A system for analyzing the stability of a slope cross section

Abstract

The application relates to the technical field of rock-soil section analysis, in particular to a slope section stability analysis system, which comprises a slope segmentation module, a multi-model analysis module, a working condition correction module and a stability coefficient calculation module. The slope segmentation module is configured with a section segmentation strategy, a virtual three-dimensional slope model is constructed by performing geological contour scanning on a slope, and a plurality of characteristic sections are generated by analyzing the virtual three-dimensional slope model. The multi-model analysis module is configured with an analysis model library, the analysis model library stores a plurality of slope space analysis models, and the structure surface of the characteristic sections is analyzed to determine corresponding rock mass structure models. The working condition correction module is configured with a slope working condition parameter library, and is used for establishing a parameter mapping relationship with the corresponding characteristic sections. The stability coefficient calculation module is configured with a safety coefficient analysis model, and the rock mass structure model and the slope working condition parameter library are used for calculation and analysis to determine the slope stability coefficients of the corresponding characteristic sections under different working conditions. The application has the effect that the accuracy and comprehensiveness of slope section stability analysis are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of rock-soil section analysis, in particular to a slope section stability analysis system. BACKGROUND

[0002] The slope anchor retaining wall structure is a kind of supporting structure combined by anchor rod and retaining wall, which is used for reinforcing slope and preventing slope instability, and is widely used in slope treatment, foundation pit support and other scenes in civil engineering. The principle is to connect the retaining wall with the stable rock or soil inside the slope through the anchor rod, and transfer the load such as soil pressure and water pressure borne by the retaining wall to the stable stratum through the tension of the anchor rod, so as to improve the overall stability of the slope.

[0003] In the related art, the slope section stability analysis mainly depends on the manual field investigation combined with the calculation of relevant rock-soil structure, or uses simple automatic acquisition equipment to scan the slope section, and matches with fixed analysis model to calculate and analyze the parameters of the slope section, so as to determine whether the rock-soil structure of the section is stable.

[0004] In view of the above related technology, the combination of manual investigation and simple automatic analysis has the problems of incomplete data collection and poor adaptability of analysis model, which is difficult to accurately reflect the actual stability state of the engineering slope. This not only may lead to safety hazards in engineering design, but also may increase the engineering cost due to over-conservative design, or cause safety accidents such as slope instability due to insufficient evaluation, which seriously affects the safety and economy of the project. SUMMARY

[0005] In order to improve the accuracy and comprehensiveness of the slope section stability analysis, the present application provides a slope section stability analysis system.

[0006] In the first aspect, the present application provides a slope section stability analysis system, which adopts the following technical scheme:

[0007] A slope section stability analysis system, comprising:

[0008] A slope segmentation module is configured with a section segmentation strategy, a virtual three-dimensional slope model is constructed by scanning the geological profile of the slope, and the geological features, slope height and slope direction of the virtual three-dimensional slope model are analyzed to generate a plurality of characteristic segments;

[0009] A multi-model analysis module is configured with an analysis model library, the analysis model library stores a plurality of slope space analysis models, and the structure surface of the characteristic segment is analyzed by the slope space analysis model to determine the corresponding rock mass structure model;

[0010] The working condition correction module is configured with a slope working condition parameter library, the slope working condition parameter library includes slope self-weight, ground load, fracture water pressure under the action of rainstorm and seismic force parameters, and the slope working condition parameter library and the corresponding feature section establish a parameter mapping relationship;

[0011] The stability coefficient calculation module is configured with a safety coefficient analysis model, and analyzes according to the rock mass structure model and the slope working condition parameter library to determine the slope stability coefficient of the corresponding feature section under different working conditions.

[0012] By adopting the above technical scheme, the virtual three-dimensional slope model is constructed by the slope segmentation module, the feature section is generated, the rock mass structure model is matched by the multi-model analysis module, the working condition parameters are associated by the working condition correction module, and the stability coefficient under different working conditions is determined by the stability coefficient calculation module, so that the fine segmentation and multi-working condition adaptive analysis of the slope are realized, the stability coefficient is accurately calculated combined with specific geological characteristics and stress parameters, the pertinence and accuracy of the slope stability analysis are improved, reliable basis is provided for engineering design and construction, the engineering applicability of the analysis result is improved, and the dependence on personnel exploration experience is reduced.

[0013] Optionally, it also includes a slope stability optimization module, the slope stability optimization module is configured with a support distribution model, when the slope stability coefficient of the feature section is lower than the preset safety coefficient value, the support distribution parameters are generated according to the rock mass structure model for feature recognition;

[0014] The support optimization parameters in the preset support optimization database are matched according to the working condition type corresponding to the slope working condition parameters, including anchor rod spacing, anchor rod length and slope rate;

[0015] The best support distribution parameters are obtained by adjusting the support optimization parameters and the slope support distribution parameters of the corresponding feature section, so that the slope stability coefficient is higher than the preset safety coefficient value.

[0016] By adopting the above technical scheme, when the stability coefficient is lower than the preset value, the support distribution parameters are generated by the slope stability optimization module, and the parameters in the support optimization database are matched, the best support scheme is obtained by adjustment, the slope stability change can be dynamically responded, the key parameters such as anchor rod spacing and length can be optimized, the stability coefficient meets the safety requirement, and the slope instability risk is reduced, and the engineering safety and economy are balanced.

[0017] Optionally, the slope segmentation module is also configured with a virtual three-dimensional slope model construction precision grading strategy, including:

[0018] According to the preset building construction model and the feature section, position comparison analysis is performed to determine the key slope area and the conventional slope area corresponding to the key structure of the building construction model in the feature section;

[0019] The weight model with preset accuracy is used to calculate the construction weight values corresponding to the key slope region and the conventional slope region, and the construction weight values are matched with the model construction accuracy levels in the database of the model construction accuracy.

[0020] The virtual three-dimensional slope model is constructed with different accuracies based on the model construction accuracy levels.

[0021] By using the above technical solution, the virtual three-dimensional slope model construction accuracy grading strategy differentiates the key and conventional slope regions, calculates the construction weight values according to the weights, and matches the accuracy levels, so as to realize the differentiated processing of high-precision modeling of the key region and reasonable-precision modeling of the conventional region, ensure the reliability of the key region analysis, avoid unnecessary precision resource waste, and help improve the model construction efficiency and engineering practicability.

[0022] Optionally, the accuracy construction weight model is calculated by using the following formula:

[0023] ;

[0024] wherein, is the construction weight value, is a preset regional type weight coefficient, indicating the criticality of the feature section corresponding region, is a building key structure influence weight, is a preset distance attenuation coefficient of the key slope region and the building key structure, is a preset structure surface influence weight coefficient, is a preset structure surface development density coefficient, is a preset topographic complexity weight coefficient, obtained by analyzing the rock mass structure model, is a preset regional deformation sensitivity weight coefficient, obtained by table lookup method, and D is a slope body deformation sensitivity index.

