Slope section stability analysis system

Abstract

The invention relates to the technical field of rock-soil section analysis, in particular to a side slope section stability analysis system, which comprises a side slope segmentation module configured with a section segmentation strategy, constructing a virtual three-dimensional side slope model by performing geological contour scanning on a side slope, and analyzing the virtual three-dimensional side slope model to generate a plurality of characteristic sections; the multi-model analysis module is configured with an analysis model library, a plurality of slope space analysis models are stored in the analysis model library, and the multi-model analysis module is used for analyzing the structural surface of the feature section to determine a corresponding rock mass structure model; the working condition correction module is configured with a slope working condition parameter library and is used for establishing a parameter mapping relation with the corresponding characteristic section; and the stability coefficient calculation module is configured with a safety coefficient analysis model, and performs calculation and analysis according to the rock mass structure model and the slope working condition parameter library so as to determine slope stability coefficients of corresponding characteristic sections under different working conditions. The method 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 rods and retaining walls, which is used for reinforcing slopes 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 related technologies, slope section stability analysis mostly relies on manual field investigation combined with relevant rock-soil structure calculation, or uses simple automatic acquisition equipment to scan the slope section, and matches fixed analysis model to calculate and analyze the slope section parameters, so as to determine whether the rock-soil structure of the section is stable.

[0004] In view of the above related technologies, 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 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 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: A slope section stability analysis system, comprising: 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 a plurality of characteristic sections are generated by analyzing the geological features, slope height and slope direction of the virtual three-dimensional slope model; 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 section is analyzed by the slope space analysis model to determine the corresponding rock mass structure model; A 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 seismic force parameters, and the slope working condition parameter library and the corresponding characteristic section establish a parameter mapping relationship; 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.

[0007] By adopting the technical solution, 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 is 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 in combination 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.

[0008] Optionally, the slope stability optimization module is further configured with a support distribution model, and 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; The support optimization parameters in the preset support optimization database are matched based on the working condition type corresponding to the slope working condition parameters, including the anchor rod spacing, the anchor rod length and the slope rate; 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.

[0009] By adopting the technical solution, 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.

[0010] Optionally, the slope segmentation module is further configured with a virtual three-dimensional slope model construction precision grading strategy, including: The position comparison analysis is performed according to the preset building construction model and the feature 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 feature section; The construction weight value corresponding to the key slope area and the conventional slope area is calculated by the preset precision construction weight model, and the model construction precision level corresponding to the construction weight value in the preset model construction precision database is matched; The virtual three-dimensional slope model is constructed in different precisions based on the model construction precision level.

[0011] By adopting the technical scheme, the virtual three-dimensional slope model construction precision grading strategy differentiates key and conventional slope areas, calculates a construction weight value according to a weight, and matches a precision level, so as to realize differentiated processing of high-precision modeling of key areas and reasonable-precision modeling of conventional areas, guarantee the reliability of analysis of the key areas, avoid unnecessary precision resource waste, and help improve model construction efficiency and engineering practicability.

[0012] Optionally, the precision construction weight model is calculated according to the following formula. ; Among them, is a construction weight value, is a preset area type weight coefficient, indicating the key degree of the feature section corresponding area, is a building key structure influence weight, is a preset distance attenuation coefficient of the key slope area and the building key structure, is a preset structure surface influence weight coefficient, is a preset structure surface development density coefficient, is a preset terrain complexity weight coefficient, obtained by analyzing the rock mass structure model, is a preset area deformation sensitivity weight coefficient, obtained by table lookup method, and D is a slope body deformation sensitivity index.

[0013] By adopting the technical scheme, the precision construction weight model calculates a construction weight value by comprehensively considering the area type, building key structure influence, structure surface development, terrain complexity, and deformation sensitivity according to the formula. This quantitative model makes the modeling precision level division of feature sections of different importance degrees more targeted, can accurately match the importance of different areas and the modeling precision, helps improve the construction quality of the virtual three-dimensional model, and lays a precise data foundation for subsequent stability analysis.

[0014] Optionally, the section segmentation strategy includes: The rock layer 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 and formed; According to the virtual three-dimensional slope model, a plurality of different geological feature sections of the virtual three-dimensional slope model are obtained by geological feature analysis, and a feature section database is generated; According to the feature section, the geological feature analysis is performed to determine the adjacent geological complexity coefficient values of different types of geological feature sections, and the feature 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.

[0015] By adopting the technical scheme, the section segmentation strategy realizes accurate collection of slope terrain data and scientific segmentation of geological features by constructing a model through radar scanning to generate point cloud data, dividing feature sections, and screening representative geological sections for calculation, and efficiently calculates the stability coefficient through the representative sections, which helps to improve the analysis efficiency while ensuring the accuracy of the results, and provides accurate segmentation basis for slope treatment.

