Intelligent monitoring system and monitoring method for ecological environment of rock slope of highway in cold region

Abstract

The invention relates to the technical field of slope ecological monitoring, in particular to an intelligent monitoring system and method for the ecological environment of a rock slope of a highway in a cold region. According to the method, the high-precision digital elevation model is efficiently generated through an unmanned aerial vehicle remote sensing and ground measurement fusion technology, and a reliable basis is provided for analysis; based on a freeze-thaw attenuation coefficient fitted in a laboratory, the cohesive force and the internal friction angle of the rock mass are dynamically corrected, and the accuracy of stability analysis of the slope in the cold region is improved; the geological safety coefficient, the vegetation index and the soil erosion index are fused and weighted, a unified ecological risk evaluation model is constructed, integrated and intelligent comprehensive monitoring and risk evaluation are carried out on the slope geological stability and the ecological environment condition, and decision-making assistance is provided for the cold region slope ecological environment.

Description

Technical Field

[0001] This invention relates to the field of slope ecological monitoring technology, specifically to an intelligent monitoring system and method for the ecological environment of rock slopes along highways in cold regions. Background Technology

[0002] The stability and ecological protection effectiveness of rock slopes along highways in cold regions are key issues affecting the safety and sustainable development of transportation infrastructure. Shaanxi Province is long and narrow from north to south, with distinct climatic zones. The Shaanbei Plateau and northern Guanzhong Plain are typical temperate continental semi-arid and semi-humid climates, characterized by long, cold winters and hot, rainy summers, with significant annual and daily temperature variations. These unique climatic conditions, especially the frequent freeze-thaw cycles, severely impact highway rock slopes.

[0003] Intense freeze-thaw cycles are the main geological force causing the deterioration of rock slopes. In the cold regions of Shaanxi, winter temperatures can remain below 0°C for extended periods, leading to repeated freezing and thawing of water in rock fissures and joints. When water freezes, its volume expands, generating enormous frost heave pressure that continuously widens existing fissures and creates new microcracks. After the ice melts, water carrying erosion materials further infiltrates, softening the rock mass structure. After dozens or even hundreds of freeze-thaw cycles throughout the winter and into early spring, the rock mass's structural strength significantly decreases, its integrity is compromised, and ultimately, this triggers geological disasters such as surface spalling, fragmentation, and even large-scale collapses, directly threatening highway safety and smooth traffic flow.

[0004] Currently, ecological and stability monitoring of rock slopes along highways in cold regions mainly relies on static geological surveys or limited mechanical parameter testing, often failing to consider the long-term deterioration of the strength and frictional properties of rock and soil caused by the unique freeze-thaw cycle in cold regions.

[0005] Although UAV remote sensing monitoring can acquire terrain information over a wide area, point cloud data often suffers from low accuracy and high noise, resulting in unreliable 3D terrain models and limiting the precision of risk assessment.

[0006] In ecological monitoring, common vegetation cover or soil erosion analyses are usually conducted independently, lacking a comprehensive evaluation system coupled with geological stability. Therefore, they are insufficient to fully reflect the true risk status of slopes. These problems result in limitations in current technologies when dealing with highway slopes in cold regions, often leading to incomplete monitoring results, insufficient accuracy, and delayed early warnings. Summary of the Invention

[0007] To address the aforementioned shortcomings of existing technologies, this invention provides an intelligent monitoring system and method for the ecological environment of rock slopes along highways in cold regions. This system effectively solves the problem of accurate risk monitoring and early warning for rock slopes along highways in cold regions under complex freeze-thaw conditions, which is difficult to achieve in existing technologies.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] This invention provides an intelligent monitoring method for the ecological environment of rock slopes along highways in cold regions, comprising at least:

[0010] A three-dimensional terrain model is constructed using slope point cloud data;

[0011] Sampling points were set up to collect soil and rock samples, and their mechanical parameters were determined. The fracture density of the rock surface was also measured to establish a material parameter matrix. The mechanical parameters include:

[0012] Density, cohesion, internal friction angle, and permeability coefficient;

[0013] Based on the cumulative number of freeze-thaw cycles at the sampling points, the cohesion and internal friction angle in the material parameter matrix are corrected, and the material parameter matrix is ​​updated.

