with dimensions set according to the actual dimensions of the intersection zone, for example, a length of 50 meters, a width of 30 meters, and a height of 20 meters. The combination of major structural planes records the structural plane numbers and their intersection angles. The stress state characteristics record the stress state characteristics of the area. The stress concentration factor and principal stress deflection angle of the domain are recorded. All marking information is stored as a structured data table. The data table includes the region number, spatial coordinates, structural surface combination, stress parameters, and risk level fields. The risk level is comprehensively assessed based on the stress concentration factor and the degree of penetration. For example, a region with a stress concentration factor greater than 2.0 and a high degree of penetration is rated as high risk, while a region with a stress concentration factor between 1.5 and 2.0 and a medium degree of penetration is rated as medium risk. After marking is completed, a potential instability region distribution map is generated and displayed in a three-dimensional visualization form. At the same time, a data report is output for subsequent analysis.

[0067] S3. Analyze the stability evolution path of potential unstable regions under typical external disturbance sequences, implemented as follows:

[0068] In analyzing the stability evolution path of potentially unstable areas under typical external disturbance sequences, a typical external disturbance sequence incorporating rainfall infiltration and seismic dynamics is first constructed. The rainfall infiltration process is determined based on historical meteorological data and design rainfall conditions, which include an initial light rain phase and a subsequent sustained heavy rainfall phase. The rainfall intensity of the initial light rain phase is set to 5 mm / h for 24 hours, and the rainfall intensity of the subsequent sustained heavy rainfall phase is set to 15 mm / h for 48 hours. The rainfall infiltration process is simulated through changes in volumetric water content, calculated using the van Genuchten model. The model parameters include saturated water content, residual water content, and shape parameters. The saturated water content is obtained through indoor geotechnical tests, with a value range of 0.3 to 0.5; the residual water content ranges from 0.05 to 0.1; and the shape parameter ranges from 0.1 to 0.5. The seismic dynamics are determined based on the regional seismic hazard analysis results. The input seismic wave uses artificially synthesized acceleration time histories, which include components in three directions. The peak ground acceleration (PGA) is set according to the seismic intensity; for example, for a seismic intensity of 7 degrees, the PGA is set to 0.1g. The seismic wave duration is set according to the magnitude; for example, for a magnitude 6 earthquake, the duration is set to 20 seconds. A typical external disturbance sequence combines rainfall infiltration with seismic dynamics in chronological order. First, the rainfall infiltration process during the initial light rainfall phase is applied, then the seismic dynamics are applied, and finally, the rainfall infiltration process during the subsequent continuous heavy rainfall phase is applied. The duration of the entire sequence is set according to the actual working conditions; for example, the total duration is 100 hours.

[0069] Next, the dynamic response of the potentially unstable region under typical external disturbance sequences was simulated in a digital twin model. The simulation employed a fully coupled seepage-stress analysis method, establishing the interaction between the seepage and stress fields within the digital twin model. For rainfall infiltration, flow boundary conditions were applied to the model surface, with the flow rate calculated based on the ratio of rainfall intensity to the surface permeability coefficient. The surface permeability coefficient was obtained through in-situ double-ring infiltration tests, ranging from 0.1 mm to 10 mm per hour. For seismic dynamics, acceleration time histories were applied to the bottom and lateral boundaries of the model, using viscous boundary conditions to absorb boundary reflections. The dynamic response simulation employed an explicit time integration method, with the time step determined based on the model's minimum element size and wave velocity; for example, for a model with a 1-meter element size, the time step was set to 0.001 seconds. At each time step, stress, displacement, and pore water pressure data for the potentially unstable region were recorded. The stress field data included six stress components, the displacement field data included three directional displacement components, and the pore water pressure field data included the pore water pressure values ​​at each node. During the simulation, energy balance and force balance are monitored to ensure computational convergence. The energy balance error is controlled within 5%, and the force balance residual is controlled within 1%.

