A method for evaluating stability of accumulation body based on terrain reconstruction and parameter inversion

Abstract

This application discloses a method for evaluating the stability of accumulation bodies based on terrain reconstruction and parameter inversion, belonging to the field of accumulation body stability evaluation technology. The method includes: acquiring first topographic mapping data characterizing the underlying terrain before the formation of the target accumulation body, and second topographic mapping data characterizing its current landform; performing finite element simulation based on the two sets of data; extracting micro-topographic feature vectors from the first topographic mapping data; and inverting the macroscopic mechanical parameters of the accumulation body through a predetermined constitutive mapping relationship; finally, outputting the stability evaluation results using the degree of plastic zone penetration and the displacement growth rate of key points as dual indicators. This method simultaneously improves the accuracy of the accumulation body's geometric model, the reliability of parameter assignment, and the comprehensiveness of stability criteria, effectively improving the accuracy of accumulation body stability evaluation results and the identification of gradual instability processes.

Description

Technical Field

[0001] This application relates to the field of accumulation stability evaluation technology, and in particular to an accumulation stability evaluation method based on terrain reconstruction and parameter inversion. Background Technology

[0002] In the fields of water conservancy and hydropower, mining and transportation infrastructure construction, the instability of waste and landslide accumulations formed by large-scale excavation and filling can endanger engineering structures and the surrounding environment. Stability assessment is a key link in geotechnical engineering design, operation and management.

[0003] In related technologies, geometric modeling of accumulation bodies often relies on topographic data from a single period, which cannot accurately reconstruct the underlying original topography, resulting in deviations between the model boundaries and the actual strata. The assignment of physical and mechanical parameters depends on empirical estimation or a small number of indoor experiments, lacking a system parameter inversion method based on micro-topography and structural features, leading to significant uncertainty in parameter selection. Stability assessment often uses the safety factor as a single criterion, without introducing multiple quantitative indicators such as the degree of plastic zone penetration and the rate of displacement growth at key points, making it difficult to effectively identify the gradual instability process of accumulation bodies. There is an urgent need for an accumulation body stability evaluation method that can simultaneously improve the accuracy of topographic reconstruction, the reliability of parameter assignment, and the comprehensiveness of stability assessment indicators. Summary of the Invention

[0004] This application provides a method for evaluating the stability of accumulation bodies based on terrain reconstruction and parameter inversion. It can solve the problems in related technologies, such as insufficient accuracy of underlying original terrain reconstruction due to terrain data from a single period, large uncertainty in physical and mechanical parameters due to empirical estimation or limited experiments, and the inability of a single safety factor criterion to effectively identify the gradual instability process of accumulation bodies.

[0005] According to a first aspect of this application, a method for evaluating the stability of accumulated bodies based on terrain reconstruction and parameter inversion is provided, comprising:

[0006] S100: Obtain first topographic mapping data of the target accumulation body, the first topographic mapping data being used to characterize the underlying topographic morphology before the formation of the target accumulation body; S200: Obtain the second topographic mapping data of the target accumulation body, the second topographic mapping data being used to characterize the geomorphological features of the target accumulation body at the current moment; S300: Based on the first topographic mapping data and the second topographic mapping data, perform finite element simulation calculations on the target accumulation body; wherein, the calculation includes extracting micro-topographic feature vectors based on the first topographic mapping data and obtaining the macroscopic mechanical parameters of the target accumulation body through a predetermined constitutive mapping relationship. S400: Based on the finite element simulation results in step S300, the stability evaluation results are output using the degree of plastic zone penetration and the displacement growth rate of key points as dual indicators.

[0007] This application solves the problems in related technologies, such as insufficient accuracy of reconstructing the underlying terrain due to single-period terrain data, large uncertainty in physical and mechanical parameters due to reliance on empirical estimation or a small number of experiments, and difficulty in effectively identifying the gradual instability process of the accumulation body by a single safety factor criterion. By integrating the first topographic mapping data representing the underlying terrain before the formation of the accumulation body with the second topographic mapping data representing the current terrain, and extracting micro-topographic feature vectors based on the first topographic mapping data and inverting the macro-mechanical parameters of the accumulation body through a predetermined constitutive mapping relationship, and using dual indicators of plastic zone penetration degree and key point displacement growth rate for stability determination, this application achieves the technical effect of simultaneously improving the accuracy of the accumulation body geometric model, the reliability of parameter assignment and the comprehensiveness of stability criteria, and improving the accuracy of accumulation body stability evaluation results and gradual instability process identification.

[0008] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0009] To more clearly illustrate the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a flowchart illustrating a method for evaluating the stability of an accumulation body based on terrain reconstruction and parameter inversion, provided in an embodiment of this application. Detailed Implementation

[0011] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of this application, including various details to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0012] The following describes, with reference to the accompanying drawings, an embodiment of the present application of a method for evaluating the stability of an accumulation body based on terrain reconstruction and parameter inversion.

[0013] Figure 1This is a flowchart illustrating a method for evaluating the stability of an accumulation body based on terrain reconstruction and parameter inversion, provided in an embodiment of this application.

[0014] like Figure 1 As shown, the method includes the following steps: S100: Obtain first topographic mapping data of the target accumulation body, the first topographic mapping data being used to characterize the underlying topographic morphology before the formation of the target accumulation body; In some embodiments, the first topographic mapping data is used to characterize the spatial morphology of the original underlying topographic surface before the formation of the target accumulation, providing an accurate reference surface for the volume stripping of the accumulation and the reconstruction of the underlying contact surface.

[0015] When acquiring the first topographic mapping data, the topographic mapping data of the target deposit recorded in different historical periods are first collected to form a topographic mapping data time series set. The topographic mapping data time series set contains topographic mapping data units corresponding to any two different historical periods. Data units can be obtained by collecting historical topographic maps, archived remote sensing images, early aerial photographs, or pre-engineering measurement records. All collected data units must undergo digitization scanning, geometric correction, and coordinate registration to be unified to the 1985 National Elevation Datum or local independent elevation datum. For historical maps with missing coordinates or map distortion, polynomial correction or thin-plate spline transformation is performed using corresponding feature points to ensure spatial consistency of the data.

[0016] Subsequently, the topographic mapping data units in the time series set are arranged in a forward time sequence, i.e., from the earliest time to the latest time, to establish a time series, and one topographic mapping data unit is selected as the first topographic mapping data. The selection process is achieved by constructing and solving a utility objective function.

[0017] The first derivative of the objective utility function U(T) with respect to time T is calculated and set to zero to obtain the optimal time T that minimizes the objective utility function. When the calculated optimal time T exceeds the time range of the topographic mapping data time series set, the topographic mapping data cell closest to the boundary of that time range is selected as the first topographic mapping data. If multiple data cells exist that are equally close to the optimal time T, the cell with the smallest coordinate registration residual is selected first.

[0018] By constructing a time series set of topographic mapping data and using a utility objective function to dynamically select the first topographic mapping data, the accuracy and acquisition difficulty of the data can be quantitatively balanced. This effectively avoids the bias caused by subjective experience in selecting historical topographic data, provides highly adaptable benchmark data for subsequent topographic reconstruction of sedimentary bodies, and improves the reconstruction accuracy of the underlying original topography.