[0014] The safety factor of the slip surface is calculated iteratively based on the simplified Bishop method, and potential landslide hazard zones are identified based on the safety factor.

[0015] Calculate the vegetation index and soil erosion index in the landslide hazard zone;

[0016] The ecological risk level of the slope is analyzed by combining the safety factor, vegetation index and soil erosion index.

[0017] Furthermore, the method for constructing a three-dimensional terrain model using slope point cloud data is as follows:

[0018] The slope point cloud collected by aerial photography equipment is fused with the ground measurement point cloud using the iterative nearest point method to output the fused point cloud;

[0019] Noise filtering is applied to the fused point cloud to remove isolated and outlier points, generating an updated fused point cloud.

[0020] The update method for fused point clouds is as follows:

[0021] Construct a neighborhood set for each point in the fused point cloud;

[0022] If the number of neighbors of a point is lower than the threshold, the point is determined to be an isolated point and is removed.

[0023] Calculate the average elevation and standard deviation of the neighborhood set of the remaining points;

[0024] If the difference between the elevation of a point and the average elevation is greater than a threshold, the point is determined to be an anomaly and removed, and the fused point cloud is updated.

[0025] Furthermore, the method for measuring the density of rock surface fractures is as follows:

[0026] Several survey points were set up on the slope surface;

[0027] Record each visible crack at each survey point:

[0028] The crack density is defined based on the crack length within the survey point and the area of ​​the survey area at the survey point.

[0029] Furthermore, the method for correcting the cohesion and internal friction angle is as follows:

[0030] Obtain the original cohesive force and internal friction angle of the sampling point before freezing and thawing;

[0031] The cohesion and internal friction angle are updated by introducing an exponential decay of the cumulative freeze-thaw cycles at the sampling point location;

[0032] The exponential decay also includes coefficients for the decay of cohesion and internal friction angle caused by freeze-thaw cycles, wherein the coefficients are obtained by establishing a univariate linear regression model.

[0033] Furthermore, the method for determining the safety factor is as follows:

[0034] Step 1: Determine the potential slip surface based on the 3D terrain model;

[0035] The soil and rock mass above the slip surface is divided into several slices;

[0036] Extract set parameters for each slice, including:

[0037] Weight, base length, and base angle;

[0038] The weight is calculated based on the unit weight of the soil and rock mass, the bottom area of ​​the slice, and the thickness of the slice.

[0039] The effective normal stress is calculated based on the pore water pressure at the bottom of the slice and the bottom area of ​​the slice, and by introducing the normal force at the bottom of the slice.

[0040] Step 2: Set the initial safety factor of the slip surface where the slice is located as the starting point for iteration;

[0041] Step 3: Construct the single-slice friction force from cohesion, internal friction angle, and contribution correction term;

[0042] Step 4: Calculate the driving force of the slice along the sliding direction based on the weight and bottom inclination angle; define the total anti-slip force based on the sum of the components of the friction force of a single slice along the sliding direction; define the total driving force based on the sum of the components of the slice weight along the sliding direction.

[0043] The safety factor is determined based on the total anti-skid force and the total driving force.

[0044] Step 5: Calculate the iteration difference, determine whether convergence has occurred based on the iteration difference, and output the final safety factor; if convergence fails, return to step 3 to continue iteration.

[0045] Furthermore, the frictional force of the single slice is constructed based on the bottom inclination angle, the internal friction angle, and the iterative safety factor.

[0046] Furthermore, the method for determining the soil erosion index is as follows:

[0047] Rainfall factors are constructed based on rainfall events;

[0048] Obtain soil erodibility;

[0049] Slope length and slope angle are calculated based on the terrain factor formula to construct the slope length factor;

[0050] Vegetation cover factor is mapped based on vegetation index;

[0051] Water and soil conservation measures factors are constructed based on engineering management measures;

[0052] The soil erosion index is calculated by combining multiple of the aforementioned factors.

[0053] Also provided:

[0054] An intelligent monitoring system for the ecological environment of rocky slopes along highways in cold regions, applied to the aforementioned intelligent monitoring method, includes:

[0055] The terrain construction module builds a 3D terrain model using slope point cloud data;

[0056] The material parameter update module collects rock and soil samples to determine their mechanical parameters, measures the fracture density of the rock surface, and establishes a material parameter matrix. The mechanical parameters include:

[0057] Density, cohesion, internal friction angle, and permeability coefficient;

[0058] Based on the cumulative number of freeze-thaw cycles at the sampling points, the cohesion and internal friction angle in the material parameter matrix are corrected, and the material parameter matrix is ​​updated.

