Comprehensive land improvement planning method based on three-dimensional modeling and auxiliary decision analysis

Abstract

The present invention discloses a method for comprehensive land consolidation planning based on three-dimensional modeling and auxiliary decision-making analysis, which relates to the technical field of comprehensive land consolidation planning. Specifically, the method includes the following steps: based on a generated three-dimensional terrain model, identifying areas with continuous slopes and height-difference fractures at the slope foot, marking them as sensitive areas for sliding structure interference, and evenly dividing them into a number of sliding configuration sub-areas; obtaining and analyzing the slope sliding interference configuration information of each sliding configuration sub-area, evaluating the potential degree of sliding risk of each sliding configuration sub-area, and dividing each sliding configuration sub-area according to the evaluation results; based on the division results of each sliding configuration sub-area, respectively executing corresponding slope reinforcement measures and dynamically regulating them. The present invention solves the problem of risk misjudgment caused by unclear identification of the slope foot fracture, and achieves accurate identification of sliding slopes and dynamic reinforcement response.

Description

Technical Field

[0001] The present invention relates to the technical field of comprehensive land improvement planning, and in particular to a comprehensive land improvement planning method based on three-dimensional modeling and auxiliary decision analysis. Background Art

[0002] The introduction of 3D modeling technology enables a three-dimensional, refined representation of topography, current land use, and proposed remediation areas, enabling planners to more intuitively and comprehensively grasp spatial structure and land use potential. Furthermore, with the aid of decision-making support analysis methods, multiple remediation plans can be simulated, evaluated, and optimized, supporting multi-objective trade-offs and scientific decision-making, thereby improving the feasibility and implementation efficiency of remediation plans. Comprehensive land remediation planning methods based on 3D modeling and decision-making support can significantly enhance the visualization, scientific nature, and intelligence of the planning process, and are an important technical path to achieving high-quality land governance.

[0003] Existing comprehensive land remediation planning technologies based on 3D modeling and decision-making support analysis typically include multiple steps, including data collection and preprocessing, 3D model construction, spatial analysis and simulation, decision-making model design, and remediation plan generation and evaluation. First, high-precision spatial data on topography, land use, and ecological environment are acquired using remote sensing imagery, drone-based aerial surveys, and LiDAR laser radar. This data is then cleaned, classified, and integrated using a GIS platform. Subsequently, a 3D geographic information model of the remediation area is constructed using 3D modeling techniques (such as TIN, DEM, and BIM), enabling a three-dimensional visualization of features such as terrain, buildings, and land boundaries. Spatial analysis methods (such as accessibility analysis, slope analysis, and water system analysis) are then applied to identify suitable remediation areas or problem areas. In light of current land use conditions and policy constraints, a multi-objective decision-making support model is constructed, such as one based on the analytic hierarchy process (AHP), fuzzy comprehensive evaluation, or gray relational analysis, to comprehensively evaluate and rank multiple remediation plans based on economic, ecological, and social dimensions. Ultimately, the optimal remediation plan is automatically generated or selected based on the assessment results, and the results are visually output, providing managers with scientific, intuitive, and actionable decision support. The entire process achieves a closed-loop support from data to modeling, from analysis to decision-making, significantly improving the scientific nature, transparency, and implementation efficiency of comprehensive land remediation.

[0004] The existing technology has the following deficiencies:

[0005] During the terrain transformation phase, when the planning system performs stability analysis on slopes near the boundaries of the remediation area, if the modeling algorithm used during the 3D modeling phase smoothes the terrain surface, it will generate an apparently continuous and gently transitioning slope model despite a significant elevation difference at the toe of the slope. This can lead the system to mistakenly interpret the slope as structurally continuous and stable. This smoothing masked the actual elevation difference and disrupted the true identification of the shear surface at the base of the slope, which is the high-risk area for slope slip. Consequently, the system incorrectly identified this area as stable and requiring no intervention. Existing comprehensive land remediation planning technologies based on 3D modeling and decision-making support cannot dynamically adjust slope reinforcement measures based on the potential for slip risk when the slope surface is continuous but has an elevation difference at the toe. This can lead to missed identification of potential landslide points, resulting in structural loosening or even collapse after construction, leading to serious consequences such as safety accidents, project rework, and the failure of successful acceptance of the remediation project.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0007] The purpose of the present invention is to provide a land comprehensive improvement planning method based on three-dimensional modeling and auxiliary decision analysis to solve the problems in the above-mentioned background technology.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a land comprehensive improvement planning method based on three-dimensional modeling and auxiliary decision analysis, specifically comprising the following steps:

[0009] During the terrain transformation phase, a 3D terrain model was constructed by collecting terrain elevation data from the land consolidation area. Areas with sudden changes in terrain elevation were identified during the construction process, and a modeling method without smoothing was used in these areas to generate a 3D terrain model with features such as the slope foot fracture and steep profile changes.

[0010] Based on the generated 3D terrain model, the area with continuous slope surface and height difference fracture at the slope foot is identified, marked as the sensitive area of sliding structure interference, and evenly divided into several sliding configuration sub-areas;

[0011] Obtain and analyze the slope sliding interference configuration information of each sliding configuration sub-area, evaluate the potential degree of sliding risk of each sliding configuration sub-area, and divide each sliding configuration sub-area according to the evaluation results;

[0012] Based on the division results of each sliding configuration sub-area, corresponding slope reinforcement measures are implemented and dynamically regulated;

[0013] The reinforcement type, structural information, processing boundary coordinates and construction layout path corresponding to each sliding configuration sub-area are extracted to generate slope remediation and reinforcement task data for the drawing of remediation design drawings, engineering quantity calculations, construction resource allocation and construction task planning.

[0014] Preferably, based on the generated three-dimensional terrain model, identifying an area where the slope surface is continuous and a height difference fracture exists at the foot of the slope specifically comprises the following steps:

[0015] Extract the slope area in the generated 3D terrain model with the slope foot fracture and steep profile change terrain features, calculate the slope change value along the slope direction of the profile line, and identify the continuous area of the entire slope based on this;

[0016] At the toe of the profile line, the number and amplitude of elevation mutation points per unit length are calculated. When the overall slope of the profile changes continuously, and there are concentrated elevation mutation points at the toe of the profile line, and the amplitude exceeds the set threshold, the slope area is identified as an area with a continuous slope surface and an elevation fracture at the toe of the slope.

[0017] Preferably, obtaining the slope sliding interference configuration information of each sliding configuration sub-area, analyzing it, evaluating the potential degree of sliding risk of each sliding configuration sub-area, and dividing each sliding configuration sub-area according to the evaluation results, specifically includes the following steps:

[0018] Obtain the slope sliding interference configuration information of each sliding configuration sub-area and preprocess it;

[0019] Extracting terrain mutation structure information and slope shear disturbance information from the pre-processed slope sliding disturbance configuration information of each sliding configuration sub-area, and analyzing them after extraction to generate terrain mutation index and shear disturbance coefficient of each sliding configuration sub-area respectively;

[0020] A potential sliding risk assessment model is constructed based on the terrain mutation index and shear disturbance coefficient of each sliding configuration sub-area, and the sliding risk potential coefficient of each sliding configuration sub-area is generated by weighted summation;

[0021] A pre-set threshold range of the potential sliding risk coefficient is determined and compared with the generated potential sliding risk coefficient of each sliding configuration sub-area after determination. The potential sliding risk degree of each sliding configuration sub-area is evaluated based on the comparison results, and each sliding configuration sub-area is divided into a sliding stability control area, a structural disturbance buffer area and a high-sensitivity sliding warning area based on the evaluation results.