[0019] S200: Obtain the second topographic mapping data of the target accumulation body, the second topographic mapping data being used to characterize the geomorphological features of the target accumulation body at the current moment; In some embodiments, the second topographic mapping data is used to characterize the real landform of the target accumulation body at the current moment, providing a precise basis for delineating the top boundary of the accumulation body and defining the accumulation range.

[0020] The second topographic mapping data is acquired using UAV oblique photogrammetry. The UAV, equipped with a five-lens oblique camera, acquires multi-view images of the target accumulation along a pre-planned flight path. Aerial triangulation is then used to generate a dense topographic point cloud with a density of at least 50 points per square meter. Depending on the terrain complexity and engineering accuracy requirements, single-lens panoramic or intersecting flight paths can be used. The UAV platform can be fixed-wing or multi-rotor, and the point cloud density can be adjusted to 20 points per square meter or 100 points per square meter.

[0021] The generated dense point cloud is preprocessed, and morphological filtering or cloth-simulation filtering algorithms are used to remove non-ground points such as vegetation and temporary structures, retaining the true surface point data. An irregular triangular network is constructed based on the preprocessed ground point cloud, and a digital elevation model (DEM) is generated using Kriging interpolation or inverse distance weighted interpolation methods. This DEM serves as the second topographic mapping data. When dense vegetation cover prevents effective photogrammetry penetration, airborne lidar point clouds are fused for vegetation penetration compensation, or multi-temporal photogrammetric data is used to extract exposed ground points from different seasons, further improving the accuracy of the exposed ground model.

[0022] Unmanned aerial vehicle (UAV) oblique photography technology can acquire detailed topographic information of the accumulation body in a non-contact and instantaneous manner, improving the resolution and coverage integrity of the second topographic mapping data. At the same time, the compensation processing for vegetation-covered areas can effectively eliminate errors caused by surface occlusion, providing accurate top surface geometric boundaries for subsequent three-dimensional solidification reconstruction of the accumulation body.

[0023] S300: Based on the first topographic mapping data and the second topographic mapping data, perform finite element simulation calculations on the target accumulation body; wherein, the calculation includes extracting micro-topographic feature vectors based on the first topographic mapping data and obtaining the macroscopic mechanical parameters of the target accumulation body through a predetermined constitutive mapping relationship. In some embodiments, finite element simulation is performed on the target accumulation body based on first and second topographic mapping data, providing a numerical basis for stress-strain field analysis and deformation failure mechanism research of the accumulation body. The finite element simulation first constructs a three-dimensional solid model of the accumulation body. Using the second topographic mapping data as the upper surface and the first topographic mapping data as the underlying interface, the closed space enclosed by the two surfaces is filled with a solid using the difference algorithm in Boolean operations to obtain the waste accumulation body model. The solid filling uses 10-node modified quadratic tetrahedral elements for meshing. The element size is selected within the range of 1 to 5 meters based on the accumulation body thickness. At locations with abrupt topographic changes such as valleys and ledges, the element size is refined to 0.5 meters. During the solidification of the closed space, the micro-topographic features such as valley undulations and ledges in the first topographic mapping data are fully preserved without any smoothing interpolation processing to avoid local curvature distortion and contact surface morphology deviation caused by smoothing operations.

[0024] After model construction is completed, either fluid-structure interaction (FSI) or discrete-continuous coupling (CDC) calculation methods are selected based on actual engineering needs. FSI calculations are used when rainfall infiltration or groundwater seepage needs to be considered. Within each time increment step, the seepage force vector is first calculated from the current pore water pressure field. This seepage force is then applied as a volume load to the finite element nodes of the solid skeleton, and the node displacement field is solved. Subsequently, the element volumetric strain is calculated, and the pore water pressure field is updated based on the volumetric strain. This process is iterated until the normalized increments of displacement and pore pressure satisfy 10. -5 The preset convergence tolerance is set. The time increment step is automatically adjusted based on the material permeability coefficient and element size, with an initial step size ranging from 0.1 hours to 1 hour. When the accumulation contains a large number of discrete boulders, a discrete-continuous coupling scheme is adopted. Discrete elements are used to simulate the movement and collision behavior of the boulders, while continuous elements simulate the fine-grained soil matrix. The coupling analysis of the two media is achieved through the transfer of interfacial contact forces.

[0025] During the finite element simulation, the inversion of macroscopic mechanical parameters of the target accumulation body is completed simultaneously. First, based on the micro-topography and surface outcrop information reflected in the first topographic mapping data, micro-topographic feature vectors are extracted. These feature vectors include the proportion of boulder area, surface roughness index, roundness, fractal dimension, and orientation angle, extracted through multi-scale image segmentation, wavelet analysis, and box-counting dimension algorithms. The proportion of boulder area is obtained by the ratio of the boulder area to the total sample area in the binarized surface image. The surface roughness index is characterized by the ratio of the root mean square height of the laser scanning profile line to its relevant length. Roundness is quantified by the ratio of the radius of curvature of the particle profile to the radius of its largest inscribed circle. The orientation angle is the angle between the long axis of the boulder and the slope dip.

[0026] Based on the extracted micro-topographic feature vectors, a set of full-parameter constitutive mapping equations is established and solved to calculate the macroscopic mechanical parameters of the target accumulation body.

[0027] After parameter inversion, constitutive models are assigned to the target deposit, the bedrock surface represented by the first topographic mapping data, and the contact surface between them. The bedrock surface uses a linear elastic model or an elastoplastic model based on the Hawke-Brown criterion, while the target deposit uses a Mohr-Coulomb constitutive model or a Drucker-Prag model, with values ​​assigned based on macroscopic mechanical parameters calculated using the full-parameter constitutive mapping equations. The contact surface is simulated using contact elements. The friction coefficient, cohesion, and other parameters of the contact elements are corrected based on the roughness of the first topographic mapping data. First, the roughness coefficient is calculated from the surface roughness index, then multiplied by the basic friction coefficient to obtain the corrected contact surface friction coefficient. During the nonlinear iteration process, the contact elements determine slip and opening states based on the corrected friction coefficient. When the shear stress exceeds the corrected friction strength, the contact surface enters a slip state, and the shear stiffness degrades to its residual value. When the normal tensile stress exceeds the tensile strength of the contact surface, the contact surface opens, and the friction and cohesion simultaneously drop to zero.

[0028] By using the Boolean difference algorithm to reconstruct the three-dimensional solidified body of the accumulation while preserving micro-topographic features, the accuracy of the underlying slip surface geometric model and the ability to capture stress concentration zones can be improved. Based on the micro-topographic feature vectors, a full-parameter constitutive mapping equation system is constructed to invert macroscopic mechanical parameters, realizing the transformation of parameter assignment from empirical estimation to quantitative inversion and reducing the uncertainty of parameter selection. Differential simulation of the mechanical behavior of the rock-soil interface and the heterogeneity of materials inside the accumulation improves the discrimination accuracy of the slip and opening states of the contact surface. Fluid-structure interaction or discrete-continuous coupling calculation takes into account both computational efficiency and the realism of multiphase medium simulation of the accumulation, providing a reliable numerical analysis basis for subsequent stability evaluation.