[0025] By using the above technical solution, the accuracy construction weight model calculates the construction weight values by comprehensively considering the regional type, the building key structure influence, the structure surface development, the topographic complexity, and the deformation sensitivity, and this quantitative model makes the modeling accuracy level division of feature sections with different importance more targeted, can accurately match the importance of different regions and the modeling accuracy, and helps improve the construction quality of the virtual three-dimensional model and lay a precise data foundation for subsequent stability analysis.

[0026] Optionally, the section segmentation strategy includes:

[0027] The rock occurrence, joint fissure distribution, and slope height data of the slope terrain are collected by radar scanning, integrated to generate slope terrain point cloud data, and a virtual three-dimensional slope model is constructed.

[0028] According to the virtual three-dimensional slope model, a plurality of different geological characteristic sections of the virtual three-dimensional slope model are obtained by analyzing the geological characteristics, and a characteristic section database is generated;

[0029] According to the characteristic section, the adjacent geological complexity coefficient values of different types of geological characteristic sections are determined by analyzing the geological characteristics, and the characteristic section with the highest coefficient value is selected as a representative geological section;

[0030] Based on the representative geological section, a slope stability coefficient calculation module is triggered to calculate the slope stability coefficient.

[0031] By adopting the above technical solution, the section segmentation strategy constructs a model by generating point cloud data through radar scanning, divides characteristic sections, and selects a representative geological section for calculation, thereby realizing accurate collection of slope terrain data and scientific segmentation of geological characteristics, efficiently calculating the stability coefficient through the representative section, which helps to improve the analysis efficiency while ensuring the accuracy of the results, and provides accurate segmentation basis for slope treatment.

[0032] Optionally, a virtual three-dimensional slope model verification sub-strategy is further configured, including the following steps:

[0033] By analyzing and calculating the slope height, contour scanning points of different heights are determined, and a laser scanner is arranged according to a preset interval distance to scan and obtain a data group of slope terrain point cloud data;

[0034] Based on the data group, a plurality of virtual three-dimensional slope models are constructed and compared to determine the difference proportion of the model difference area, and when the difference proportion is higher than a preset allowed difference proportion, a point cloud data optimization instruction is triggered;

[0035] Based on the point cloud data optimization instruction, corresponding slope terrain point cloud data groups corresponding to the contour scanning points are re-collected to keep the model difference proportion below the allowed difference proportion.

[0036] By adopting the above technical solution, the virtual three-dimensional slope model verification sub-strategy compares a plurality of model differences and optimizes the point cloud data to ensure that the model difference proportion is below the allowed value, thereby effectively identifying and correcting model errors, ensuring the consistency of the virtual three-dimensional model and the actual slope, and helping to reduce analysis errors caused by data deviation and improve the reliability of the model.

[0037] Optionally, the safety coefficient analysis model at least includes:

[0038] A plane sliding calculation model is used to calculate the slope stability coefficient according to the sliding body weight, anti-sliding force and sliding force of the characteristic section;

[0039] A three-dimensional wedge calculation model is used to calculate the slope stability coefficient according to the wedge weight, structure surface shear strength and fracture water pressure of the characteristic section;

[0040] The stereographic projection analysis model determines whether the stability type is consistent with the preset stability type structure by analyzing the projection relationship between the structural surface and the slope surface.

[0041] By adopting the above technical solutions, the virtual 3D slope model verification sub-strategy compares the differences between multiple sets of models and optimizes point cloud data to ensure that the proportion of model differences is lower than the allowable value. This effectively identifies and corrects model errors, ensures the consistency between the virtual 3D model and the actual slope, helps reduce analysis errors caused by data deviations, and improves the reliability of the model.

[0042] Optionally, the planar sliding calculation model includes:

[0043] The planar sliding slope stability coefficient calculation formula is configured based on the planar sliding calculation model.

[0044] ;

[0045] ;

[0046] ;

[0047] ;

[0048] ;

[0049] in, This is the slope stability coefficient. This refers to the sliding force per unit width of the characteristic segment caused by gravity and other external forces. This refers to the anti-slip force per unit width of the sliding body corresponding to the characteristic segment, caused by gravity and other external forces. The weight per unit width of the sliding body corresponding to the characteristic segment. The vertical additional load per unit width of the sliding body corresponding to the characteristic segment is applied. The slope angle corresponding to the feature segment. This represents the total water pressure per unit width corresponding to the characteristic segment. This represents the total water pressure per unit width on the steeply dipping crack surface at the trailing edge corresponding to the characteristic segment. The internal friction angle of the sliding surface. The characteristic segment corresponds to the cohesion of the sliding surface of the sliding body. The length of the sliding surface of the sliding body corresponding to the characteristic segment. This represents the horizontal load per unit width of the sliding body corresponding to the characteristic segment. This refers to the water filling height on the steeply dipping crack surface at the trailing edge corresponding to the characteristic segment. The water weight corresponding to the characteristic segment.

[0050] By adopting the technical scheme, the plane sliding calculation model calculates the stability coefficient through a series of formulas, covers multiple parameters such as self-weight, additional load and water pressure, thereby accurately quantifying the anti-sliding force and sliding force of the plane sliding slope, fully considering the influence of the terrain, load and hydrological conditions, and helping to improve the accuracy of the stability analysis of the single structural plane slope, and providing a reliable basis for the safety evaluation of the slope.

[0051] Optionally, the three-dimensional wedge calculation model comprises:

[0052] The three-dimensional wedge calculation model is configured with a three-dimensional wedge slope stability coefficient calculation formula:

[0053] ;

[0054] ;

[0055] ;

[0056] ;

[0057] wherein, is the gravity of the three-dimensional wedge corresponding to the feature section, is the measured height of the three-dimensional wedge corresponding to the feature section, is the height of the three-dimensional wedge corresponding to the feature section, is the stability coefficient of the rock mass, A, B, C and D respectively represent the virtual vertex position coordinates of the wedge ABD and BCD, c1 and c2 are respectively the unit cohesion of the two structural planes, is the sine inclination angle of the structural plane corresponding to the three-dimensional wedge, is the cosine inclination angle of the structural plane corresponding to the three-dimensional wedge, represents the tangent value of the internal friction angle of the two structural planes.

[0058] By adopting the technical scheme, the three-dimensional wedge calculation model calculates the wedge volume, gravity and stability coefficient through a formula, covers the shear strength of the structural plane, accurately reflects the stress state of the wedge formed by the two structural planes, fully considers the correlation between the wedge geometric characteristics and the mechanical properties of the structural plane, and helps to improve the accuracy of the stability analysis of the wedge failure slope, and provides a targeted reference for support design.