[0059] The safety factor analysis module calculates the safety factor of the slip surface iteratively based on the simplified Bishop method and identifies potential landslide hazard zones based on the safety factor.

[0060] The ecological risk assessment module calculates the vegetation index and soil erosion index in landslide hazard areas;

[0061] The ecological risk level of the slope is analyzed by combining the safety factor, vegetation index and soil erosion index.

[0062] A computer device includes a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the method.

[0063] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method.

[0064] The technical solution provided by this invention has the following advantages compared with the known prior art:

[0065] By constructing a high-precision three-dimensional terrain model and combining it with freeze-thaw cycle tests to correct the mechanical parameters of the soil and rock, the freeze-thaw degradation effect is introduced into the stability calculation, which is more in line with the actual working conditions of slopes in cold regions. At the same time, ecological indicators such as vegetation cover changes and soil erosion are incorporated into the risk assessment, and a comprehensive ecological risk value model is established to achieve a unified evaluation of geological and ecological factors, and to realize comprehensive monitoring and accurate early warning of rock slopes for highways in cold regions under dynamic environments. Attached Figure Description

[0066] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0067] Figure 1 This is a schematic diagram of the overall method of the present invention. Detailed Implementation

[0068] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0069] In Shaanxi Province, due to its long and narrow shape from north to south, the region exhibits distinct geographical and climatic zones. Northern Shaanxi and the northern Guanzhong Plain, in particular, have a typical temperate continental semi-arid and semi-humid climate, characterized by long, cold winters, large diurnal temperature variations, and widespread seasonal permafrost. These unique climatic conditions subject the rock slopes along highways in this region to intense freeze-thaw cycles over extended periods, creating a persistent and highly destructive geological force.

[0070] The destructive mechanism of freeze-thaw cycles on rock slopes is mainly manifested as follows: water seeping into rock fissures and pores freezes into ice at low temperatures, causing it to expand and generate enormous frost heave forces, leading to the expansion of primary fissures and the initiation of new micro-fissures; as temperatures rise, the ice melts, water seeps in again, the rock structure becomes loose, and its mechanical properties deteriorate significantly. This repeated cycle of freezing, expansion, thawing, and contraction affects mechanical parameters of the rock mass, such as the internal friction angle and cohesion.

[0071] In addition, freeze-thaw cycles can damage the shallow soil structure on which slope vegetation depends for survival, exacerbate soil erosion, lead to vegetation degradation, and weaken the reinforcing effect of plant roots on the shallow soil, thus creating a vicious cycle in which declining geological stability and ecological degradation exacerbate each other.

[0072] To address the aforementioned issues, this embodiment aims to construct a high-precision digital elevation model of the slope based on multi-source data fusion technology combining UAV multispectral remote sensing, 3D laser scanning, and ground measurement. It also intelligently identifies fracture development, establishes a rock mass mechanical parameter attenuation model considering freeze-thaw cycles, and dynamically updates material properties simultaneously. Finally, the multi-source information is integrated into a simplified Bishop method for accurate geological model calculation and analysis. Finally, based on vegetation indices and soil erosion indicators, a comprehensive risk assessment system for geomechanical stability and ecological environment health is constructed, providing decision-making support for the ecological environment of rock slopes.

[0073] The present invention will be further described below with reference to embodiments.

[0074] Example 1 (see) Figure 1 A method for intelligent monitoring of the ecological environment of rock slopes along highways in cold regions, comprising at least:

[0075] For the slope planning route, a UAV flies along the planned route and uses a multispectral camera to collect slope point clouds, recording the three-dimensional coordinates of each point to establish a point cloud dataset. GPS / IMU information is used to perform preliminary registration of the point cloud collected by the UAV. The ground measurement point cloud and the UAV point cloud are then fused using the Iterative Closest Point (ICP) algorithm to obtain a fused point cloud. The overall registration accuracy of the fused point cloud is guaranteed to be within 5-10 cm. This provides a unified coordinate for generating a 3D DEM. It should be noted that the fused point cloud needs to be noise filtered to remove isolated and outlier points.