[0022] Preferably, the logic for obtaining the terrain mutation index of each sliding configuration sub-area is as follows:

[0023] The terrain mutation structure information is extracted from the pre-processed slope sliding interference configuration information of each sliding configuration sub-area, including the number of height difference mutation points in each sliding configuration sub-area, the average mutation amplitude of all height difference mutation points and the total length of the profile line, and calibrated as, 、 and , Indicates the The number of height difference mutation points in the sliding configuration sub-area, Indicates the The average mutation amplitude of all height difference mutation points in the sliding configuration sub-region, Indicates the The total length of the cross-section in the sliding configuration sub-area, , is a positive integer;

[0024] Calculate the terrain mutation index of each sliding configuration sub-area. The specific calculation formula is as follows: Where, For the The topographic mutation index of the sliding configuration sub-region.

[0025] Preferably, the logic for obtaining the shear disturbance coefficient of each sliding configuration sub-region is as follows:

[0026] The slope shear disturbance information is extracted from the pre-processed slope sliding interference configuration information of each sliding configuration sub-region, including the slope angle of each slope segment in each sliding configuration sub-region and the average slope angle of all slope segments, and calibrated as and , Indicates the In the sliding configuration sub-area The slope angle of each slope section, Indicates the The average value of the slope angles of all slope segments in a sliding configuration sub-area, , , and are all positive integers;

[0027] Calculate the shear disturbance coefficient of each sliding configuration sub-region. The specific calculation formula is as follows: Where, For the The shear perturbation coefficient of the sliding configuration sub-region.

[0028] Preferably, the terrain mutation index of each sliding configuration sub-area generated is and shear disturbance coefficient A sliding risk potential assessment model is constructed, and the sliding risk potential coefficient of each sliding configuration sub-area is generated by weighted summation. The specific calculation formula is as follows: Where, For the The sliding risk potential coefficient of the sliding configuration sub-area, and are the topographic mutation indices of each sliding configuration sub-area and shear disturbance coefficient The non-zero weight coefficient of .

[0029] Preferably, a preset threshold interval of the potential coefficient of sliding risk is determined , and after determination, the sliding risk potential coefficient of each sliding configuration sub-area generated Comparison is performed, and the potential degree of sliding risk of each sliding configuration sub-area is evaluated based on the comparison results. Based on the evaluation results, each sliding configuration sub-area is divided into a sliding stability control area, a structural disturbance buffer area, and a high-sensitivity sliding warning area. The specific comparison analysis and division are as follows:

[0030] like , the potential degree of sliding risk of the sliding configuration sub-region is low, and the sliding configuration sub-region is divided into a sliding stability control region;

[0031] like , the potential degree of sliding risk of the sliding configuration sub-area is medium, and the sliding configuration sub-area is divided into a structural disturbance buffer area;

[0032] like The potential degree of sliding risk of the sliding configuration sub-area is high, and the sliding configuration sub-area is divided into a high-sensitivity sliding warning area.

[0033] Preferably, based on the division results of each sliding configuration sub-area, corresponding slope reinforcement measures are respectively implemented, specifically:

[0034] For the sliding configuration sub-area classified as the sliding stability control area, the slope reinforcement measures implemented are as follows: maintaining the original slope structure unchanged, not setting up rigid structural support, and using ecological restoration methods to treat the slope surface. Ecological restoration methods include surface soil reinforcement, turf laying, and slope drainage ditch installation;

[0035] For the sliding configuration sub-area classified as a structural disturbance buffer zone, the slope reinforcement measures implemented are as follows: flexible support structures and shallow drainage structures are used. The flexible support structures include shear-resistant plant belts, ecological grids, and slope surface mesh anchoring structures. The shallow drainage structures include permeable brick guide belts, stone retaining walls, and anti-slide piles at the slope toe.

[0036] For the sliding configuration sub-areas classified as high-sensitivity slip warning areas, the slope reinforcement measures implemented are as follows: strong support structures and active protection structures are adopted. Strong support structures include reinforced concrete gravity retaining walls, anchor pile-sheet support structures, and anti-slide piles. Active protection structures include slope surface wire rope net coverings, vertical drainage wells, and surface interception drainage ditches.

[0037] It is dynamically regulated. Specifically, based on the sensor data, deformation monitoring data and construction progress feedback results deployed in the sliding configuration sub-area, the potential coefficient of sliding risk is updated in real time. According to the updated results, the structural type, strength level and layout range of the implemented slope reinforcement measures are adjusted to respond to the changing trend of the sliding risk level and the spatial migration characteristics.

[0038] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0039] 1. This invention addresses the existing problem of terrain smoothing during 3D modeling, which obscures elevation fracture information at the slope foot and causes the system to misjudge the slope's stability. By integrating the "identification of sudden elevation change areas" and "disabling smooth modeling" strategies into the terrain modeling phase, this method generates a high-fidelity 3D terrain model that accurately reflects the abrupt structural changes at the slope foot. This method effectively preserves the geometric anomalies of potential sliding slopes, providing a realistic and structurally complete spatial data foundation for subsequent identification of slip-sensitive areas and decision-making on reinforcement strategies. It overcomes the critical flaw of conventional models that mistakenly approximate high-risk slopes as continuous transitional surfaces.

[0040] 2. By constructing two quantitative parameters, the "terrain mutation index" and the "shear disturbance coefficient," combined with three-dimensional profile analysis and slope discreteness modeling, this method achieves the first quantitative modeling of the potential sliding trend in sliding configuration subregions. A sliding risk potential coefficient model is constructed based on the weighting of these two parameters, enabling the automatic identification and zoning of slope sliding risk levels. Compared to existing methods that rely solely on empirical evidence or single slope judgment, this method offers significant advantages in structural identification accuracy, scientific risk classification, and response sensitivity. It demonstrates greater reliability and generalizability, particularly in complex slope environments with hidden mutations at the toe or significant local disturbances.

[0041] 3. This invention establishes a risk-based zoning-based reinforcement measure classification and matching mechanism, supplemented by a dynamic control strategy. This allows the system to dynamically update the slip risk potential coefficient based on real-time sensor data and deformation monitoring feedback, and automatically adjust the structural type, strength level, and layout range of implemented reinforcement measures, achieving full lifecycle closed-loop control of slip-sensitive areas. Furthermore, the system supports the automatic extraction of reinforcement task data and the linked generation of remediation drawings, quantity calculations, and resource scheduling, establishing a data link throughout the entire process from modeling and analysis to risk assessment, reinforcement execution, and construction management. This significantly improves the intelligence level, planning response efficiency, and construction implementation capabilities of land remediation projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction to the drawings required for use in the embodiments will be given below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0043] Figure 1 It is a flow chart of the comprehensive land improvement planning method based on three-dimensional modeling and auxiliary decision analysis of the present invention.

[0044] Figure 2 This is a method mind map of the land comprehensive improvement planning method based on three-dimensional modeling and auxiliary decision analysis of the present invention. DETAILED DESCRIPTION

[0045] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0046] The present invention provides Figure 1 and Figure 2 The comprehensive land consolidation planning method based on three-dimensional modeling and auxiliary decision analysis shown in the figure specifically includes the following steps:

[0047] During the terrain transformation phase, a 3D terrain model was constructed by collecting terrain elevation data from the land consolidation area. Areas with sudden changes in terrain elevation were identified during the construction process, and a modeling method without smoothing was used in these areas to generate a 3D terrain model with features such as the slope foot fracture and steep profile changes.

