A tailings dam slope anti-sliding stability analysis method based on swedish slice method

Abstract

The present application relates to the technical field of tailing pond safety and geotechnical engineering, and discloses a tailing pond dam slope anti-sliding stability analysis method based on Sweden slice method. The method comprises the following steps: fusing dam slope form point cloud, stratum lithology profile and multi-period pore water pressure monitoring sequence through field domain conversion technology, and constructing a unified three-dimensional space-time analysis model. Based on high-precision point cloud data, a candidate sliding trend surface is intelligently generated, and slice sectioning is performed. The geotechnological parameters and pore water pressure changing with time and space are given to each slice, and a dynamic slice state set is formed. The principle of Sweden slice method is used for cyclic iteration calculation, the stability evolution under different working conditions is simulated, and the stability results of each sliding surface are output. Finally, the dominant sliding surface and the most unfavorable working condition are identified, and accurate basis is provided for reinforcement decision. The method realizes deep fusion of multi-source data and dynamic simulation of stability state, and improves the accuracy and engineering applicability of the analysis results.

Description

Technical Field

[0001] This invention relates to the fields of tailings dam safety and geotechnical engineering technology, specifically a method for analyzing the anti-sliding stability of tailings dam slopes based on the Swedish slice method. Background Technology

[0002] Stability analysis of tailings dam slopes is a crucial step in ensuring safe mine production. Traditional analysis methods primarily rely on limit equilibrium theories such as the Swedish slice method. In conventional practice, analysis is usually based on limited geological exploration borehole data, generating representative two-dimensional profiles and assigning them fixed soil strength parameters obtained empirically or through limited testing. Pore water pressure is often calculated using assumed static water level or a fixed value under certain conditions, without dynamically linking it to actual, time-varying hydrological monitoring data of the reservoir area. This approach highly simplifies the three-dimensional dam morphology, complex geological distribution, and dynamic seepage field, resulting in a computational model that differs from the actual engineering conditions and struggles to accurately capture the true spatial morphology of potential slip surfaces and the most dangerous conditions.

[0003] Existing technical solutions have shortcomings. On the one hand, the data utilization is characterized by fragmentation and staticity. Data from dam morphology measurement, geological exploration, and seepage pressure monitoring are independent of each other, making them difficult to integrate during analysis. This isolated data state means that stability assessments can only reflect the static safety state of a specific cross-section at a specific moment, failing to reflect the dynamic evolution of the dam body under the influence of factors such as reservoir water level fluctuations and rainfall infiltration during operation. On the other hand, the search strategy for potential slip surfaces relies too heavily on human experience and simple geometric assumptions. Conventional methods often presuppose circular slip surfaces on two-dimensional profiles for searching, ignoring the detailed features and three-dimensional spatial effects of the actual dam slope surface morphology. This may overlook non-circular slip surfaces controlled by actual topography or hidden weak zones, limiting the reliability and early warning value of the analysis results. Summary of the Invention

[0004] The purpose of this invention is to provide a method for analyzing the anti-sliding stability of tailings dam slopes based on the Swedish slice method, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, this invention provides a method for analyzing the anti-sliding stability of tailings dam slopes based on the Swedish slice method, the method comprising:

[0006] The method begins with a field transformation of multi-source exploration data of the target tailings dam body, which includes dam slope morphology point cloud, stratigraphic lithology profile and multi-period pore water pressure monitoring sequence;

[0007] Slip trend surface construction is performed on the point cloud of dam slope morphology after field transformation to generate a series of candidate slip trend surfaces. For each candidate slip trend surface, a striped mesh is performed to obtain the geometric element representation of each striped mesh.

[0008] Combining the aforementioned stratigraphic lithology profile with multi-stage pore water pressure monitoring sequences, the geometric element representation of each striped grid is assigned a soil strength parameter representation that evolves over time, generating a set of comprehensive state representations for the striped grid.

[0009] The comprehensive state representation set of the striped mesh is imported into a dynamic evolution simulation process constructed based on the Swedish strip method principle. The dynamic evolution simulation process covers iterative calculation of the stress state of the striped mesh and finally outputs the anti-slip stability simulation results covering all candidate slip trend surfaces.

[0010] The risk level of the anti-sliding stability simulation results is interpreted to identify the dominant potential slip surface and its corresponding most unfavorable load condition. Based on the dominant potential slip surface and the most unfavorable load condition, a decision basis for dam slope reinforcement is generated.

[0011] Preferably, the step of constructing a slip trend surface on the point cloud of the dam slope morphology after the field transformation is completed generates a series of candidate slip trend surfaces, including:

[0012] Within the analysis area defined by the dam slope morphology point cloud, multiple slip start control points and slip end control points are set up;

[0013] Based on the preset geometric constraint rules of the slip surface, multiple initial slip curves that satisfy geometric continuity are generated between each set of slip start control points and slip end control points.

[0014] The slip trend optimizer is invoked to perform potential energy minimization optimization for each initial slip curve. The potential energy minimization optimization is based on the estimated weight distribution of the sliced ​​grid and the soil parameters for iterative adjustment.

[0015] When the potential energy minimization optimization process reaches the convergence condition, the optimized slip curve is output, and all optimized slip curves are summarized into a series of candidate slip trend surfaces.

[0016] Preferably, the step of performing strip meshing for each candidate slip trend surface to obtain a geometric feature representation of each strip mesh includes:

[0017] Along the slip volume range defined by the candidate slip trend surface, the mesh is divided according to the vertical striping direction to generate multiple adjacent striped meshes;

[0018] Extract the boundary vertex coordinates of each striped grid, and calculate the bottom edge tilt angle, height, and width of the striped grid based on the boundary vertex coordinates;

[0019] Calculate the area and centroid position of the striped grid based on the bottom edge inclination angle, height, and width of the striped grid.

[0020] The boundary vertex coordinates, bottom edge inclination angle, area, and centroid position of the segmented grid are combined and encoded to form the geometric element representation of each segmented grid.

[0021] Preferably, by combining the stratigraphic lithology profile with the multi-stage pore water pressure monitoring sequence, a time-evolving soil strength parameter representation is assigned to the geometric element representation of each sliced ​​grid, generating a comprehensive state representation set for the sliced ​​grid, including:

[0022] Map the centroid position in the geometric element representation of each grid segment to the stratigraphic lithology profile, and determine the stratigraphic number and the corresponding shear strength parameter benchmark value of each grid segment location;

[0023] From the multi-period pore water pressure monitoring sequence, the pore water pressure values ​​of each grid segment location in different monitoring periods were extracted;

[0024] Based on the pore water pressure values ​​during different monitoring periods, the baseline values ​​of the shear strength parameters are effectively stress-corrected to obtain the dynamic shear strength parameters of each grid segment under different monitoring periods.