[0029] S400: Based on the finite element simulation results in step S300, the stability evaluation results are output using the degree of plastic zone penetration and the displacement growth rate of key points as dual indicators.

[0030] In some embodiments, based on the finite element simulation results obtained in step S300, a comprehensive judgment is made using both the degree of plastic zone penetration and the displacement growth rate at key points, outputting the stability evaluation results of the target accumulation body. This evaluation system breaks through the limitations of the traditional single safety factor criterion, effectively identifying the gradual instability process of the accumulation body, and providing multi-dimensional quantitative basis for engineering design, operation and maintenance, and reinforcement.

[0031] The degree of plastic zone penetration is quantitatively characterized by the plastic strain threshold. Plastic strain is a core indicator reflecting the irreversible plastic deformation stage of soil and rock materials. When the plastic zone extends from the slope toe to the slope crest, forming a continuous penetration zone, the deposit will experience overall instability and failure. In this method, the plastic strain threshold is set differently according to the engineering level: 2% for routine projects, 1% for important projects, and 5% for temporary projects. By extracting the plastic zone distribution cloud map and shear strain increment contour lines from the finite element calculation results, the development range and penetration status of the plastic zone can be intuitively determined.

[0032] The displacement growth rate at key points is a dynamic indicator reflecting the deformation development trend of the accumulation body. Key points are located in deformation-sensitive areas such as the upper and middle parts of the slope, the edge of the slope platform, and potential slip surfaces within the accumulation body. The displacement growth rate is obtained by extracting the displacement-time history of key points obtained from finite element analysis and calculating the displacement increment per unit time. The preset threshold for the displacement growth rate is also adjusted according to the engineering level: 5 mm / day for conventional projects, 2 mm / day for important projects, and 10 mm / day for temporary projects.

[0033] The stability evaluation employs a strict "AND" logic dual judgment criterion. When the finite element simulation results show that the plastic strain representing the continuity of the plastic zone exceeds a preset threshold, and simultaneously the displacement growth rate at key points exceeds a preset rate, the evaluation result indicates that the target accumulation does not meet the stability requirements. If the plastic zone does not form a continuous continuous band, or the displacement at key points gradually converges over time, the accumulation is judged to meet the stability requirements. During the evaluation process, the bearing capacity of the foundation represented by the first topographic mapping data is simultaneously assessed. The ratio of the foundation stress obtained from the finite element calculation to the allowable bearing capacity is used as the criterion. When this ratio is not greater than 1 and the displacement rate approaches zero, the foundation bearing capacity is judged to meet the engineering requirements.

[0034] By using a dual-indicator approach of determining the degree of plastic zone penetration and the rate of displacement growth at key points, this method replaces the traditional single safety factor method. It can more comprehensively capture the evolution characteristics of progressive instability of the accumulation body, significantly improve the reliability and sensitivity of stability evaluation results, and simultaneously complete the foundation bearing capacity assessment, providing complete quantitative support for site utilization and reinforcement decisions under top loading conditions.

[0035] Compared with related technologies, in this embodiment, first topographic mapping data of the target deposit is acquired, which characterizes the underlying topographic morphology before the formation of the target deposit; second topographic mapping data of the target deposit is acquired, which characterizes the current geomorphological morphology of the target deposit; based on the first and second topographic mapping data, finite element simulation is performed on the target deposit; this includes extracting micro-geomorphic feature vectors based on the first topographic mapping data and inverting the macroscopic mechanical parameters of the target deposit through a predetermined constitutive mapping relationship; based on the finite element simulation results, stability evaluation results are output using the degree of plastic zone penetration and the displacement growth rate of key points as dual indicators. This can solve the problems in related technologies where the accuracy of reconstructing the underlying original topography is insufficient due to topographic data from a single period, physical and mechanical parameters rely on empirical estimation or have large uncertainties due to a small number of experiments, and a single safety factor criterion is difficult to effectively identify the gradual instability process of the deposit. It achieves the technical effect of simultaneously improving the accuracy of the deposit's geometric model, the reliability of parameter assignment, and the comprehensiveness of the stability criterion, thereby improving the accuracy of the deposit stability evaluation results and the identification of the gradual instability process.

[0036] As a specific implementation of this application, based on the basic solution, step S100 is further defined to include: S110: Obtain the time series set of topographic mapping data recorded in different historical periods of the target accumulation body; S120: Sort the various topographic mapping data units in the topographic mapping data time series set in forward time order, and select one topographic mapping data unit as the first topographic mapping data.

[0037] Specifically, in step S110, various topographic mapping data of the target accumulation body formed in different historical periods are comprehensively collected and integrated to form a topographic mapping data time series set. The topographic mapping data time series set contains topographic mapping data units corresponding to any two different historical periods. The data sources cover historical paper topographic maps, archived satellite remote sensing images, early aerial photographs, and on-site measurement records from the early stages of engineering construction. All collected raw data must undergo standardized preprocessing. First, paper maps are converted into digital format through digital scanning. Then, geometric correction and coordinate registration operations are performed to align the spatial coordinate system of all data units to the 1985 National Elevation Datum or the local independent elevation datum of the project location. For historical maps with missing coordinate information or paper stretching distortion, corresponding feature points such as road intersections, building corners, and ridgeline intersections are selected from the maps. Polynomial correction or thin-plate spline transformation methods are used for geometric correction to eliminate spatial position deviations caused by paper distortion and ensure spatial consistency of each topographic mapping data unit.

[0038] In step S120, all topographic mapping data units in the topographic mapping data time series set are arranged in a forward order according to their recording time, that is, a continuous time series is established from the earliest historical record time to the latest historical record time. Based on the established time series, one topographic mapping data unit is selected as the first topographic mapping data. The selection process prioritizes data completeness and spatial accuracy, giving priority to topographic mapping data units that cover the entire target deposit area, have no obvious data gaps, and have small coordinate registration residuals. When multiple data units meet the basic requirements, the unit whose recording time is closest to the formation time of the target deposit is selected first, so as to restore the original topographic morphology before the formation of the deposit to the greatest extent.

[0039] Beneficial effects: By constructing a time-series set of topographic mapping data covering multiple historical periods, it can provide rich alternative resources for the selection of primary topographic mapping data, avoiding the failure of topographic reconstruction due to missing or insufficient accuracy of a single data point; by adopting a forward time sorting method to manage data units in an orderly manner, it can clearly present the evolution of the topography over time, making it easier to quickly select reference data that best fits the original state before the formation of the accumulation body, effectively improving the accuracy and applicability of primary topographic mapping data.

[0040] As a specific implementation of this application, based on the basic scheme, step S120 is further defined as selecting the first topographic mapping data by constructing and solving a utility objective function; The utility objective function is expressed as: ; in, For the utility objective function, The instability coefficient, Let β be the accuracy asymptotic function, and let β be the difficulty weighting coefficient. It is a difficulty exponential function. For each topographic mapping data unit The maximum value in T is the time length between the recording time of each topographic mapping data unit and the acquisition time of the second topographic mapping data; Solve for the utility objective function The optimal time T with the minimum value is selected, and the topographic mapping data unit whose initial recording time is closest to the optimal time T is selected as the first topographic mapping data.

