Slope displacement monitoring method and system for loess area high and steep slope

Abstract

The invention provides a slope displacement monitoring method and system for a high and steep slope in a loess area, and the method comprises the steps: carrying out the preprocessing of data through a point cloud denoising algorithm according to an initial point cloud data set, filtering noise points and irrelevant reflection points caused by a complex terrain, and generating an optimized refined point cloud data set; aiming at the refined point cloud data set, constructing a digital surface model of the side slope by utilizing a three-dimensional reconstruction algorithm, carrying out local detail correction on the model in combination with soil loosening characteristics of a loess area, and determining a preliminary morphological structure of the side slope; performing time sequence data acquisition on the preliminary morphological structure to obtain slope three-dimensional model data of multiple time periods, and analyzing the dynamic evolution trend of morphological change to obtain slope deformation feature distribution; according to the slope deformation characteristic distribution, a finite element analysis method is adopted to conduct simulation calculation on stress distribution of the high and steep slope, the stability risk of a deformation area is judged, and potential landslide risk points are determined.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a slope displacement monitoring method and system for high and steep slopes in loess areas. Background Art

[0002] Monitoring the displacement of high and steep slopes in loess regions is a crucial component of geological disaster prevention and control. Its research is directly related to slope stability and the safety of the surrounding environment, and is crucial for protecting people's lives and property. Slope instability often leads to geological hazards such as landslides. This is particularly challenging in loess regions due to the complex terrain and loose soil. Therefore, research into methods and technologies in this area is particularly urgent.

[0003] Currently, traditional slope displacement monitoring methods rely on point measurements or simple two-dimensional analysis, which makes it difficult to fully reflect the overall morphological changes of slopes. This is especially true in complex terrain conditions such as the high and steep slopes of the Loess Plateau. Existing methods often suffer from insufficient accuracy and limited coverage. These limitations make it difficult for monitoring data to accurately capture subtle slope deformations, which in turn affects the timeliness and reliability of disaster warnings.

[0004] Against this backdrop, the primary challenge facing research is obtaining comprehensive slope information in complex terrain. Due to the undulating terrain and steep slopes of the loess plateau, traditional measurement methods struggle to cover the entire slope area, limiting the integrity of data collection. Incomplete data further hinders accurate analysis of slope morphological changes, rendering monitoring results incapable of truly reflecting the dynamic evolution of the slope. This interdependent problem, from data acquisition to analytical modeling, has become a key bottleneck hindering the advancement of monitoring technology.

[0005] Therefore, how to achieve all-round data collection of high and steep slopes in the loess region and construct high-precision morphological models through innovative technical means has become a key issue that needs to be solved urgently. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a slope displacement monitoring method and system for high and steep slopes in loess areas.

[0007] To achieve the above object, the present invention adopts the following technical solutions:

[0008] A slope displacement monitoring method for high and steep slopes in loess areas mainly includes:

[0009] Using drones equipped with high-resolution laser scanning equipment, we performed multi-angle flight missions over steep slopes in the loess region, acquiring 3D point cloud data covering the entire slope. We performed a comprehensive scan of the complex terrain, targeting topographical constraints, and generated an initial point cloud dataset.

[0010] Based on the initial point cloud dataset, a point cloud denoising algorithm is used to preprocess the data, filter out noise points and irrelevant reflection points caused by complex terrain, and generate an optimized refined point cloud dataset;

[0011] Based on the refined point cloud dataset, a digital surface model of the slope was constructed using a 3D reconstruction algorithm. Local detail corrections were performed on the model based on the loose soil characteristics of the loess region to determine the initial morphological structure of the slope.

[0012] By collecting time series data of the preliminary morphological structure, we can obtain three-dimensional slope model data for multiple periods, analyze the dynamic evolution trend of morphological changes, and obtain the distribution of slope deformation characteristics.

[0013] Based on the distribution of slope deformation characteristics, the finite element analysis method is used to simulate and calculate the stress distribution of high and steep slopes, judge the stability risk of the deformation area, and identify potential landslide risk points.

[0014] The present invention also provides a slope displacement monitoring system for high and steep slopes in loess areas, comprising:

[0015] The data acquisition module is used to carry out multi-angle flight missions over the steep slopes in the loess region using drones equipped with high-resolution laser scanning equipment. This module acquires 3D point cloud data covering the entire slope and comprehensively scans the terrain restrictions under complex terrain to obtain an initial point cloud dataset.