[0025] The geometric element representation of each striped grid is spatiotemporally aligned and bound to its dynamic shear strength parameters under different monitoring periods to form a comprehensive state representation set of the striped grid.

[0026] Preferably, the step of importing the comprehensive state representation set of the sliced ​​mesh into a dynamic evolution simulation process constructed based on the Swedish slice method principle, wherein the dynamic evolution simulation process includes iterative calculation of the stress state of the sliced ​​mesh, including:

[0027] From the set of comprehensive state descriptions of the striped grid, select all the state descriptions of the striped grid under a target monitoring period in order of monitoring period;

[0028] Based on the static equilibrium principle of the Swedish slice method, the normal and tangential force components along the slip surface of each slice grid under the target monitoring period are calculated.

[0029] By summing the tangential force components and the anti-slip force components of all grid segments, the overall anti-slip safety factor corresponding to the current candidate slip trend surface under the target monitoring period is calculated.

[0030] Traverse all candidate slip trend surfaces, repeat the above calculation process, and generate a set of overall anti-slip safety factors corresponding to all candidate slip trend surfaces under the target monitoring period;

[0031] Repeat the above calculation process for the next monitoring period until all monitoring periods have been calculated. The final output calculation results constitute the anti-skid stability simulation results.

[0032] Preferably, the step of interpreting the risk level of the anti-slip stability simulation results to identify the dominant potential slip surface and its corresponding most unfavorable load condition includes:

[0033] From the anti-skid stability simulation results, the minimum overall anti-skid safety factor value calculated under all monitoring periods was selected;

[0034] Locate the candidate slip trend surface corresponding to the minimum overall anti-slip safety factor value, and mark the candidate slip trend surface as the preliminary dominant potential slip surface;

[0035] Locate the monitoring period corresponding to the minimum overall anti-slip safety factor value, and mark the monitoring period as the preliminary most unfavorable load condition;

[0036] Cluster analysis was performed on the simulation results of other candidate slip trend surfaces in the vicinity of the preliminary dominant potential slip surface to verify the spatial uniqueness and stability of the preliminary dominant potential slip surface;

[0037] Trend analysis was performed on the simulation results of adjacent monitoring periods before and after the preliminary most unfavorable load condition to verify the temporal representativeness of the preliminary most unfavorable load condition, and finally the dominant potential slip surface and the most unfavorable load condition were determined.

[0038] Preferably, the basis for generating dam slope reinforcement decisions based on the dominant potential slip surface and the most unfavorable load conditions includes:

[0039] Extract the comprehensive state description of the striped mesh corresponding to the dominant potential slip surface, and filter out the striped mesh state under the most unfavorable load condition;

[0040] Calculate the contribution and sensitivity index of each grid segment to the overall anti-slip safety factor under the most unfavorable load condition;

[0041] Based on the aforementioned contribution and sensitivity indices, all striped grids are sorted, and several target striped grids that play a key control role in overall stability are identified.

[0042] Based on the spatial distribution of the target grid and its soil strength parameters, a list of targeted reinforcement measures is generated, which constitutes the core content of the decision-making basis for the dam slope reinforcement.

[0043] Preferably, the calculation of the contribution and sensitivity index of each grid segment to the overall anti-slip safety factor under the most unfavorable load condition includes:

[0044] While keeping the other strip grid states unchanged, the soil strength parameter of a single target strip grid is increased, the overall anti-sliding safety factor is recalculated, and the safety factor increment is obtained. The safety factor increment is defined as the contribution index of the target strip grid.

[0045] While keeping the other striped grid states unchanged, a small perturbation is applied to the geometric element representation of the target striped grid, the overall anti-slip safety factor is recalculated, and the safety factor change rate is obtained. The safety factor change rate is defined as the sensitivity index of the target striped grid.

[0046] Repeat the above calculation process for all grid segments to obtain the contribution index and sensitivity index for each grid segment.

[0047] Preferably, the method further includes a step of virtually implementing the decision-making basis for dam slope reinforcement:

[0048] Based on the list of reinforcement measures recommendations in the decision-making basis for dam slope reinforcement, modify the soil parameters or geometry of the corresponding target grid in the dam slope numerical model;

[0049] Based on the modified dam slope numerical model, the entire analysis process from the construction of the slip trend surface to the generation of anti-sliding stability simulation results was re-executed.

[0050] The recalculated anti-slip stability simulation results are compared with the original anti-slip stability simulation results to evaluate the effectiveness of the reinforcement measures.

[0051] Based on the effectiveness assessment results, the list of reinforcement measures recommended in the decision-making basis for dam slope reinforcement is optimized and adjusted to generate an optimized dam slope reinforcement scheme.

[0052] Preferably, comparing the recalculated anti-skid stability simulation results with the original anti-skid stability simulation results to evaluate the effectiveness of the reinforcement measures includes:

[0053] Compare the changes in the overall anti-slip safety factor corresponding to the dominant potential slip surface before and after reinforcement;

[0054] Compare the changes in the minimum overall anti-slip safety factor of other candidate slip trend surfaces before and after reinforcement;

[0055] If the overall anti-slip safety factor after reinforcement meets the preset safety threshold and does not induce new potential slip surfaces with lower safety factors, then the reinforcement measures are deemed effective.

[0056] Conversely, if the reinforcement measures are not effective, they are deemed ineffective or require further optimization, and the specific quantitative indicators for effectiveness assessment are recorded.

[0057] Compared with the prior art, the beneficial effects of the present invention are:

[0058] A unified field transformation was performed on dam slope morphology point clouds, stratigraphic lithology profiles, and multi-period pore water pressure monitoring sequences to establish a spatiotemporal collaborative analysis framework integrating geometric, lithological, and seepage dynamic information. This allows each computational unit to be associated with its actual spatial location, the lithology of its constituent strata, and the pore water pressure value evolving over time. This elevates the traditional static, homogeneous computational model into a refined numerical model capable of reflecting the spatial variability of dam materials and the dynamic processes of the seepage field. Stability calculations based on this model have input conditions closer to actual engineering conditions, simulating the dam slope response under different load conditions, thereby accurately identifying the time-varying patterns of stability and the most unfavorable load combinations.