[0048] During the terrain transformation phase, comprehensive elevation data collection for the remediation area can be achieved by integrating multi-source spatial data acquisition methods, including oblique photography images acquired by drone aerial surveys, high-density point cloud data acquired by laser radar (LiDAR), GNSS-measured elevation points, cross-sectional data collected by ground survey stations, and existing DEM data. After receiving this elevation data, the software platform uses a digital surface modeling (DSM) and digital elevation model (DEM) overlay processing algorithm to preprocess the data, such as data filtering, deduplication, coordinate unification, and error correction, to ensure the consistency and compatibility of the various data types. Subsequently, by constructing an irregular triangulated network (TIN) or raster interpolation algorithm, the elevation points are mapped into a continuous surface structure, generating a preliminary three-dimensional terrain model covering the remediation area. This model provides information on the overall shape and local slope of the terrain, providing a raw terrain representation for subsequent structural feature analysis.

[0049] During the initial construction of the 3D terrain model, the software can calculate the elevation difference and spatial slope between adjacent cells for each grid cell or triangular facet by setting a height gradient analysis threshold. When multiple consecutive cells experience sudden elevation changes within a small area (such as elevation differences exceeding a set multiple, or local slope mutation values exceeding a set multiple of the average slope), they are automatically identified as areas of sudden elevation changes. In these areas, the modeling algorithm switches to a conservative strategy, disabling global or regional smoothing interpolation algorithms and instead using original elevation point reconstruction facets or minimum curvature fitting to avoid smoothing landform features such as slope toe fractures, platforms, and steep slopes. At the same time, the edge angular expression of the model can be emphasized based on the boundary curvature enhancement algorithm to ensure that complete steep terrain information is retained in the model's visual and data structures, ultimately outputting a refined 3D model containing realistic terrain mutation details.

[0050] The fundamental purpose of adopting the above approach is to ensure that the model can truly express the possible sliding risk structures in the remediation area, especially the slope foot fracture and slope mutation areas. In actual remediation projects, slope sliding often does not occur in the overall slope continuous area, but is concentrated in the area where the slope foot height difference changes sharply and is concealed by the traditional model smoothing treatment; if conventional modeling methods are used, these key structures will be mistakenly expressed as gentle slope continuous surfaces, and thus misjudged as "stable areas" in subsequent analysis, resulting in failure to identify sliding hazards and burying serious engineering safety hazards. By actively identifying height mutations and suppressing smooth interpolation processing during the model construction stage, the "root cause" of the terrain characteristics of sliding risks can be retained at the data level, laying a high-precision and reliable spatial data foundation for subsequent sliding structure identification, risk assessment and reinforcement strategy formulation, which is an indispensable prerequisite for ensuring the stability of remediation projects and the reliability of assessments.

[0051] Based on the generated 3D terrain model, the area with continuous slope surface and height difference fracture at the slope foot is identified, marked as the sensitive area of sliding structure interference, and evenly divided into several sliding configuration sub-areas;

[0052] In this embodiment, based on the generated three-dimensional terrain model, identifying areas with continuous slopes and height differences at the foot of the slope includes the following steps:

[0053] Extract the slope area in the generated 3D terrain model with the slope foot fracture and steep profile change terrain features, calculate the slope change value along the slope direction of the profile line, and identify the continuous area of the entire slope based on this;

[0054] This software extracts profiles of slope areas within 3D terrain models characterized by toe fractures and steep profile changes, and calculates the slope gradient along the profile line. The software automatically identifies slope areas by using the 3D terrain model's raster data or irregular triangulated network (TIN) structure. After identifying the slope's spatial boundaries, it extracts profile lines perpendicular to the slope direction at regular intervals. Each profile line consists of a number of consecutive elevation sampling points. The software traverses the elevation differences and horizontal distances between adjacent points on the profile line, using a segmented slope calculation formula to determine the slope value for each segment. The software further analyzes the slope gradient's positional variation along the profile line to determine whether the gradient remains stable within a continuous spatial segment (i.e., the gradient difference falls within a continuity threshold). When most profile lines meet these continuity criteria, the corresponding area is identified as a "whole-slope continuous region." This process is necessary because, in actual slope stability assessments, sliding risk typically occurs not across the entire slope surface but rather in the localized terrain change section at the slope foot. Therefore, by calculating the slope change value and identifying the overall slope continuity zone, subsequent elevation change analysis can be limited to the local area at the slope foot, avoiding mistakenly analyzing areas with insufficient overall continuity, thereby improving the accuracy and efficiency of sliding structure identification. This process requires no human intervention and can be fully implemented in 3D modeling software using terrain analysis modules, raster algorithms, and profile data calculation algorithms.

[0055] At the toe of the profile line, the number and amplitude of elevation mutation points per unit length are calculated. When the overall slope of the profile changes continuously, and there are concentrated elevation mutation points at the toe of the profile line, and the amplitude exceeds the set threshold, the slope area is identified as an area with a continuous slope surface and an elevation fracture at the toe of the slope.

[0056] To identify potential elevation discontinuities within the toe section of a profile, the software automatically locates the toe section within the extracted profile. This section identifies the lower region of the profile where the slope begins to taper or where it meets the bottom plateau. This starting point is typically determined by analyzing the second-order derivative of the profile slope. Within this toe section, the software monitors elevation changes at a set unit length (e.g., every two meters), scanning the elevation difference between adjacent sampling points point by point. When the elevation difference exceeds a preset threshold (e.g., a multiple of the local average slope), it is recorded as a discontinuity point. The software then counts the number of discontinuity points within the unit length and calculates the average amplitude of these discontinuity points. If the combined conditions of continuous slope change, dense discontinuity points within the toe section (i.e., the number per unit length exceeds a set density), and the amplitude exceeds a set threshold, the software automatically identifies the corresponding slope area as a potential sliding structure with a continuous slope surface but a elevation discontinuity at the toe. This approach can identify steep fracture risk points during the 3D modeling phase that are not captured by traditional smooth modeling, allowing for early intervention in slip risk analysis. This process leverages algorithms such as slope difference analysis, elevation jump statistics, and spatial distribution clustering, which can be integrated into GIS analysis or 3D modeling software. It offers a high degree of automation and versatility, avoids subjective judgment bias, and effectively improves the depth and accuracy of slope stability analysis.

[0057] After identifying an area where the slope surface is continuous and there is a height difference fracture at the toe of the slope, the spatial analysis and terrain structure annotation functions in the software can be used to calibrate the area as a sensitive area for sliding structure interference. In specific implementation, the system will extract the closed terrain fragments in the three-dimensional model based on the spatial coordinate boundaries corresponding to the slope profile that has met the identification conditions, and assign a unique identification number to the area. At the same time, it will be classified as a "sliding structure interference sensitive area" so that it can be oriented in the subsequent sliding risk assessment and reinforcement strategy matching. After the calibration is completed, in order to ensure the balanced distribution and computational efficiency of the sliding risk assessment, the sensitive area can be divided into several sliding configuration sub-areas by using an equidistant grid division or an equal-length segmentation method based on the main axis direction of the region. Each sub-area inherits the numbering information of the original area and has independent spatial boundaries and profile structure information, becoming the basic unit for subsequent sliding interference configuration information extraction and risk level assessment. The core purpose of this calibration and division approach is to structure and unitize the original slip-sensitive areas, avoiding the inundation of details in a holistic analysis while enhancing the refinement of slip risk analysis. It also provides a clear spatial object carrier for the configuration of differentiated reinforcement measures, ensuring that reinforcement strategies accurately correspond to actual risk points. This process can be automated using terrain zoning algorithms, feature annotation systems, and spatial segmentation tools integrated into the 3D modeling platform, offering the advantages of high efficiency, controllability, and standardized processing.