[0059] Optionally, the model matching rule of the analysis model library comprises:

[0060] when the feature section has a single structural plane, a plane sliding model is matched;

[0061] when the feature section has two groups of intersecting structural planes, a three-dimensional wedge model is matched;

[0062] When the characteristic section needs to analyze the spatial relationship between the structural plane and the slope surface, the stereographic projection model is matched.

[0063] By adopting the above technical scheme, the matching rule of the analysis model library is clear, and the single structural plane, two groups of intersecting structural planes and the spatial relationship analysis are associated with the corresponding model, so that the accurate matching of the analysis model and the slope characteristics is realized, the blindness of model selection is avoided, and the efficiency and accuracy of the stability analysis are improved, and it is ensured that the slopes with different structural plane characteristics can be evaluated by suitable models.

[0064] In summary, the present application has at least one of the following beneficial technical effects:

[0065] 1. The virtual three-dimensional slope model is constructed by the slope segmentation module, and the characteristic section is generated, the rock mass structure model is matched by the multi-model analysis module, the working condition correction module is associated with the working condition parameters, and the stability coefficient calculation module determines the stability coefficient under different working conditions, so that the fine segmentation and multi-working condition adaptive analysis of the slope are realized, the stability coefficient is accurately calculated combined with the specific geological characteristics and stress parameters, the pertinence and accuracy of the slope stability analysis are improved, reliable basis is provided for engineering design and construction, the engineering applicability of the analysis results is improved, and the dependence on personnel exploration experience is reduced;

[0066] 2. When the stability coefficient is lower than the preset value, the slope stability optimization module generates support distribution parameters and matches the parameters in the support optimization database, adjusts to obtain the best support scheme, can dynamically respond to the change of slope stability, optimizes key parameters such as anchor rod spacing and length, ensures that the stability coefficient meets the safety requirements, and helps to reduce the risk of slope instability and balance the engineering safety and economy;

[0067] 3. The virtual three-dimensional slope model construction precision grading strategy differentiates between key and conventional slope areas, calculates the construction weight value according to the weight, and matches the precision level, so as to realize the differential treatment of high-precision modeling in key areas and reasonable-precision modeling in conventional areas, ensure the reliability of analysis in key areas, avoid unnecessary precision resource waste, and improve the model construction efficiency and engineering practicability. BRIEF DESCRIPTION OF DRAWINGS

[0068] Figure 1 is a module connection diagram of the slope cross-section stability analysis system in the present application.

[0069] Figure 2 is a method flowchart of steps S100 to S102 in the present application.

[0070] Figure 3 is a method flowchart of steps S200 to S202 in the present application.

[0071] Figure 4is the method flowchart of steps S300 to S303 in this application.

[0072] Figure 5 is the method flowchart of steps S400 to S402 in this application. DETAILED DESCRIPTION

[0073] For the purpose, technical solutions and advantages of the present application to be more clearly understood, the following will combine the drawings of the specification Figures 1-5 and examples to further illustrate the present application. It should be understood that the specific examples described herein are only used to explain the present application and not to limit the present application.

[0074] The embodiments of the present application will be further described in detail below with reference to the drawings of the specification.

[0075] The embodiments of the present application disclose a slope section stability analysis system, a virtual three-dimensional slope model is constructed by a slope segmentation module and a characteristic section is generated, a rock mass structure model is matched by a multi-model analysis module, a working condition correction module is associated with working condition parameters, a stability coefficient calculation module determines the stability coefficient under different working conditions, fine segmentation and multi-working condition adaptive analysis of the slope are realized, the stability coefficient is accurately calculated in combination with specific geological characteristics and stress parameters, which helps to improve the pertinence and accuracy of slope stability analysis, provides a reliable basis for engineering design and construction, and increases the engineering applicability of the analysis result.

[0076] Referring to Figure 1 A slope section stability analysis system includes the following modules:

[0077] A slope segmentation module is configured with a section segmentation strategy, a virtual three-dimensional slope model is constructed by performing a geological contour scan on the slope, and a number of characteristic sections are generated by analyzing the geological characteristics, slope height, and slope direction of the virtual three-dimensional slope model;

[0078] The geological contour scan is a high-precision three-dimensional data acquisition of the slope using laser scanning or geological radar technology, generating a virtual three-dimensional model of the slope. This model can truly reflect the geological characteristics of the slope, including the distribution of rock layers, slope height, and slope direction, etc. Through analysis of the model, the system can identify different characteristic sections of the slope, which will provide basic data for subsequent stability analysis and support design.

[0079] When specifically executed, the system first performs a comprehensive geological scan on the slope to obtain its three-dimensional data. Then, the geological characteristics in the model are analyzed by algorithm, the areas with similar geological characteristics are identified, and are divided into a number of characteristic sections. For example, if there is obvious rock layer change in a section of the slope, the system will automatically divide it into an independent characteristic section for more detailed analysis.

[0080] The purpose of this strategy is to provide accurate geological data for subsequent analysis and design. By dividing the slope in detail, it ensures the pertinence and effectiveness of subsequent processing. The specific section segmentation strategy is further disclosed in subsequent steps.

[0081] A multi-model analysis module is configured with an analysis model library that stores several slope spatial analysis models. The structure surface of the characteristic section is analyzed by the slope spatial analysis model to determine the corresponding rock mass structure model.

[0082] The analysis model library is a database containing various slope spatial analysis models, covering different types of slope structure analysis methods. By analyzing the structure surface of the characteristic section, the system can determine the most suitable rock mass structure model for subsequent stability evaluation.

[0083] In specific implementation, the system matches the geometric characteristics and geological information of the characteristic section with the models in the analysis model library, and selects the optimal analysis model. For example, when there is a single structure surface in the characteristic section, the system matches the plane sliding model for analysis; if there are two groups of intersecting structure surfaces in the characteristic section, the system matches the three-dimensional wedge model; and when the spatial relationship between the structure surface and the slope surface needs to be analyzed, the system matches the stereographic projection model.

[0084] The model matching rules of the analysis model library include:

[0085] When there is a single structure surface in the characteristic section, match the plane sliding model. When there are two groups of intersecting structure surfaces in the characteristic section, match the three-dimensional wedge model. When the spatial relationship between the structure surface and the slope surface needs to be analyzed, match the stereographic projection model.

[0086] A working condition correction module is configured with a slope working condition parameter library, which includes slope self-weight, surface load, fissure water pressure under the action of rainstorm, and seismic force parameters. The slope working condition parameter library and the corresponding characteristic section establish a parameter mapping relationship.

[0087] The slope working condition parameter library refers to a collection of various external loading and environmental condition parameters that affect the stability of the slope. These parameters are quantitative descriptions of the working conditions of the slope under different times and environments.

[0088] Slope self-weight refers to the inherent weight of rock-soil mass under the action of gravity, which is a basic internal force affecting the stability of the slope. It is usually calculated from the density of the rock mass and the geometric size of the slope.