[0076] The method for performing noise filtering on the fused point cloud to remove isolated and outlier points and generate an updated fused point cloud is as follows:

[0077] For each fused point cloud, construct its neighborhood set:

[0078] N i ={p j ∈P|||pj -p i ||<r}

[0079] Where, N i Represents the neighborhood set, containing all elements in p. i Points within a neighborhood radius r, i = 1, ..., N, where N represents the total number of points in the fused point cloud, p i p represents the i-th fused point cloud. j Indicates with p i The fused point cloud within the neighborhood, where P represents the set of fused point clouds, r represents the neighborhood radius, determined based on the density of the fused point cloud (e.g., 0.5–2 m), ||p j -p i || indicates that the calculation is performed using Euclidean distance.

[0080] Determine the number of neighbors for each point |N i | is usually the number of elements in the neighborhood set;

[0081] If |N i |<k min k min If the threshold is represented, then p is determined. i Isolated points, due to errors in UAV / ground measurements, may result in floating points. Isolated points can cause mesh spikes and abnormal slopes, affecting subsequent simulation analysis. Therefore, isolated points are removed to ensure the local continuity of the fused point cloud. This is achieved through anomaly detection, including:

[0082] Calculate the average elevation:

[0083]

[0084] in, p i The average elevation of the surrounding neighboring points, z j Represents the neighborhood point p j Elevation;

[0085] Calculate the standard deviation:

[0086]

[0087] Where, σ i p i The neighborhood elevation standard deviation, measuring p i The greater the standard deviation of the elevation dispersion in the neighborhood, the greater the local elevation fluctuation. i p i Elevation (vertical coordinates);

[0088] So, if λ·σ iLet λ represent the threshold term used for judgment, and λ represent the decision coefficient (usually taken as 2-3, reflecting λ times the standard deviation). Then, the decision p... i This is an outlier;

[0089] Therefore, isolated points and outliers can be removed from the fused point cloud set P to generate an updated fused point cloud set P′.

[0090] A preliminary terrain mesh is generated using triangulation or Triangulated Lrregular Network methods. The mesh density is adjusted according to the slope characteristics; typically, the mesh is denser in areas with more rock fissures and sparser in gentler areas. A preliminary triangular mesh DEM is then output. Elevation interpolation (such as Kriging or bilinear interpolation) is used to smooth the preliminary triangular mesh DEM to eliminate measurement noise. High resolution is maintained for important potential landslide areas, while minor areas can be appropriately downsampled. Elevation continuity and slope consistency are checked, and then a high-precision three-dimensional terrain model DEM can be output.

[0091] Based on the constructed three-dimensional terrain model (DEM), sampling points can be set up along the edge of the soil and rock mass every 50-100m to collect samples. Then, the physical and mechanical parameters of the soil and rock mass samples are determined in the laboratory, including the density (drying-weighing method or bulk density method), cohesion, internal friction angle (triaxial shear test), and permeability coefficient (variable head permeability test). Then, the density of rock surface fractures is measured by laser scanner, thereby generating a material parameter matrix, including density, cohesion, internal friction angle, permeability coefficient, and fracture density.

[0092] The method for measuring fracture density is as follows:

[0093] Several survey points were set at fixed intervals on the slope surface, covering areas with different slopes, orientations, and lithologies to avoid sample point bias. Then, for each survey point, each visible crack was recorded.

[0094] Length L d (Actual length along the fracture), aperture (maximum gap of the fracture), and dip angle (angle of inclination of the fracture surface);

[0095] Define fracture density:

[0096]

[0097] Among them, D a L represents the fracture density at the survey point. d A represents the length of the d-th fracture within the survey point. a This indicates the area of ​​the surveyed region at the survey point.

[0098] Furthermore, with the accumulation of freeze-thaw cycles, the number of microcracks inside the soil and rock mass gradually increases, and the repeated freezing and expansion followed by thawing and shrinkage of pore water causes the shear strength parameters, namely cohesion and internal friction angle, to gradually deteriorate. Therefore, the cohesion and internal friction angle of the soil and rock mass are adjusted according to the number of freeze-thaw cycles at the sampling points.