[0016] The point cloud preprocessing module is used to preprocess the data based on the initial point cloud dataset using a point cloud denoising algorithm to filter out noise points and irrelevant reflection points caused by complex terrain, and generate an optimized refined point cloud dataset;

[0017] The 3D modeling module is used to construct a digital surface model of the slope using a 3D reconstruction algorithm based on the refined point cloud dataset. The model is then corrected for local details based on the loose soil characteristics of the loess region to determine the initial morphological structure of the slope.

[0018] The dynamic analysis module is used to collect time series data of the preliminary morphological structure, obtain three-dimensional slope model data for multiple periods, analyze the dynamic evolution trend of morphological changes, and obtain the distribution of slope deformation characteristics;

[0019] The risk assessment module is used to simulate and calculate the stress distribution of high and steep slopes based on the distribution of slope deformation characteristics using the finite element analysis method, determine the stability risk of the deformation area, and identify potential landslide risk points.

[0020] This method uses multi-angle flight to collect high-resolution three-dimensional point cloud data, denoises and optimizes the initial data in complex terrain, and constructs an accurate digital surface model. Local corrections are performed based on loess characteristics, and dynamic trends in slope deformation are analyzed through time series analysis. Finally, finite element methods are used to simulate stress distribution, determine stability risks, and identify potential landslide points. This method achieves comprehensive scanning, precise modeling, and dynamic monitoring of high and steep slopes in loess regions, providing reliable technical support for slope stability assessment and landslide risk warning, and plays a vital role in ensuring engineering safety in loess regions. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 The present invention is a flow chart of a slope displacement monitoring method for high and steep slopes in loess areas. DETAILED DESCRIPTION

[0022] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0023] Example 1:

[0024] like Figure 1 As shown, an embodiment of the present invention provides a slope displacement monitoring method for high and steep slopes in loess areas, comprising:

[0025] In step S101, a high-resolution laser scanning device is mounted on a UAV to perform a multi-angle flight mission over a high and steep slope in the loess area to obtain three-dimensional point cloud data covering the entire slope. A comprehensive scan is performed based on the terrain restrictions under complex terrain to obtain an initial point cloud dataset.

[0026] Using a drone flight path planning system, an optimized multi-angle flight path is generated to capture comprehensive scanning data for the complex terrain features of steep slopes in the loess region. Based on this optimized path, the drone, equipped with a laser scanner, is driven to perform scanning operations, collecting 3D data of the steep slopes in high-resolution mode and forming a preliminary point cloud. Point cloud registration technology is then used to spatially align the data from the different perspectives of the multi-angle flight, generating a unified 3D point cloud dataset. De-noising is performed on this unified 3D point cloud dataset to filter out non-surface interference points in the complex terrain, resulting in a refined point cloud structure. If the refined point cloud structure contains missing data, interpolation techniques are used to supplement the missing areas to generate a complete point cloud model of the slope terrain. Based on this complete point cloud model, the distribution of steep slope features under terrain constraints is analyzed to determine topographic trends in key areas. Based on these trends, a support vector machine algorithm is used to classify the point cloud data and determine the stability distribution characteristics of the slope terrain.

[0027] For example, when planning drone paths for steep slopes in loess regions, terrain modeling software combined with digital elevation model data can analyze the slope's gradient, aspect, and elevation differences to generate a multi-angle flight path. For example, if the slope is 100 meters high and 60 degrees steep, the system can plan a layered flight path from bottom to top, with each layer separated by 5 meters. A 30-degree lateral offset angle is also set to ensure coverage of both the front and side areas of the slope. This path optimization effectively improves the comprehensiveness of the scanned data and reduces blind spots.

[0028] In one possible implementation, a drone equipped with a laser scanner can operate in high-resolution mode, with a point cloud density of 1,000 points per square meter, to capture subtle cracks or loose areas on the slope. During flight, the device records point cloud data in real time, achieving over 95% coverage. This high-precision data acquisition provides a reliable foundation for subsequent analysis.

[0029] For example, a feature-based registration algorithm can be used to align the initial point cloud data from different angles. Assuming a 2-centimeter offset between the data collected from three different viewpoints, significant feature points on the slope, such as rock corners, can be extracted and aligned to achieve a final error of less than 0.5 centimeters. This approach significantly improves the overall consistency of the point cloud data.

[0030] In one possible implementation, denoising can be achieved through statistical filtering techniques, which removes non-surface features such as vegetation or bird interference. Assuming that 10% of the points in the point cloud data are noise, filtering can eliminate 90% of the invalid data, preserving the true topographic structure of the slope. This process effectively improves data quality and lays the foundation for subsequent modeling.