[0089] Surface load refers to external loads applied on the surface of the slope, such as the weight of buildings, the load of vehicles, the weight of earth piles, etc. These loads increase the stress of the slope and may reduce its stability.

[0090] Rainfall-induced fissure water pressure refers to the water pressure formed in the structure plane and pore when the rainfall penetrates into the slope rock mass. The water pressure reduces the effective stress of the rock mass, thereby reducing its shear strength, which is an important inducement for slope instability. This parameter is usually related to rainfall intensity, duration, and permeability of the rock mass.

[0091] Seismic force parameter refers to the inertial force acting on the slope rock mass when an earthquake occurs. The size and direction of these forces depend on the intensity and frequency of the earthquake and the location of the rock mass. Seismic force is a key factor leading to dynamic instability of the slope.

[0092] Parameter mapping relationship is a mechanism that associates the data in the slope working condition parameter library with the various characteristic sections that have been divided. Each characteristic section may face different combinations of working conditions, and the parameter mapping relationship ensures that each characteristic section can accurately obtain the specific working condition parameter values corresponding to it.

[0093] When executed, the system first loads the pre-stored slope working condition parameter library, which may contain parameter values under different rainfall levels, such as light rain, moderate rain, heavy rain, different earthquake intensities, and different ground load conditions. Then, the system will look up the most suitable working condition parameters for the specific characteristic section being analyzed according to its geographical location, slope direction, lithology, and other information obtained from the slope segmentation module. For example, if a characteristic section is located on a windward slope and there is a forecast of heavy rainfall in the near future, the system will set the fissure water pressure parameter under heavy rainfall to a higher value; if the region is in a seismically active zone, the corresponding seismic force parameter will be introduced. This mapping relationship ensures that each characteristic section can obtain the most relevant working condition parameters based on its own characteristics.

[0094] The stability coefficient calculation module is configured with a safety factor analysis model that analyzes the rock mass structure model and the slope working condition parameter library to determine the slope stability coefficient of the corresponding characteristic section under different working conditions.

[0095] Safety factor analysis model: This is a mathematical model used to evaluate the stability of a slope, and its core is to calculate the safety factor by comparing the ratio of the anti-slide force to the sliding force acting on the potential sliding body. A safety factor greater than 1 indicates that the slope is stable, and a safety factor less than 1 indicates that the slope is unstable. Common safety factor analysis models include those based on the limit equilibrium method, such as the Fellenius method, the Bishop method, the Janbu method, numerical analysis methods, etc.

[0096] Rock mass structure model: This is a model determined and described by the multi-model analysis module, which contains the geometry, distribution, and combination of structure planes within a specific characteristic section, such as the plane sliding model and the wedge model. It directly provides geometric input for the macroscopic mechanical behavior of the rock mass to the safety factor analysis model.

[0097] Slope working condition parameter library: This is provided by the working condition correction module, which is a collection of parameters for a specific feature section under different loading and environmental conditions, such as water pressure, seismic force, ground load, etc. These parameters are directly input into the safety factor analysis model to simulate the response of the slope under different working conditions.

[0098] Determine the slope stability coefficient of the corresponding feature section under different working conditions: This is an iterative and computational process. The system will repeatedly call the safety factor analysis model for each feature section and the various potential working conditions corresponding to that feature section, such as: no rainfall, light rain, moderate rain, heavy rain, no earthquake, moderate earthquake, strong earthquake. In each call, the rock mass structure model determined by the multi-model analysis module and the specific parameters under the working condition provided by the working condition correction module are used to finally calculate the slope stability coefficient of the feature section under the specific working condition.

[0099] Specific implementation: The system first traverses all the divided feature sections. For each feature section, it calls the rock mass structure model obtained by the multi-model analysis module and obtains the parameters of the feature section under different working conditions from the working condition correction module. Then, the system inputs these models and parameters into the selected safety factor analysis model. For example, the system first calculates the stability coefficient of the feature section under normal conditions using the no rainfall, no earthquake working condition parameters. Then, it calculates the stability coefficient under the influence of water pressure using the heavy rain working condition parameters. Then, it may calculate the dynamic stability coefficient under the action of earthquake using the earthquake working condition parameters. Through a series of calculations, a clear stability coefficient value is obtained for each feature section under various possible working conditions.

[0100] The purpose of this step is to comprehensively consider the inherent geological structure characteristics of the slope and the dynamic changes of the external environment, and quantitatively evaluate the stability level of the slope under different situations through scientific mechanical models, providing core basis for subsequent risk assessment and decision-making.

[0101] Reference Figure 2 In order to target slope structures with insufficient slope stability coefficients, a slope stability optimization module is also configured, which is configured with a support distribution model and uses the following process steps:

[0102] Step S100: When the slope stability coefficient of a feature section is lower than the preset safety coefficient value, generate support distribution parameters according to feature recognition based on the rock mass structure model;

[0103] The rock mass structure model refers to a mathematical or computerized description of the actual mechanical properties and geometric configurations of the slope rock mass, which includes the lithology, joint development conditions such as joint opening degree, filling conditions, roughness, and key geological parameters such as rock mass grade, block size and shape. For example, for a slope dominated by large, intact rock blocks, the rock mass structure model may be more inclined to describe the contact relationship between blocks; for a slope with dense joint development and broken rock mass, the model will focus on describing the density and connectivity of the joint network.

[0104] Feature identification is an analysis of the input rock mass structure model to identify key geological features that affect slope stability. This may include identifying weak interlayers, potential sliding surfaces, rock mass fracture zones, high permeability fracture zones, etc. For example, in the model, a through-going joint zone with a large opening degree may be identified as a high-risk feature because it is a potential sliding channel.

[0105] Support distribution parameters refer to the geometric and mechanical properties used to describe how to arrange support structures such as anchor rods, sprayed concrete, soil nailing walls, etc. on the surface or inside the slope. They include the type, size, number, spacing, inclination, length, anchoring force, etc. of the support units. For example, for anchor rod support, the support distribution parameters may include the horizontal and vertical spacing of the anchor rods, the length of the anchor rods, the hole diameter of the anchor rods, and the length of the anchoring section and grouting pressure, etc.

[0106] In implementation, the system receives input slope rock mass structure model data and scans and analyzes the model through built-in algorithms such as GIS-based analysis, preliminary iteration of finite element modeling, etc. When the stability coefficient of a certain feature section is identified to be lower than the preset safety coefficient value, for example, the stability coefficient is less than 1.3, the system will start the feature identification program. The program will determine the most suitable support form and its preliminary arrangement rules for the area based on detailed information of the model, such as the density of the joint network, the friction coefficient of the joint surface, the rock mass strength, etc. in the feature section. For example, if a set of nearly parallel joints with a large opening degree is identified in the feature section, the system will determine that high-strength anchoring is needed to pass through these joints, and accordingly generate preliminary suggestions for anchor rod spacing and length.