[0099] c(t) = c0e -αF(t)

[0100] φ(t)=φ0e -βF(t )

[0101] Where c(t) and φ(t) represent the cohesion and internal friction angle at the t-th cycle, respectively; e represents the natural constant; c0 and φ0 represent the original cohesion and internal friction angle of the sampling point before freezing and thawing, respectively; F(t) represents the cumulative number of freeze-thaw cycles at the sampling point; and α and β represent the coefficients of cohesion and internal friction angle attenuation caused by each freeze-thaw cycle. It should be noted that α and β usually need to be estimated through laboratory fitting, i.e., freeze-thaw cycle tests are conducted on the soil and rock samples in the laboratory, such as triaxial tests or direct shear tests after 0, 10, 20, and 50 freeze-thaw cycles, to obtain the test data points (including cohesion, internal friction angle, and internal friction angle). Since the cohesion decays exponentially, a natural logarithmic transformation is applied to the cohesion. After the transformation, new data points are constructed (cohesion logc after natural logarithmic transformation and number of freeze-thaw cycles). A univariate linear regression model is established, with the number of freeze-thaw cycles as the independent variable and logc as the dependent variable. By running the regression, regression coefficients (intercept and slope) are obtained. The intercept of the regression coefficients is mapped to the cohesion before freeze-thaw. The slope is then transformed by changing its sign, i.e., taking its negative value, to obtain the decay intensity, i.e., α. Similarly, the same applies to β. In this way, the optimal estimates of α and β can be obtained, and thus the updated material parameter matrix can be obtained.

[0102] Furthermore, the 3D terrain model (DEM) calculates the safety factor of the slip surface for each slice (describing a small volume of the slope, i.e., a slice along the potential slip surface) using the simplified Bishop method, resulting in:

[0103] Step 1: Determine the potential slip surface based on the 3D terrain model (DEM). This is usually done by defining the arc with the center, radius, and start and end points, and then connecting them with piecewise curves.

[0104] The soil and rock mass above the slip surface is divided into multiple slices, numbered with index b = 1, ..., m, where m represents the total number of slices;

[0105] Therefore, set parameters can be extracted for each slice, including:

[0106] Weight W b =γ b ·A b ·hb

[0107] Where, γ b A represents the unit weight of soil and rock mass. b h represents the base area of ​​the slice. b Indicates the slice thickness;

[0108] Base length l b and the bottom inclination angle α b ;

[0109] Calculate the effective normal stress:

[0110]

[0111] Where, σ b Represents the effective normal stress, N b The normal force on the bottom surface of the slice (initially approximated as W) represents the force. b cosα b ), u b This represents the pore water pressure (pore water pressure gauge) at the bottom of the slice. It should be noted that this formula is based on the effective stress principle, and the total stress is... Separating pore water pressure from the stress in the soil and rock mass (which can be decomposed into total stress and pore water pressure), pore water pressure does not increase the friction within the soil and rock mass. Instead, it partially offsets the normal force, thereby reducing the friction contribution of the slice. Through this treatment, the influence of pore water pressure can be accurately considered when iteratively calculating the friction force and total anti-sliding force of a single slice. This makes the safety factor based on the simplified Bishop method more realistically reflect the landslide stability of the soil and rock mass under freeze-thaw cycles or saturation conditions.

[0112] Step 2: Set the initial safety factor FS of the slip plane where the slice is located. (0) =1.0, as the starting point of the iteration, set the iteration counter k=0, and set the convergence threshold, which is usually 0.001.

[0113] Step 3: In the simplified Bishop method, the normal force on the bottom surface of the slice is affected by the distribution of sliding force. There is a coupling relationship between normal stress and friction. Therefore, the friction force of a single slice needs to be corrected by iterative calculation of FS. If the static value is used directly, the overall anti-slip force will be underestimated or overestimated. By segmenting and iterating, the anti-slip force calculation is ensured to be accurate, avoiding deviation of the safety factor of the entire landslide. Thus:

[0114] For a single slice, the frictional force consists of its corresponding cohesion, internal friction angle, and contribution correction term:

[0115]

[0116] in, c represents the frictional force (shear strength) of the slice in the k-th iteration, reflecting the contribution of that slice to the overall anti-slip force. b (t) represents the cohesive strength of the slice after freeze-thaw correction, l b φ represents the length of the bottom surface of the slice. b (t) represents the internal friction angle after freeze-thaw correction, q b G represents the transverse width of the slice along the slope, and G represents the correction term considering the contribution of the normal force on the bottom surface of the slice.