[0016] Optionally, a virtual three-dimensional slope model verification sub-strategy is further configured, including the following steps: The profile scanning points of different heights are determined by analyzing and calculating the slope height, and the laser scanner is arranged at a preset interval distance to scan to obtain a data group of slope terrain point cloud data; The difference proportions of the model difference regions are determined by comparing and analyzing a plurality of virtual three-dimensional slope models constructed based on the data group, and when the difference proportion is higher than a preset allowed difference proportion, a point cloud data optimization instruction is triggered; The corresponding slope terrain point cloud data group of the corresponding profile scanning points is re-collected based on the point cloud data optimization instruction to keep the model difference proportion below the allowed difference proportion.

[0017] By adopting the technical scheme, the virtual three-dimensional slope model verification sub-strategy compares a plurality of model differences, optimizes the point cloud data, ensures that the model difference proportion is below the allowed value, effectively identifies and corrects the model error, and ensures the consistency of the virtual three-dimensional model and the actual slope, which helps to reduce the analysis error caused by data deviation and improves the reliability of the model.

[0018] Optionally, the safety factor analysis model at least includes: A plane sliding calculation model calculates the slope stability coefficient according to the sliding body weight, anti-sliding force and sliding force of the feature section; A three-dimensional wedge calculation model calculates the slope stability coefficient according to the wedge weight, structural plane shear strength and fissure water pressure of the feature section; An equatorial projection analysis model determines whether the projection relationship between the structural plane and the slope surface is consistent with the preset stable type structure.

[0019] By adopting the technical scheme, the virtual three-dimensional slope model verification sub-strategy compares a plurality of model differences, optimizes the point cloud data, ensures that the model difference proportion is below the allowed value, can effectively identify and correct the model error, and ensure the consistency of the virtual three-dimensional model and the actual slope, which helps to reduce the analysis error caused by data deviation and improves the reliability of the model.

[0020] Optionally, the plane sliding calculation model includes: A plane sliding slope stability coefficient calculation formula is configured based on the plane sliding calculation model: ; ; ; ; ; wherein, is a slope stability coefficient, is a gravity of a unit width of a sliding body corresponding to a characteristic section and a sliding force caused by other external forces, is an anti-sliding force of a unit width of a sliding body corresponding to a characteristic section, is a unit width of a sliding body corresponding to a characteristic section, is a unit width of a sliding body corresponding to a characteristic section, is a sliding surface inclination angle corresponding to a characteristic section, is a total water pressure of a unit width corresponding to a characteristic section, is a total water pressure of a unit width on a trailing edge steep crack surface corresponding to a characteristic section, is a sliding surface internal friction angle, is a sliding surface cohesion of a sliding body corresponding to a characteristic section, is a sliding surface length of a sliding body corresponding to a characteristic section, is a unit width of a sliding body corresponding to a characteristic section, is a water filling height on a trailing edge steep crack surface corresponding to a characteristic section, is a water body specific gravity corresponding to a characteristic section.

[0021] By adopting the above 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 topography, load, and hydrological conditions, and helping to improve the accuracy of the stability analysis of the single structural surface slope, and providing a reliable basis for the safety evaluation of such slope.

[0022] Optionally, the three-dimensional wedge calculation model comprises: The three-dimensional wedge calculation model is configured with a three-dimensional wedge slope stability coefficient calculation formula: ; ; ; ; wherein, is a three-dimensional wedge gravity corresponding to a characteristic section, is a measured height of a three-dimensional wedge corresponding to a characteristic section, is a three-dimensional wedge height corresponding to a characteristic section, A, B, C, D represent the coordinates of the virtual top points of the wedge-shaped bodies ABD and BCD respectively, 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.

[0023] By adopting the above technical scheme, the three-dimensional wedge-shaped body calculation model calculates the wedge-shaped body volume, gravity and stability coefficient through a formula, covers parameters such as structural surface shear strength, accurately reflects the stress state of the wedge formed by the two structural surfaces, fully considers the correlation between the wedge geometric characteristics and the structural surface mechanical properties, and helps to improve the accuracy of the wedge-shaped failure slope stability analysis, and provides targeted reference for support design.

[0024] Optionally, the model matching rule of the analysis model library comprises: when a single structural surface exists in the characteristic section, a plane sliding model is matched; when two groups of intersecting structural surfaces exist in the characteristic section, a three-dimensional wedge-shaped body model is matched; when the spatial relationship between the structural surface and the slope surface needs to be analyzed in the characteristic section, a stereographic projection model is matched.

[0025] By adopting the above technical scheme, the matching rule of the analysis model library clearly associates the single structural surface, the two groups of intersecting structural surfaces and the spatial relationship analysis with the corresponding model, so as to realize accurate matching of the analysis model and the slope characteristics, avoid blindness in model selection, help to improve the efficiency and accuracy of the stability analysis, and ensure that the slope with different structural surface characteristics can be evaluated by using a suitable model.