[0070] Then, the dynamic expansion pattern of the plastic region and the cooperative change characteristics of displacement vectors during the dynamic response process are extracted. The dynamic expansion pattern of the plastic region is obtained by tracking the equivalent plastic strain distribution. The equivalent plastic strain is calculated based on the second invariant of the plastic strain tensor. When the equivalent plastic strain value exceeds 0.001, it is determined to be a plastic region. The extraction process is carried out according to the time series, and the plastic region distribution map is output every hour to analyze the spatial expansion direction, expansion speed, and connectivity of the plastic region. The expansion direction is determined by the movement trajectory of the centroid of the plastic region, the expansion speed is calculated by the area change rate of the plastic region in adjacent time steps, and the connectivity is evaluated by the topological structure analysis of the plastic region. The cooperative change characteristics of displacement vectors are obtained by calculating the consistency index of the displacement vector field. The consistency index is based on the directional similarity and magnitude correlation of the displacement vectors. The directional similarity is calculated by vector dot product, and the magnitude correlation is calculated by correlation coefficient. The extraction process is carried out according to the same time series to analyze the cooperative evolution law of the displacement vector field, including the spatial distribution of cooperative regions, the temporal change of the degree of cooperation, and the moment of cooperative abrupt change. The degree of collaboration is represented by a consistency index, which ranges from 0 to 1. The closer the value is to 1, the higher the collaboration.

[0071] Finally, the stability evolution path is determined based on the dynamic expansion pattern of the plastic zone and the synergistic change characteristics of the displacement vector. The stability evolution path is constructed by comprehensively evaluating the plastic zone expansion state and the displacement synergistic state. First, a stability evaluation matrix is ​​established, with rows corresponding to different time steps and columns corresponding to plastic zone expansion indices and displacement synergistic indices. The plastic zone expansion indices include the expansion area ratio, expansion direction, and connectivity, while the displacement synergistic indices include the average consistency index and the proportion of synergistic regions. Then, cluster analysis is used to identify patterns in the evaluation matrix. The cluster analysis is based on Euclidean distance to measure sample similarity, and the number of clusters is determined according to the elbow rule. The identified typical patterns correspond to different stability states, including stable state, critical state, and unstable state. The stable state is characterized by slow plastic zone expansion and high displacement synergistic; the critical state is characterized by accelerated plastic zone expansion and decreased displacement synergistic; and the unstable state is characterized by complete plastic zone penetration and loss of displacement synergistic. The final determined stability evolution path is represented in time series form, including the stability state and state transition time at each time point. The state transition time is determined based on the abrupt change points of the indicators. The abrupt change points are identified through sliding window variance analysis, with the window size set to 5 time steps. The stability evolution path output is a spatiotemporal evolution map and data tables for subsequent correlation strength assessment.

[0072] S4. Evaluate the correlation strength between the internal stress-displacement response mode and the formation of the potential sliding surface in the stability evolution path, implemented as follows:

[0073] In assessing the correlation between internal stress-displacement response patterns and the formation of potential sliding surfaces within the stability evolution path, internal stress-displacement response patterns are first extracted based on the stability evolution path. The stability evolution path originates from previous analysis steps and includes time-series stability state data and corresponding stress and displacement field data. The extraction process is performed for the stability state at each time step, focusing on extracting data at critical and instability points. Internal stress-displacement response patterns are characterized by the spatiotemporal distribution features of the stress and displacement fields. The stress field data includes six stress components: normal stress and shear stress, while the displacement field data includes displacement components in three directions. The extraction method employs eigenvalue decomposition (EVD), which is calculated based on the covariance matrix of the stress tensor and displacement vector. The covariance matrix has dimensions of 6×6 corresponding to the stress field and 3×3 corresponding to the displacement field. After EVA, the eigenvectors corresponding to the three largest eigenvalues ​​are selected as the primary response patterns. The magnitude of the eigenvalue indicates the contribution of the pattern to the overall response; for example, patterns with eigenvalues ​​greater than 10% of the sum of all eigenvalues ​​are retained. The extracted response modes include stress concentration mode, displacement coordination mode and stress-displacement coupling mode. Each mode is stored in the form of a spatial distribution map. The grid resolution of the distribution map is set according to the model accuracy, for example, a 5m × 5m grid is used.