[0059] Instead of pre-setting regular slip surfaces on a simplified model, slip trend surfaces are constructed based on point cloud data of the dam slope morphology after field transformation. Utilizing the geometric features such as slope and curvature contained in high-precision 3D surface point clouds, a series of candidate slip trend surfaces that are consistent with the actual topography and potential geological structures are generated. This changes the prior assumptions about the shape and location of the slip surface, expanding the search scope from abstract mathematical spatial constraints to a physically meaningful engineering geological space. The dominant potential slip surfaces identified in this way are more likely to conform to the actual instability mechanism of the dam body, improving the accuracy and reliability of dangerous slip surface location and providing precise spatial targeting for targeted reinforcement design. Attached Figure Description

[0060] Figure 1 This is a schematic diagram illustrating the working principle of the tailings dam slope anti-sliding stability analysis method based on the Swedish slice method described in this invention.

[0061] Figure 2 A flowchart for constructing the slip trend surface;

[0062] Figure 3 Flowchart generated for the integrated state representation of the striped grid;

[0063] Figure 4 A time-series risk trend analysis diagram for the anti-sliding stability of tailings dam slope;

[0064] Figure 5 A comparison chart of the anti-sliding safety factor before and after tailings dam slope reinforcement measures. Detailed Implementation

[0065] 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 skilled in the art without creative effort are within the scope of protection of the present invention.

[0066] Please see Figure 1 This invention provides a method for analyzing the anti-sliding stability of tailings dam slopes based on the Swedish slice method. The method includes: performing a field transformation on multi-source exploration data of the target tailings dam body. The multi-source exploration data includes dam slope morphology point clouds, stratigraphic lithology profiles, and multi-period pore water pressure monitoring sequences. The field transformation unifies the exploration data from different sources to the same spatial reference system, ensuring data consistency in geometry and attributes. Slip trend surface construction is performed on the transformed dam slope morphology point cloud, generating a series of candidate slip trend surfaces. For each candidate slip trend surface, slice meshing is performed to obtain the geometric element representation of each slice mesh. Combining the stratigraphic lithology profiles and multi-period pore water pressure monitoring sequences, a soil strength parameter representation that evolves over time is assigned to the geometric element representation of each slice mesh, generating a comprehensive state representation set of the slice mesh. The comprehensive state representation set of the sliced ​​grid is imported into a dynamic evolution simulation process constructed based on the Swedish slice method. This process involves iterative calculations of the stress state of the sliced ​​grid, ultimately outputting anti-sliding stability simulation results covering all candidate slip trend surfaces. The anti-sliding stability simulation results are then interpreted for risk levels, identifying the dominant potential slip surfaces and their corresponding most unfavorable load conditions. Based on these dominant potential slip surfaces and the most unfavorable load conditions, a decision-making basis for dam slope reinforcement is generated.

[0067] Example 1: See Figure 2Within the analysis area defined by the dam slope morphology point cloud, multiple slip initiation control points and slip termination control points are established. These control points are determined based on the dam slope topographic features and historical slip data. According to preset slip surface geometric constraints, multiple initial slip curves satisfying geometric continuity are generated between each set of slip initiation and slip termination control points. The geometric constraints include curvature restrictions and endpoint tangent directions. A slip trend optimizer is invoked to perform potential energy minimization optimization on each initial slip curve. This optimization is based on the estimated weight distribution of the striped grid and soil parameters, iteratively adjusting the local shape of the slip curve using a gradient descent algorithm. When the potential energy minimization optimization reaches convergence, the optimized slip curve is output. The convergence condition is set to a potential energy change less than a threshold. All optimized slip curves are then aggregated into a series of candidate slip trend surfaces. Along the slip volume defined by the candidate slip trend surfaces, a grid is generated perpendicular to the striping direction, producing multiple adjacent striped grids. The striping direction is parallel to the dam slope orientation. For each striped grid, the coordinates of its boundary vertices are extracted, and the base angle, height, and width of the striped grid are calculated based on these coordinates using plane geometry and triangulation methods. The area and centroid position of the striped grid are calculated based on its base angle, height, and width; the area is obtained through polygon segmentation. The boundary vertices, base angle, area, and centroid position of each striped grid are combined and encoded to form a geometric representation of each striped grid, using a structured data table format.

[0068] In practical implementation, the analysis area is defined based on the point cloud of the dam slope morphology after field transformation, covering the instability range from the dam crest to the dam toe. Within the analysis area, multiple slip initiation control points and slip termination control points are set up according to topographic transition characteristics and engineering experience. Slip initiation control points are often located near the dam crest or at the interface of abrupt material property changes, while slip termination control points are located at the dam slope toe or specific locations on the slope surface. According to the preset geometric constraint rules of the slip surface, multiple initial slip curves satisfying geometric continuity are generated between each set of slip initiation control points and slip termination control points. The geometric constraint rules require that the slip curves be second-order continuous and have a radius of curvature greater than a critical value. The generation method can be spline curve interpolation or circular arc combination. The slip trend optimizer is called to perform potential energy minimization optimization on each initial slip curve. The slip trend optimizer takes the initial slip curve as input and iteratively adjusts the shape of the slip curve based on the estimated weight distribution of the striped grid and soil parameters. The goal of the potential energy minimization optimization process is to find the surface shape that makes the potential energy of the slip body tend to the extreme value. Iterative adjustment is achieved through numerical optimization algorithms. In each iteration, the weight of the striped grid along the current slip curve and the torque it generates are recalculated.

[0069] It can be understood that the process of minimizing potential energy can be expressed as finding the shape of the slip surface that minimizes the total potential energy function of the system. The total potential energy function of the system is defined as:

[0070]

[0071] in: This represents the total potential energy of the slip body system. Indicates the total number of grid segments. Indicates the first Estimated weight of each grid segment Indicates the first The vertical coordinates of the centroids of each individual grid in the global coordinate system. The convergence condition is reached when the potential energy minimization optimization process is achieved, i.e., the total potential energy of the system obtained from two consecutive iterations. When the change in slip is less than a set threshold, the optimization process stops and the optimized slip curve is output. Summarizing all optimized slip curves forms a series of candidate slip trend surfaces for subsequent analysis.