[0058] Obtain and analyze the slope sliding interference configuration information of each sliding configuration sub-area, evaluate the potential degree of sliding risk of each sliding configuration sub-area, and divide each sliding configuration sub-area according to the evaluation results;

[0059] In this embodiment, the slope sliding interference configuration information of each sliding configuration sub-area is obtained and analyzed to evaluate the potential degree of sliding risk of each sliding configuration sub-area. The sliding configuration sub-area is divided according to the evaluation results. Specifically, the following steps are included:

[0060] Obtain the slope sliding interference configuration information of each sliding configuration sub-area and preprocess it;

[0061] After delineating the sensitive areas for sliding structural interference and partitioning the sliding configuration sub-areas, the software's built-in 3D analysis module performs cross-sectional structural analysis and terrain feature extraction on each sliding configuration sub-area, thereby obtaining slope sliding interference configuration information. Specifically, the system extracts multiple equally spaced profile lines along the main direction of the sub-area and reads parameters for each grid cell or slope segment sampling node along the profile line, including elevation, slope angle, aspect angle, curvature, and the distribution of sudden changes in slope toe elevation. All sampled data is structured and recorded as a sliding configuration feature dataset and bounded to its corresponding spatial location information, forming the "slope sliding interference configuration information" for each sliding configuration sub-area. This information fully describes the geometry, changing trends, and sudden changes within the sub-area, serving as the input for subsequent risk assessment modeling. The entire process is automated using the cross-sectional analysis tools and spatial feature extraction algorithms built into the GIS platform and 3D terrain modeling software.

[0062] Initially acquired slope sliding interference configuration information often suffers from data noise, inconsistent scales, outlier interference, and inconsistent data formats. Direct use in sliding risk analysis can lead to model misjudgments or distorted outputs. Therefore, preprocessing is essential to improve the accuracy and robustness of subsequent calculations. Preprocessing primarily involves four operations: first, removing outliers and interpolating missing values in elevation and slope values within the profile data to ensure data continuity and reliability; second, clustering and filtering the coordinates of discontinuity points to eliminate isolated elevation discontinuities caused by discrete errors; third, unifying data units, projection coordinates, and resolution generated from different data sources to ensure a consistent spatial reference system; and fourth, normalizing slope segment data to map each dimension to a standardized interval to facilitate comparability in index and coefficient calculations. These operations can be automated using the data cleaning plug-in, statistical analysis tools, and spatial correction modules within the terrain modeling platform, ensuring high-quality, low-redundancy, and quantifiable input information for each sliding configuration subregion, providing a reliable data foundation for subsequent high-precision sliding risk assessment.

[0063] Extracting terrain mutation structure information and slope shear disturbance information from the pre-processed slope sliding disturbance configuration information of each sliding configuration sub-area, and analyzing them after extraction to generate terrain mutation index and shear disturbance coefficient of each sliding configuration sub-area respectively;

[0064] After preprocessing the slope sliding disturbance configuration information of each sliding configuration sub-area, the key terrain parameters contained therein can be classified and extracted through the spatial feature extraction module, terrain analysis plug-in, or dedicated algorithm tools in the software to construct terrain mutation structure information and slope shear disturbance information. The specific extraction process includes: First, for the elevation and slope data, the software can set a calculation window at the slope foot section of the sub-area, extract the number of elevation mutation points per unit length and the mutation amplitude of each mutation point, and calculate the profile line length to form a structural index group reflecting the mutation density and intensity characteristics, which constitutes the "terrain mutation structure information"; second, for the slope angle data of each slope segment within the sub-area, the software will calculate the slope fluctuation value between adjacent slope segments through the multi-segment sliding window method, and combine it with the average slope angle of the entire profile. Based on the normalized difference processing based on trigonometric functions (such as sine transform), it forms shear disturbance trend characteristic data, which constitutes the "slope shear disturbance information." The software can separate, aggregate, and name the two pieces of information mentioned above according to field labels in the configuration information to generate structured output for subsequent index and coefficient calculations. The entire process can be automatically implemented through built-in functions in a 3D modeling platform or digital terrain analysis system, making it highly efficient and controllable.

[0065] A potential sliding risk assessment model is constructed based on the terrain mutation index and shear disturbance coefficient of each sliding configuration sub-area, and the sliding risk potential coefficient of each sliding configuration sub-area is generated by weighted summation;

[0066] A pre-set threshold range of the potential sliding risk coefficient is determined and compared with the generated potential sliding risk coefficient of each sliding configuration sub-area after determination. The potential sliding risk degree of each sliding configuration sub-area is evaluated based on the comparison results, and each sliding configuration sub-area is divided into a sliding stability control area, a structural disturbance buffer area and a high-sensitivity sliding warning area based on the evaluation results.

[0067] The software platform's historical sample analysis and statistical modeling capabilities enable the determination of threshold intervals for the potential sliding risk coefficient. This approach involves clustering analysis and regression modeling of sliding configuration sub-regions with known risk levels based on a library of typical slope sliding cases. The system first imports a large number of sliding cases verified by field measurements or existing engineering data and normalizes the known terrain mutation index and shear disturbance coefficient in each case to form a risk-level annotated dataset. The software then identifies risk intervals within the sample data using algorithms such as multivariate regression, K-means clustering, or Gaussian mixture models, extracting the dividing points between different risk levels as preliminary threshold candidates. The system then performs confidence interval validation and sensitivity analysis on the candidate threshold intervals to ensure that each interval boundary maintains stable discrimination and low false positive rates across different terrain types. Finally, the system divides the range of the potential sliding risk coefficient into multiple intervals, for example, [0, A] for low risk, [A, B] for medium risk, and [B, +∞) for high risk. This creates a standardized threshold interval configuration table that can be used by the sliding risk assessment module. The entire process is completed through the "risk training model management tool" or "historical data regression module" integrated in the sliding risk analysis software, which has data-driven, automatic archiving, dynamic update and other capabilities to ensure the adaptability and generalization of the sliding risk assessment model under different terrain and remediation area conditions.

[0068] In this embodiment, the logic for obtaining the terrain mutation index of each sliding configuration sub-area is as follows:

[0069] The terrain mutation structure information is extracted from the pre-processed slope sliding interference configuration information of each sliding configuration sub-area, including the number of height difference mutation points in each sliding configuration sub-area, the average mutation amplitude of all height difference mutation points, and the total length of the profile line, and is calibrated as 、 and , Indicates the The number of height difference mutation points in the sliding configuration sub-area, Indicates the The average mutation amplitude of all height difference mutation points in the sliding configuration sub-region, Indicates the The total length of the cross-section in the sliding configuration sub-area, , is a positive integer;

[0070] In the sliding risk analysis system, profile analysis and height difference identification operations can be performed on each sliding configuration sub-area through 3D terrain modeling software or GIS analysis platform, and core data such as the number of height difference mutation points in the sub-area, the average mutation amplitude of all height difference mutation points, and the total length of the profile line can be automatically obtained. The specific acquisition method is as follows: First, the system extracts a profile line along the main profile direction based on the spatial boundary of the sliding configuration subregion. This profile line consists of a series of sampling points with spatial coordinates and elevation values. Then, the system calculates the elevation difference between adjacent sampling points to determine whether there is an elevation change. When the elevation difference exceeds the system-set change identification threshold (for example, the elevation change is greater than a certain multiple of the average slope difference), the location is identified as an elevation change point and counted in the number of change points, forming the "number of elevation change points in each sliding configuration subregion." Next, the elevation difference values of all change points in the subregion are statistically averaged to generate the "average change amplitude of all elevation change points," which is used to quantify the intensity of the change. Finally, the system records the total length of the profile line during extraction as the "total profile length" data input for normalizing the structural scale. This data acquisition is automatically completed through the terrain analysis system's embedded profile extraction module, slope break identification algorithm, and spatial path analysis tool. This provides high resolution, high consistency, and high repeatability, providing accurate and structured quantitative input for the subsequent calculation of the terrain change index.