[0117] for FS (k) This represents the safety factor for the k-th iteration.

[0118] Step 4: Summarize the calculation results for each segment to obtain the safety factor of the overall slip surface:

[0119] Slicing driving force T along the sliding direction b :

[0120] Tb=Wb·sin α b

[0121] The sum of the frictional forces of the individual slices along the sliding direction, i.e., the total anti-slip force R. (k) :

[0122]

[0123] Based on the sum of the components of the slice weight along the sliding direction, i.e., the total driving force D. F :

[0124]

[0125] Build a safety factor:

[0126]

[0127] Step 5: Calculate the iteration difference ΔFS to prevent iteration non-convergence or overcomputation and avoid numerical uncertainty.

[0128] ΔFS=|FS (k+1) -FS (k) |

[0129] Judgment based on iterative difference:

[0130] If the iteration difference is less than the iteration threshold, the iteration converges, and the safety factor FS of the final slip surface is output. (k+1) ;

[0131] Otherwise, continue iterating, i.e., update the iteration counter to k+1 and use FS. (k+1) Use this as the initial value for the next iteration, and return to step 3 to continue the iteration.

[0132] Therefore, the safety factor FS is compared with the preset safety threshold:

[0133] If the value is greater than the safety threshold (safety and uncertainty need to be considered, i.e., the safety threshold is usually slightly greater than 1, such as 1.1-1.3), the corresponding slip surface is generally considered to have a landslide risk, that is, the area covered by the slip surface is marked as a potential landslide hazard zone (the area covered by the slip surface is the range of soil that may slide, which can be projected on GIS or DEM to form the boundary of the landslide hazard zone), otherwise it is a stable zone.

[0134] Furthermore, since landslide hazard areas are usually accompanied by vegetation destruction, and vegetation cover is an important indicator of ecological risk, vegetation cover monitoring will be conducted in the identified landslide hazard areas:

[0135] Calculate the current vegetation index NDVI:

[0136]

[0137] Wherein, NIR represents the reflectance in the near-infrared band, and RED represents the reflectance in the red band;

[0138] Therefore, the change in the exponent is calculated as follows:

[0139] ΔNDVI=NDVI-NDVI0

[0140] Where ΔNDVI represents the change in the index, and NDVI0 represents the historical average index;

[0141] If ΔNDVI is less than 0, it indicates that the NDVI index has decreased, suggesting vegetation degradation; conversely, it indicates vegetation recovery.

[0142] Furthermore, freeze-thaw cycles and precipitation in cold regions accelerate soil erosion, further impacting slope stability and ecosystems. Therefore, the soil erosion index EI is calculated within landslide risk zones:

[0143] EI = R·K·LS·C·H

[0144] Where R represents the rainfall factor, which describes the energy contribution of rainfall to soil erosion during a certain period in the landslide hazard zone; K represents soil erodibility, which describes the sensitivity of soil to rainfall erosion, and is determined by measuring the erodibility coefficient of soil samples in the experimental field; LS represents the slope length factor, which describes the influence of slope size and slope length on soil erosion; C represents the vegetation cover factor, which describes the coefficient of vegetation's protective effect on soil; and H represents the soil and water conservation measures factor, which describes the impact of engineering management measures on reducing erosion. It is usually set according to whether there are engineering management measures such as terraces, slope protection, and retaining walls on the actual slope. The more measures there are, the larger H is, and 0 < H ≤ 1.

[0145]

[0146] Among them, E f I represents the rainfall capacity of the f-th rainfall event. f The intensity of the f-th rainfall event is represented by f, which represents the sequence number of the rainfall event. It describes each independent rainfall event within the statistical period. Soil erosion is mainly driven by the kinetic energy of falling rainfall. The energy and intensity of a single rainfall event determine the scouring force of water flow on soil particles. Since a single rainfall event is insufficient to reflect the overall erosion threat, the overall rainfall factor is obtained by accumulating multiple rainfall events within a time period, which reflects the impact of rainfall on soil erosion.