[0031] For example, interpolation can be used to fill in areas with missing data based on the geometric characteristics of neighboring point clouds. For example, if 5% of the point cloud data at the top of a slope is missing due to occlusion, the missing portion can be inferred from the curvature and height trends of neighboring points to reconstruct a complete model. This approach ensures the continuity of terrain data.

[0032] In one possible implementation, when analyzing the distribution of slope topographic features, the slope change rate can be extracted in conjunction with a point cloud model to identify key areas. If a slope change rate exceeds 10%, the area can be marked as a potential landslide risk zone. This analysis helps pinpoint unstable areas.

[0033] For example, when using a support vector machine algorithm to classify point cloud data, slopes can be divided into stable and potentially unstable areas based on the point cloud's geometric characteristics and density distribution. For example, an area with low point cloud density and a sudden change in slope gradient could be classified as a high-risk area. This classification method provides a scientific basis for slope stability assessment, facilitating risk warning and management decisions.

[0034] In step S102 , a point cloud denoising algorithm is used to pre-process the data based on the initial point cloud dataset, thereby filtering out noise points and irrelevant reflection points caused by complex terrain, and generating an optimized refined point cloud dataset.

[0035] A preliminary analysis of the initial point cloud data reveals distribution characteristics and outlier distribution, and determines the initial range of the noise point set. Based on the noise point set range determined by this preliminary analysis, the voxel grid filtering method employed in the point cloud denoising algorithm is used to grid the initial point cloud data, generating voxelized point cloud grouping data. Within the voxelized point cloud grouping data, if the point cloud density within a voxel falls below a preset threshold, it is marked as a potential noise point, and the point cloud group requiring further processing is determined. After obtaining the marked potential noise point data, statistical filtering is applied to remove outliers that deviate from the main point cloud distribution, resulting in preliminary refined point cloud data. Based on this preliminary refined point cloud data, the distribution characteristics of terrain influence and reflection interference are analyzed. If discontinuity in the point cloud distribution is detected within a specific area, secondary denoising is performed to determine an optimized point cloud subset. The optimized point cloud subsets are then integrated and smoothing techniques are used to adjust the point cloud boundaries, resulting in the final refined point cloud data. For the final refined point cloud data, analyze its point cloud quality characteristics, determine the data integrity and distribution uniformity, and generate an optimized point cloud dataset that meets business needs.

[0036] For example, when processing point cloud data of steep slopes in loess regions, preliminary analysis of the initial point cloud data is particularly important. The purpose of preliminary analysis is to identify distribution characteristics and outliers in the data. By statistically analyzing the density distribution of the point cloud, it is possible to determine which areas may contain noise.

[0037] For example, in a point cloud covering a 1,000-square-meter slope area, if the number of points in a small area is significantly lower than that in the surrounding area, it may mean that the data was obstructed or affected by equipment jitter during collection, and it should be marked as a preliminary noise point set. This analysis helps provide direction for subsequent processing.

[0038] For example, after determining the range of the noise point set, the initial point cloud data is processed using a voxel grid filtering method. Voxel grid filtering works by dividing the point cloud data into small three-dimensional grids, for example, each with a side length of 0.1 meter, and then counting the number of points within each grid. If the number of points within a grid is less than a preset threshold, such as 5 points, it is marked as a potential noise point. This method is particularly suitable for complex terrain on loess slopes and can effectively distinguish sparse noise from dense surface data.

[0039] For example, when processing voxelized point cloud grouping data, statistical filtering can be further applied to the marked potential noise points. Statistical filtering is based on the local density distribution of the point cloud and removes outliers that deviate from the main distribution.

[0040] For example, within a voxel grid, if the number of points within a 0.2-meter neighborhood radius of a point is less than 50% of the average value, it is considered an outlier and removed. This method can further refine the data and reduce the impact of terrain reflection interference.

[0041] For example, if discontinuity is detected in a specific area of ​​initially refined point cloud data, such as missing data at the top of a slope due to vegetation obstructing the data, secondary denoising can be performed. This secondary denoising can be combined with local density analysis to remove isolated point clusters and ensure data continuity.

[0042] For example, for a missing area of ​​10 square meters, after analyzing the distribution characteristics of the surrounding point cloud, it can be confirmed whether it is a non-surface interference point.

[0043] For example, when integrating the optimized point cloud subsets, smoothing technology is used to adjust the point cloud boundaries. The principle of smoothing is to adjust the coordinates of the boundary points by weighted averaging the positions of neighboring points to make them more consistent with the natural transition of the terrain.

[0044] For example, at the corners of a slope, if the point cloud boundary appears jagged, smoothing can be used to make the boundary lines more natural and improve data quality.