[0071] Calculate the terrain mutation index of each sliding configuration sub-area. The specific calculation formula is as follows: Where, For the The topographic mutation index of the sliding configuration sub-region.

[0072] This formula is used to quantitatively evaluate the The severity of the sudden change in the terrain at the toe of the slope in the sliding configuration sub-area. Indicates the total number of all elevation mutation points in the sub-region, which is used to reflect the frequency of mutation events in the terrain structure of the region; Represents the average mutation amplitude of these mutation points, and its square The introduction of highlights the nonlinear effect of mutation intensity on risk, that is, the larger the amplitude, the greater its effect on structural instability; Indicates the total length of the sub-area section line, which is obtained by taking the square root of On the one hand, it acts as a normalization factor to normalize the risk amplitude of sub-regions of different lengths, and on the other hand, it suppresses the deviation of the results caused by the natural increase of the number of mutation points in the long section. is the mutation intensity index, and then divided by To achieve scale adjustment, the results are comparable. On this basis, the natural logarithm function is introduced to prevent the value from being and The formula amplifies the maximum value, while improving the resolution of the intermediate value range and enhancing the distribution level of sliding risk. Therefore, this formula can provide a unified, accurate, and structurally sensitive assessment of the intensity of terrain mutations in each sliding configuration sub-region in the software system, supporting the scientific classification of sliding risk levels.

[0073] No. The topographic mutation index of the sliding configuration sub-region It reflects the discontinuity and drastic changes in the terrain structure of the slope foot section of the sub-region and is a key structural indicator in the assessment of the potential degree of sliding risk. A larger value indicates a greater number of elevation change points and a greater amplitude of change within the sliding configuration subregion, or a concentration of multiple significant fault change sections within a relatively short section length. This topographic feature generally indicates a higher local shear concentration and slip-driving potential within the subregion, resulting in a corresponding increase in the probability of slip and the severity of damage.

[0074] The upward trend of can be regarded as a quantitative expression of the reduced stability of the internal structure of the sub-region, and plays a role in strengthening the weight of the influence of slope structure mutation in the sliding risk potential coefficient model. In other words, The larger the value, the higher the potential degree of sliding risk, and the more likely it is to be classified as a medium or high risk area, and reinforcement measures should be prioritized to prevent structural instability. It is one of the key determinants of “structural mutation-induced sliding tendency” in sliding risk assessment.

[0075] In this embodiment, the logic for obtaining the shear disturbance coefficient of each sliding configuration sub-region is as follows:

[0076] The slope shear disturbance information is extracted from the pre-processed slope sliding interference configuration information of each sliding configuration sub-region, including the slope angle of each slope segment in each sliding configuration sub-region and the average slope angle of all slope segments, and calibrated as and , Indicates the In the sliding configuration sub-area The slope angle of each slope section, Indicates the The average value of the slope angles of all slope segments in a sliding configuration sub-area, , , and are all positive integers;

[0077] Within a sliding configuration subregion, the slope angles of each slope segment and the average slope angles of all segments can be automatically extracted and calculated using slope analysis tools integrated into terrain modeling software or a geological information system (GIS). Specifically, the system first divides the sliding configuration subregion into several continuous slope segments along the main slope surface, based on the boundary contours of the subregion. Each segment consists of two elevation sampling points, one starting point and one ending point, with a clear spatial location and geometry. After building a 3D terrain model, the software calculates the slope angle for each segment. This is done by using the inverse tangent function to calculate the slope angle, typically expressed in radians or degrees, by taking the ratio of the elevation difference between the starting and ending points to the horizontal distance. This creates a dataset of "slope angles for each slope segment within each sliding configuration subregion." The system then performs an arithmetic average of the slope angles of all segments within the subregion to obtain the average slope angle of all segments, which is used to characterize the central orientation of the slope trend across the entire subregion. This process can efficiently capture the spatial distribution characteristics of slopes, which not only facilitates the analysis of slope continuity but also provides basic data support for the subsequent calculation of shear disturbance coefficients. The entire operation can be automatically completed by the profile construction algorithm, slope statistics module and spatial topology analysis function built into the software platform, and is highly stable, repeatable and scalable.

[0078] Calculate the shear disturbance coefficient of each sliding configuration sub-region. The specific calculation formula is as follows: Where, For the The shear perturbation coefficient of the sliding configuration sub-region.

[0079] The shear disturbance coefficient The design goal is to quantify the The degree of slope fluctuation between the internal slope segments of a sliding configuration sub-region is used to reveal its potential shear instability. Indicates the sub-area The slope angle of each slope section is Represents the average value of the slope angles of all slope sections in the sub-area. Since the slope angle is essentially an angular quantity, directly performing difference and square operations is physically ambiguous. Therefore, the formula introduces a sine function to convert the angle value into a dimensionless value with actual physical meaning, namely the ratio of the vertical component of the slope, thereby quantifying the fluctuation. represents the perturbation deviation of each slope segment relative to the mean slope direction, while the square term emphasizes the nonlinear effect of the perturbation amplitude on the shear instability trend. Sum over all slope segments and divide by the total number of slope segments , the average disturbance energy value is obtained in the form of standard deviation, and the final 0.75-power processing serves as a power function-type sensitivity adjustment mechanism, which is used to enhance the hierarchical expression of moderate disturbances in the overall assessment while suppressing the amplification effect of extreme slope sections. A larger exponent indicates a more uneven slope distribution, a more complex shear trend, and a higher slip risk. Therefore, this calculation method achieves high-precision modeling of slope disturbance characteristics while ensuring mathematical rationality and physical interpretability, making it an indispensable structural disturbance indicator in the quantitative assessment of slip risk.

[0080] No. Shear perturbation coefficient of the sliding configuration sub-region It reflects the degree of fluctuation of slope angles between slope sections within the sub-area and is a key indicator for measuring the continuity and shear stability of slope structure. Larger values indicate more significant differences in slope gradients within the subregion, unstable slope trends, and frequent local gradient abrupt changes, suggesting the presence of complex shear stress distribution and potential structural dislocations within the region. Since slip is typically induced by shear concentration and gradient abrupt changes, the greater the slope gradient disturbance, the worse the structural integrity and the higher the potential slip risk. By weighted summing the square deviations of the sine values of each slope section and introducing nonlinear power function transformation, we can keenly capture the microscopic disturbance changes on the slope surface and enhance its impact on the risk assessment results. In the sliding risk potential coefficient model, it is responsible for identifying whether the internal disturbance trend of the slope surface has the driving force for sliding. The higher the value, the more likely the sliding configuration sub-area will be classified as a medium- or high-risk area, and reinforcement and monitoring strategies will be prioritized in subsequent remediation planning.