[0041] Specifically, in step S120, after completing the forward sorting of the topographic mapping data units by time, a quantified utility objective function is constructed. Through mathematical solution, the first topographic mapping data is automatically and objectively selected, avoiding the subjectivity and arbitrariness of manual selection and ensuring the scientificity and consistency of the benchmark data selection.

[0042] The utility objective function comprehensively considers two core dimensions: the accuracy of terrain data and the difficulty of acquiring it. Its expression is: ; In the formula, The smaller the value of the objective function, the higher the overall utility of the terrain data at the corresponding time. The instability coefficient is used to characterize the degree of instability of terrain evolution over time. Its value ranges from 0.6 to 0.9, with the upper limit used for mountainous areas with drastic terrain changes and the lower limit used for plains with gentle terrain. As an accuracy asymptotic function, it monotonically decreases with increasing time length T, reflecting that the further away the historical topographic data is from the current time, the lower its accuracy in reflecting the original topography before the formation of the accumulation body. It is a difficulty exponential function that monotonically increases with the time length T, reflecting that the older the terrain data, the greater the difficulty in digitizing, correcting and registering it. For all topographic mapping data units The maximum value in the range is used to normalize the difficulty index. T represents the time elapsed between the recording time of each topographic data unit and the acquisition time of the second topographic data, in years.

[0043] Regarding the utility objective function Taking the first derivative of time T and setting it to zero, we solve the differential equation to obtain the optimal time T that minimizes the utility objective function. The optimal time T is the best balance point that comprehensively weighs data accuracy and acquisition difficulty, and the corresponding data can minimize the workload and cost of data processing while ensuring sufficient accuracy.

[0044] Based on the calculated optimal time T, in the sorted topographic mapping data time series set, the topographic mapping data unit whose initial recording time is closest to the optimal time T is selected as the first topographic mapping data. When the calculated optimal time T exceeds the time range of the topographic mapping data time series set, the topographic mapping data unit closest to the boundary of that time range is selected. If multiple data units are equally close to the optimal time T, the unit with the smallest coordinate registration residual is selected first, with the coordinate registration residual controlled within 0.5 meters to ensure that the spatial accuracy of the data meets engineering requirements.

[0045] By constructing a quantitative utility objective function to achieve automatic selection of the first topographic mapping data, the accuracy of the data and the difficulty of acquisition can be quantitatively balanced, and the model boundary deviation caused by subjective experience selection can be completely eliminated. This provides stable and reliable benchmark data for high-precision reconstruction of the original terrain under the accumulation body, while taking into account the economy and efficiency of engineering implementation.

[0046] As a specific implementation of this application, based on the basic solution, step S200 is further defined to include: The target accumulation body is obtained by using oblique photography of UAVs, and a digital elevation model of the terrain is generated based on the terrain point cloud. The digital elevation model of the terrain is used as the second terrain mapping data.

[0047] Specifically, in step S200, UAV oblique photography technology is used to obtain high-precision geomorphic information of the target accumulation body at the current moment, and a digital elevation model of the geomorphology is generated as the second topographic mapping data, providing accurate geometric basis for delineating the top boundary of the accumulation body and defining the accumulation range.

[0048] The UAV oblique photography system is equipped with a five-lens oblique camera that flies along a pre-planned flight path covering the entire target accumulation area, simultaneously acquiring multi-view images from five different perspectives: vertical, forward-looking, backward-looking, left-looking, and right-looking. During flight, the camera exposure time is synchronized with the UAV's positioning system to ensure that each image carries precise position and attitude information. Aerial triangulation is used to match corresponding feature points in the multi-view images, generating a dense terrain point cloud with a density of no less than 50 points per square meter. Depending on the terrain complexity and engineering precision requirements, the shooting scheme can be adjusted, employing single-lens panoramic shooting or cross-flight methods to acquire images. The UAV platform can be either fixed-wing or multi-rotor, and the point cloud density can be adjusted accordingly to 20 points per square meter or 100 points per square meter.

[0049] The generated dense point cloud is preprocessed using morphological filtering or cloth-based simulation filtering algorithms to distinguish between ground points and non-ground points, removing non-ground interference points such as vegetation, temporary structures, and construction machinery, while retaining true surface point data. When the surface is covered by dense vegetation and photogrammetry cannot effectively penetrate the vegetation layer to obtain ground information, airborne lidar point cloud data is fused for vegetation penetration compensation, or multi-temporal photogrammetric data is used to extract exposed ground points during periods of sparse vegetation in different seasons to supplement and improve the ground point cloud data and eliminate terrain distortion caused by surface occlusion.

[0050] An irregular triangular mesh is constructed based on the preprocessed ground point cloud. Using Kriging interpolation or inverse distance weighted interpolation, the discrete point cloud data is converted into a continuous raster surface, generating a digital elevation model (DEM). This model fully preserves the detailed geomorphological features of the deposit, such as slope undulations, gully development, and platform boundaries. Its spatial resolution matches the point cloud density, accurately reflecting the current geomorphological morphology of the target deposit and serving directly as secondary topographic mapping data for subsequent analysis.

[0051] Beneficial effects: Unmanned aerial vehicle (UAV) oblique photogrammetry technology enables non-contact and highly efficient acquisition of detailed topographic information across the entire accumulation body, significantly improving the resolution and coverage integrity of the second topographic mapping data. Compensation processing for vegetation-covered areas effectively solves the problem of missing topographic data caused by surface occlusion, ensuring the accuracy of the digital elevation model of the topography and providing accurate top surface geometric boundaries for subsequent 3D solidification reconstruction of the accumulation body.

[0052] As a specific implementation of this application, based on the basic solution, step S300 is further defined to include: S310: Based on the first topographic mapping data and the second topographic mapping data, the closed space enclosed by the second topographic mapping data and the first topographic mapping data is materialized using the difference algorithm in Boolean operation to obtain a waste dump model. S320: Based on the waste slag accumulation model, perform fluid-structure interaction or discrete-continuous interaction calculations.

[0053] Specifically, step S310 uses the difference algorithm in Boolean operations to construct a three-dimensional solid model of the slag heap based on the first and second topographic survey data. The difference algorithm in Boolean operations refers to a geometric operation method that fills the closed area enclosed by two spatial surfaces with a solid, removing overlapping parts to obtain the target solid. In practice, the second topographic survey data, representing the current landform, is used as the upper surface, and the first topographic survey data, representing the underlying original topography, is used as the lower interface. The closed space enclosed by the two surfaces is filled with a solid to obtain the slag heap model. The solid filling uses 10-node modified quadratic tetrahedral elements for meshing. The element size is selected within the range of 1 to 5 meters based on the overall thickness of the slag heap. At locations with abrupt topographic changes such as valleys and drops, the element size is refined to 0.5 meters to ensure the calculation accuracy in stress concentration areas. During the process of solidifying the enclosed space, the micro-geomorphic features such as valleys, undulations, and drops in the first topographic mapping data are fully preserved without any smoothing interpolation processing. This avoids local curvature distortion and deviation of the underlying contact surface morphology caused by smoothing operations, ensuring that the model boundary is highly consistent with the actual stratum morphology.