[0026] In summary, the present application includes at least one of the following beneficial technical effects: 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, the stability coefficient calculation module determines the stability coefficient under different working conditions, the 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 the slope stability analysis, provides a reliable basis for engineering design and construction, improves the engineering applicability of the analysis result, and reduces the dependence on personnel exploration experience; 2. The slope stability optimization module generates support distribution parameters when the stability coefficient is lower than the preset value, matches the parameters in the support optimization database, adjusts to obtain the best support scheme, can dynamically respond to changes in slope stability, optimizes key parameters such as anchor rod spacing and length, ensures that the stability coefficient meets the safety requirements, helps to reduce the risk of slope instability, and balances engineering safety and economy; 3. The virtual three-dimensional slope model construction precision grading strategy differentiates between key and conventional slope areas, calculates construction weight values according to weights and matches precision levels to realize differentiated processing of high-precision modeling in key areas and reasonable-precision modeling in conventional areas, ensures the reliability of analysis in key areas while avoiding unnecessary precision resource waste, and helps to improve model construction efficiency and engineering practicability. BRIEF DESCRIPTION OF DRAWINGS

[0027] Figure 1 is a schematic diagram of the module connection of the slope section stability analysis system in the present application.

[0028] Figure 2 is a method flowchart of steps S100 to S102 in the present application.

[0029] Figure 3 is a method flowchart of steps S200 to S202 in the present application.

[0030] Figure 4 is a method flowchart of steps S300 to S303 in the present application.

[0031] Figure 5 is a method flowchart of steps S400 to S402 in the present application. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application. Figures 1-5 DETAILED DESCRIPTION

[0033] The embodiments of the present application will be further described in detail below with reference to the accompanying drawings.

[0034] The embodiments of the present application disclose a slope section stability analysis system, which constructs a virtual three-dimensional slope model through a slope segmentation module and generates a characteristic section, matches a rock mass structure model through a multi-model analysis module, associates working condition parameters through a working condition correction module, determines the stability coefficient under different working conditions through a stability coefficient calculation module, realizes fine segmentation and multi-working condition adaptive analysis of the slope, accurately calculates the stability coefficient in combination with specific geological characteristics and stress parameters, helps to improve the pertinence and accuracy of slope stability analysis, and provides a reliable basis for engineering design and construction to increase the engineering applicability of the analysis results.

[0035] Referring to Figure 1 A slope section stability analysis system includes the following modules: A slope segmentation module is configured with a section segmentation strategy, a virtual three-dimensional slope model is constructed by performing a geological profile scan on the slope, and a number of characteristic sections are generated by analyzing the geological features, slope height, and slope direction of the virtual three-dimensional slope model; The geological profile 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 features 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.

[0036] In specific implementation, the system first performs a comprehensive geological scan on the slope to obtain its three-dimensional data. Then, through algorithmic analysis of the geological features in the model, it identifies areas with similar geological features and divides them into a number of characteristic sections. For example, if a section of the slope has obvious changes in rock layers, the system will automatically divide it into independent characteristic sections for more detailed analysis.

[0037] 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.

[0038] A multi-model analysis module is configured with an analysis model library, which stores a number of 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; The analysis model library is a database containing a variety of 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.

[0039] In specific implementation, the system matches the geometric features and geological information of the characteristic section with the models in the analysis model library, selecting the optimal analysis model. For example, when the characteristic section has a single structure surface, the system will match a plane sliding model for analysis; if the characteristic section has two intersecting structure surfaces, it will match a three-dimensional wedge model; and when the spatial relationship between the structure surface and the slope surface needs to be analyzed, it will match an orthographic projection model.

[0040] The model matching rules of the analysis model library include: When a characteristic segment has a single structural plane, the matching plane sliding model is used. When a characteristic segment has two intersecting structural planes, the matching three-dimensional wedge model is used. When a characteristic segment needs to analyze the spatial relationship between structural planes and slope surfaces, the matching stereographic projection model is used.

[0041] The working condition correction module is configured with a slope working condition parameter library, and the slope working condition parameter library includes parameters of slope self-weight, ground load, fissure water pressure under the action of rainstorm, and earthquake force. 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.

[0042] The slope self-weight refers to the inherent weight of the 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.

[0043] The ground load refers to the external load 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.

[0044] The fissure water pressure under the action of rainstorm refers to the water pressure formed in the structural plane and pore when rainwater penetrates into the internal part of the slope rock mass. 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.

[0045] The earthquake force parameter refers to the inertial force acting on the slope rock mass when an earthquake occurs, and the size and direction of these forces depend on the intensity, frequency of the earthquake, and the location of the rock mass. Earthquake force is a key factor leading to dynamic instability of the slope.

[0046] The parameter mapping relationship is a mechanism that associates the data in the slope working condition parameter library with the various characteristic segments that have been divided. Each characteristic segment may face different combinations of working conditions, and the parameter mapping relationship ensures that each characteristic segment can accurately obtain the specific working condition parameter values corresponding to it.