[0107] This step is to generate preliminary and targeted support design guidance in unstable areas of the slope based on accurate understanding of the actual rock mass conditions, providing a basis for subsequent more detailed optimization.

[0108] Step S101: match the support optimization parameters in the preset support optimization database, including anchor rod spacing, anchor rod length and slope rate, based on the working condition type corresponding to the slope working condition parameters;

[0109] The support optimization database is a collection of support design parameters for various working conditions, covering support schemes for different types of slopes. Working condition parameters include slope gradient, soil type, and groundwater level, factors that directly affect the effectiveness of the support design. By matching working condition types, the system can quickly find the most suitable support optimization parameters for the current slope conditions.

[0110] During execution, the system compares the real-time monitored slope condition parameters with preset parameters in the database to select the optimal support scheme. For example, under certain conditions, if the slope has a large gradient and the soil moisture is high, the system may select shorter anchor bolts and a denser anchor bolt spacing to enhance the support effect.

[0111] The purpose of this step is to ensure the scientific validity and effectiveness of the support design through data matching, thereby providing a strong guarantee for the stability of the slope.

[0112] Step S102: Adjust the support distribution parameters according to the support optimization parameters and the slope support distribution parameters of the corresponding characteristic section to obtain the optimal support distribution parameters, so as to keep the slope stability coefficient higher than the preset safety factor value.

[0113] The optimal support distribution parameters are the result of dynamic adjustments made by comprehensively considering the optimized support parameters and the actual conditions of the characteristic sections. This process involves rearranging the support structure and adjusting its parameters to ensure maximum support effectiveness.

[0114] In practice, the system will adjust the support distribution of the feature section according to the support optimization parameters determined in step S101. For example, if the system suggests increasing the number of anchor bolts and shortening the anchor bolt spacing, the system will automatically calculate a new support layout scheme and generate corresponding construction guidelines.

[0115] The purpose of this step is to ensure that the slope stability coefficient is always higher than the preset safety factor value by dynamically adjusting the support parameters, thereby effectively preventing slope instability and ensuring project safety.

[0116] Reference Figure 3 The slope segmentation module is also equipped with a virtual 3D slope model construction accuracy grading strategy, including:

[0117] Step S200: Perform a position comparison analysis based on the preset building construction model and feature segments to determine the key slope area and conventional slope area in the feature segments that correspond to the key structure of the building construction model;

[0118] Step S201: Calculate the construction weight values ​​corresponding to the key slope area and the conventional slope area using the preset accuracy construction weight model, and match the model construction accuracy level corresponding to the construction weight values ​​in the preset model construction accuracy database;

[0119] Step S202: Based on the model construction accuracy level, construct three-dimensional models of different accuracy for the virtual three-dimensional slope model.

[0120] The accuracy-based weighted model is calculated using the following formula:

[0121] ;

[0122] in, To construct weight values, These are preset region type weighting coefficients, representing the criticality of the region corresponding to the feature segment. Weighting of the building's critical structural influence. This is the distance attenuation coefficient between the pre-defined critical slope area and the critical building structure. The weighting coefficients for the influence of the structural surfaces are set. The set structural surface development density coefficient, The set terrain complexity weighting coefficients were obtained by analyzing the rock mass structure model. The regional deformation sensitivity weight coefficient is obtained by looking up a table, where D is the slope deformation sensitivity index.

[0123] Reference Figure 4 The cross-section segmentation strategy employs the following steps:

[0124] Step S300: The rock strata attitude, joint and fissure distribution and slope height data of the slope topography are collected by radar scanning, integrated to generate slope topography point cloud data, and a virtual three-dimensional slope model is constructed.

[0125] Radar scanning is a technology that uses radar waves to perform non-contact measurement and imaging of targets. Examples include LiDAR and ground-based radar, which can penetrate certain vegetation cover to obtain high-precision three-dimensional spatial information of the ground and rock surfaces.

[0126] The attitude of rock strata refers to parameters describing the spatial orientation of rock strata within the geological formation, typically including the strike and dip angle. The strike is the direction of the line of intersection between the rock strata and the horizontal plane, while the dip angle is the maximum slope angle of the rock strata and its direction of dip.

[0127] Joint and fracture distribution refers to the spatial location, density, orientation, dip angle, and interrelationships of various structural planes such as cracks and faults existing in the slope rock mass. These structural planes often form the basis of the potential sliding surface of the slope.

[0128] Slope height refers to the height of a slope at a certain point perpendicular to the horizontal plane, that is, the distance from the foot of the slope to the top of the slope.

[0129] Data integration is the process of collecting, calibrating, and registering various geological and topographical data collected from different sensors such as radar, GPS, and total station, to form a unified and coordinated dataset.

[0130] Slope terrain point cloud data is the raw data obtained through radar scanning or other three-dimensional scanning techniques. It consists of a large number of three-dimensional coordinate points, each representing a specific location on the slope surface, which can be used to accurately depict the undulating morphology of the slope.

[0131] The virtual three-dimensional slope model is a three-dimensional geometric model constructed based on the integrated point cloud data, using professional modeling software such as CAD, GIS, and geological modeling software. This model can visually display information such as the real terrain, rock layer interface, and structure surface distribution of the slope.

[0132] During execution, the system deploys radar scanning equipment to systematically scan the target slope, such as unmanned aerial laser radar. During the scanning process, the equipment emits radar signals to the slope and receives the reflected signals, determining the three-dimensional coordinates of the measurement points by calculating the round-trip time of the signals. At the same time, sensors such as IMU and GPS on the equipment record the attitude and position information during scanning, which is used for geographical registration of the scanning data. In addition, if conditions permit, handheld radar or survey drilling data may be used to obtain detailed information on rock layer occurrence and joint fissure. All collected point cloud data coordinates and related geological parameters, including rock layer occurrence, joint information, etc., are imported into the data processing platform. Through point cloud processing algorithms, the original data is denoised, filtered, and key information representing rock layer occurrence, joint fissure distribution, and slope height is identified and extracted. Finally, using these processed data, a complete virtual three-dimensional model of the slope is constructed through triangulation, interpolation, and other methods.

[0133] Step S301: Perform geological feature analysis based on the virtual three-dimensional slope model to obtain multiple characteristic segments of different geologies of the virtual three-dimensional slope model, and generate a characteristic segment database;

[0134] Geological feature analysis refers to the in-depth interpretation and evaluation of the constructed virtual three-dimensional slope model to identify various geological information contained therein. This includes analyzing the lithology, rock mass structure, slope, slope direction, rock layer inclination, and possible weak interlayers or potential sliding zones in different regions.

[0135] Different geological characteristic segments are the division of the entire virtual three-dimensional slope model into several representative and relatively homogeneous regions based on their geological composition, structural characteristics, and geometric morphology, such as slope, slope height, and slope direction. These regions are referred to as characteristic segments. For example, a slope may be divided into a steep slope segment dominated by joint A and a gentle slope segment dominated by layer B.