[0045] For example, in the quality feature analysis of the final refined point cloud data, data integrity and distribution uniformity are key indicators. Integrity can be judged by statistically analyzing the point cloud coverage.

[0046] For example, if 99% of a slope area's surface is covered by data, it is considered highly complete. This analysis helps ensure that the data meets business needs and provides a reliable foundation for subsequent terrain characterization studies.

[0047] For example, when generating an optimized point cloud dataset, the data resolution can be adjusted according to business needs.

[0048] For example, in a loess slope stability analysis, if the focus is on a critical landslide area, the point cloud density in that area can be increased to 100 points per square meter, while other areas remain at 50 points per square meter. This differentiated processing optimizes data storage and processing efficiency.

[0049] In step S103 , a digital surface model of the slope is constructed using a 3D reconstruction algorithm for the refined point cloud data set. Local details of the model are corrected based on the loose soil characteristics of the loess region to determine the preliminary morphological structure of the slope.

[0050] Using the refined point cloud dataset, a digital surface model of the slope is constructed using a 3D reconstruction algorithm to obtain preliminary surface morphological data. Based on the constructed digital surface model and the loose soil properties of the loess region, local detail areas with potential deviations in the model are identified, determining the detail ranges requiring correction. Pre-set correction rules are then used to locally adjust the digital surface model within these identified detail areas, determining the corrected surface detail data. This corrected surface detail data is then combined with the overall structural characteristics of the slope to generate a preliminary slope morphological and structural model. Using this preliminary slope morphological and structural model, the impact of loose soil properties on the morphological structure is analyzed. If the impact exceeds a preset threshold, a secondary correction is performed on the localized area to obtain adjusted morphological and structural data. Based on this adjusted morphological and structural data, the density distribution characteristics of the point cloud dataset are used to verify the local consistency of the model and determine whether the final slope morphological structure conforms to the actual terrain characteristics. This verified slope morphological and structural data, combined with the overall framework of the digital surface model, is used to generate a complete slope morphological and structural description, ultimately determining the final digital model.

[0051] For example, when constructing a digital surface model of a slope from a refined point cloud dataset, one can first analyze the slope's surface geometry by examining the spatial distribution characteristics of the point cloud data. Suppose the point cloud data covers a loess slope area, with a point density of 100 points per square meter at the top and only 30 points per square meter at the bottom due to occlusion. To address this uneven distribution, a triangulated 3D reconstruction method can be used to convert the point cloud data into a continuous surface mesh, ensuring a preliminary representation of the slope's surface morphology.

[0052] For example, when identifying model deviations based on the loose soil characteristics of loess regions, we can focus on the sudden changes in localized slope areas. For example, if we discover an area with unusually sparse point cloud distribution in the middle of a slope, this could be due to measurement deviation caused by loose soil. In this case, by comparing the point cloud density and slope changes in the surrounding areas, we can mark this area as a detailed area requiring correction, providing a basis for subsequent adjustments.

[0053] For example, when correcting for localized detail, we can use neighborhood-based smoothing. For example, if the slope in a certain area is unusually steep and inconsistent with the overall slope trend, we can smooth the height values ​​in that area by referencing the mean height of the point cloud within a 5-meter radius. This will make the surface details more consistent with the actual terrain characteristics. This approach helps improve the local consistency of the model.

[0054] For example, when generating a preliminary slope morphological and structural model, a comprehensive analysis can be conducted based on the overall structural characteristics. Assuming the slope is steep at the top and gentle at the bottom, a hierarchical structural model can be constructed by extracting key characteristic points at the top, middle, and bottom of the slope. This hierarchical approach can better reflect the morphological patterns of the loess slope and lay the foundation for subsequent analysis.

[0055] For example, when analyzing the impact of loose soil properties on morphological structure, a threshold can be set. For example, if the local deformation of the slope exceeds 5%, a secondary correction is required. For example, if a certain area of ​​the model surface has a significant depression due to loose soil, secondary adjustments can be made by increasing the local point cloud sampling density or introducing auxiliary measurement data to ensure model accuracy.

[0056] For example, when verifying the local consistency of a model, the point cloud density distribution characteristics can be used for comparison. For example, if the top of a slope has a higher point cloud density, resulting in a smooth model surface, while the bottom has a lower density, resulting in a slightly rougher surface, then comparing the morphological continuity of these two areas can determine whether further optimization is needed. This verification method helps ensure the consistency of the model with the actual terrain.

[0057] For example, to ultimately generate a complete description of the slope's morphological structure, the digital surface model can be fused with the adjusted structural data. Assuming the model generated through these steps clearly reflects the slope's hierarchical characteristics and local details, it can be used as the final result for subsequent geological analysis or engineering planning. This complete description provides reliable data support for related operations.