[0047] When executed, the system first loads a 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 for the most suitable working condition parameters for the specific feature section being analyzed according to its geographical location, slope direction, lithology, and other information obtained from the slope segmentation module in the parameter library. For example, if a feature section is located on a windward slope and there is a forecast of heavy rain in the near future, the system will set the fissure water pressure parameter under heavy rain 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 feature section can obtain the most relevant working condition parameters based on its own characteristics.

[0048] A stability coefficient calculation module is configured with a safety factor analysis model to analyze based on 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.

[0049] Safety factor analysis model: This is a mathematical model used to evaluate the stability of a slope, which calculates 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 limit equilibrium methods, such as the Fellenius method, the Bishop method, the Janbu method, numerical analysis methods, etc.

[0050] 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 structural surfaces within a specific feature section, such as the plane sliding model, wedge model, etc. It directly provides geometric input for the macroscopic mechanical behavior of the rock mass to the safety factor analysis model.

[0051] Slope working condition parameter library: This is provided by the working condition correction module and contains a set 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.

[0052] Determination of 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 that working condition provided by the working condition correction module are used to finally calculate the slope stability coefficient of the feature section under that specific working condition.

[0053] In specific implementation: the system will first traverse all the divided feature segments. For each feature segment, it will call the rock mass structure model obtained by the multi-model analysis module, and obtain the parameters of the feature segment under different working conditions from the working condition correction module. Then, the system will input these models and parameters into the selected safety factor analysis model. For example, the system will first calculate the stability factor of the feature segment under normal conditions using the parameters of the no rainfall, no earthquake working condition. Then, it will calculate the stability factor considering the influence of water pressure using the heavy rain working condition parameters. Then, it may calculate the dynamic stability factor under the action of earthquake using the earthquake working condition parameters. Through a series of calculations, a clear stability factor value is obtained for each feature segment under various possible working conditions.

[0054] 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.

[0055] Reference Figure 2 In order to target the slope structure with insufficient slope stability coefficient, a slope stability optimization module is also configured, wherein the slope stability optimization module is configured with a support distribution model, and the following process steps are adopted: Step S100: When the slope stability coefficient of the feature segment is lower than the preset safety factor value, generate support distribution parameters according to feature recognition based on the rock mass structure model; The rock mass structure model refers to the mathematical or computerized description of the actual mechanical properties and geometric shape of the slope rock mass, which includes the lithology, joint development conditions such as joint opening degree, filling conditions, roughness, as well as key geological parameters such as rock mass grade, block size and shape. For example, for a slope dominated by large and complete rock blocks, the rock mass structure model may tend 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.

[0056] Feature recognition 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, and high permeability fracture zones. For example, in the model, a through-going joint zone with large opening degree may be identified as a high-risk feature because it is a potential sliding channel.

[0057] 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 unit. For example, for anchor rod support, support distribution parameters may include the horizontal and vertical spacing of anchor rods, the length of anchor rods, the hole diameter of anchor rods, and the length of the anchoring section and grouting pressure, etc.

[0058] In implementation, the system receives input slope rock mass structure model data and can scan and analyze the model through built-in algorithms such as GIS-based analysis, preliminary iteration of finite element modeling, etc. When it identifies that the stability coefficient of a certain feature section is 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 region according to the detailed information of the model, such as the density of joint network, the friction coefficient of joint surface, the strength of rock mass, etc. in the feature section. For example, if a set of nearly parallel joints with large opening 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.

[0059] 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.

[0060] Step S101: match the support optimization parameters in the preset support optimization database based on the working condition type corresponding to the slope working condition parameters, including anchor rod spacing, anchor rod length and slope rate; The support optimization database is a database containing support design parameters under various working conditions, covering support schemes for different types of slopes. Working condition parameters include slope gradient, soil type, groundwater level, etc., which directly affect the effectiveness of support design. By matching the working condition type, the system can quickly find the most suitable support optimization parameters for the current slope conditions.

[0061] When executed, the system compares the real-time monitored slope working condition parameters with the preset parameters in the database and selects the optimal support scheme. For example, in a certain working condition, if the slope gradient is large and the soil humidity is high, the system may choose shorter anchor rods and denser anchor rod spacing to enhance the support effect.

[0062] The role of this step is to ensure the scientificity and effectiveness of support design through data matching, providing a strong guarantee for the stability of the slope.

[0063] Step S102: Adjust the support distribution parameters of the corresponding characteristic section according to the support optimization parameters to obtain the optimal support distribution parameters to maintain the slope stability coefficient above the preset safety factor value.

[0064] The optimal support distribution parameters are the result of dynamic adjustment by considering the support optimization parameters and the actual situation of the characteristic section. This process involves rearrangement and parameter adjustment of the support structure to maximize the support effect.

[0065] In specific implementation, the system will adjust the support distribution of the characteristic section according to the support optimization parameters determined in step S101. For example, if the system suggests increasing the number of anchor rods and shortening the anchor rod spacing, the system will automatically calculate a new support arrangement scheme and generate corresponding construction guidance.

[0066] The purpose of this step is to ensure that the stability coefficient of the slope is always higher than the preset safety factor value by dynamically adjusting the support parameters, thereby effectively preventing slope instability and ensuring project safety.