[0136] The feature segment database is a structured dataset that stores information about each identified feature segment. This database typically contains the number of each feature segment, boundary information, primary lithology, structural characteristics of the rock mass, such as the occurrence of dominant structural planes, density, slope height, aspect, slope, and other key descriptive attributes.

[0137] In execution, the system receives the virtual three-dimensional slope model generated by S300. Then, according to the preset section segmentation strategy, the model is analyzed for geological features. This usually involves cutting sections in the model and analyzing the lithological changes, distribution density and strike of structural planes on each section. At the same time, the slope height, slope, aspect and other geometric parameters of each area are also analyzed. Based on these analysis results, the system will divide the slope into several feature segment corresponding areas with similar geological characteristics. For example, the system may identify a region with sandstone lithology, the main structural plane consistent with the slope, and a large slope. Another area may be mudstone with less developed structural planes and a gentle slope. Each identified feature segment and its related geological feature information is stored in a special feature segment database, providing structured input for subsequent analysis.

[0138] Step S302: Perform geological feature analysis according to the feature segments to determine the adjacent geological complexity coefficient values of different types of geological feature segments and filter out the feature segment with the highest coefficient value as the representative geological segment;

[0139] The adjacent geological complexity coefficient value is a quantitative indicator that measures the degree of difference in geological properties between two adjacent feature segments. Its calculation may be based on multiple factors, such as: lithological differences between different feature segments, differences in structural plane occurrence, differences in slope and aspect, and the smoothness or tortuosity of geological interfaces. A high complexity coefficient value indicates that the geological features of two adjacent feature segments are significantly different, meaning that there may be complex geological structures or stability changes at their boundaries.

[0140] The filtering process involves comparing and sorting all calculated adjacent geological complexity coefficient values to find the one with the highest or lowest value.

[0141] The feature segment with the highest coefficient value as the representative geological segment means that the system will preferentially select those feature segments with the greatest geological difference at their boundaries with the surrounding area as the representative or key segment in the stability analysis of the entire slope. This is because areas with higher geological complexity are often the weakest or most critical links in slope stability.

[0142] In implementation: the system will first traverse the feature segment database generated in step S301, identify all adjacent feature segment pairs. For each pair of adjacent feature segments, the system will apply a pre-defined geological complexity evaluation model to calculate the adjacent geological complexity coefficient value between them according to their internal geological characteristics, such as lithology, structure surface occurrence, slope, slope direction, etc. For example, if a feature segment is steep broken rock layer, and its adjacent feature segment is flat complete rock layer, the complexity coefficient value between them will be high. Conversely, if two adjacent feature segments are very similar in geology, the complexity coefficient value will be low.

[0143] After calculating the complexity coefficient values of all adjacent feature segments, the system will sort these values and select the feature segment with the highest complexity coefficient value, which is identified as the representative geological segment. This means that this feature segment is considered to be the representative of the key information that reveals the overall stability of the slope because of its unique geological performance that is most different from the surrounding environment.

[0144] The purpose of this step is to identify the area that is most representative in geology or most likely to be a weak link in stability from multiple feature segments, so as to concentrate the subsequent stability calculation resources on the most critical area, improve the efficiency and pertinence of the analysis.

[0145] Step S303: Trigger the stability coefficient calculation module based on the representative geological segment to calculate the slope stability coefficient.

[0146] The representative geological segment is the feature segment with geological representation determined according to the screening of the adjacent geological complexity coefficient value in step S302. The geological characteristics of this segment are most likely to have a key impact on the stability of the entire slope.

[0147] Triggering the stability coefficient calculation module is to pass the calculation task to a module specially used for slope stability analysis. This module will usually use various mechanical analysis methods, such as limit equilibrium method, numerical simulation, to calculate the stability coefficient of the slope according to the input geological model, working condition parameters, etc.

[0148] Slope stability coefficient calculation is to quantitatively evaluate the ability of the slope to resist sliding or deformation through scientific mechanical calculation methods. The stability coefficient is an important indicator to measure the stability of the slope, usually expressed as the ratio of anti-sliding force to sliding force, or other forms of dimensionless parameters.

[0149] In implementation, the system takes the representative geological section determined in step S302 as the core input and passes it to an independent stability coefficient calculation module. In the process of passing, the geological model of the representative section and various slope working condition parameters that may affect its stability, such as water pressure, earthquake force, etc., are also attached. These parameters are usually extracted from the working condition database according to the geographical location and characteristics of the representative section. After receiving these information, the stability coefficient calculation module will select the appropriate analysis model and conduct detailed mechanical calculation to finally obtain the slope stability coefficient of the representative section under specific working conditions.

[0150] With reference to Figure 5 The slope segmentation module is also configured with a virtual three-dimensional slope model verification sub-strategy, including the following steps:

[0151] Step S400: Determine the contour scanning points at different heights by analyzing and calculating the slope height, and arrange the laser scanners at a preset interval distance to scan and obtain the data set of slope terrain point cloud data;

[0152] Slope height analysis and calculation is to obtain the vertical height information at different positions of the slope. This process identifies the contour scanning points on the slope surface, which represent the geometric undulations of the slope terrain.

[0153] Contour scanning points are three-dimensional coordinate points on the slope surface that are accurately measured. They collectively form the geometric contour of the slope terrain.

[0154] The preset interval distance is the spatial distance between the measurement points of the laser scanner when scanning the terrain. This interval determines the density of the scanning data, which in turn affects the accuracy of the model construction.

[0155] The laser scanner is an advanced terrain measurement device that accurately obtains distance and position information of the target by emitting a laser beam and measuring the round-trip time of the reflected signal, thereby generating high-density and high-precision three-dimensional point cloud data.

[0156] The data set of slope terrain point cloud data is a collection of original three-dimensional coordinate points collected by the laser scanner, which faithfully records the undulating shape and height information of the slope surface.

[0157] In specific execution, first, the slope height is analyzed and calculated to determine the key contour scanning points at different height ranges on the slope. For example, the system identifies the scanning points at the toe of the slope, the top of the slope, and the key contour line positions where the slope changes significantly. Subsequently, according to the preset interval distance, the laser scanners are evenly arranged between these key points. These scanners will work synchronously, emit laser beams, and receive reflected signals, recording the measured three-dimensional coordinate point data to form a complete data set containing a large number of point clouds. These data sets will be used to construct the three-dimensional model of the slope.

[0158] Step S401: Compare and analyze several virtual three-dimensional slope models constructed based on data sets to determine the difference proportion of the model difference area. When the difference proportion is higher than the preset allowed difference proportion, trigger the point cloud data optimization instruction;

[0159] The data set is a set of point cloud data of the slope terrain collected by the laser scanner in step S300.