[0058] Step S104 , by collecting time series data of the preliminary morphological structure, obtaining three-dimensional slope model data of multiple time periods, analyzing the dynamic evolution trend of the morphological changes, and obtaining the slope deformation characteristic distribution.

[0059] Using a time series data acquisition system, data on the initial slope morphology is collected over multiple time periods to construct an initial slope morphology dataset, generating raw data records for multiple time periods. Based on this initial slope morphology dataset, 3D reconstruction technology is used to process the multi-period data to generate 3D slope models, determining the spatial morphology of the slope at different time periods. Dynamic morphological analysis methods are applied to the 3D slope models to extract differential features between models at different time periods and identify patterns in morphological evolution trends. If the morphological evolution trend exceeds a preset threshold, information processing is used to perform a deep comparison of these differential features to determine the specific distribution of abnormal deformation areas. Based on the distribution of these abnormal deformation areas and combined with the time series data, the dynamic evolution of deformation features over multiple time periods is analyzed to generate a spatiotemporal distribution map of the deformation features. Using this spatiotemporal distribution map, a support vector machine algorithm is used to classify and predict deformation trends, identifying potential high-risk areas. Based on the distribution of high-risk areas and combined with the dynamic analysis results, a comprehensive assessment of slope deformation characteristics is generated to identify key areas of concern for slope stability changes.

[0060] For example, when acquiring multi-period data on the initial slope morphology through a time series data acquisition system, one could imagine deploying multiple high-precision sensors on a slope in a loess region. At three key time points—spring, summer, and autumn—the sensors would record displacement and moisture changes on the slope surface daily, accumulating an initial dataset containing thousands of records. This data would cover the slope's morphological changes across the different seasons, providing a wealth of raw information for subsequent analysis.

[0061] For example, when using 3D reconstruction technology to process data from multiple time periods, point cloud data processing software can be used to convert the collected displacement data into 3D coordinates, thereby constructing spatial models of the slope at different time periods. For example, suppose the spring model shows an overall slope tilt of 15 degrees, while in summer, due to rainfall, the tilt increases to 18 degrees in some areas. This subtle change can be visually represented in the model, laying the foundation for morphological evolution analysis.

[0062] For example, when applying morphological dynamic analysis to extract the differences in the models across time periods, a comparison of the spring and summer models revealed an increase in displacement of approximately 5 centimeters in a local area. Combined with rainfall data, this was inferred to be deformation caused by loose soil. This analysis helps determine whether the trend in slope morphology evolution is abnormal, providing a basis for subsequent treatment.

[0063] For example, when performing a deep comparison of differential features to identify areas of abnormal deformation, we can focus on the central area of ​​the slope, where the displacement is large. Combined with time series data, we can find that the deformation in this area continues to increase after summer rainfall, reaching 1.2 times the preset threshold, thus marking it as an abnormal area. This comparison method can accurately locate problem areas.

[0064] For example, when analyzing the temporal and spatial distribution of deformation characteristics, a distribution map can be drawn to indicate the displacement changes of the abnormal area over three time periods. For example, the central region went from a stable state in spring to a continuous sinking in autumn, with a cumulative deformation of 8 centimeters. This distribution map provides an intuitive basis for subsequent predictions.

[0065] For example, when using a support vector machine algorithm to classify and predict deformation trends, historical data can be used as a training set to predict that deformation in the central region could increase to 10 centimeters within the next month, thereby designating it as a high-risk area. This prediction method can identify potential problem areas in advance and provide a reference for protective measures.

[0066] For example, when generating comprehensive assessment data on slope deformation characteristics, dynamic analysis results and forecast data can be combined to assess the central region as having a high risk of stability decline, recommending it as a key area of ​​focus. This assessment method helps rationally allocate resources, implement targeted measures, and improve the efficiency of slope management.

[0067] Step S105 , based on the distribution of slope deformation characteristics, a finite element analysis method is used to simulate and calculate the stress distribution of the steep slope, determine the stability risk of the deformation area, and identify potential landslide risk points.