[0081] In this embodiment, the terrain mutation index of each sliding configuration sub-area generated is and shear disturbance coefficient A sliding risk potential assessment model is constructed, and the sliding risk potential coefficient of each sliding configuration sub-area is generated by weighted summation. The specific calculation formula is as follows: Where, For the The sliding risk potential coefficient of the sliding configuration sub-area, and are the topographic mutation indices of each sliding configuration sub-area and shear disturbance coefficient The non-zero weight coefficient of .

[0082] Complete the terrain mutation index for each sliding configuration sub-area and shear disturbance coefficient After the calculation, the software system can build a sliding risk potential assessment model based on weighted summation to generate the sliding risk potential coefficient of each sub-area. During the implementation process, the system will call two types of structural information: terrain mutation structure information and slope shear disturbance information, corresponding to and , and combine them according to the preset weight coefficient. Weight coefficient and Respectively and The importance weights in risk assessment are both non-zero and satisfy To ensure the normalization and physical comparability of the model results. The setting of weight coefficients can be obtained through historical landslide case regression analysis, expert scoring method or risk sensitivity test: if the main characteristics of the assessment area are dense slope foot mutations and dominant terrain faults, then Higher weight; if the slope in the area fluctuates greatly and the structural disturbance is dominant, the Weight. The system will and Substitution The weighted model is calculated using a formula to form a standardized sliding risk potential coefficient, which is used to quantitatively grade and compare sub-regional risk levels and formulate dynamic control strategies. This weighted model can be automatically executed within the sliding risk analysis platform using the "parameter fusion module" or "indicator integration function," eliminating the need for human intervention and demonstrating high adaptability and engineering scalability.

[0083] In this embodiment, the preset sliding risk potential coefficient threshold range is determined , and after determination, the sliding risk potential coefficient of each sliding configuration sub-area generated Comparison is performed, and the potential degree of sliding risk of each sliding configuration sub-area is evaluated based on the comparison results. Based on the evaluation results, each sliding configuration sub-area is divided into a sliding stability control area, a structural disturbance buffer area, and a high-sensitivity sliding warning area. The specific comparison analysis and division are as follows:

[0084] like , the potential degree of sliding risk of the sliding configuration sub-region is low, and the sliding configuration sub-region is divided into a sliding stability control region;

[0085] This situation indicates that the sub-region is at a low level in terms of both the degree of terrain mutation and slope shear disturbance. The slope foot structure is continuous and the profile slope distribution is stable. The interior of the region does not show typical slip configuration characteristics or abnormal stress concentration trends. This type of area is usually located in the stable zone of the overall slope structure or away from the active fault zone. It is the safest type of area in the remediation area. In actual planning, this type of area does not require the deployment of active reinforcement measures. It can be treated by setting basic monitoring points, maintaining the natural slope shape, or carrying out ecological restoration (such as turf and topsoil water retention) to maximize engineering cost savings while maintaining the original landform characteristics.

[0086] like , the potential degree of sliding risk of the sliding configuration sub-area is medium, and the sliding configuration sub-area is divided into a structural disturbance buffer area;

[0087] This indicates that while the area does not meet the criteria for high-risk structural mutations or extreme slope disturbances, it exhibits a certain degree of topographic complexity and slope discontinuity, with a slight shear slip trend or local slope foot mutations. Such areas are in the early stages of slip development or subcritical states, presenting certain potential risks and should not be left unattended. In remediation planning, they should be considered interference buffer zones, adopting a parallel treatment strategy of flexible reinforcement and drainage intervention, such as laying plant blankets, setting up anti-shearing strips, local anchoring or micro-retaining structures, supplemented by medium-frequency deformation monitoring, to prevent them from evolving into high-risk areas.

[0088] like The potential degree of sliding risk of the sliding configuration sub-area is high, and the sliding configuration sub-area is divided into a high-sensitivity sliding warning area.

[0089] This situation indicates that there are a large number of elevation mutation points within the area, and the mutation amplitude is significant. At the same time, the slope gradients between slope sections change dramatically and the disturbances are concentrated, forming a typical unstable slip structure. Such areas are often located in terrain fracture zones, geological fault edges, or hydrological erosion active areas, and have significant landslide-inducing conditions. If not intervened in time, structural sliding or slope collapse is very likely to occur, threatening the safety of the project and the surrounding environment. In the remediation plan, such areas should be listed as first-level remediation targets, and strong support measures should be adopted, such as constructing gravity retaining walls, reinforced concrete anchor piles, soil nail walls, or active slope protection nets, and configuring continuous deformation monitoring devices to achieve all-weather data perception and dynamic risk response to ensure the safety and stability of regional slopes.

[0090] Based on the division results of each sliding configuration sub-area, corresponding slope reinforcement measures are implemented and dynamically regulated;

[0091] In this embodiment, based on the division results of each sliding configuration sub-area, corresponding slope reinforcement measures are respectively implemented, specifically:

[0092] For the sliding configuration sub-area classified as the sliding stability control area, the slope reinforcement measures implemented are as follows: maintaining the original slope structure unchanged, not setting up rigid structural support, and using ecological restoration methods to treat the slope surface. Ecological restoration methods include surface soil reinforcement, turf laying, and slope drainage ditch installation;

[0093] Within the slip stability control zone, where the potential sliding risk coefficient is at its lowest level and the overall slope structural stability is high, rigid support structural reinforcement measures are not required. Instead, ecological restoration measures are employed for slope stabilization to improve ground cover, prevent surface erosion, and maintain ecological functions. At the software level, the measure matching module within the slip risk analysis system schedules treatment rules for the sliding configuration sub-areas classified as slip stability control zones. Specifically, the system automatically accesses a library of ecological restoration measures based on the regional classification results and selects a default combination of non-structural measures for these low-risk areas. Furthermore, the system automatically identifies slope drainage directions and runoff paths, generating a network for slope drainage ditch layouts by combining the 3D terrain model with slope spatial data. Slope parameters such as sunlight, aspect, and soil type are analyzed to determine suitable areas for turf laying and soil conservation requirements. This information is then used to determine the thickness of the surface cover reinforcement and the extent of turf vegetation coverage. The resulting ecological restoration measure configuration is generated within the software as a task configuration sheet, which is then exported as a structured document for subsequent design drawings and construction planning. This approach can not only effectively protect stable slopes with low intervention, but also reduce engineering investment and improve the ecological sustainability of the remediation area. It is the most economical and environmentally friendly treatment strategy for areas with low sliding risk.

[0094] For the sliding configuration sub-area classified as a structural disturbance buffer zone, the slope reinforcement measures implemented are as follows: flexible support structures and shallow drainage structures are used. The flexible support structures include shear-resistant plant belts, ecological grids, and slope surface mesh anchoring structures. The shallow drainage structures include permeable brick guide belts, stone retaining walls, and anti-slide piles at the slope toe.