[0067] Referring to Figure 3 , the slope segmentation module is also configured with a virtual three-dimensional slope model construction precision grading strategy, including: Step S200: According to the preset building construction model and the characteristic section, perform position comparison analysis to determine the key slope area and the general slope area corresponding to the key structure of the building construction model in the characteristic section; Step S201: Calculate the construction weight value corresponding to the key slope area and the general slope area according to the preset precision construction weight model, and match the model construction precision level corresponding to the construction weight value in the preset model construction precision database; Step S202: Based on the model construction precision level, perform three-dimensional model construction of the virtual three-dimensional slope model with different precision.

[0068] Wherein, the precision construction weight model is calculated by the following formula: ; Wherein, is the construction weight value, is the preset regional type weight coefficient, indicating the key degree of the corresponding area of the characteristic section, is the building key structure influence weight, is the preset distance attenuation coefficient of the key slope area and the building key structure, is the set structure surface influence weight coefficient, is the set structure surface development density coefficient, is the set terrain complexity weight coefficient, which is obtained by analyzing the rock mass structure model, The deformation sensitivity weight coefficient of the set region is obtained by a table lookup method, and D is a slope body deformation sensitivity index.

[0069] Referring to Figure 4 The section segmentation strategy adopts the following method steps: Step S300: Integrate the rock occurrence, joint fissure distribution, and slope height data of the slope terrain collected by radar scanning to generate slope terrain point cloud data, and construct a virtual three-dimensional slope model; Radar scanning is a technology that uses radar waves to non-contact measure and image target objects. For example, LiDAR (Laser Imaging Detection and Ranging) and ground radar. It can penetrate certain vegetation cover and obtain high-precision three-dimensional spatial information of the ground and rock surface.

[0070] Rock occurrence refers to parameters describing the spatial orientation of rock layers in the stratum, usually including the strike and dip angle of the rock layer. The strike is the direction of the intersection line of the rock layer and the horizontal plane, and the dip angle is the maximum slope angle and the direction of the inclination of the rock layer.

[0071] Joint fissure distribution refers to the spatial position, density, strike, dip angle, and mutual relationship between various cracks, faults, and other structural planes in the slope rock mass. These structural planes often serve as the basis for potential sliding surfaces of the slope.

[0072] Slope height refers to the height perpendicular to the horizontal plane at a certain position of the slope, i.e., the distance from the slope foot to the slope top.

[0073] Data integration is the process of collecting, calibrating, and registering various geological and topographic data collected from different sensors such as radar, GPS, and total station to form a unified and coordinated data set.

[0074] Slope terrain point cloud data is the raw data obtained by radar scanning or other three-dimensional scanning techniques. It consists of a large number of three-dimensional coordinate points, each representing a specific position on the slope surface and can be used to accurately depict the undulating shape of the slope.

[0075] The virtual three-dimensional slope model is a three-dimensional geometric model constructed by professional modeling software based on the integrated point cloud data. For example, using CAD, GIS software, and geological modeling software for modeling. This model can visually display information such as the real terrain, rock layer interface, and structural plane distribution of the slope.

[0076] In execution, the system deploys radar scanning devices, such as unmanned aerial laser radars, to systematically scan the target slope. During the scanning process, the device emits radar signals towards 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, the device's sensors, such as IMU and GPS, record the attitude and position information during scanning, which is used for geographic registration of the scanning data. In addition, if conditions permit, handheld radars or exploration drilling data may also be used to obtain detailed information about rock layer attitudes and joint fissures. All collected point cloud data coordinates and related geological parameters, including rock layer attitudes, 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 attitudes, 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.

[0077] Step S301: Perform geological feature analysis based on the virtual three-dimensional slope model to obtain multiple different geological feature segments of the virtual three-dimensional slope model, and generate a feature segment database; Geological feature analysis refers to 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.

[0078] Different geological feature 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, geometric shapes such as slope, slope height, slope direction, etc. These regions are referred to as feature 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.

[0079] The feature segment database is a structured data set that stores information related to each identified feature segment. This database typically includes the number of each feature segment, boundary information, main lithology, rock mass structural characteristics such as the occurrence of dominant structural planes, density, slope height, slope direction, slope, and other key descriptive attributes.

[0080] In execution, the system receives the virtual three-dimensional slope model generated by S300. Then, according to the pre-set section segmentation strategy, the model is analyzed for geological features. This usually involves cutting profiles in the model and analyzing the lithology changes, distribution density and strike of structural planes on each profile. Meanwhile, the geometric parameters such as slope height, slope gradient, slope aspect of each region are also analyzed. Based on these analysis results, the system will divide the slope into several corresponding regions of feature segments with similar geological features. For example, the system may identify a region with sandstone lithology, the main structural plane consistent with the slope, and a large slope gradient. Another region may be mudstone with less structural plane development and a gentle slope. Each identified feature segment and its related geological feature information will be stored in a special feature segment database, providing structured input for subsequent analysis.