[0068] Topographic data and deformation characteristics of high and steep slopes are obtained. A pre-established geological model is used to preliminarily delineate the distribution patterns and determine the distribution of deformation zones. Based on the distribution of deformation zones, the finite element method is used to simulate the stress distribution within the slope, determining the location and intensity distribution of stress concentration areas. Based on the location and intensity distribution of stress concentration areas, combined with deformation characteristic data, a quantitative stability risk analysis is performed to determine the local stability level of the slope. If the stability level falls below a preset threshold, high-risk points within the deformation zone are further identified to determine the distribution of potential landslides. Based on the distribution of potential landslides, risk points are prioritized using a risk assessment model, combining stress distribution and topographic data, and key monitoring areas are identified. Based on the distribution of key monitoring areas, a support vector machine algorithm is used to predict the dynamic trends of risk points and determine the evolutionary characteristics of potential landslides. Based on the evolutionary characteristics of potential landslides, combined with stability risk and stress distribution data, the slope analysis results are comprehensively compared to determine the overall risk level of the high and steep slope.

[0069] For example, when acquiring topographic data and deformation characteristics of steep slopes, a high-precision laser scanner mounted on a drone can be used to comprehensively scan the slope surface, acquiring point cloud data and constructing a digital terrain model of the slope. For deformation characteristic data, differential interferometry radar technology can be used to monitor minute displacements on the slope surface. For example, if a region experiences a cumulative displacement of 5 mm over three months, it is marked as a potential deformation area. Using a pre-established geological model, the deformation area can be initially delineated. For example, if the slope is divided into upper, middle, and lower regions, deformation is more concentrated in the middle region.

[0070] For example, when using the finite element method to simulate stress distribution, a slope can be divided into multiple grid cells. Geotechnical parameters such as the elastic modulus and Poisson's ratio are then input to simulate the stress state of the slope under its own weight and external loads. For example, if the simulation results show that the maximum principal stress in a certain area is concentrated in the middle of the slope, and the strength exceeds 80% of the rock mass shear strength, this area is identified as a stress concentration area. This process helps identify weak links within the slope.

[0071] For example, to quantify stability levels, deformation data and stress distribution can be combined to assess local stability using a safety factor method. If the safety factor for a region falls below a preset threshold of 1.2, it is considered unstable and requires further identification of high-risk locations. For example, if the extraction results reveal three locations in the central region with rapid deformation rates, these locations are designated as potential landslide locations.

[0072] For example, when assessing the distribution of potential landslides, a risk assessment model can prioritize locations. For example, if terrain slope and stress data indicate a location in the middle of the area has the highest risk and is ranked first, it can be designated as a key monitoring area. This ranking helps rationally allocate monitoring resources.

[0073] For example, when using a support vector machine algorithm to predict the dynamic changes of risk points, a model can be trained based on historical deformation data to predict the displacement trend of a specific point over the next month. If the predicted results indicate a 20% increase in the displacement rate, the potential evolutionary characteristics of the landslide can be inferred. This prediction provides data support for early warning.

[0074] For example, when comprehensively comparing the overall risk level of a slope, stability data, stress distribution, and evolution characteristics can be integrated. If multiple indicators in the central region exceed the safe range, the overall risk level is determined to be high. This comprehensive analysis helps to fully understand the slope's condition and provide a basis for subsequent protective measures.

[0075] Example 2:

[0076] The present invention also provides a slope displacement monitoring system for high and steep slopes in loess areas, comprising:

[0077] The data acquisition module is used to carry out multi-angle flight missions over the steep slopes in the loess region using drones equipped with high-resolution laser scanning equipment. This module acquires 3D point cloud data covering the entire slope and comprehensively scans the terrain restrictions under complex terrain to obtain an initial point cloud dataset.

[0078] The point cloud preprocessing module is used to preprocess the data based on the initial point cloud dataset using a point cloud denoising algorithm to filter out noise points and irrelevant reflection points caused by complex terrain, and generate an optimized refined point cloud dataset;

[0079] The 3D modeling module is used to construct a digital surface model of the slope using a 3D reconstruction algorithm based on the refined point cloud dataset. The model is then corrected for local details based on the loose soil characteristics of the loess region to determine the initial morphological structure of the slope.

[0080] The dynamic analysis module is used to collect time series data of the preliminary morphological structure, obtain three-dimensional slope model data for multiple periods, analyze the dynamic evolution trend of morphological changes, and obtain the distribution of slope deformation characteristics;

[0081] The risk assessment module is used to simulate and calculate the stress distribution of high and steep slopes based on the distribution of slope deformation characteristics using the finite element analysis method, determine the stability risk of the deformation area, and identify potential landslide risk points.