[0074] Next, we analyze the principal stress direction deflection characteristics and displacement vector field divergence characteristics in the internal stress-displacement response model. The principal stress direction deflection characteristics are obtained by calculating the changes in the principal stress direction angles. These angles include dip and tilt angles, with the dip angle ranging from 0 to 360 degrees and the tilt angle ranging from 0 to 90 degrees. The deflection characteristic analysis is based on time-series data, calculating the variation amplitude of the principal stress direction angles between adjacent time steps. This variation amplitude is calculated using the angle difference formula, taking into account the combined changes in dip and tilt angles. The displacement vector field divergence characteristics are obtained by calculating the divergence values ​​of the displacement vectors. These divergence values ​​represent the degree of divergence or convergence of the displacement field. The divergence calculation uses the central difference method, calculating the displacement vector divergence at each grid point in a three-dimensional mesh. Positive and negative divergence values ​​represent displacement divergence and convergence, respectively. The analysis also includes feature statistics, such as calculating the average, maximum, and standard deviation of the deflection angles, and calculating the extreme values ​​and gradients of the divergence values. These statistical characteristics are used to characterize the dynamic properties of the response pattern. For example, a standard deviation of deflection angle greater than 15 degrees indicates drastic changes in direction, and a divergence gradient greater than 0.001 per second indicates rapid changes in the displacement field.

[0075] Then, the principal stress direction deflection characteristics and displacement vector field divergence characteristics are spatially coupled with the potential slip surface development location. The potential slip surface development location is derived from the previous geological structure surface identification results and stored in three-dimensional spatial coordinates. The spatial coupling analysis first establishes a spatial distribution matrix of the principal stress direction deflection angle and displacement vector field divergence value. The distribution matrix discretizes the analysis area into a regular grid, with the grid size set according to the model accuracy, for example, using a 10m × 10m × 10m cube grid. Each grid cell stores the average principal stress direction deflection angle and the average displacement vector field divergence value for that area. The average value is calculated using the arithmetic mean method, considering the values ​​of all data points within the grid. Next, the spatial coordinate information of the potential slip surface development location is spatially superimposed with the distribution matrix. The superposition analysis uses a spatial interpolation method, such as the inverse distance weighted interpolation method, with the interpolation radius set to 20 meters. For each potential slip surface development location, the principal stress direction deflection amplitude and displacement vector field divergence value at that location are extracted. The extracted values ​​are obtained through interpolation calculations, for example, taking the weighted average of the values ​​of the four nearest grid cells, with the weights set based on the reciprocal of the distance. After the calculation is completed, a coupling data table is generated. The data table includes the sliding surface number, spatial coordinates, deflection amplitude, divergence value and coupling coefficient. The coupling coefficient is calculated by multiplying the deflection amplitude and the divergence value and is used to characterize the strength of the interaction between the two.

[0076] Finally, the correlation strength between the internal stress-displacement response mode and the formation of the potential sliding surface was quantified using the grey relational analysis method. The grey relational analysis used the development degree of the potential sliding surface as a reference sequence and the principal stress direction deflection characteristics and displacement vector field divergence characteristics as comparison sequences. The reference sequence data came from geological survey results, including sliding surface size, connectivity, and activity indicators, which were quantified using an expert scoring method with scores ranging from 1 to 10. The comparison sequence data came from spatial coupling analysis results, including deflection amplitude and divergence values. Before analysis, the sequence data were standardized using a mean-based method, i.e., each sequence value was divided by the average value of that sequence. The grey relational coefficient was calculated using the standard grey relational model, with the correlation coefficient formula based on the sequence difference and resolution coefficient, where the resolution coefficient was set to 0.5. The correlation strength was quantified by the average value of the correlation coefficients, calculated using the arithmetic mean method. The correlation strength value ranged from 0 to 1, with larger values ​​indicating stronger correlations. For example, a correlation strength greater than 0.7 was considered a strong correlation, between 0.5 and 0.7 a moderate correlation, and less than 0.5 a weak correlation. The quantification results are output as a correlation strength matrix, with rows corresponding to different sliding surfaces and columns corresponding to different response modes, for use in subsequent chain effect analysis. The entire analysis process ensures the integrity of the data chain, forming a closed loop from response mode extraction to correlation strength quantification, and all intermediate data are stored in a structured format for verification.