[0072] In some embodiments, after constructing the slip trend surface, a strip mesh is generated for each candidate slip trend surface. Along the slip volume defined by the candidate slip trend surface, the mesh is divided according to the vertical striping direction, generating multiple adjacent strip meshes. The striping direction is typically parallel to the dam axis direction to ensure consistency of the computational model. The boundary vertex coordinates of each strip mesh are extracted. These coordinates are obtained by calculating the intersection of the vertical stripe plane with the surface topography and the slip trend surface. Based on the boundary vertex coordinates, the bottom edge inclination angle, height, and width of the strip mesh are calculated. The bottom edge inclination angle is the angle between the tangent to the slip trend surface at the bottom midpoint of the strip mesh and the horizontal plane; the height is the vertical distance between the top midpoint and the bottom midpoint of the strip mesh; and the width is the projected width of the strip mesh on the horizontal plane.

[0073] Optionally, the area and centroid position of the striped mesh are calculated based on its base angle, height, and width. The area of ​​the striped mesh can be obtained by numerical integration after approximating its cross-section as a trapezoid or polygon, while the centroid position is calculated based on the principle of area moments to determine the centroid coordinates of its cross-section. The boundary vertex coordinates, base angle, area, and centroid position of the striped mesh are combined and encoded to form a geometric representation of each striped mesh. This combination encoding can use arrays or structured data structures to ensure the integrity and accessibility of the geometric information.

[0074] Example 2: See Figure 3The centroid position in the geometric feature representation of each striped grid is mapped to the stratigraphic lithology profile, determining the stratigraphic number and corresponding shear strength parameter benchmark value for each striped grid location. This mapping is achieved through a spatial interpolation algorithm. From multi-period pore water pressure monitoring sequences, pore water pressure values ​​for each striped grid location at different monitoring periods are extracted, and a match based on monitoring point coordinates and timestamps is obtained. Based on the pore water pressure values ​​at different monitoring periods, effective stress correction is applied to the shear strength parameter benchmark value to obtain the dynamic shear strength parameters of each striped grid under different monitoring periods, following the Mohr-Coulomb strength criterion. The geometric feature representation of each striped grid and its dynamic shear strength parameters under different monitoring periods are spatiotemporally aligned and bound, forming a comprehensive state representation set for the striped grid. The binding uses a time series index. From the comprehensive state representation set of the striped grid, all state representations of the striped grids under a target monitoring period are selected in chronological order. Based on the static equilibrium principle of the Swedish slice method, the normal and tangential force components along the slip surface of each slice grid under the target monitoring period are calculated, taking into account the effects of soil self-weight and pore water pressure. The sum of the tangential force components and the sum of the anti-slip force components of all slice grids are summarized to calculate the overall anti-slip safety factor corresponding to the current candidate slip trend surface under the target monitoring period. The anti-slip force components are determined based on dynamic shear strength parameters. The calculation process is repeated for all candidate slip trend surfaces, generating a set of overall anti-slip safety factors corresponding to all candidate slip trend surfaces under the target monitoring period. The traversal uses a loop iterative structure. The above calculation process is repeated for the next monitoring period until all monitoring periods are calculated. The final output calculation results constitute the anti-slip stability simulation results, which are stored in the form of a multidimensional array.

[0075] In practice, the centroid position in the geometric representation of each strip grid is mapped to the stratigraphic lithology profile, determining the stratigraphic number and corresponding shear strength parameter benchmark value for each strip grid location. This mapping process is achieved by establishing an inclusion relationship query between spatial coordinates and the polygonal region of the stratigraphic profile. From multi-period pore water pressure monitoring sequences, the pore water pressure values ​​for each strip grid location at different monitoring periods are extracted. This extraction is performed using a nearest neighbor matching algorithm between the spatial coordinates of the monitoring point and the centroid coordinates of the strip grid, and the corresponding timestamps are recorded. Based on the pore water pressure values ​​at different monitoring periods, the effective stress correction is applied to the shear strength parameter benchmark value, obtaining the dynamic shear strength parameters for each strip grid at different monitoring periods. The correction process follows the effective stress principle and strength criterion in soil mechanics.

[0076] In some embodiments, the effective stress correction can be quantified according to the Mohr-Coulomb strength criterion, and the cohesion and internal friction angle in the dynamic shear strength parameters are determined through the effective stress state. It can be understood that, for a specific monitoring period... Next The dynamic shear strength parameters of each grid segment can be calculated as follows:

[0077]

[0078] in: Indicates the first The first monitoring period Shear strength on the bottom sliding surface of each grid segment This represents the cohesion under the corresponding effective stress state. Indicates the first Effective normal stress on the bottom sliding surface of each grid This represents the internal friction angle under the corresponding effective stress state. Effective normal stress. The value is obtained by subtracting the pore water pressure corresponding to the monitoring period from the total normal stress at the bottom of the sliced ​​grid. The geometric feature representation of each sliced ​​grid is spatiotemporally aligned and bound to its dynamic shear strength parameters under different monitoring periods to form a comprehensive state representation set of the sliced ​​grid. The binding operation is achieved by creating an attribute dictionary indexed by the monitoring period for each sliced ​​grid in the data structure.

[0079] From the comprehensive state description set of the sliced ​​grid, all sliced ​​grid state descriptions for a target monitoring period are selected in sequence according to the monitoring period order, which is logically arranged based on the time of data acquisition. Based on the static equilibrium principle of the Swedish slice method, the normal and tangential force components along the slip surface of each sliced ​​grid under the target monitoring period state are calculated. The weight of the sliced ​​grid is obtained based on its area in its geometric element description and the preset soil unit weight. The decomposition of normal and tangential forces is based on the dip angle of the bottom edge of the sliced ​​grid. The sum of the tangential force components and the sum of the anti-slip force components of all sliced ​​grids are summarized to calculate the overall anti-slip safety factor corresponding to the current candidate slip trend surface under the target monitoring period. The anti-slip force is determined by the dynamic shear strength at the bottom of each sliced ​​grid. It is obtained by multiplying its length by the length of its base and summing the results.