[0147]

[0148] Where L represents the slope length, which is the horizontal projection length from the top of the slope to the bottom of the slope, θ represents the slope angle, and zs represents the slope length index, which is usually 0.4-0.6. Its value depends on the slope; the larger the slope, the larger its value tends to be. The calculation formula of the slope length factor LS comes from the topographic factor calculation formula in the Universal Soil Loss Equation (USLE) and its improved version, the Revised Universal Soil Loss Equation (RUSLE). 22.13 represents the baseline slope length, which is a standardized constant derived from the original experimental conditions of USLE.

[0149] C can be mapped using the calculated vegetation index NDVI. If the NDVI index data is not in the range of 0-1, it is normalized. The vegetation cover factor C = e is obtained according to the formula. -α1NDVI α1 represents the weighting coefficient.

[0150] It should be noted that when calculating the soil erosion index EI using the above formula, all data must be normalized to a single dimension before calculation to ensure accuracy.

[0151] Finally, based on the previously calculated data, a comprehensive assessment of the slope's ecological environment risk can be made, resulting in:

[0152]

[0153] Where RI represents the ecological risk value of the slope, and FS max This represents the maximum safety factor. It reflects the intensity of geological risk, that is, the smaller the FS, the higher the risk. NDVI and EI are both normalized dimensionless data, ranging from 0 to 1. ω1, ω2, and ω3 represent the corresponding weight coefficients.

[0154] Then, the calculated slope ecological risk value RI is compared with the corresponding threshold to determine the risk level of the slope ecological environment. Generally, low risk is green (corresponding to daily inspection), medium-low risk is blue-yellow (increased frequency of drone or sensor detection), medium-high risk is orange (requires on-site verification and handling by personnel), and high risk is red (emergency, traffic restriction). This enables early warning and risk control of slope ecological environment.

[0155] Also provided:

[0156] An intelligent monitoring system for the ecological environment of rocky slopes along highways in cold regions, applied to the aforementioned intelligent monitoring method, includes:

[0157] The terrain construction module builds a 3D terrain model using slope point cloud data;

[0158] The material parameter update module collects rock and soil samples to determine their mechanical parameters, measures the fracture density of the rock surface, and establishes a material parameter matrix. The mechanical parameters include:

[0159] Density, cohesion, internal friction angle, and permeability coefficient;

[0160] Based on the cumulative number of freeze-thaw cycles at the sampling points, the cohesion and internal friction angle in the material parameter matrix are corrected, and the material parameter matrix is ​​updated.

[0161] The safety factor analysis module calculates the safety factor of the slip surface where the slice is located iteratively based on the simplified Bishop method, and identifies potential landslide hazard zones based on the safety factor.

[0162] The ecological risk assessment module calculates the vegetation index and soil erosion index in landslide hazard areas;

[0163] The ecological risk level of the slope is analyzed by combining the safety factor, vegetation index and soil erosion index.

[0164] A computer device includes a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the method.

[0165] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method.

[0166] Specifically, the intelligent monitoring method in the embodiments is described in detail here.

[0167] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the protection scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent monitoring method for the ecological environment of rocky slopes along highways in cold regions, characterized in that, include: A three-dimensional terrain model is constructed based on slope point cloud data; Sampling points were set up to collect soil and rock samples, and their mechanical parameters were determined. The fracture density of the rock surface was also measured to establish a material parameter matrix. The mechanical parameters include: Density, cohesion, internal friction angle, and permeability coefficient; Based on the cumulative number of freeze-thaw cycles at the sampling points, the cohesion and internal friction angle in the material parameter matrix are corrected, and the material parameter matrix is ​​updated. Based on the updated material parameter matrix, the safety factor of the slip surface is calculated iteratively using the simplified Bishop method, and landslide hazard zones are identified based on the safety factor. Calculate the vegetation index and soil erosion index in the landslide hazard zone; The ecological risk level of the slope is analyzed by combining the safety factor, vegetation index and soil erosion index.

2. The intelligent monitoring method for the ecological environment of rock slopes along highways in cold regions according to claim 1, characterized in that, The method for constructing a three-dimensional terrain model based on slope point cloud data is as follows: The slope point cloud collected by aerial photography equipment is fused with the ground measurement point cloud using the iterative nearest point method to output the fused point cloud; Noise filtering is applied to the fused point cloud to remove isolated and outlier points, generating an updated fused point cloud. The update method for fused point clouds is as follows: Construct a neighborhood set for each point in the fused point cloud; If the number of neighbors of a point is lower than the threshold, the point is determined to be an isolated point and is removed. Calculate the average elevation and standard deviation of the neighborhood set of the remaining points; If the difference between the elevation of a point and the average elevation is greater than a threshold, the point is determined to be an anomaly and removed, and the fused point cloud is updated.