[0054] Step S320: Based on the completed waste stockpile model, select either fluid-structure interaction (FSI) or discrete-continuous coupling (CD-CC) calculation method according to actual engineering needs. When the impact of rainfall infiltration and groundwater seepage on the stability of the stockpile needs to be considered, FSI calculation is used. FSI calculation employs a sequential coupling method. Within each time increment step, the seepage force vector is first calculated from the current pore water pressure field. This seepage force is then applied as a volume load to the finite element nodes of the solid skeleton, and the node displacement field is solved. Next, the element volumetric strain is calculated based on the node displacements, and the pore water pressure field is updated based on the volumetric strain. This process is iterated repeatedly until the normalized increments of displacement and pore pressure satisfy 10. -5 The preset convergence tolerance is set. The time increment step is automatically adjusted based on the material permeability coefficient and element size, with an initial step size ranging from 0.1 hours to 1 hour. When the accumulation contains a large number of discrete stones with large particle sizes, and the interlocking and collision between the stones plays a controlling role in the overall stability, a discrete-continuous coupling scheme is adopted. Discrete elements are used to simulate the movement, collision, and interlocking behavior of the stones, while continuous elements are used to simulate the fine-grained soil matrix. The coupling analysis of the two media is achieved through the transfer of interfacial contact forces, balancing computational efficiency with the simulation realism of the multiphase medium mechanical behavior of the accumulation.

[0055] By using Boolean difference algorithms to reconstruct the three-dimensional solidified body of the accumulation body and fully preserve the micro-topographic features, the accuracy of the underlying slip surface geometric model and the ability to capture stress concentration zones can be significantly improved. The fluid-structure interaction or discrete-continuous interaction calculation method can be flexibly selected according to engineering conditions, which can realistically simulate the mechanical response characteristics of different types of accumulation bodies, taking into account both computational efficiency and simulation accuracy, and providing a reliable numerical analysis basis for subsequent stability evaluation.

[0056] As a specific implementation of this application, based on the basic scheme, step S310 is further defined to retain the micro-topographic features in the first topographic mapping data when performing closed space materialization. The micro-topographic features include valley undulations and / or ridges.

[0057] Specifically, in step S310, during the process of solidifying the closed space using the Boolean difference algorithm, the undulations and micro-landform features of the valleys and ridges in the first topographic mapping data are fully preserved, the geometric distortion caused by traditional smooth interpolation is eliminated, and the underlying contact surface model is highly consistent with the actual stratum morphology.

[0058] Gullies and undulations refer to naturally developed linear depressions in the underlying original topography, typically ranging from 0.5 to 5 meters in depth and 2 to 20 meters in width. They serve as primary channels for groundwater seepage within the deposit and are also weak points on potential slip surfaces. Steps and ledges refer to steep landforms formed by sudden changes in elevation, generally ranging from 1 to 10 meters in height with a slope greater than 60 degrees. These tend to form stress concentration zones at the toe, inducing local shear failure. Traditional modeling methods often use Kriging interpolation or moving averages to smooth the original topographic data to simplify mesh generation and calculation. This smooths out micro-topographic features like gullies and steps and ledges, distorting the curvature of the underlying contact surface and consequently affecting the accuracy of stress and displacement field calculations.

[0059] In this embodiment, the underlying interface surface is directly constructed using the original elevation points from the first topographic mapping data during the solidification reconstruction, without any global or local smoothing interpolation operations. During the mesh generation stage, local mesh refinement is performed in the areas containing valleys and ledges, reducing the cell size from the conventional 1-5 meters to 0.5 meters to ensure the mesh accurately fits the geometry of the micro-topography. For complex ledges, a gradual cell size transition is used to avoid computational convergence problems caused by abrupt changes in cell size. This processing method can completely restore the undulations and steep changes of the underlying contact surface, accurately simulating the contact morphology between the deposit and the bedrock.

[0060] By preserving the undulations and micro-geomorphic features of the valleys and ridges in the first topographic mapping data, the local curvature distortion and contact surface morphology deviation caused by smooth interpolation can be effectively eliminated, significantly improving the accuracy of the underlying slip surface geometric model, accurately capturing the location of stress concentration areas and groundwater seepage channels, providing a realistic geometric boundary for subsequent finite element calculations, and avoiding the stability evaluation results that are biased towards danger due to model simplification.

[0061] As a specific implementation of this application, based on the basic scheme, step S300 is further defined by inverting the macroscopic mechanical parameters through the following sub-steps: S321: Based on the first topographic mapping data, extract feature vectors, wherein the feature vectors include at least one of the following: area ratio of boulders, surface roughness index, roundness, fractal dimension, and directional arrangement angle; S322: Establish and solve the full-parameter constitutive mapping equation system to calculate the macroscopic mechanical parameters, which include at least one of macroscopic porosity, macroscopic density, macroscopic cohesion, macroscopic internal friction angle, macroscopic elastic modulus, macroscopic Poisson's ratio, and anisotropic strength reduction factor.

[0062] Specifically, step S321 extracts multi-dimensional feature vectors reflecting the internal structure of the accumulation body based on the first topographic mapping data and the accompanying surface outcrop information. The feature vectors include the proportion of boulder area, surface roughness index, roundness, fractal dimension, and orientation angle. Each index is extracted using a corresponding quantitative algorithm. The proportion of boulder area is obtained by binarizing the surface image using a multi-scale image segmentation algorithm and calculating the ratio of the boulder area to the total sample area, with a value ranging from 0 to 1. The surface roughness index is characterized by the ratio of the root mean square height of the laser scanning profile line to its relevant length, reflecting the degree of surface undulation and roughness. Roundness is quantified by the ratio of the radius of curvature of the particle profile to the radius of the largest inscribed circle, with a value ranging from 0 to 1; a larger value indicates better roundness. The fractal dimension is calculated using a box-counting dimension algorithm, characterizing the complexity and self-similarity of the accumulation body's surface morphology, with a value ranging from 2.0 to 2.5. The directional arrangement deviation angle is the angle between the long axis of the boulders and the slope inclination, reflecting the directional arrangement characteristics of the boulders within the accumulation body, and its value ranges from 0° to 90°.

[0063] Step S322: Based on the extracted feature vectors, establish and solve the full-parameter constitutive mapping equation system to calculate the macroscopic mechanical parameters of the accumulation body. The equation system contains 8 interrelated calculation formulas, systematically mapping the intrinsic relationship between micro-topographic features and macroscopic mechanical parameters.

[0064] By extracting multidimensional micro-topographic feature vectors and constructing a set of full-parameter constitutive mapping equations, quantitative inversion of the macroscopic mechanical parameters of the accumulation body was achieved. This completely changed the traditional parameter assignment method that relied on empirical estimation or a small number of indoor tests, effectively reducing the parameter uncertainty caused by the inability to sample or the discreteness of the test, and providing an accurate and reliable material parameter basis for finite element numerical simulation.