[0080] Optional, overall anti-slip safety factor For the The candidate slip trend surface at the first The calculation expression for each monitoring period is the ratio of the sum of anti-slip moments to the sum of sliding moments. Under the assumption of the Swedish slice method that inter-slice forces are not considered, the sum of moments can be simplified to the sum of forces multiplied by the same moment arm. The calculation process is repeated for all candidate slip trend surfaces to generate a set of overall anti-slip safety factors corresponding to all candidate slip trend surfaces for the target monitoring period. The above calculation process is repeated for the next monitoring period until all monitoring periods are calculated. The final output calculation results constitute the anti-slip stability simulation results, which are a safety factor matrix indexed by the monitoring period and slip surface number. In some embodiments, the sliced ​​grid integrated state representation set is the core data carrier connecting multi-source spatiotemporal data with quantitative stability calculations. The dynamic evolution simulation process completes the simulation of all working conditions by iteratively traversing this set.

[0081] Example 3: From the anti-slip stability simulation results, the minimum overall anti-slip safety factor value calculated for all monitoring periods is selected. The selection is achieved through a comparison algorithm. Candidate slip trend surfaces corresponding to the minimum overall anti-slip safety factor value are located and marked as preliminary dominant potential slip surfaces. Location is based on the index relationship in the simulation results. The monitoring period corresponding to the minimum overall anti-slip safety factor value is located and marked as the preliminary most unfavorable load condition. The monitoring period identifier is a time stamp from the pore water pressure monitoring sequence. Cluster analysis is performed on the simulation results of other candidate slip trend surfaces in the vicinity of the preliminary dominant potential slip surface to verify the spatial uniqueness and stability of the preliminary dominant potential slip surface. The cluster analysis uses the density peak detection method. Trend analysis is performed on the simulation results of adjacent monitoring periods before and after the preliminary most unfavorable load condition to verify the temporal representativeness of the preliminary most unfavorable load condition. The trend analysis includes sliding window statistics. Finally, the dominant potential slip surface and the most unfavorable load condition are determined.

[0082] In practical implementation, the risk level interpretation of the anti-slip stability simulation results is performed. The minimum overall anti-slip safety factor value calculated for all monitoring periods is selected from the simulation results. This selection is achieved by traversing the safety factor matrix indexed by the monitoring period and slip surface number and applying a global minimum value search algorithm. Candidate slip trend surfaces corresponding to the minimum overall anti-slip safety factor value are located and marked as preliminary dominant potential slip surfaces. This location process is completed based on the mapping relationship between slip surface indices and monitoring period indices stored in the safety factor matrix. The monitoring period corresponding to the minimum overall anti-slip safety factor value is located and marked as the preliminary most unfavorable load condition. The monitoring period information is derived from the original time stamps of the pore water pressure monitoring sequence.

[0083] It is understandable that cluster analysis is performed on the simulation results of other candidate slip trend surfaces in the vicinity of the initially dominant potential slip surface. This cluster analysis is used to verify the spatial uniqueness and stability of the initially dominant potential slip surface. In some embodiments, the vicinity is defined based on the Euclidean distance between the center points of the slip surfaces. The cluster analysis employs an eigenvector-based method, where the eigenvectors contain the horizontal coordinates of the geometric center of the candidate slip trend surfaces and their corresponding average overall anti-slip safety factor. By analyzing the set of eigenvectors using a clustering algorithm, if the overall anti-slip safety factor of the category to which the initially dominant potential slip surface belongs is significantly lower than that of other members in the same category, and this category is well separated from other categories in the feature space, then the initially dominant potential slip surface is verified to have spatial uniqueness and representativeness.

[0084] In practical implementation, trend analysis is performed on the simulation results of adjacent monitoring periods before and after the initial most unfavorable load condition. This trend analysis verifies the temporal representativeness of the initial most unfavorable load condition. The trend analysis constructs a sliding window centered on the monitoring point of the initial most unfavorable load condition over time, calculating the average value of the overall anti-slip safety factor of all candidate slip surfaces within the window and its variation characteristics over time. Optionally, the evaluation index used to quantify the time-series risk trend can be designed as a composite function reflecting the safety factor level and its rate of change, with the following expression:

[0085]

[0086] in: This represents the time-series risk assessment indicators within the sliding window. This represents the average value of the overall anti-slip safety factor for all candidate sliding surfaces within the sliding window. It is a dimensionless constant used to adjust for the effect of the rate of change. This represents the mean of the absolute values ​​of the first-order differences of the overall anti-slip safety factor within the window over time. (Time-series risk assessment index) A smaller value indicates a lower average safety factor or a significant downward trend within that time period. The monitoring point where the initial most unfavorable load condition is located typically corresponds to a time-series risk assessment index. A local minimum point. If the initial most unfavorable load case is determined to be a significant low point in the sliding window analysis, and the time-series risk assessment index of its preceding and following windows... The "trough" pattern, which first declines and then rises, is verified through time representativeness. Finally, by combining the dual verification results of spatial cluster analysis and time trend analysis, the unique dominant potential slip surface and the most unfavorable load condition are determined.

[0087] See Figure 4This is a time-series risk trend analysis chart for the anti-sliding stability of a tailings dam slope. Monitoring period 6 represents the risk "bottom": the minimum safety factor drops to 0.8 (far below the safety threshold of 1.2), and the time-series risk assessment index simultaneously drops to 0.85, verifying that this monitoring period represents the most unfavorable load condition. The safety factor and risk index are negatively correlated: the lower the safety factor, the smaller the risk assessment index (higher risk), consistent with the logic that "the safety factor is the core driving factor of risk." The minimum safety factor for all monitoring periods is below the safety threshold of 1.2, indicating that the dam slope is in an unsafe state overall, requiring priority reinforcement for the conditions in monitoring period 6. This type of time-series analysis chart is used in the most unfavorable time-condition verification stage of the anti-sliding stability of tailings dam slopes. By visualizing the time evolution of the safety factor and risk, it accurately identifies the monitoring period with the highest risk, serving as the core data basis for subsequent "dominant slip surface identification" and "reinforcement measure formulation."