[0160] The several virtual three-dimensional slope models are three-dimensional slope geometric models generated by different data sets or different processing methods. This may include using data from different scanning periods or using different point cloud processing algorithms to construct.

[0161] The comparison and analysis is a mutual comparison of the virtual three-dimensional slope models constructed by different sources or processing methods to evaluate their similarity and difference.

[0162] The model difference area refers to the area where two or more models have significant differences in geometric shape, key feature point position, etc. during the comparison process.

[0163] The difference proportion refers to the proportion of the model difference area in the overall model area. This proportion value quantifies the inconsistency between the models.

[0164] The preset allowed difference proportion is a threshold that specifies the maximum acceptable difference between models in model comparison analysis. Exceeding this threshold indicates that the consistency of the models is insufficient and needs to be corrected.

[0165] The point cloud data optimization instruction is a signal or command that the system issues when the model comparison result shows that the difference exceeds the allowed range, requiring the reacquisition or optimization of part of the point cloud data.

[0166] When executed, the system will use the data set obtained in step S300 to attempt to construct several different virtual three-dimensional slope models, possibly by repeated scanning of the same area or using different processing algorithms. Then, these models will be subjected to precise geometric comparison. Through algorithm analysis, areas with significant differences in shape, height, or key geological features are identified. Then, the proportion of these difference areas in the total area or total volume of the model is calculated. If this difference proportion exceeds the preset allowed difference proportion, for example, if the slope height difference of two models in a certain key slope section exceeds 1 meter, which is not allowed, the system will trigger the point cloud data optimization instruction. The purpose of this step is to evaluate the consistency of the slope models generated by different data sources or processing methods, identify possible measurement or processing errors, and provide a basis for decision-making for model optimization.

[0167] Step S402: Re-collecting the corresponding slope terrain point cloud data set corresponding to the profile scanning points based on the point cloud data optimization instruction to keep the model difference proportion below the allowed difference proportion.

[0168] The point cloud data optimization instruction is issued by step S401, indicating the need to re-collect or process the slope terrain point cloud data.

[0169] Re-collecting the corresponding slope terrain point cloud data set corresponding to the profile scanning points refers to re-scanning and data collection of relevant profile scanning points using a laser scanner again in the model difference area identified in S401 or around these areas.

[0170] To keep the model difference proportion below the allowed difference proportion is to improve the consistency between all constructed slope models by optimizing point cloud data, so that the difference proportion is controlled within an acceptable range, thereby ensuring that the final constructed virtual three-dimensional slope model is accurate and reliable.

[0171] In the specific implementation process, when the point cloud data optimization instruction is triggered in step S401, the system will locate the area that needs to be optimized according to the requirements of the instruction, especially the model difference area identified in S401. Then, the laser scanner will be instructed to scan the profile scanning points in these areas more finely and repeatedly. The high-density point cloud data set re-collected will be integrated into the existing data or replace the original inaccurate data. Then, the system will re-construct the virtual three-dimensional slope model based on the optimized data set and perform comparison analysis again. Repeat this process until the difference proportion between all constructed models is below the pre-set allowed difference proportion.

[0172] The purpose of this step is to actively correct and improve the data quality, ensuring that the final constructed virtual three-dimensional slope model can accurately reflect the topography and geological features of the actual slope.

[0173] The safety factor analysis model in the stability coefficient calculation module at least includes:

[0174] The planar sliding calculation model calculates the slope stability coefficient based on the weight of the sliding body, the anti-sliding force and the sliding force of the feature section;

[0175] The planar sliding calculation model includes:

[0176] The planar sliding slope stability coefficient calculation formula is configured based on the planar sliding calculation model:

[0177] ;

[0178] ;

[0179] ;

[0180] ;

[0181] ;

[0182] wherein, is the slope stability coefficient, is the gravity and other external force induced downslope force of the unit width of the sliding body corresponding to the characteristic section, is the anti-sliding force of the unit width of the sliding body corresponding to the characteristic section, is the unit width of the sliding body corresponding to the characteristic section, is the unit width of the sliding body corresponding to the characteristic section, is the inclination angle of the sliding surface corresponding to the characteristic section, is the total water pressure of the unit width corresponding to the characteristic section, is the total water pressure of the unit width on the steep rear edge crack surface corresponding to the characteristic section, is the internal friction angle of the sliding surface, is the sliding surface cohesion of the sliding body corresponding to the characteristic section, is the sliding surface length of the sliding body corresponding to the characteristic section, is the unit width of the sliding body corresponding to the characteristic section, is the water filling height on the steep rear edge crack surface corresponding to the characteristic section, is the water body density corresponding to the characteristic section.

[0183] The three-dimensional wedge calculation model is used to calculate the slope stability coefficient according to the wedge weight, the structural plane shear strength and the crack water pressure of the characteristic section;

[0184] The three-dimensional wedge calculation model includes:

[0185] The three-dimensional wedge calculation model is configured with a three-dimensional wedge slope stability coefficient calculation formula:

[0186] ;

[0187] ;

[0188] ;

[0189] ;

[0190] wherein, is the three-dimensional wedge gravity corresponding to the characteristic section, is the measured height of the three-dimensional wedge corresponding to the characteristic section, is the three-dimensional wedge height corresponding to the characteristic section, A, B, C, D are the coordinates of the virtual top points of the wedge-shaped bodies ABD and BCD, c1 and c2 are the unit cohesion of the two structural surfaces respectively, is the sine dip angle of the structural surface corresponding to the three-dimensional wedge-shaped body, is the cosine dip angle of the structural surface corresponding to the three-dimensional wedge-shaped body, is the tangent value of the internal friction angle of the two structural surfaces.

[0191] The stereographic projection analysis model is used to determine whether the projection relationship between the structural surface and the slope surface is consistent with the preset stable type structure.

[0192] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional modules is exemplified, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device and unit described above can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0193] The embodiment of the present application provides a computer readable storage medium, which stores a computer program capable of being loaded and executed by a processor to execute a slope section stability analysis system.

[0194] The computer storage medium includes, for example, a U disk, a mobile hard disk, a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a magnetic disk or an optical disk, and various media capable of storing program codes.

[0195] Based on the same inventive concept, the embodiment of the present application provides an intelligent terminal, which includes a memory and a processor, and the memory stores a computer program capable of being loaded and executed by the processor to execute a slope section stability analysis system.

[0196] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional modules is exemplified, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device and unit described above can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0197] The above are only preferred embodiments of the present application, not intended to limit the protection scope of the present application, any feature disclosed in the specification (including the abstract and the drawings) can be replaced by other equivalent or similar features, unless specifically stated otherwise. That is, each feature is only an example of a series of equivalent or similar features, unless specifically stated otherwise.