[0081] Step S302: Analyze the geological features 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; The adjacent geological complexity coefficient value is a quantitative indicator that measures the difference in geological properties between two adjacent feature segments. Its calculation may be based on multiple factors, such as: lithology difference, structural plane occurrence difference, slope gradient and aspect difference, and geological interface smoothness or tortuosity. 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.

[0082] Filtering means comparing and sorting all calculated adjacent geological complexity coefficient values to find the one with the highest or lowest value.

[0083] 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 attention-worthy links in slope stability.

[0084] In specific implementation: the system first traverses the feature segment database generated in step S301 to identify all pairs of adjacent feature segments. For each pair of adjacent feature segments, the system will apply a pre-defined geological complexity evaluation model to calculate their adjacent geological complexity coefficient value based on their internal geological features such as lithology, structural plane occurrence, slope gradient, and slope aspect. For example, if one feature segment is a steep fractured rock layer and its adjacent feature segment is a gentle intact rock layer, their complexity coefficient value will be high. Conversely, if two adjacent feature segments are very similar in geology, the complexity coefficient value will be low.

[0085] After calculating the complexity coefficient values of all adjacent feature segments, the system sorts these values and selects the one 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 revealing the overall stability of the slope because of its unique geological performance that is most different from the surrounding environment.

[0086] 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 subsequent stability calculation resources on the most critical area, improve the efficiency and pertinence of analysis.

[0087] Step S303: Trigger the stability coefficient calculation module based on the representative geological segment to calculate the slope stability coefficient.

[0088] The representative geological segment is determined according to the selection of the adjacent geological complexity coefficient value in step S302, and it is a feature segment with geological representation. The geological characteristics of this paragraph are most likely to have a key impact on the stability of the entire slope.

[0089] Triggering the stability coefficient calculation module is to pass the calculation task to a module specially used for slope stability analysis. This module usually uses various mechanical analysis methods such as limit equilibrium method and numerical simulation to calculate the stability coefficient of the slope according to the input geological model, working condition parameters, etc.

[0090] Slope stability coefficient calculation is to quantitatively evaluate the ability of the slope to resist sliding or deformation through scientific mechanical calculation method. 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.

[0091] In implementation, the system takes the representative geological segment determined in step S302 as the core input and passes it to the independent stability coefficient calculation module. In the process of passing, the geological model of the representative feature segment and various slope working condition parameters that may affect its stability, such as water pressure, seismic 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 segment. 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 feature segment under the specific working condition.

[0092] Reference Figure 5 The slope segmentation module is also configured with a virtual three-dimensional slope model verification sub-strategy, including the following steps: Step S400: Determine the contour scanning points at different heights by slope height analysis and calculation, and arrange the laser scanners at preset interval distances for scanning to obtain the data set of slope terrain point cloud data; 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.

[0093] Contour scanning points are three-dimensional coordinate points on the slope surface that are accurately measured, and they collectively constitute the geometric contour of the slope terrain.

[0094] 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.

[0095] The laser scanner is an advanced terrain measurement device that accurately obtains the 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.

[0096] 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.

[0097] In specific implementation, first, the slope is subjected to slope height analysis and calculation 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 positions where the slope changes significantly. Subsequently, according to the preset interval distance, the laser scanners are uniformly arranged between these key points. These scanners will work synchronously, emit laser beams, and receive reflected signals, and record 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.

[0098] Step S401: Compare and analyze a number of virtual three-dimensional slope models constructed based on the data sets to determine the difference proportion of the model difference area, and when the difference proportion is higher than the preset allowed difference proportion, trigger the point cloud data optimization instruction; The data set is the collection of slope terrain point cloud data collected by the laser scanner in step S300.

[0099] The number of 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.

[0100] The comparison analysis is to compare the virtual three-dimensional slope models constructed from different sources or processing methods to assess their similarities and differences.

[0101] The model difference area refers to the area where two or more models have significant differences in geometry, key feature point positions, etc. during the comparison process.

[0102] The difference ratio refers to the proportion of the model difference area to the overall model area. This ratio value quantifies the inconsistency between the models.

[0103] The preset allowed difference ratio is a threshold that specifies the maximum acceptable difference between models in model comparison analysis. Exceeding this threshold indicates insufficient consistency between models and requires correction.

[0104] 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 reacquisition or optimization of part of the point cloud data.

[0105] 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 repeating scanning 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 to the total area or total volume of the model is calculated. If the difference ratio exceeds the preset allowed difference ratio, for example, if the height difference of two models at a 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 assess the consistency of slope models generated by different data sources or processing methods, identify possible measurement or processing errors, and provide a basis for model optimization decisions.

[0106] Step S402: Re-collect 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 ratio below the allowed difference ratio.

[0107] The point cloud data optimization instruction is issued by step S401, indicating the need to reacquire or process the slope terrain point cloud data.

[0108] 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 around the model difference areas identified in S401 or around these areas.