[0077] S5. Based on correlation strength simulation, the chain effect of sequential activation of the main control structural surfaces involved in the potential sliding surface under external disturbance is identified. The structural surface region that plays a transmission and pivotal role in the chain effect is determined as the key influence region, and implemented as follows:

[0078] In the chain effect simulation of the sequential activation of the controlling structural planes involved in the potential sliding surface under external disturbance, based on correlation strength, the controlling structural planes that need to be focused on for analysis are first screened out based on correlation strength. Correlation strength data comes from previous evaluation steps and includes the correlation strength values ​​formed by each structural plane and the sliding surface, ranging from 0 to 1. A correlation strength threshold is set during the screening process, determined based on statistical distribution; for example, the upper quartile of all correlation strength values ​​is used as the threshold, and structural planes are retained when the correlation strength value is greater than 0.7. The geological characteristics and spatial distribution of the structural planes are also considered during screening. Geological characteristics include the scale, continuity, and mechanical properties of the structural planes. The scale of the structural planes is quantified by its extension length and influence range; continuity is assessed by the structural plane continuity index; and mechanical properties are characterized by the structural plane shear strength parameters. The screened controlling structural planes are arranged in descending order of correlation strength value and grouped into primary controlling structural planes and secondary controlling structural planes. The grouping boundary is determined based on the natural discontinuity of the correlation strength values. The final list of controlling structural surfaces includes structural surface number, spatial location, geological features, and correlation strength value, for use in subsequent dynamic response analysis.

[0079] Next, the dynamic response of the controlling structural surface under a typical external disturbance sequence was simulated in a digital twin model. The typical external disturbance sequence was derived from a previously constructed load sequence including rainfall infiltration and seismic dynamics. The simulation employed a fully coupled seepage-stress-damage analysis method. Contact characteristics of the controlling structural surface were set in the digital twin model, including normal stiffness, tangential stiffness, and friction angle. The normal stiffness was set according to the properties of the infill material; for example, it was set to 10 GPa / m for an unfilled structural surface and 1 GPa / m for a silt-filled structural surface. The dynamic response simulation used an explicit dynamic method, with the time step automatically determined based on the minimum element size and material wave velocity. For example, for a model with an element size of 0.5 meters, the time step was set to 0.0005 seconds. During the simulation, the stress state, displacement response, and damage evolution data of each controlling structural surface were recorded. The stress state was characterized by the average stress of the structural surface elements, the displacement response by the relative displacement of the structural surface, and the damage evolution by tracking damage variables. The simulation continues throughout the entire typical external disturbance sequence, and the output data time interval is adjusted according to the disturbance intensity. For example, it outputs once every 10 minutes during the heavy rainfall phase and once every 0.1 seconds during the earthquake phase.

[0080] Then, the stress redistribution characteristics and plastic zone continuity characteristics of the controlling structural surfaces during the dynamic response process are analyzed. Specifically, this involves extracting stress tensor variation data and plastic strain distribution data around each controlling structural surface according to time steps. Stress tensor variation data is obtained by calculating the stress tensor difference between adjacent time steps, including changes in normal stress and shear stress. Plastic strain distribution data is characterized by equivalent plastic strain values, which are calculated based on the second invariant of the plastic strain tensor. The temporal variation law of stress state between adjacent controlling structural surfaces is analyzed to determine the stress redistribution propagation path. The temporal variation law is determined by time series correlation analysis of stress changes, using the Pearson correlation coefficient. A correlation coefficient with an absolute value greater than 0.8 is considered a strong correlation. The stress redistribution propagation path is determined based on the correlation strength and temporal relationship. For example, if the stress change of structural surface A leads the stress change of structural surface B, and the correlation coefficient between the two is greater than 0.8, then the stress is considered to propagate from structural surface A to structural surface B. The expansion and connectivity of the plastic region among the controlling structural surfaces are traced. This expansion and connectivity process is analyzed through the spatiotemporal evolution of plastic strain isosurfaces. The isosurface threshold is set according to the material's yield strength; for example, an equivalent plastic strain value of 0.005 is taken as the boundary of the plastic region. The expansion path is determined based on the temporal order and spatial connection relationship of the plastic regions, and connectivity is evaluated using topological analysis methods.

[0081] Subsequently, the chain effect of sequential activation of the controlling structural surfaces was identified based on stress redistribution characteristics and plastic zone continuity characteristics. Chain effect identification was based on the spatiotemporal correlation analysis of stress propagation paths and plastic zone expansion paths. The activation order was determined according to the abrupt change time of the structural surface stress state and the appearance time of the plastic zone. The abrupt change time was identified by the peak stress change rate, which was detected using sliding window variance analysis with a window size of 5 time steps. Chain effect modes included series activation, parallel activation, and hybrid activation modes. The series activation mode was characterized by the sequential activation of structural surfaces in a linear order; the parallel activation mode was characterized by the simultaneous activation of multiple structural surfaces; and the hybrid activation mode combined series and parallel characteristics. The identification process also considered activation intensity, which was quantified by comprehensively considering the stress change amplitude and the plastic zone expansion rate. The stress change amplitude was taken as the absolute value of the stress difference before and after the abrupt change, and the plastic zone expansion rate was calculated by the growth rate of the plastic zone area per unit time. Chain effect verification is conducted by comparing the activation time difference and spatial location relationship of different structural surfaces. Structural surfaces with a time difference less than the critical time interval and a spatial distance less than the influence radius are considered to have a chain effect. The critical time interval is estimated based on the disturbance propagation speed, and the influence radius is determined based on the scale of the structural surface.