[0095] Within the structural disturbance buffer zone, the slope experiences a certain degree of topographic abruptness and slope disturbance, but has not yet reached a high risk of slip instability. Therefore, a combination of flexible support structures and shallow drainage structures is required to improve slope stability and optimize drainage conditions. At the software level, the system automatically retrieves the corresponding reinforcement strategy template based on the risk level identified for this area and executes the digital layout of the flexible support and drainage structures. Specifically, the system first analyzes the slope distribution, aspect, and cover thickness of the sub-regions in the 3D terrain model to identify suitable vegetation areas and concentrated zones of slope structural disturbance. Based on this, the distribution paths of shear-resistant vegetation belts and ecological grids are planned. Simultaneously, based on the results of slope toe and slope curvature analysis, a map of anchor points for the slope cover grid is generated. Regarding drainage design, the system determines the layout direction of the permeable brick guide belt and the concentrated drainage point at the toe of the slope based on slope flow simulation calculations. Combining the slope toe profile height difference with the structural load characteristic parameters, the software automatically generates a linear layout plan and parameter configuration table for the masonry retaining wall and anti-slip piles. Through the above method, the whole process from risk level identification to reinforcement measure map generation is software-based output, ensuring that the coordinated goals of slope structure stability and drainage safety are achieved with lower rigidity intervention in medium-risk areas, controlling the development of slippage while maintaining the ecological characteristics of the slope and the ability to adjust local deformation.

[0096] For the sliding configuration sub-areas classified as high-sensitivity slip warning areas, the slope reinforcement measures implemented are as follows: strong support structures and active protection structures are adopted. Strong support structures include reinforced concrete gravity retaining walls, anchor pile-sheet support structures, and anti-slide piles. Active protection structures include slope surface wire rope net coverings, vertical drainage wells, and surface interception drainage ditches.

[0097] In high-sensitivity slip warning areas, where the potential slip risk coefficient exceeds the warning threshold, the slope structure exhibits significant discontinuities, sudden elevation changes, and concentrated shear disturbances, making it highly susceptible to geological hazards such as landslides and collapses. Therefore, a combined reinforcement strategy of strong support structures and active protection structures must be adopted to achieve both rigid control of slope structural safety and drainage and unloading capabilities. At the software implementation level, based on the classification results of the potential slip risk coefficient, specific strategy templates from the reinforcement measures library for high-risk areas can be called upon to automatically match various high-strength support and protection components. In specific operations, the software first analyzes the height difference fracture location, slope foot displacement trend and potential shear surface extension range in the three-dimensional terrain model, and automatically arranges the structural line type and foundation anchor points of reinforced concrete gravity retaining wall and anchor pile plate support based on these parameters. At the same time, according to the geometric characteristics of the slope profile and the inclination of the sliding surface, the position, burial depth and resistance parameters of the anti-sliding piles are determined; in terms of protective structure generation, the system identifies the exposed area of the high-level sliding surface through slope gradient and exposure analysis, and automatically plans the coverage range of the slope wire rope net and the layout density of the anchor nodes; at the same time, combined with the rainwater flow path and groundwater outcrop points, the vertical drainage well layout coordinates and the flow direction topology map of the surface interception drainage ditch are generated; all structural design schemes form a visual model in the system and are output as a standardized structural configuration file for construction drawing, construction unit scheduling and bill of quantities generation. Through this fully automated, parametric design approach, accurate identification, high-intensity intervention, and real-time response control of slopes in high-risk areas are achieved, effectively preventing slope structural instability and ensuring the overall safety and long-term sustainability of land reclamation projects.

[0098] It is dynamically regulated. Specifically, based on the sensor data, deformation monitoring data and construction progress feedback results deployed in the sliding configuration sub-area, the potential coefficient of sliding risk is updated in real time. According to the updated results, the structural type, strength level and layout range of the implemented slope reinforcement measures are adjusted to respond to the changing trend of the sliding risk level and the spatial migration characteristics.

[0099] The core purpose of dynamic control is to continuously optimize the adaptability and responsiveness of slope reinforcement measures to changes in the risk status of sliding configuration sub-regions during the implementation and operation of the remediation project. This ensures timely, accurate, and quantifiable reinforcement strategy adjustments for different regions as the sliding risk level dynamically evolves. At the software implementation level, the system first connects to multi-source monitoring devices deployed on-site to collect real-time sensor data, including displacement sensors, inclinometers, groundwater level gauges, and strain gauges. It also obtains slope displacement curves and profile deformation trend data output by digital deformation monitoring equipment (such as total stations or GNSS monitoring stations). This data, combined with reinforcement implementation and progress feedback uploaded to the construction progress management system, forms a unified monitoring and response data stream. The system regularly performs automatic normalization and trend fitting on this data, comparing and analyzing the magnitude of changes in the monitoring data with the original input conditions (TDF and SDC) in the sliding risk potential coefficient model to determine whether the risk level has transitioned or expanded spatially. Once a sub-region's SRPC value is determined to exceed the original classification interval threshold, the system automatically triggers the re-matching logic of the slope reinforcement measures library. Combining the region's current structural type and remaining construction capacity, it recommends and outputs new reinforcement adjustment suggestions. These include increasing the reinforcement strength level (such as increasing the number of anchor bolts or increasing pile diameter), converting the structural type (such as switching from flexible support to rigid support), and expanding the deployment range (such as expanding the wire mesh coverage area or extending the drainage system path). This mechanism not only enables data-driven adaptive reinforcement control but also continuously monitors and dynamically optimizes risk control strategies during the operation of the remediation project, significantly improving the management efficiency and response speed of slope stability in high-risk areas.

[0100] The reinforcement type, structural information, processing boundary coordinates and construction layout path corresponding to each sliding configuration sub-area are extracted to generate slope remediation and reinforcement task data for the drawing of remediation design drawings, engineering quantity calculations, construction resource allocation and construction task planning.

[0101] To achieve integrated digital delivery and construction execution for slope remediation, the system, after completing risk assessments and reinforcement implementation decisions for each sliding configuration sub-area, extracts the matched reinforcement type, structural parameters, reinforcement zone boundary coordinates, and construction layout paths within each sub-area, thereby generating structured slope remediation and reinforcement task data. This process is implemented within the software platform through a task data generation module. First, based on the division of the sliding configuration sub-areas, the system retrieves the reinforcement type and specific structural configuration parameters from the structural matching records, including the type of reinforcement structure (such as gravity retaining wall, wire mesh, anti-slide piles), corresponding structural dimensions, anchorage depth, and resistance level. Second, the system automatically obtains the spatial coordinate point list of the reinforcement treatment boundary based on the projected boundary of the reinforcement area on the 3D terrain model, combining the original terrain point cloud data with the toe line extraction results. Furthermore, by analyzing construction accessibility, road aspect, and toe connection conditions, the system plans mechanical or manual work paths using a shortest path algorithm, forming a layout network for construction execution. The above data will be encapsulated into a standardized task data structure in the software and coded and managed by layer, object type and construction attributes.

[0102] The generated slope remediation and reinforcement task data will serve as direct input for the construction design and resource allocation of remediation projects. Its main uses include: first, automatically outputting remediation design layers to achieve a graphical representation of the slope reinforcement scheme, facilitating design review and approval filing; second, based on the structural volume and layout range in the task data, calling the engineering quantity calculation module to automatically calculate earthwork volume, concrete volume, anchor rod length, etc.; third, through linkage with the construction resource library, achieving precise matching of various types of machinery and equipment, material consumption, and labor types, and building a resource allocation plan; fourth, outputting a construction task scheduling plan based on the layout path and project zoning logic, clarifying construction priorities, node control points, and dynamic adjustment interfaces. The deep integration of sliding risk assessment results with construction execution data through the software system not only improves the digitalization of remediation design and the efficiency of construction preparation, but also provides full-process support for subsequent project progress monitoring and construction quality traceability, establishing a complete technical closed loop from assessment decision-making to project execution.