[0088] Example 4: Extract the comprehensive state representation of the strip grid corresponding to the dominant potential slip surface, and select the strip grid states under the most unfavorable load condition based on timestamp matching. Calculate the contribution and sensitivity index of each strip grid to the overall anti-slip safety factor under the most unfavorable load condition, while keeping the states of other strip grids unchanged. While keeping the states of other strip grids unchanged, increase the soil strength parameter representation of a single target strip grid, recalculate the overall anti-slip safety factor, and obtain the safety factor increment. Define the safety factor increment as the contribution index of the target strip grid, with the increase set as a preset percentage. While keeping the states of other strip grids unchanged, apply a small perturbation to the geometric element representation of the target strip grid, recalculate the overall anti-slip safety factor, and obtain the safety factor change rate. Define the safety factor change rate as the sensitivity index of the target strip grid. The perturbation is achieved through random offset of vertex coordinates. Traverse all strip grids and repeat the above calculation process to obtain the contribution and sensitivity indices of each strip grid. The traversal uses an iterative loop. Based on contribution and sensitivity indices, all grid segments are ranked, identifying several target grid segments that play a key controlling role in overall stability. The ranking is based on weighted scores of the indices. According to the spatial distribution of the target grid segments and their soil strength parameters, a list of targeted reinforcement measures is generated. This list constitutes the core content of the dam slope reinforcement decision-making process, including recommendations for reinforcement locations and parameter adjustments.

[0089] In practice, based on the identified dominant potential slip surface and the most unfavorable load condition, a decision-making basis for dam slope reinforcement is generated. The comprehensive state description of the strip grid corresponding to the dominant potential slip surface is extracted, and the strip grid state under the most unfavorable load condition is selected. The contribution and sensitivity index of each strip grid to the overall anti-sliding safety factor under the most unfavorable load condition are calculated, while keeping the states of other strip grids unchanged. While keeping the states of other strip grids unchanged, the soil strength parameter description of a single target strip grid is increased, for example, by increasing the cohesion value in the dynamic shear strength parameter of the target strip grid by a preset proportion. The overall anti-sliding safety factor is then recalculated, and the safety factor increment is defined as the contribution index of the target strip grid. While keeping other grid states unchanged, a small relative perturbation is applied to the geometric representation of the target grid, such as adjusting the width or height parameters of the target grid by a preset ratio. The overall anti-slip safety factor is then recalculated, and the ratio of the change in safety factor to the change in parameters is obtained. This ratio is defined as the sensitivity index of the target grid.

[0090] The above calculation process is repeated across all grid cells to obtain the contribution and sensitivity indices for each grid cell. In some embodiments, all grid cells are sorted based on their contribution and sensitivity indices to identify several target grid cells that play a key control role in overall stability. Optionally, the sensitivity index... For the The calculation of a single grid segment can be defined as the ratio of the relative rate of change of the safety factor to the relative rate of change of the geometric parameters, and its expression is:

[0091]

[0092] in: Indicates the first Sensitivity index for individual grid segments This represents the overall anti-skid safety factor calculated under the original conditions. Indicates the first The overall anti-slip safety factor is obtained by recalculating a certain geometric parameter of a grid after applying a small perturbation. Indicates the first The original value of a selected geometric parameter of a segmented mesh. This represents the amount of small perturbation applied to the geometric parameter. The contribution and sensitivity indices can be normalized and then weighted and summed according to preset weights to obtain the comprehensive evaluation score for each grid segment. See Table 1 for a sample of the results ranked based on the comprehensive evaluation score.

[0093] Table 1: Ranking of Indicators Affecting Stability of Sliced ​​Grids

[0094] ;

[0095] It is understandable that a list of targeted reinforcement measures is generated based on the spatial distribution of the target grid and its soil strength parameters. In some embodiments, for grids with high contribution indices located in the lower part of the landslide, the reinforcement measure recommendation list suggests using slope reinforcement or anti-slide piles to increase their anti-slide resistance; for grids with high sensitivity indices located in the upper part of the landslide, the reinforcement measure recommendation list suggests using grouting or laying geosynthetic materials to improve their mechanical parameters.

[0096] Example 5: Based on the list of reinforcement measures recommended in the dam slope reinforcement decision-making criteria, the soil parameters or geometry of the corresponding target grid in the dam slope numerical model are modified. This modification is achieved by directly updating the model database. Based on the modified dam slope numerical model, the entire analysis process from the construction of the slip trend surface to the generation of anti-slip stability simulation results is re-executed, and the same algorithm modules are called again. The recalculated anti-slip stability simulation results are compared with the original anti-slip stability simulation results to evaluate the effectiveness of the reinforcement measures. The comparison is completed through difference calculation. The change in the overall anti-slip safety factor corresponding to the dominant potential slip surface before and after reinforcement is compared, and the change is expressed as a percentage. The change in the minimum overall anti-slip safety factor of other candidate slip trend surfaces before and after reinforcement is compared, and the change is recorded as the safety factor difference. If the overall anti-slip safety factor after reinforcement meets the preset safety threshold and does not induce new potential slip surfaces with lower safety factors, the reinforcement measures are deemed effective. The safety threshold is set based on engineering specifications. Conversely, if the reinforcement measures are deemed ineffective or require further optimization, the specific quantitative indicators for effectiveness assessment are recorded, including the safety factor improvement and the amount of change in the slip surface. Based on the effectiveness assessment results, the list of recommended reinforcement measures in the dam slope reinforcement decision-making process is optimized and adjusted to generate an optimized dam slope reinforcement scheme. This optimization and adjustment is achieved through an iterative feedback mechanism.

[0097] In practice, based on the list of reinforcement measures recommended in the dam slope reinforcement decision-making criteria, the soil parameters or geometric morphology of the corresponding target grid segments in the dam slope numerical model are modified. This modification is accomplished by directly accessing and updating the database records storing the geometric and attribute information of the dam slope numerical model. The specific implementation of the dam slope numerical model is based on the field transformation of multi-source exploration data of the target tailings dam body. This model integrates dam slope morphology point clouds, stratigraphic lithology profiles, and multi-period pore water pressure monitoring sequences, forming a unified three-dimensional spatiotemporal data structure. The model stores the geometric element representations of each grid segment, including boundary vertex coordinates, bottom dip angle, area, and centroid location, as well as soil strength parameter representations that evolve over time, such as dynamic shear strength parameters. During modification, the system, based on the list of reinforcement measures recommended in the dam slope reinforcement decision-making criteria, directly accesses the corresponding records in the database through the unique identifier or spatial coordinate index of the grid segment, updating the soil parameters of the target grid segment in real time to ensure that the model data is synchronized with the reinforcement measures. Based on the modified dam slope numerical model, the entire analysis process, from constructing the slip trend surface to generating anti-slip stability simulation results, was re-executed. The same algorithm modules and computational logic as the original analysis were used to ensure process consistency. The recalculated anti-slip stability simulation results were compared with the original results to evaluate the effectiveness of the reinforcement measures. The comparison was performed programmatically by comparing the corresponding element values ​​of the two safety factor matrices.