[0082] Finally, structural plane regions that serve as hubs for stress transfer and plastic zone connectivity in the chain effect are marked as key influence areas. The hub function is evaluated using the centrality indices of structural planes in the chain effect, including stress transfer centrality and plastic zone connectivity centrality. Stress transfer centrality is calculated based on the betweenness centrality of the structural plane in the stress propagation network, which represents the frequency of the structural plane's appearance in the stress propagation path. Plastic zone connectivity centrality is calculated based on the degree centrality of the structural plane in the extended plastic zone network, which represents the number of plastic zones connected by the structural plane. A hub function threshold is set during the marking process, determined based on the statistical distribution of the centrality indices; for example, the top 20% of both betweenness centrality and degree centrality are used as the threshold. The spatial extent of the key influence areas is determined through structural plane influence domain analysis. The influence domain employs a buffer zone analysis method, with the buffer radius set according to the structural plane scale and geological conditions; for example, for a structural plane with an extension length of 50 meters, the buffer radius is set to 25 meters. The marking results are represented in the form of three-dimensional spatial regions, including information such as region number, spatial boundaries, hub function level, and chain effect participation, for use in the subsequent generation of pre-reinforcement schemes. All analytical data are stored in a structured database to ensure data traceability and verification.

[0083] S6. Based on the key impact areas, generate a regional slope pre-reinforcement plan with the goal of breaking the chain effect, and implement it as follows:

[0084] In generating regional slope pre-reinforcement schemes based on key influence areas to prevent chain effects, the first step is to analyze the stress transmission paths and plastic zone connectivity channels within these key influence areas. The key influence areas are structural surfaces identified in previous steps that serve as stress transmission and plastic zone connectivity hubs within the chain effect. Stress transmission path analysis is based on time-series stress field data from a digital twin model, determining the path direction by extracting principal stress direction changes and stress value gradient distribution. Specific methods include constructing a stress propagation network model, where network nodes correspond to core points within the key influence areas. Connections between nodes are established based on stress correlation coefficients, obtained by calculating the Pearson correlation coefficient of stress changes in adjacent areas. A Pearson correlation coefficient threshold of 0.75 or higher indicates a strong correlation. Plastic zone connectivity channel analysis is based on plastic strain distribution data, determining the channel range by tracing the spatial connectivity of equivalent plastic strain isosurfaces. The isosurface generation employs a moving cube algorithm, with the isosurface threshold set according to the material's yield characteristics; for example, for soil and rock materials, an equivalent plastic strain value of 0.01 is used as the channel boundary identification standard. The analysis process also includes evaluating the topological properties of paths and channels, such as calculating the betweenness centrality of paths and the connectivity of channels. Betweenness centrality represents the transit importance of a path in the network, and connectivity is quantified by the number of channel branches. The final determined stress transfer paths and plastic zone penetration channels are represented in the form of three-dimensional linear and planar elements, with strength grade attributes attached. The strength grade is determined based on the path stress flow rate and the channel plastic strain value.