Claims

1. A slope cross-sectional stability analysis system, characterized in that, Comprise: The slope segmentation module is configured with a section segmentation strategy, a virtual three-dimensional slope model is constructed by scanning the geological profile of the slope, and a plurality of characteristic sections are generated by analyzing the geological characteristics, slope height and slope direction of the virtual three-dimensional slope model; The multi-model analysis module is configured with an analysis model library, the analysis model library stores a plurality of slope space analysis models, and the structure surface of the characteristic section is analyzed by the slope space analysis model to determine the corresponding rock mass structure model; The working condition correction module is configured with a slope working condition parameter library, the slope working condition parameter library includes slope self weight, ground load, fissure water pressure under the action of rainstorm and earthquake force parameters, and the slope working condition parameter library and the corresponding characteristic section establish parameter mapping relationship; The stability coefficient calculation module is configured with a safety factor analysis model, which analyzes the rock mass structure model and the slope working condition parameter library to determine the slope stability coefficient of the corresponding characteristic section under different working conditions; The safety factor analysis model at least includes: The plane sliding calculation model calculates the slope stability coefficient according to the sliding body self weight, anti sliding force and sliding force of the characteristic section; The three-dimensional wedge calculation model calculates the slope stability coefficient according to the wedge weight, structure surface shear strength and fissure water pressure of the characteristic section; The polar projection analysis model determines whether the projection relationship between the structure surface and the slope surface is consistent with the preset stable type structure; The plane sliding calculation model includes: The plane sliding slope stability coefficient calculation formula is configured based on the plane sliding calculation model: ； ； ； ； ； wherein, is the slope stability coefficient, is the gravity and other external force induced downslope force per unit width of the sliding body corresponding to the characteristic section, is the anti-sliding force per unit width of the sliding body corresponding to the characteristic section, is the unit width self-weight of the sliding body corresponding to the characteristic section, is the vertical additional load per unit width of the sliding body corresponding to the characteristic section, is the sliding surface inclination angle corresponding to the characteristic section, is the total water pressure per unit width corresponding to the characteristic section, is the total water pressure per unit width on the trailing edge steep crack surface corresponding to the characteristic section, is the internal friction angle of the sliding surface, is the sliding surface cohesion of the sliding body corresponding to the characteristic section, is the sliding surface length of the sliding body corresponding to the characteristic section, is the horizontal load per unit width of the sliding body corresponding to the characteristic section, is the water filling height on the trailing edge steep crack surface corresponding to the characteristic section, is the water body specific gravity corresponding to the characteristic section. The three-dimensional wedge calculation model includes: The three-dimensional wedge slope stability coefficient calculation formula is configured based on the three-dimensional wedge calculation model: ； ； ； ； wherein, is the gravity of the three-dimensional wedge corresponding to the characteristic section, is the measured height of the three-dimensional wedge corresponding to the characteristic section, is the height of the three-dimensional wedge corresponding to the characteristic section, is the stability coefficient of the rock mass, A, B, C, D represent the coordinates of the virtual vertexes of the wedge ABD and BCD respectively, c1 and c2 are the unit cohesion of the two structural planes respectively, is the sine dip angle of the structural plane corresponding to the three-dimensional wedge, is the cosine dip angle of the structural plane corresponding to the three-dimensional wedge, represents the tangent value of the internal friction angle of the two structural planes.

2. The system for analyzing the stability of a slope cross section according to claim 1, wherein It also includes a slope stability optimization module, the slope stability optimization module is configured with a support distribution model, when the slope stability coefficient of the characteristic section is lower than the preset safety coefficient value, the support distribution parameters are generated according to the rock mass structure model for feature recognition; The support optimization parameters in the preset support optimization database are matched according to the working condition type corresponding to the slope working condition parameters, including anchor rod spacing, anchor rod length and slope rate; The best support distribution parameters are obtained by adjusting the support optimization parameters and the slope support distribution parameters of the corresponding characteristic section, so that the slope stability coefficient is higher than the preset safety coefficient value.

3. The system for analyzing the stability of a slope cross-section according to claim 1, wherein The slope segmentation module is also configured with a virtual three-dimensional slope model construction precision grading strategy, including: The position comparison analysis is carried out according to the preset building construction model and the characteristic section, so as to determine the key slope area and the conventional slope area corresponding to the key structure of the building construction model in the characteristic section; The construction weight value corresponding to the model construction precision level in the preset model construction precision database is matched by constructing the weight model with the preset precision to construct the key slope area and the conventional slope area. The virtual three-dimensional slope model is constructed with different precision based on the model construction precision level.

4. The system for slope profile stability analysis of claim 3, wherein The precision construction weight model is calculated by the following formula: ； wherein, is a weight value, is a preset regional type weight coefficient, indicating the key degree of the feature section corresponding region, is a building key structure influence weight, is a preset distance attenuation coefficient of the key slope region and the building key structure, is a set structure surface influence weight coefficient, is a set structure surface development density coefficient, is a set terrain complexity weight coefficient, obtained by analyzing the rock mass structure model, is a set regional deformation sensitivity weight coefficient, obtained by table lookup method, and D is a slope body deformation sensitivity index.

5. The system for slope profile stability analysis of claim 1, wherein, The section segmentation strategy includes: The rock layer occurrence, joint fissure distribution and slope height data of the slope terrain are integrated to generate slope terrain point cloud data, and a virtual three-dimensional slope model is constructed; According to the virtual three-dimensional slope model, a plurality of different geological characteristic sections of the virtual three-dimensional slope model are obtained by analyzing the geological characteristics, and a characteristic section database is generated; According to the characteristic sections, the adjacent geological complexity coefficient values of different types of geological characteristic sections are determined by analyzing the geological characteristics, and the characteristic section with the highest coefficient value is selected as a representative geological section; Based on the representative geological section, a stability coefficient calculation module is triggered to calculate the slope stability coefficient.

6. A system for analyzing the stability of a slope cross-section according to claim 5, wherein A virtual three-dimensional slope model verification sub-strategy is also configured, including the following steps: By analyzing and calculating the slope height, the contour scanning points of different heights are determined, and the laser scanner is arranged according to the preset interval distance to scan and obtain the data group of the slope terrain point cloud data; Based on the data group, a plurality of virtual three-dimensional slope models are constructed for comparison and analysis to determine the difference proportion of the model difference area, and when the difference proportion is higher than the preset allowed difference proportion, a point cloud data optimization instruction is triggered; Based on the point cloud data optimization instruction, the corresponding slope terrain point cloud data group corresponding to the contour scanning point is re-collected to keep the model difference proportion below the allowed difference proportion.

7. The system for slope profile stability analysis of claim 1, wherein The model matching rules of the analysis model library include: When the characteristic section has a single structural plane, a plane sliding model is matched; When the characteristic section has two intersecting structural planes, a three-dimensional wedge model is matched; When the characteristic section needs to analyze the spatial relationship between the structural plane and the slope surface, an orthographic projection model is matched.

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