[0098] In practice, the change in the overall anti-slip safety factor corresponding to the dominant potential slip surface is compared before and after reinforcement, and the change is quantified and recorded as a percentage or absolute difference. The change in the minimum overall anti-slip safety factor of other candidate slip trend surfaces is also compared before and after reinforcement. This requires scanning the newly generated safety factor matrix after reinforcement, finding the minimum safety factor corresponding to all slip surfaces, and comparing it with the corresponding minimum value in the original matrix. It is understandable that the effectiveness evaluation process requires a comprehensive quantitative index to characterize the comparison results; this index can be defined as the degree of difference in reinforcement effect. Differences in reinforcement effect The calculation formula can be designed as follows:

[0099]

[0100] in: Indicates the degree of difference in reinforcement effect. This represents the minimum overall anti-slip safety factor corresponding to all candidate slip surfaces in the original anti-slip stability simulation results. This represents the minimum overall anti-slip safety factor corresponding to all candidate slip surfaces in the recalculated anti-slip stability simulation results. This indicates the number of slip surfaces in the original results where the overall anti-slip safety factor is lower than the preset safety threshold. This indicates the number of slip surfaces whose overall anti-slip safety factor is lower than the same safety threshold in the recalculated results. and This is an adjustment factor used to balance the two effects. If the overall anti-skid safety factor after reinforcement is at its minimum... The number of slip surfaces that meet the preset safety threshold and whose overall anti-slip safety factor is lower than the safety threshold in the recalculated results. If no increase is observed, the reinforcement measures are deemed effective. In some embodiments, the preset safety threshold is determined based on industry standards and engineering design requirements. Conversely, if the overall anti-slip safety factor after reinforcement is at its minimum value... The number of slip surfaces that are still below the safety threshold, or whose overall anti-slip safety factor is below the safety threshold in the recalculated results. If the increase is observed, the reinforcement measures are deemed ineffective or require further optimization, and the degree of difference in reinforcement effect is recorded. The specific value is used as a quantitative indicator.

[0101] Optionally, based on the effectiveness assessment results, the list of recommended reinforcement measures in the decision-making basis for dam slope reinforcement can be optimized and adjusted to generate an optimized dam slope reinforcement scheme. The optimization and adjustment are based on the degree of difference in reinforcement effects. The analysis of the numerical values ​​and their constituent terms is conducted. If the improvement in the safety factor is insufficient, the improvement range of the soil parameters in the corresponding target grid is increased. If a new potential slip surface is induced, the spatial location or type of the reinforcement measures is adjusted. In some embodiments, the optimization adjustment is achieved through an iterative feedback mechanism, that is, the list of reinforcement measure recommendations is modified based on the current effectiveness assessment results, and then the virtual implementation and evaluation process is executed again until the generated dam slope reinforcement scheme meets all preset stability improvement targets.

[0102] See Figure 5 This is a comparison chart of the anti-sliding safety factors before and after tailings dam slope reinforcement measures. The safety factors of all sliding surfaces significantly improved after reinforcement: before reinforcement, most sliding surfaces (such as 1-5) had a safety factor below 1.5; after reinforcement, all exceeded the safety threshold, and some sliding surfaces (such as 9 and 15) improved to above 2.0. Sliding surface 4, which had the highest risk before reinforcement (safety factor 1.2), improved to 1.6 after reinforcement, changing from "unsafe" to "safe." The safety factor improvement was even greater in high-risk areas, reflecting the targeted nature of the reinforcement measures. This type of comparison chart is used in the effectiveness evaluation stage of tailings dam slope reinforcement schemes. By visualizing the changes in safety factors before and after reinforcement, the stability effect of the measures on each potential sliding surface is verified, which is the core basis for judging whether the reinforcement scheme meets the standards.

[0103] It should be noted that, in this document, relational terms such as "first" and "second" are used only 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 process, method, article, or apparatus.

[0104] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for analyzing the anti-sliding stability of tailings dam slopes based on the Swedish slice method, characterized in that, The method begins with a field transformation of multi-source exploration data of the target tailings dam body, which includes dam slope morphology point cloud, stratigraphic lithology profile and multi-period pore water pressure monitoring sequence; Slip trend surfaces are constructed on the point cloud of the dam slope morphology after the field transformation, generating a series of candidate slip trend surfaces. For each candidate slip trend surface, a striped mesh is then performed to obtain the geometric feature representation of each striped mesh, including: Within the analysis area defined by the dam slope morphology point cloud, multiple slip start control points and slip end control points are set up; Based on the preset geometric constraint rules of the slip surface, multiple initial slip curves that satisfy geometric continuity are generated between each set of slip start control points and slip end control points. The slip trend optimizer is invoked to perform potential energy minimization optimization for each initial slip curve. The potential energy minimization optimization is based on the estimated weight distribution of the sliced ​​grid and the soil parameters for iterative adjustment. When the potential energy minimization optimization process reaches the convergence condition, the optimized slip curve is output, and all optimized slip curves are summarized into a series of candidate slip trend surfaces; Along the slip volume range defined by the candidate slip trend surface, the mesh is divided according to the vertical striping direction to generate multiple adjacent striped meshes; Extract the boundary vertex coordinates of each striped grid, and calculate the bottom edge tilt angle, height, and width of the striped grid based on the boundary vertex coordinates; Calculate the area and centroid position of the striped grid based on the bottom edge inclination angle, height, and width of the striped grid. The boundary vertex coordinates, bottom edge inclination angle, area, and centroid position of the segmented grid are combined and encoded to form the geometric element representation of each segmented grid; Combining the aforementioned stratigraphic lithology profile with multi-stage pore water pressure monitoring sequences, the geometric element representation of each striped grid is assigned a soil strength parameter representation that evolves over time, generating a set of comprehensive state representations for the striped grid. The comprehensive state representation set of the striped mesh is imported into a dynamic evolution simulation process constructed based on the Swedish strip method principle. The dynamic evolution simulation process covers iterative calculation of the stress state of the striped mesh and finally outputs the anti-slip stability simulation results covering all candidate slip trend surfaces. The risk level of the anti-sliding stability simulation results is interpreted to identify the dominant potential slip surface and its corresponding most unfavorable load condition. Based on the dominant potential slip surface and the most unfavorable load condition, a decision basis for dam slope reinforcement is generated.