[0065] As a specific embodiment of this application, based on the basic scheme, the fully parameter constitutive mapping equation system is further defined to include at least one of the following formulas: Formulas for calculating historical consolidation and degradation evolution coefficients: ; In the formula, The coefficient for historical consolidation and deterioration evolution. The time-consolidation rate, The optimal historical time length is obtained by dynamically optimizing the equation based on spatiotemporal data. For reference time, As a disaster damage factor, The instability coefficient; Macroscopic porosity calculation formula: ; In the formula, Macroscopic porosity, The initial reference porosity is in a completely loose state. For fractal dimension, The sensitivity index of porosity to fractal dimension. The coefficient for historical consolidation and deterioration evolution; Macro density calculation formula: ; In the formula, For macroscopic density, This represents the percentage of the area occupied by the boulders. The natural density of a pure block of stone. The natural density of the fine-grained soil matrix. Macroscopic porosity; Formula for calculating macroscopic cohesion: ; In the formula, For macroscopic cohesion, For the basic cohesion of fine-grained soil matrix, This represents the percentage of the area occupied by the boulders. The conversion coefficient of the interlocking cohesion of the blocks (KPa). R is the surface roughness index, and R is the roundness. The coefficient for historical consolidation and deterioration evolution; Formula for calculating the macroscopic internal friction angle: ; In the formula, The angle of internal friction is the macroscopic angle. The basic angle of friction for pure fine-grained soil. The maximum additional angle of friction that can be provided by a pure block stone skeleton, , is the weighting coefficient for the content and geometric morphology of the boulders, where + =1; This represents the percentage of the area occupied by the boulders. R is the surface roughness index, and R is the roundness. The coefficient for historical consolidation and deterioration evolution; Formula for calculating macroscopic elastic modulus: ; In the formula, For macroscopic elastic modulus, This is the reference elastic modulus of the matrix soil. Macroscopic porosity, This is the stiffness enhancement index of the skeleton. This represents the percentage of the area occupied by the boulders. The coefficient for historical consolidation and deterioration evolution; Macroscopic Poisson's ratio calculation formula: ; In the formula, For macroscopic Poisson ratio, , Poisson's ratio for fine-grained soil and rock, respectively. This is the Poisson's pressure correction factor. This represents the percentage of the area occupied by the boulders. The coefficient for historical consolidation and deterioration evolution; Formula for calculating the anisotropic strength reduction factor: ; In the formula, This is the anisotropic strength reduction factor. The minimum reduction factor, The anisotropic sensitivity coefficient is... The directional arrangement is at an angle.

[0066] Specifically, the full-parameter constitutive mapping equations establish a quantitative coupling relationship between the micro-geomorphic features, temporal evolution effects, and macroscopic mechanical parameters of the accumulation body, realizing a systematic inversion from observable surface features to internal mechanical properties. The physical meaning, parameter connotations, and calculation logic of each formula are as follows: Formulas for calculating historical consolidation and degradation evolution coefficients: ; In the formula, It represents the historical consolidation and degradation evolution coefficient, which comprehensively characterizes the consolidation and hardening processes and the damage and degradation caused by geological disasters that occur over time after the formation of the accumulation body. The time consolidation rate reflects the rate of consolidation of the accumulation body by its own weight. The value ranges from 0.5 to 1.5. The upper limit is used for accumulation bodies with high fine soil content, and the lower limit is used for accumulation bodies with high boulder content. The disaster damage factor is 0 for deposits with no historical disaster records, and 0 for deposits that have experienced disasters such as earthquakes, rainstorms, and landslides, depending on the intensity of the disaster. The optimal historical time length is obtained by dynamic optimization equation based on spatiotemporal data, which is the optimal time interval between the initial recording time of the selected first topographic mapping data and the acquisition time of the second topographic mapping data. For reference, one year is used. This is the instability coefficient, with a value ranging from 0 to 1.

[0067] Macroscopic porosity calculation formula: ; In the formula, Macroscopic porosity reflects the degree of pore development within the accumulation body. The initial reference porosity is in a completely loose state, with a value ranging from 0.1 to 0.5, and can be calibrated through on-site test pit water injection tests. is the fractal dimension, which characterizes the complexity of the surface morphology of the accumulation. The sensitivity index of porosity to fractal dimension ranges from 0.2 to 0.5, reflecting the degree of influence of fractal dimension on porosity. This represents the historical consolidation and degradation evolution coefficient. Consolidation reduces porosity, while damage increases it.

[0068] Macro density calculation formula: ; In the formula, The macroscopic density is obtained by weighted averaging of the densities of the boulders and the fine-grained soil matrix, combined with porosity correction. This represents the percentage of the area occupied by the boulders. Let be the natural density of the pure block stone, and be a constant. Let be the natural density of the fine-grained soil matrix, and be a constant. The macroscopic porosity is the highest porosity, and the higher the porosity, the lower the overall density of the packing.

[0069] Formula for calculating macroscopic cohesion: ; In the formula, It is macroscopic cohesion, which is composed of the cohesion of the fine-grained soil matrix and the interlocking cohesion between the boulders. For the basic cohesion of fine-grained soil matrix, This represents the percentage of the area occupied by the boulders. The conversion coefficient of the interlocking cohesion of the blocks can be obtained through regression analysis of a large-scale direct shear test. In this embodiment... The value ranges from 5 kPa to 50 kPa. R is the surface roughness index, and R is the roundness. The rougher the surface of the stone and the worse the roundness, the stronger the interlocking effect and the greater the cohesion. This represents the historical consolidation and degradation evolution coefficient. Consolidation enhances cohesion, while damage weakens it.

[0070] Formula for calculating the macroscopic internal friction angle: ; In the formula, It is the macroscopic internal friction angle, which consists of the basic friction angle of fine-grained soil and the additional friction angle provided by the boulders skeleton. The basic angle of friction for pure fine-grained soil. The maximum additional angle of friction that can be provided by a pure block stone skeleton, , is the weighting coefficient for the content and geometric morphology of the boulders, where + =1; This represents the percentage of the area occupied by the boulders. R is the surface roughness index, and R is the roundness. The coefficient represents the historical consolidation and degradation evolution. Consolidation increases the internal friction angle, while damage decreases it.

[0071] Formula for calculating macroscopic elastic modulus: ; In the formula, It is the macroscopic elastic modulus, reflecting the ability of an aggregate to resist elastic deformation. This is the reference elastic modulus of the matrix soil. Macroscopic porosity, The stiffness enhancement index is used in this embodiment. The value range is from 50MPa to 500MPa. This represents the percentage of the area occupied by the boulders. This represents the coefficient for historical consolidation and deterioration evolution.

[0072] Macroscopic Poisson's ratio calculation formula: ; In the formula, Poisson's ratio is the macroscopic ratio that reflects the lateral deformation to the longitudinal deformation of an aggregate. , These are the Poisson's ratios for fine-grained soil and rock, respectively, ranging from 0.30 to 0.35 and 0.20 to 0.25. This is the Poisson pressure correction factor, with a value ranging from -0.02 to 0.02. This represents the percentage of the area occupied by the boulders. This represents the coefficient for historical consolidation and deterioration evolution.