[0082] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A slope displacement monitoring method for high and steep slopes in loess areas, characterized in that: include: Using drones equipped with high-resolution laser scanning equipment, we performed multi-angle flight missions over steep slopes in the loess region, acquiring 3D point cloud data covering the entire slope. We performed a comprehensive scan of the complex terrain, targeting topographical constraints, and generated an initial point cloud dataset. Based on the initial point cloud dataset, a point cloud denoising algorithm is used to preprocess the data, filter out noise points and irrelevant reflection points caused by complex terrain, and generate an optimized refined point cloud dataset; Based on the refined point cloud dataset, a digital surface model of the slope was constructed using a 3D reconstruction algorithm. Local detail corrections were performed on the model based on the loose soil characteristics of the loess region to determine the initial morphological structure of the slope. By collecting time series data of the preliminary morphological structure, we can obtain three-dimensional slope model data for multiple periods, analyze the dynamic evolution trend of morphological changes, and obtain the distribution of slope deformation characteristics. Based on the distribution of slope deformation characteristics, the finite element analysis method is used to simulate and calculate the stress distribution of high and steep slopes, judge the stability risk of the deformation area, and identify potential landslide risk points.

2. The slope displacement monitoring method for high and steep slopes in loess areas according to claim 1 is characterized in that: The method uses a drone equipped with high-resolution laser scanning equipment to perform multi-angle flight missions over the steep slopes in the loess region to obtain three-dimensional point cloud data covering the entire slope. It performs a comprehensive scan of the terrain restrictions under complex terrain and obtains an initial point cloud dataset, including: The UAV flight path planning system generates optimized multi-angle flight paths based on the complex terrain features of high and steep slopes in the loess area, acquiring comprehensive scanning range data. Based on the generated optimized path, the drone equipped with the laser scanning device is driven to perform the scanning task, collecting 3D data of the high and steep slope in high-resolution mode to form a preliminary point cloud collection; For the preliminary point cloud collection, point cloud registration technology is used to spatially align the different perspective data from multi-angle flights to obtain a unified three-dimensional point cloud dataset of the slope; By denoising the unified 3D point cloud dataset, the interference points of non-surface features in complex terrain are filtered out to determine the refined point cloud structure data; If there are missing data areas in the refined point cloud structure data, the missing parts are supplemented by interpolation technology to obtain a complete slope terrain point cloud model; Based on the complete slope terrain point cloud model, analyze the distribution of high and steep slope characteristics under terrain restrictions and determine the terrain change trend in key areas; According to the terrain change trend in key areas, the support vector machine algorithm is used to classify the point cloud data and determine the stability distribution characteristics of the slope terrain.

3. The slope displacement monitoring method for high and steep slopes in loess areas according to claim 1 is characterized in that: The point cloud denoising algorithm is used to pre-process the data based on the initial point cloud dataset, filter out noise points and irrelevant reflection points caused by complex terrain, and generate an optimized refined point cloud dataset, including: By performing a preliminary analysis on the initial point cloud data, the distribution characteristics and abnormal point distribution in the data are obtained, and the preliminary range of the noise point set is determined; According to the range of noise point sets obtained by preliminary analysis, the voxel grid filtering method in the point cloud denoising algorithm is used to grid the initial point cloud data to obtain voxelized point cloud grouping data; For voxelized point cloud group data, if the point cloud density within a voxel is lower than the preset threshold, it will be marked as a potential noise point, and the point cloud group that needs further processing will be determined; Obtain the marked potential noise point data, apply statistical filtering methods to remove abnormal points that deviate from the main point cloud distribution, and obtain preliminary refined point cloud data; Based on the initially refined point cloud data, the distribution characteristics of terrain influence and reflection interference are analyzed. If the point cloud distribution in a specific area is detected to be discontinuous, a secondary denoising process is performed to determine the optimized point cloud subset. By integrating the optimized point cloud subsets and adjusting the point cloud boundaries using smoothing technology, the final refined point cloud data is obtained; For the final refined point cloud data, analyze its point cloud quality characteristics, determine the data integrity and distribution uniformity, and generate an optimized point cloud dataset that meets business needs.

4. The slope displacement monitoring method for high and steep slopes in loess areas according to claim 1 is characterized in that: The method involves constructing a digital surface model of the slope using a 3D reconstruction algorithm for the refined point cloud dataset, performing local detail correction on the model based on the loose soil characteristics of the loess region, and determining the preliminary morphological structure of the slope, including: The digital surface model of the slope is constructed using the refined point cloud dataset using a 3D reconstruction algorithm to obtain preliminary surface morphology data. Based on the constructed digital surface model and the loose soil characteristics of the loess region, local detail areas that may have deviations in the model are identified, and the range of details that need to be corrected is obtained; For the identified local detail areas, the preset correction rules are used to locally adjust the digital surface model to determine the corrected surface detail data; Obtain the corrected surface detail data and generate a preliminary slope morphological and structural model based on the overall structural characteristics of the slope morphology; The influence of loose soil characteristics on the morphological structure is analyzed through the preliminary slope morphological structure model. If the influence exceeds the preset threshold, a secondary correction is performed on the local area to obtain the adjusted morphological structure data. Based on the adjusted morphological structure data, the density distribution characteristics of the point cloud dataset are used to verify the local consistency of the model and determine whether the final slope morphological structure conforms to the actual terrain characteristics; The verified slope morphological and structural data are used in combination with the overall framework of the digital surface model to generate a complete slope morphological and structural description and determine the final digital model results.