[0109] The purpose of keeping the model difference ratio below the allowable difference ratio is to improve the consistency between all constructed slope models by optimizing the point cloud data, control the difference ratio within an acceptable range, and ensure that the finally constructed virtual three-dimensional slope model is accurate and reliable.

[0110] In the implementation process, when the point cloud data optimization instruction is triggered in 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 perform more detailed and repetitive scanning on the contour scanning points in these areas. 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 reconstruct the virtual three-dimensional slope model based on the optimized data set and perform comparison analysis again. Repeat this process until the difference ratio between all constructed models is below the preset allowable difference ratio.

[0111] The purpose of this step is to actively correct and improve the data quality, and ensure that the finally constructed virtual three-dimensional slope model can accurately reflect the topography and geological features of the actual slope.

[0112] The safety factor analysis model in the stability coefficient calculation module at least includes: A plane sliding calculation model is configured to calculate the slope stability coefficient according to the weight of the sliding body, the anti-sliding force and the sliding force of the feature section; The plane sliding calculation model includes: The plane sliding calculation model is configured with a plane sliding slope stability coefficient calculation formula: ; ; ; ; ; Wherein, is the slope stability coefficient, is the sliding force of the feature section corresponding to the unit width gravity and other external forces of the sliding body, is the anti-sliding force of the feature section corresponding to the unit width gravity and other external forces of the sliding body, is the unit width self-weight of the feature section corresponding sliding body, is the unit width vertical additional load of the feature section corresponding sliding body, is the inclination angle of the feature section corresponding sliding surface, is the total water pressure per unit width of the feature section, is the total water pressure per unit width of the feature section corresponding to the rear edge steep crack surface, is an internal friction angle of the sliding surface, is a corresponding sliding body sliding surface cohesion of the characteristic segment, is a sliding surface length of the characteristic segment corresponding sliding body, is a characteristic segment corresponding sliding body unit width horizontal load, is a characteristic segment corresponding water filling height on the trailing edge steeply inclined fracture surface, is a characteristic segment corresponding water body specific weight.

[0113] The three-dimensional wedge calculation model is used to calculate the slope stability coefficient according to the wedge weight of the characteristic segment, the shear strength of the structural surface, and the fracture water pressure; The three-dimensional wedge calculation model includes: The three-dimensional wedge calculation model is configured with a three-dimensional wedge slope stability coefficient calculation formula based on the three-dimensional wedge calculation model: ; ; ; ; wherein, is a three-dimensional wedge gravity corresponding to the characteristic segment, is a three-dimensional wedge measured height corresponding to the characteristic segment, is a three-dimensional wedge height corresponding to the characteristic segment, is a stability coefficient of the rock mass, A, B, C, and D represent the coordinates of the virtual vertex positions of the wedge ABD and BCD respectively, and c1 and c2 are the unit cohesion of the two structural surfaces respectively, is a sine inclination angle of the structural surface corresponding to the three-dimensional wedge, is a cosine inclination angle of the structural surface corresponding to the three-dimensional wedge, represents the tangent value of the internal friction angle of the two structural surfaces.

[0114] 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 stability type structure.

[0115] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of functional modules is exemplified, and in actual application, the above-mentioned 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.

[0116] The embodiment of the present application provides a computer readable storage medium, which stores a computer program capable of being loaded by a processor and executing a slope section stability analysis system.

[0117] The computer storage medium includes, for example, a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media capable of storing program codes.

[0118] Based on the same inventive concept, the embodiment of the present application provides an intelligent terminal, which comprises a memory and a processor, and the memory stores a computer program capable of being loaded by the processor and executing the slope section stability analysis system.

[0119] Those skilled in the art can clearly understand that, for the convenience and brevity, only the division of the above functional modules is taken as an example for description, 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 above-described system, device and unit can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0120] The above are preferred embodiments of the present application, and the protection scope of the present application is not limited thereto, any feature disclosed in the specification (including the abstract and the drawings) can be replaced by other equivalent or similar features unless specifically described. That is, each feature is only an example of a series of equivalent or similar features unless specifically described.

Claims

1. A slope cross-sectional stability analysis system, characterized in that, include: The slope segmentation module is configured with a cross-section segmentation strategy. It constructs a virtual three-dimensional slope model by scanning the geological contour of the slope, and analyzes the geological features, slope height, and slope aspect of the virtual three-dimensional slope model to generate several feature segments. The multi-model analysis module is equipped with an analysis model library, which stores several slope spatial analysis models. The structural surfaces of characteristic segments are analyzed through the slope spatial analysis models to determine the corresponding rock mass structure model. The working condition correction module is equipped with a slope working condition parameter library, which includes parameters such as slope self-weight, surface load, fissure water pressure under rainstorm, and seismic force. The slope working condition parameter library establishes a parameter mapping relationship with the corresponding feature segment. The stability coefficient calculation module is equipped with a safety factor analysis model. It performs analysis based on 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.