[0073] Formula for calculating the anisotropic strength reduction factor: ; In the formula, It is the anisotropic strength reduction factor, which reflects the strength anisotropy of the accumulation body caused by the directional arrangement of the boulders. The minimum reduction factor, The anisotropic direction sensitivity coefficient ranges from 0.05 to 0.3, with a larger value for the stronger the orientation of the stone. The directional arrangement is at an angle.

[0074] This system of fully parameter constitutive mapping equations couples time evolution effects, micro-topographical features, and macroscopic mechanical responses. Each formula has a clear physical meaning, and the parameters can be obtained through field observations and conventional experiments, enabling quantitative inversion of all mechanical parameters of the accumulation body. Compared to traditional empirical estimation methods, it can more accurately reflect the heterogeneity and anisotropy of the accumulation body, significantly reducing the uncertainty of parameter assignment and providing a solid material parameter foundation for finite element numerical simulation.

[0075] As a specific implementation of this application, based on the basic solution, step S300 is further defined to include: S323: Assign constitutive models to the target accumulation, the bedrock surface represented by the first topographic mapping data, and the contact surface between the two; The contact surface is simulated using contact elements, and the parameters of the contact elements are corrected based on the roughness of the first topographic mapping data; the constitutive model of the target accumulation body is assigned values ​​based on the macroscopic mechanical parameters calculated by the full-parameter constitutive mapping equations.

[0076] Specifically, in step S323, after completing the inversion of macroscopic mechanical parameters, appropriate constitutive models are assigned to different components of the numerical model to simulate the mechanical behavior of the target accumulation body, bedrock surface, and their contact surface in a differentiated manner, ensuring that the finite element calculation can truly reflect the interaction mechanism between the accumulation body and the underlying strata.

[0077] The constitutive model of the target accumulation adopts either the Mohr-Coulomb constitutive model or the Drucker-Prag constitutive model. The model parameters are assigned values ​​entirely based on the macroscopic mechanical parameters calculated from the full-parameter constitutive mapping equations, including macroscopic porosity, macroscopic density, macroscopic cohesion, macroscopic internal friction angle, macroscopic elastic modulus, macroscopic Poisson's ratio, and anisotropic strength reduction factor. For accumulations with obvious zoning characteristics, the macroscopic mechanical parameters of the corresponding regions can be calculated and assigned values ​​according to the differences in micro-topographic features of different regions, accurately simulating the material heterogeneity inside the accumulation.

[0078] The underlying bedrock surface represented by the first topographic mapping data is modeled using either a linear elastic constitutive model or an elastoplastic constitutive model based on the Hawke-Brown criterion. The linear elastic model is suitable for hard bedrock with good integrity and high strength; its parameters include the bedrock's elastic modulus, Poisson's ratio, and density, which can be determined from engineering geological survey data. The Hawke-Brown elastoplastic model is suitable for weathered bedrock with well-developed joints and poor integrity, and can more accurately simulate the plastic failure behavior of bedrock.

[0079] The contact surface between the target deposit and the bedrock surface is simulated using surface-to-surface contact elements. The core parameters of the contact elements, such as friction coefficient and cohesion, are corrected based on the surface roughness of the first topographic survey data. First, the roughness coefficient is calculated from the surface roughness index of the first topographic survey data. The roughness coefficient is a linear function of the surface roughness index. Then, the basic friction coefficient is multiplied by the roughness coefficient to obtain the corrected contact surface friction coefficient. The contact surface cohesion is also adjusted accordingly based on the surface roughness; the greater the roughness, the higher the contact surface cohesion. During the nonlinear iteration process, the contact elements automatically determine the state: when the shear stress of the contact surface exceeds the corrected friction strength, the contact surface enters a slip state, at which point the shear stiffness automatically degenerates to its residual value; when the normal tensile stress of the contact surface exceeds the tensile strength of the contact surface, the contact surface opens, at which point both friction and cohesion simultaneously drop to zero.

[0080] By assigning appropriate constitutive models to the target deposit, bedrock surface, and contact surface, and correcting the contact surface parameters based on terrain roughness, it is possible to simulate the mechanical behavior and interfacial interactions of different media in a differentiated manner. This significantly improves the accuracy of distinguishing the slip and opening states of the contact surface, realistically reproduces the slip-separation failure process of the deposit along the underlying interface, and provides more reliable numerical calculation results for stability evaluation.

[0081] As a specific implementation of this application, based on the basic scheme, step S400 is further defined such that when the plastic strain in the finite element simulation result exceeds a preset threshold and the displacement growth rate of the key point exceeds a preset rate, an evaluation result that does not meet the stability requirements is output.

[0082] Specifically, step S400 uses a dual judgment criterion of "AND" logic between the plastic strain threshold and the displacement growth rate of key points to quantitatively evaluate the stability of the target accumulation, avoid misjudgment by a single indicator, and accurately identify the overall instability risk of the accumulation.

[0083] Plastic strain is a direct representation of irreversible plastic deformation in soil and rock masses. When a continuous plastic zone forms from the toe to the crest of the slope, the accumulation mass will lose its overall stability. The preset threshold for plastic strain is set differently based on the engineering safety level: 2% for conventional water conservancy and hydropower projects and transportation infrastructure projects; 1% for Class I projects involving important buildings or densely populated areas; and 5% for Class III projects such as temporary spoil heaps. The plastic strain distribution cloud map of the entire model is extracted from the finite element analysis results to identify the development range and extension path of the plastic zone, and to determine whether a continuous plastic zone has formed from the toe to the crest.

[0084] The displacement growth rate at key points reflects the dynamic development trend of the aggregate deformation and is a core indicator for judging the precursors of instability. Key points are preferentially placed in the most deformation-sensitive parts of the aggregate, including the deformation concentration zone in the upper and middle parts of the slope, the tensile crack zone at the edge of the slope platform, and the middle and rear sections of the potential slip surface inside the aggregate. Based on the displacement-time history curve of key points obtained from finite element analysis, the displacement increment per unit time is calculated to obtain the displacement growth rate. The preset threshold for the displacement growth rate is also matched to the engineering level: 5 mm / day for conventional engineering, 2 mm / day for Class I engineering, and 10 mm / day for Class III engineering.

[0085] The stability evaluation employs a strict dual-condition criterion. Only when both conditions are simultaneously met in the finite element simulation results will the evaluation result indicate that the target accumulation does not meet the stability requirements: first, the maximum plastic strain characterizing the plastic zone exceeds a preset threshold for the corresponding engineering grade; second, the displacement growth rate at at least one key monitoring point exceeds a corresponding preset rate. If either condition is not met—that is, the plastic zone does not form a continuous continuous band, or the displacement growth rate at all key points is below the threshold and tends to converge over time—then the target accumulation is deemed to meet the stability requirements.

[0086] By using a dual AND logic to determine the plastic strain and displacement growth rate, it is possible to identify the degree of plastic failure development within the accumulation body and capture the dynamic evolution trend of deformation. This effectively eliminates misjudgments caused by local plastic deformation or instantaneous disturbances, significantly improving the accuracy and reliability of identifying the overall instability risk of the accumulation body, and providing clear quantitative basis for engineering early warning and reinforcement decisions.