[0085] Then, a support structure layout scheme capable of cutting off stress transmission paths and blocking the through-channels in the plastic zone is determined. The type of support structure is selected based on geological conditions and engineering requirements; common types include prestressed anchors, anti-slide piles, and retaining walls. The layout scheme is determined based on the spatial location and mechanical properties of the stress transmission paths and the through-channels in the plastic zone. For stress transmission paths, the cutting point is selected at the bottleneck location of the path, which is identified through path flow analysis. Path flow calculation is based on the flux integral of the stress vector. For through-channels in the plastic zone, the blocking point is selected at the narrowest point or intersection of the channel. The narrowest point is determined by calculating the channel width, with the width threshold set at 60% of the average channel width. Support structure parameters include geometric dimensions and mechanical properties. Geometric dimensions, such as anchor length and diameter, are set according to the scale of the affected area. For example, for an area with an affected range of 50 meters, the anchor length is set to 20 meters and the diameter to 0.1 meters. Mechanical properties, such as pull-out force and shear strength, are calculated based on the path stress level and the plastic strain value of the channel. The safety factor for pull-out force is 2.0, and the safety factor for shear strength is 1.5. The layout optimization adopted an iterative method. After the initial layout, the cutting and blocking effects were verified through numerical simulation. The effect evaluation indicators included the degree of stress redistribution and the plastic zone reduction rate. The degree of stress redistribution was calculated by the ratio of the stress difference before and after the cutting point, and the plastic zone reduction rate was calculated by the rate of change of the plastic zone area before and after the blocking point. During the optimization process, the position and parameters of the support structure were adjusted until the indicators met the requirements, such as a stress difference ratio greater than 80% and a plastic zone reduction rate greater than 70%.

[0086] Finally, a regional slope pre-reinforcement range plan, including support structure type and spatial layout parameters, is generated based on the support structure layout scheme. The pre-reinforcement range plan is output in the form of engineering drawings and data tables. The drawings include a plan view and cross-sectional view of the support structure, and the data tables include the type code, spatial coordinates, azimuth, and performance parameters of each support structure. The spatial layout parameters are derived from the optimized support structure layout scheme, specifically including the three-dimensional coordinates of the support structure center point, installation angle, and spacing. The center point coordinates are obtained through geological coordinate system transformation, with transformation parameters based on regional control point measurement data. The installation angle is set according to the support structure type; for example, the inclination angle of the anchor bolts is determined based on the potential sliding surface attitude, ranging from 10 degrees to 45 degrees. The spacing is calculated based on the influence radius of the support structure, which is determined through numerical experiments or empirical formulas; for example, the anchor bolt spacing is taken as 1.5 times the influence radius. The scheme generation process also includes economic and construction feasibility assessments. Economic assessment is based on the reinforcement cost per unit area, with cost data derived from local material prices and labor quotas. Construction feasibility considers on-site operating conditions and equipment limitations; for example, lightweight support structures are preferred in steep areas. The final regional slope pre-reinforcement scheme integrates all support structure information and includes construction sequence suggestions and monitoring requirements. The construction sequence is determined based on the activation order of the chain effect, and the monitoring requirements include the placement of displacement and stress monitoring points. The scheme's output format is compatible with subsequent engineering design software, ensuring direct use in construction drawing design.

[0087] All calculations involved in the embodiments are dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to the actual situation.

[0088] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0089] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and inventive constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0090] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0091] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0092] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0093] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for determining the scope of regional slope pre-reinforcement, characterized in that, Includes the following steps: S1. Obtain a digital twin model of the regional slope; S2. Identify the network of geological structural surfaces in the digital twin model, and identify the intersection of structural surfaces with both continuity and obstruction as potential instability areas by analyzing stress redistribution characteristics; S3. Analyze the stability evolution path of potential unstable regions under typical external disturbance sequences; S4. Evaluate the correlation strength between the internal stress-displacement response mode and the formation of the potential sliding surface in the stability evolution path; S5. Based on the correlation strength simulation, the chain effect in which the main control structural surfaces involved in the potential sliding surface are activated sequentially under external disturbances will identify the structural surface regions that play a transmission and pivotal role in the chain effect as key influence regions. S6. Based on the key affected areas, generate regional slope pre-reinforcement range schemes with the goal of breaking the chain effect.

2. The method for determining the scope of regional slope pre-reinforcement according to claim 1, characterized in that, Obtain a digital twin model of the regional slope, including: A three-dimensional geological structure model of the regional slope was constructed based on geological survey data; An initial numerical model is generated by integrating topographic survey data with a three-dimensional geological structure model. The construction of the digital twin model is completed by assigning parameters to the soil and rock mass and setting boundary conditions; The geological survey data includes data on the distribution of soil and rock layers and data on the occurrence of geological structural surfaces.

3. The method for determining the scope of regional slope pre-reinforcement according to claim 1, characterized in that, Identify the network of geological structural surfaces in the digital twin model, and identify potential instability areas at the intersection of structural surfaces with both continuity and obstruction by analyzing stress redistribution characteristics, including: Extracting the geological structural surface network based on a digital twin model; Simulate the stress field and analyze stress redistribution characteristics in a digital twin model; Identify the intersection of geological structural surfaces with both continuity and obstruction in the stress release path based on stress redistribution characteristics; The intersection of geological structural surfaces where continuity and obstruction coexist are marked as potential instability areas.