5. The slope displacement monitoring method for high and steep slopes in loess areas according to claim 1 is characterized in that: The above method acquires the slope 3D model data of multiple periods by collecting the time series data of the preliminary morphological structure, analyzes the dynamic evolution trend of the morphological change, and obtains the slope deformation characteristic distribution, including: Through the time series data acquisition system, multi-period data acquisition is carried out on the preliminary morphology of the slope, an initial slope morphology data set is constructed, and original data records of multiple periods are obtained; Based on the initial slope morphology data set, 3D reconstruction technology is used to process the multi-period data to generate slope 3D model data and determine the spatial morphology representation of the slope at different time periods. Based on the three-dimensional slope model data, the morphological dynamic analysis method is applied to extract the difference characteristics between the models in different time periods and determine the changing laws of the morphological evolution trend; If the change in the morphological evolution trend exceeds the preset threshold, the difference features are deeply compared through the information processing link to obtain the specific distribution of the abnormal deformation area; Based on the distribution of abnormal deformation areas and combined with time series data, the dynamic evolution of deformation characteristics in multiple time periods is analyzed to obtain the spatiotemporal distribution map of deformation characteristics; Through the spatiotemporal distribution map of deformation characteristics, the support vector machine algorithm is used to classify and predict deformation trends and determine the distribution of potential high-risk areas; Based on the distribution of high-risk areas and combined with dynamic analysis results, comprehensive assessment data of slope deformation characteristics are generated to determine key areas of concern for slope stability changes.

6. The slope displacement monitoring method for high and steep slopes in loess areas according to claim 1 is characterized in that: According to the distribution of slope deformation characteristics, the finite element analysis method is used to simulate and calculate the stress distribution of high and steep slopes, judge the stability risk of the deformation area, and identify potential landslide risk points, including: Obtain topographic data and deformation characteristic data of high and steep slopes, make a preliminary division of distribution patterns through pre-established geological models, and obtain the range distribution of deformation areas; According to the range distribution of deformation area, the finite element method is used to simulate the stress distribution inside the slope to determine the location and intensity distribution of the stress concentration area; Based on the location and intensity distribution of stress concentration areas and combined with deformation characteristic data, the stability risk is quantitatively analyzed to determine the local stability level of the slope; If the stability level is lower than the preset threshold, high-risk points in the deformation area are further extracted to obtain the distribution range of potential landslides; Based on the distribution range of potential landslides, combined with stress distribution and terrain data, risk points are prioritized through risk assessment models to identify key monitoring areas; Based on the distribution of key monitoring areas, the support vector machine algorithm is used to predict the dynamic change trend of risk points and obtain the evolution characteristics of potential landslides; By analyzing the evolution characteristics of potential landslides, combined with stability risk and stress distribution data, a comprehensive comparison of slope analysis results is conducted to determine the overall risk level of steep slopes.

7. A slope displacement monitoring system for high and steep slopes in loess areas, characterized by: include: The data acquisition module is used to carry out multi-angle flight missions over the steep slopes in the loess region using drones equipped with high-resolution laser scanning equipment. This module acquires 3D point cloud data covering the entire slope and comprehensively scans the terrain restrictions under complex terrain to obtain an initial point cloud dataset. The point cloud preprocessing module is used to preprocess the data based on the initial point cloud dataset using a point cloud denoising algorithm to filter out noise points and irrelevant reflection points caused by complex terrain, and generate an optimized refined point cloud dataset; The 3D modeling module is used to construct a digital surface model of the slope using a 3D reconstruction algorithm based on the refined point cloud dataset. The model is then corrected for local details based on the loose soil characteristics of the loess region to determine the initial morphological structure of the slope. The dynamic analysis module is used to collect time series data of the preliminary morphological structure, obtain three-dimensional slope model data for multiple periods, analyze the dynamic evolution trend of morphological changes, and obtain the distribution of slope deformation characteristics; The risk assessment module is used to simulate and calculate the stress distribution of high and steep slopes based on the distribution of slope deformation characteristics using the finite element analysis method, determine the stability risk of the deformation area, and identify potential landslide risk points.

Images