2. The slope cross-sectional stability analysis system according to claim 1, characterized in that, It also includes a slope stability optimization module, which is equipped with a support distribution model. When the slope stability coefficient of a characteristic segment is lower than the preset safety factor value, the support distribution parameters are generated by feature identification based on the rock mass structure model. Based on the working type corresponding to the slope working parameters, the support optimization parameters in the preset support optimization database are matched, including anchor spacing, anchor length and slope ratio. The optimal support distribution parameters are obtained by adjusting the support optimization parameters and the slope support distribution parameters of the corresponding characteristic sections, so as to keep the slope stability coefficient higher than the preset safety factor value.

3. The slope cross-sectional stability analysis system according to claim 1, characterized in that, The slope segmentation module is also configured with a virtual 3D slope model construction accuracy grading strategy, including: Based on the preset building construction model and feature segments, a positional comparison analysis is performed to determine the key slope areas and conventional slope areas in the feature segments that correspond to the key structures of the building construction model. The weight model is constructed with a preset accuracy to calculate the construction weight values ​​corresponding to the key slope area and the ordinary slope area, and then matched with the model construction accuracy level corresponding to the construction weight values ​​in the preset model construction accuracy database. Based on the model construction accuracy level, three-dimensional models of virtual three-dimensional slope models with different levels of accuracy are constructed.

4. The slope cross-sectional stability analysis system according to claim 3, characterized in that, The accuracy-based weighted model is calculated using the following formula: ； in, To construct weight values, These are preset region type weight 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.

5. The slope cross-sectional stability analysis system according to claim 1, characterized in that, The cross-section segmentation strategy includes: Data on the rock strata attitude, joint and fissure distribution, and slope height of the slope were collected by radar scanning, integrated to generate point cloud data of the slope topography, and a virtual three-dimensional slope model was constructed. Based on the geological feature analysis of the virtual 3D slope model, multiple feature segments with different geological characteristics of the virtual 3D slope model are obtained, and a feature segment database is generated. Geological feature analysis is performed based on the feature segments to determine the adjacent geological complexity coefficient values ​​of different types of geological feature segments and to select the feature segment with the highest coefficient value as the representative geological segment. The slope stability coefficient is calculated based on the representative geological segmentation trigger stability coefficient calculation module.

6. The slope cross-sectional stability analysis system according to claim 5, characterized in that, It also includes a virtual 3D slope model verification sub-strategy, which includes the following steps: By performing slope height analysis and calculation, contour scanning points at different heights are determined, and laser scanners are arranged at preset intervals to scan and obtain a data set of slope topographic point cloud data. Comparative analysis is performed on several virtual 3D slope models constructed based on the data set to determine the proportion of differences in the model difference areas. When the proportion of differences is higher than the preset allowable proportion of differences, a point cloud data optimization command is triggered. Based on the point cloud data optimization instructions, the corresponding slope terrain point cloud data group for the corresponding contour scan points is re-collected to keep the model difference ratio below the allowable difference ratio.

7. The slope cross-sectional stability analysis system according to claim 1, characterized in that, The safety factor analysis model includes at least: The planar sliding calculation model calculates the slope stability coefficient based on the self-weight of the sliding body, the anti-sliding force, and the sliding force of the characteristic segment. The three-dimensional wedge calculation model calculates the slope stability coefficient based on the wedge weight of the characteristic segment, the shear strength of the structural surface, and the fissure water pressure. 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.

8. The slope cross-sectional stability analysis system according to claim 7, characterized in that, The planar sliding calculation model includes: The planar sliding slope stability coefficient calculation formula is configured based on the planar sliding calculation model. ； ； ； ； ； 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. This represents the cohesion of the sliding surface of the characteristic segment. 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.

9. A slope cross-sectional stability analysis system according to claim 8, characterized in that, The three-dimensional wedge calculation model includes: The calculation model based on the three-dimensional wedge includes a formula for calculating the stability coefficient of the three-dimensional wedge slope. ； ； ； ； in, For the three-dimensional wedge-shaped gravity corresponding to the feature segment, The measured height of the three-dimensional wedge corresponding to the feature segment. The height of the three-dimensional wedge corresponding to the feature segment. Let be the stability coefficient of the rock mass, A, B, C, and D represent the virtual vertex coordinates of wedges ABD and BCD, respectively, and c1 and c2 represent the unit cohesion of the two structural planes. To correspond to the sinusoidal tilt angle of the structural surface of the three-dimensional wedge, To correspond to the cosine tilt angle of the structural surface of the three-dimensional wedge, This represents the tangent of the friction angle between two structural surfaces.

10. A slope cross-sectional stability analysis system according to claim 7, characterized in that, The model matching rules of the analysis model library include: When a feature segment has a single structural surface, a planar sliding model is used for matching. When there are two sets of intersecting structural surfaces in a feature segment, a three-dimensional wedge model is matched. When the spatial relationship between the structural surface and the slope needs to be analyzed for the feature segment, the stereographic projection model is matched.

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