4. The method for determining the scope of regional slope pre-reinforcement according to claim 3, characterized in that, Identifying the intersection of geological structural surfaces with both continuity and obstruction in the stress release path based on stress redistribution characteristics includes: extracting the distribution characteristics of the maximum shear stress contour lines in the digital twin model to determine the spatial distribution of the stress release path; combining principal stress direction field analysis to identify obstructive geological structural surfaces in the stress transmission process; and judging the continuity of stress release based on the degree of density variation of stress contour lines in the intersection area of ​​structural surfaces.

5. The method for determining the scope of regional slope pre-reinforcement according to claim 1, characterized in that, The stability evolution path of potential instability regions under typical external perturbation sequences is analyzed, including: Construct a typical external disturbance sequence that includes rainfall infiltration process and seismic dynamics; Simulate the dynamic response of a potentially unstable region under typical external disturbance sequences in a digital twin model; Extract the dynamic expansion mode of the plastic zone and the cooperative change characteristics of displacement vectors during the dynamic response process; The stability evolution path is determined based on the dynamic expansion mode of the plastic zone and the cooperative change characteristics of the displacement vector.

6. The method for determining the scope of regional slope pre-reinforcement according to claim 1, characterized in that, Assess the correlation strength between internal stress-displacement response modes and potential slip surface formation in the stability evolution path, including: Extracting internal stress-displacement response modes based on stability evolution paths; Analyze the principal stress direction deflection characteristics and displacement vector field divergence characteristics in the internal stress-displacement response mode; Spatial coupling analysis was performed on the principal stress direction deflection characteristics and displacement vector field divergence characteristics with the potential sliding surface development location. The correlation strength between the internal stress-displacement response mode and the potential sliding surface is quantified using the grey relational analysis method.

7. The method for determining the scope of regional slope pre-reinforcement according to claim 6, characterized in that, The spatial coupling analysis of the principal stress direction deflection characteristics and displacement vector field divergence characteristics with the potential sliding surface development location includes: establishing a spatial distribution matrix of the principal stress direction deflection angle and displacement vector field divergence value; performing spatial superposition analysis of the spatial coordinate information of the potential sliding surface development location with the distribution matrix; and calculating the principal stress direction deflection amplitude and displacement vector field divergence value at each potential sliding surface location.

8. The method for determining the scope of regional slope pre-reinforcement according to claim 1, characterized in that, Based on correlation strength simulation, a chain effect is observed where the controlling structural surfaces involved in the potential sliding surface are sequentially activated under external disturbances. The structural surface regions that play a transmitting and pivotal role in this chain effect are identified as key influence regions, including: Based on the correlation strength, the key control structure surfaces that need to be analyzed are selected. Simulate the dynamic response process of the master control structure surface under the action of typical external disturbance sequences in a digital twin model; The stress redistribution characteristics and plastic zone continuity characteristics of the main control structural surfaces during the dynamic response process are analyzed. Identify the chain effect of sequential activation of the main controlling structural surfaces based on stress redistribution characteristics and plastic zone continuity characteristics; The structural surface regions that bear the functions of stress transmission and plastic zone connection in the chain effect are marked as key influence areas.

9. The method for determining the scope of regional slope pre-reinforcement according to claim 8, characterized in that, The analysis of stress redistribution characteristics and plastic zone connectivity characteristics of the master control structural surfaces during dynamic response includes: extracting stress tensor change data and plastic strain distribution data around each master control structural surface according to the time step; analyzing the temporal variation law of stress state between adjacent master control structural surfaces to determine the stress redistribution propagation path; and tracing the expansion and connectivity process of the plastic zone between each master control structural surface.

10. The method for determining the scope of regional slope pre-reinforcement according to claim 1, characterized in that, Based on the key impact areas, regional slope pre-reinforcement schemes are generated with the goal of breaking the chain effect, including: Analyze the stress transfer path and plastic zone connectivity in key affected areas during the chain effect; Determine the support structure layout scheme that can cut off the stress transmission path and block the through channel of the plastic zone; Generate a regional slope pre-reinforcement range scheme that includes the support structure type and spatial layout parameters based on the support structure layout scheme.

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