2. The tailings dam slope anti-sliding stability analysis method based on the Swedish slice method according to claim 1, characterized in that, The method combines the stratigraphic lithology profile with multi-stage pore water pressure monitoring sequences, assigning time-evolving soil strength parameters to the geometric elements of each grid segment, generating a comprehensive state representation set for the grid segments, including: Map the centroid position in the geometric element representation of each grid segment to the stratigraphic lithology profile, and determine the stratigraphic number and the corresponding shear strength parameter benchmark value of each grid segment location; From the multi-period pore water pressure monitoring sequence, the pore water pressure values ​​of each grid segment location in different monitoring periods were extracted; Based on the pore water pressure values ​​during different monitoring periods, the baseline values ​​of the shear strength parameters are effectively stress-corrected to obtain the dynamic shear strength parameters of each grid segment under different monitoring periods. The geometric element representation of each striped grid is spatiotemporally aligned and bound to its dynamic shear strength parameters under different monitoring periods to form a comprehensive state representation set of the striped grid.

3. The tailings dam slope anti-sliding stability analysis method based on the Swedish slice method according to claim 2, characterized in that, The process of importing the comprehensive state representation set of the sliced ​​mesh into a dynamic evolution simulation process constructed based on the Swedish slice method principle, wherein the dynamic evolution simulation process includes iterative calculation of the stress state of the sliced ​​mesh, including: From the set of comprehensive state descriptions of the striped grid, select all the state descriptions of the striped grid under a target monitoring period in order of monitoring period; Based on the static equilibrium principle of the Swedish slice method, the normal and tangential force components along the slip surface of each slice grid under the target monitoring period are calculated. By summing the tangential force components and the anti-slip force components of all grid segments, the overall anti-slip safety factor corresponding to the current candidate slip trend surface under the target monitoring period is calculated. Traverse all candidate slip trend surfaces, repeat the above calculation process, and generate a set of overall anti-slip safety factors corresponding to all candidate slip trend surfaces under the target monitoring period; Repeat the above calculation process for the next monitoring period until all monitoring periods have been calculated. The final output calculation results constitute the anti-skid stability simulation results.

4. The tailings dam slope anti-sliding stability analysis method based on the Swedish slice method according to claim 3, characterized in that, The risk level interpretation of the anti-slip stability simulation results, identifying the dominant potential slip surface and its corresponding most unfavorable load condition, includes: From the anti-skid stability simulation results, the minimum overall anti-skid safety factor value calculated under all monitoring periods was selected; Locate the candidate slip trend surface corresponding to the minimum overall anti-slip safety factor value, and mark the candidate slip trend surface as the preliminary dominant potential slip surface; Locate the monitoring period corresponding to the minimum overall anti-slip safety factor value, and mark the monitoring period as the preliminary most unfavorable load condition; Cluster analysis was performed on the simulation results of other candidate slip trend surfaces in the vicinity of the preliminary dominant potential slip surface to verify the spatial uniqueness and stability of the preliminary dominant potential slip surface; Trend analysis was performed on the simulation results of adjacent monitoring periods before and after the preliminary most unfavorable load condition to verify the temporal representativeness of the preliminary most unfavorable load condition, and finally the dominant potential slip surface and the most unfavorable load condition were determined.

5. The tailings dam slope anti-sliding stability analysis method based on the Swedish slice method according to claim 4, characterized in that, The basis for generating dam slope reinforcement decisions based on the dominant potential slip surface and the most unfavorable load conditions includes: Extract the comprehensive state description of the striped mesh corresponding to the dominant potential slip surface, and filter out the striped mesh state under the most unfavorable load condition; Calculate the contribution and sensitivity index of each grid segment to the overall anti-slip safety factor under the most unfavorable load condition; Based on the aforementioned contribution and sensitivity indices, all striped grids are sorted, and several target striped grids that play a key control role in overall stability are identified. Based on the spatial distribution of the target grid and its soil strength parameters, a list of targeted reinforcement measures is generated, which constitutes the core content of the decision-making basis for the dam slope reinforcement.

6. The tailings dam slope anti-sliding stability analysis method based on the Swedish slice method according to claim 5, characterized in that, The calculation of the contribution and sensitivity index of each grid segment to the overall anti-slip safety factor under the most unfavorable load condition includes: While keeping the other strip grid states unchanged, the soil strength parameter of a single target strip grid is increased, the overall anti-sliding safety factor is recalculated, and the safety factor increment is obtained. The safety factor increment is defined as the contribution index of the target strip grid. While keeping the other striped grid states unchanged, a small perturbation is applied to the geometric element representation of the target striped grid, the overall anti-slip safety factor is recalculated, and the safety factor change rate is obtained. The safety factor change rate is defined as the sensitivity index of the target striped grid. Repeat the above calculation process for all grid segments to obtain the contribution index and sensitivity index for each grid segment.

7. The tailings dam slope anti-sliding stability analysis method based on the Swedish slice method according to claim 6, characterized in that, The method also includes a step of virtually implementing the decision-making basis for dam slope reinforcement: Based on the list of reinforcement measures recommendations in the decision-making basis for dam slope reinforcement, modify the soil parameters or geometry of the corresponding target grid in the dam slope numerical model; Based on the modified dam slope numerical model, the entire analysis process from the construction of the slip trend surface to the generation of anti-sliding stability simulation results was re-executed. The recalculated anti-slip stability simulation results are compared with the original anti-slip stability simulation results to evaluate the effectiveness of the reinforcement measures. Based on the effectiveness assessment results, the list of reinforcement measures recommended in the decision-making basis for dam slope reinforcement is optimized and adjusted to generate an optimized dam slope reinforcement scheme.

8. The tailings dam slope anti-sliding stability analysis method based on the Swedish slice method according to claim 7, characterized in that, The comparison of the recalculated anti-slip stability simulation results with the original anti-slip stability simulation results to evaluate the effectiveness of the reinforcement measures includes: Compare the changes in the overall anti-slip safety factor corresponding to the dominant potential slip surface before and after reinforcement; Compare the changes in the minimum overall anti-slip safety factor of other candidate slip trend surfaces before and after reinforcement; If the overall anti-slip safety factor after reinforcement meets the preset safety threshold and does not induce new potential slip surfaces with lower safety factors, then the reinforcement measures are deemed effective. Conversely, if the reinforcement measures are not effective, they are deemed ineffective or require further optimization, and the specific quantitative indicators for effectiveness assessment are recorded.

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