[0087] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0088] The above are merely specific embodiments of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to these embodiments, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for evaluating the stability of accumulation bodies based on terrain reconstruction and parameter inversion, characterized in that, include: S100: Obtain first topographic mapping data of the target accumulation body, the first topographic mapping data being used to characterize the underlying topographic morphology before the formation of the target accumulation body; S200: Obtain the second topographic mapping data of the target accumulation body, the second topographic mapping data being used to characterize the geomorphological features of the target accumulation body at the current moment; S300: Based on the first topographic mapping data and the second topographic mapping data, perform finite element simulation calculations on the target accumulation body; wherein, the calculation includes extracting micro-topographic feature vectors based on the first topographic mapping data and obtaining the macroscopic mechanical parameters of the target accumulation body through a predetermined constitutive mapping relationship. S400: Based on the finite element simulation results in step S300, the stability evaluation results are output using the degree of plastic zone penetration and the displacement growth rate of key points as dual indicators.

2. The method for evaluating the stability of accumulation bodies based on terrain reconstruction and parameter inversion according to claim 1, characterized in that, Step S100 includes: S110: Obtain the time series set of topographic mapping data recorded in different historical periods of the target accumulation body; S120: Sort the various topographic mapping data units in the topographic mapping data time series set in forward time order, and select one topographic mapping data unit as the first topographic mapping data.

3. The method for evaluating the stability of accumulation bodies based on terrain reconstruction and parameter inversion according to claim 2, characterized in that, In step S120, the first topographic mapping data is selected by constructing and solving the utility objective function. The utility objective function is expressed as: ； in, For the utility objective function, The instability coefficient, Let β be the accuracy asymptotic function, and let β be the difficulty weighting coefficient. It is a difficulty exponential function. For each topographic mapping data unit The maximum value in T is the time length between the recording time of each topographic mapping data unit and the acquisition time of the second topographic mapping data; Solve for the utility objective function The optimal time T with the minimum value is selected, and the topographic mapping data unit whose initial recording time is closest to the optimal time T is selected as the first topographic mapping data.

4. The method for evaluating the stability of accumulation bodies based on terrain reconstruction and parameter inversion according to claim 1, characterized in that, Step S200 includes: The target accumulation body is obtained by using oblique photography of UAVs, and a digital elevation model of the terrain is generated based on the terrain point cloud. The digital elevation model of the terrain is used as the second terrain mapping data.

5. The method for evaluating the stability of accumulation bodies based on terrain reconstruction and parameter inversion according to claim 1, characterized in that, Step S300 includes: S310: Based on the first topographic mapping data and the second topographic mapping data, the closed space enclosed by the second topographic mapping data and the first topographic mapping data is materialized using the difference algorithm in Boolean operation to obtain a waste dump model. S320: Based on the waste slag accumulation model, perform fluid-structure interaction or discrete-continuous interaction calculations.

6. The method for evaluating the stability of accumulation bodies based on terrain reconstruction and parameter inversion according to claim 5, characterized in that, In step S310, when the enclosed space is solidified, the micro-topographic features in the first topographic mapping data are retained, and the micro-topographic features include valley undulations and / or drops.

7. The method for evaluating the stability of accumulation bodies based on terrain reconstruction and parameter inversion according to claim 5, characterized in that, In step S300, the macroscopic mechanical parameters are inverted through the following sub-steps: S321: Based on the first topographic mapping data, extract feature vectors, wherein the feature vectors include at least one of the following: area ratio of boulders, surface roughness index, roundness, fractal dimension, and directional arrangement angle; S322: Establish and solve the full-parameter constitutive mapping equation system to calculate the macroscopic mechanical parameters, which include at least one of macroscopic porosity, macroscopic density, macroscopic cohesion, macroscopic internal friction angle, macroscopic elastic modulus, macroscopic Poisson's ratio, and anisotropic strength reduction factor.

8. The method for evaluating the stability of accumulation bodies based on terrain reconstruction and parameter inversion according to claim 7, characterized in that, The system of fully parameter constitutive mapping equations includes at least one of the following formulas: Formulas for calculating historical consolidation and degradation evolution coefficients: ； In the formula, The coefficient for historical consolidation and deterioration evolution. The time-consolidation rate, The optimal historical time length is obtained by dynamically optimizing the equation based on spatiotemporal data. For reference time, As a disaster damage factor, The instability coefficient; Macroscopic porosity calculation formula: ； In the formula, Macroscopic porosity, The initial reference porosity is in a completely loose state. For fractal dimension, The sensitivity index of porosity to fractal dimension. The coefficient for historical consolidation and deterioration evolution; Macro density calculation formula: ； In the formula, For macroscopic density, This represents the percentage of the area occupied by the boulders. The natural density of a pure block of stone. This represents the natural density (constant) of the fine-grained matrix soil. Macroscopic porosity; Formula for calculating macroscopic cohesion: ； In the formula, For macroscopic cohesion, For the basic cohesion of fine-grained soil matrix, This represents the percentage of the area occupied by the boulders. The conversion coefficient of the interlocking cohesion of the stones. R is the surface roughness index, and R is the roundness. The coefficient for historical consolidation and deterioration evolution; Formula for calculating the macroscopic internal friction angle: ； In the formula, The angle of internal friction is the macroscopic angle. The basic angle of friction for pure fine-grained soil. The maximum additional angle of friction that can be provided by a pure block stone skeleton, , is the weighting coefficient for the content and geometric morphology of the boulders, where + =1; This represents the percentage of the area occupied by the boulders. R is the surface roughness index, and R is the roundness. The coefficient for historical consolidation and deterioration evolution; Formula for calculating macroscopic elastic modulus: ； In the formula, For macroscopic elastic modulus, This is the reference elastic modulus of the matrix soil. Macroscopic porosity, The stiffness enhancement index is the skeleton stiffness. This represents the percentage of the area occupied by the boulders. The coefficient for historical consolidation and deterioration evolution; Macroscopic Poisson's ratio calculation formula: ； In the formula, For macroscopic Poisson ratio, , Poisson's ratio for fine-grained soil and rock, respectively. This is the Poisson's pressure correction factor. This represents the percentage of the area occupied by the boulders. The coefficient for historical consolidation and deterioration evolution; Formula for calculating the anisotropic strength reduction factor: ； In the formula, This is the anisotropic strength reduction factor. The minimum reduction factor, The anisotropic sensitivity coefficient is... The directional arrangement is at an angle.

9. The method for evaluating the stability of accumulation bodies based on terrain reconstruction and parameter inversion according to claim 7, characterized in that, Step S300 further includes: S323: Assign constitutive models to the target accumulation, the bedrock surface represented by the first topographic mapping data, and the contact surface between the two; The contact surface is simulated using contact elements, and the parameters of the contact elements are corrected based on the roughness of the first topographic mapping data; the constitutive model of the target accumulation body is assigned values ​​based on the macroscopic mechanical parameters calculated by the full-parameter constitutive mapping equations.

10. The method for evaluating the stability of accumulation bodies based on terrain reconstruction and parameter inversion according to claim 1, characterized in that, In step S400, when the plastic strain in the finite element simulation result exceeds a preset threshold and the displacement growth rate of the key point exceeds a preset rate, an evaluation result that does not meet the stability requirements is output.

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