3. The intelligent monitoring method for the ecological environment of rock slopes along highways in cold regions according to claim 1, characterized in that, The method for measuring the density of rock surface fractures is as follows: Several survey points were set up on the slope surface; Record each visible crack at each survey point: The crack density is defined based on the crack length within the survey point and the area of ​​the survey area at the survey point.

4. The intelligent monitoring method for the ecological environment of rock slopes along highways in cold regions according to claim 1, characterized in that, The method for correcting the cohesion and internal friction angle is as follows: Obtain the original cohesive force and internal friction angle of the sampling point before freezing and thawing; The cohesion and internal friction angle are updated by introducing an exponential decay of the cumulative freeze-thaw cycles at the sampling point location; The exponential decay also includes coefficients for the decay of cohesion and internal friction angle caused by freeze-thaw cycles, wherein the coefficients are obtained by establishing a univariate linear regression model.

5. The intelligent monitoring method for the ecological environment of rock slopes along highways in cold regions according to claim 1, characterized in that, The method for determining the safety factor is as follows: Step 1: Determine the potential slip surface based on the 3D terrain model; The soil and rock mass above the slip surface is divided into several slices; Extract set parameters for each slice, including: Weight, base length, and base angle; The weight is calculated based on the unit weight of the soil and rock mass, the bottom area of ​​the slice, and the thickness of the slice. The effective normal stress is calculated based on the pore water pressure at the bottom of the slice and the bottom area of ​​the slice, and by introducing the normal force at the bottom of the slice. Step 2: Set the initial safety factor of the slip surface where the slice is located as the starting point for iteration; Step 3: Construct the single-slice friction force from cohesion, internal friction angle, and contribution correction term; Step 4: Calculate the driving force of the slice along the sliding direction based on the weight and bottom inclination angle; define the total anti-slip force based on the sum of the components of the friction force of a single slice along the sliding direction; define the total driving force based on the sum of the components of the slice weight along the sliding direction. The safety factor is determined based on the total anti-skid force and the total driving force. Step 5: Calculate the iteration difference, determine whether convergence has occurred based on the iteration difference, and output the final safety factor; if convergence fails, return to step 3 to continue iteration.

6. The intelligent monitoring method for the ecological environment of rock slopes along highways in cold regions according to claim 5, characterized in that, The friction force of the single slice is constructed based on the bottom inclination angle, the internal friction angle, and the iterative safety factor.

7. The intelligent monitoring method for the ecological environment of rock slopes along highways in cold regions according to claim 1, characterized in that, The method for determining the soil erosion index is as follows: Rainfall factors are constructed based on rainfall events; Obtain soil erodibility; Slope length and slope angle are calculated based on the terrain factor formula to construct the slope length factor; Vegetation cover factor is mapped based on vegetation index; Water and soil conservation measures factors are constructed based on engineering management measures; The soil erosion index is calculated by combining multiple of the aforementioned factors.

8. An intelligent monitoring system for the ecological environment of rock slopes along highways in cold regions, applied to the steps of the method described in any one of claims 1-7, characterized in that, include: The terrain construction module builds a 3D terrain model using slope point cloud data; The material parameter update module collects rock and soil samples to determine their mechanical parameters, measures the fracture density of the rock surface, and establishes a material parameter matrix. The mechanical parameters include: Density, cohesion, internal friction angle, and permeability coefficient; Based on the cumulative number of freeze-thaw cycles at the sampling points, the cohesion and internal friction angle in the material parameter matrix are corrected, and the material parameter matrix is ​​updated. The safety factor analysis module calculates the safety factor of the slip surface iteratively based on the simplified Bishop method and identifies potential landslide hazard zones based on the safety factor. The ecological risk assessment module calculates the vegetation index and soil erosion index in landslide hazard areas; The ecological risk level of the slope is analyzed by combining the safety factor, vegetation index and soil erosion index.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-7.

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