[0103] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0104] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions according to the embodiments of the present application are fully or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means (e.g., infrared, wireless, microwave, etc.). A computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0105] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0106] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0107] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are merely illustrative. For example, the division of units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0108] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0109] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0110] The above are only specific embodiments of the present application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A comprehensive land improvement planning method based on three-dimensional modeling and auxiliary decision analysis, characterized by: The specific steps include: During the terrain transformation phase, a 3D terrain model was constructed by collecting terrain elevation data from the land consolidation area. Areas with sudden changes in terrain elevation were identified during the construction process, and a modeling method without smoothing was used in these areas to generate a 3D terrain model with features such as the slope foot fracture and steep profile changes. Based on the generated 3D terrain model, the area with continuous slope surface and height difference fracture at the slope foot is identified, marked as the sensitive area of sliding structure interference, and evenly divided into several sliding configuration sub-areas; Obtain and analyze the slope sliding interference configuration information of each sliding configuration sub-area, evaluate the potential degree of sliding risk of each sliding configuration sub-area, and divide each sliding configuration sub-area according to the evaluation results; The specific steps include: Obtain the slope sliding interference configuration information of each sliding configuration sub-area and preprocess it; Extracting terrain mutation structure information and slope shear disturbance information from the pre-processed slope sliding disturbance configuration information of each sliding configuration sub-area, and analyzing them after extraction to generate terrain mutation index and shear disturbance coefficient of each sliding configuration sub-area respectively; A potential sliding risk assessment model is constructed based on the terrain mutation index and shear disturbance coefficient of each sliding configuration sub-area, and the sliding risk potential coefficient of each sliding configuration sub-area is generated by weighted summation; Determine a pre-set threshold range of the potential sliding risk coefficient, and compare it with the generated potential sliding risk coefficient of each sliding configuration sub-area after determination. Evaluate the potential sliding risk degree of each sliding configuration sub-area based on the comparison results, and divide each sliding configuration sub-area into a sliding stability control area, a structural disturbance buffer area, and a high-sensitivity sliding warning area based on the evaluation results. The logic for obtaining the terrain mutation index of each sliding configuration sub-area is as follows: The terrain mutation structure information is extracted from the pre-processed slope sliding interference configuration information of each sliding configuration sub-area, including the number of height difference mutation points in each sliding configuration sub-area, the average mutation amplitude of all height difference mutation points, and the total length of the profile line, and is calibrated as 、 and , Indicates the The number of height difference mutation points in the sliding configuration sub-area, Indicates the The average mutation amplitude of all height difference mutation points in the sliding configuration sub-region, Indicates the The total length of the cross-section in the sliding configuration sub-area, , is a positive integer; Calculate the terrain mutation index of each sliding configuration sub-area. The specific calculation formula is as follows: Where, For the Topographic mutation index of each sliding configuration sub-area; The logic for obtaining the shear disturbance coefficient of each sliding configuration sub-region is as follows: The slope shear disturbance information is extracted from the pre-processed slope sliding interference configuration information of each sliding configuration sub-region, including the slope angle of each slope segment in each sliding configuration sub-region and the average slope angle of all slope segments, and calibrated as and , Indicates the In the sliding configuration sub-area The slope angle of each slope section, Indicates the The average value of the slope angles of all slope segments in a sliding configuration sub-area, , , and are all positive integers; Calculate the shear disturbance coefficient of each sliding configuration sub-region. The specific calculation formula is as follows: Where, For the The shear perturbation coefficient of each sliding configuration sub-region; Based on the division results of each sliding configuration sub-area, corresponding slope reinforcement measures are implemented and dynamically regulated; The reinforcement type, structural information, processing boundary coordinates and construction layout path corresponding to each sliding configuration sub-area are extracted to generate slope remediation and reinforcement task data for the drawing of remediation design drawings, engineering quantity calculations, construction resource allocation and construction task planning.

2. The method for comprehensive land improvement planning based on three-dimensional modeling and auxiliary decision analysis according to claim 1 is characterized in that: Based on the generated 3D terrain model, identify areas with continuous slopes and height differences at the toe of the slope. This involves the following steps: Extract the slope area in the generated 3D terrain model with the slope foot fracture and steep profile change terrain features, calculate the slope change value along the slope direction of the profile line, and identify the continuous area of the entire slope based on this; At the toe of the profile line, the number and amplitude of elevation mutation points per unit length are calculated. When the overall slope of the profile changes continuously, and there are concentrated elevation mutation points at the toe of the profile line, and the amplitude exceeds the set threshold, the slope area is identified as an area with a continuous slope surface and an elevation fracture at the toe of the slope.

3. The method for comprehensive land improvement planning based on three-dimensional modeling and auxiliary decision analysis according to claim 2 is characterized in that: The terrain mutation index of each sliding configuration sub-area generated and shear disturbance coefficient A sliding risk potential assessment model is constructed, and the sliding risk potential coefficient of each sliding configuration sub-area is generated by weighted summation. The specific calculation formula is as follows: Where, For the The sliding risk potential coefficient of the sliding configuration sub-area, and are the topographic mutation indices of each sliding configuration sub-area and shear disturbance coefficient The non-zero weight coefficient of .

4. The method for comprehensive land improvement planning based on three-dimensional modeling and auxiliary decision analysis according to claim 3 is characterized in that: Determine the pre-set threshold range of the potential coefficient of sliding risk , and after determination, the sliding risk potential coefficient of each sliding configuration sub-area generated Comparison is performed, and the potential degree of sliding risk of each sliding configuration sub-area is evaluated based on the comparison results. Based on the evaluation results, each sliding configuration sub-area is divided into a sliding stability control area, a structural disturbance buffer area, and a high-sensitivity sliding warning area. The specific comparison analysis and division are as follows: like , the potential degree of sliding risk of the sliding configuration sub-region is low, and the sliding configuration sub-region is divided into a sliding stability control region; like , the potential degree of sliding risk of the sliding configuration sub-area is medium, and the sliding configuration sub-area is divided into a structural disturbance buffer area; like The potential degree of sliding risk of the sliding configuration sub-area is high, and the sliding configuration sub-area is divided into a high-sensitivity sliding warning area.

5. The method for comprehensive land improvement planning based on three-dimensional modeling and auxiliary decision analysis according to claim 4 is characterized in that: Based on the division results of each sliding configuration sub-area, corresponding slope reinforcement measures are implemented respectively, specifically: For the sliding configuration sub-area classified as the sliding stability control area, the slope reinforcement measures implemented are as follows: maintaining the original slope structure unchanged, not setting up rigid structural support, and using ecological restoration methods to treat the slope surface. Ecological restoration methods include surface soil reinforcement, turf laying, and slope drainage ditch installation; For the sliding configuration sub-area classified as a structural disturbance buffer zone, the slope reinforcement measures implemented are as follows: flexible support structures and shallow drainage structures are used. The flexible support structures include shear-resistant plant belts, ecological grids, and slope surface mesh anchoring structures. The shallow drainage structures include permeable brick guide belts, stone retaining walls, and anti-slide piles at the slope toe. For the sliding configuration sub-areas classified as high-sensitivity slip warning areas, the slope reinforcement measures implemented are as follows: strong support structures and active protection structures are adopted. Strong support structures include reinforced concrete gravity retaining walls, anchor pile-sheet support structures, and anti-slide piles. Active protection structures include slope surface wire rope net coverings, vertical drainage wells, and surface interception drainage ditches. It is dynamically regulated. Specifically, based on the sensor data, deformation monitoring data and construction progress feedback results deployed in the sliding configuration sub-area, the potential coefficient of sliding risk is updated in real time. According to the updated results, the structural type, strength level and layout range of the implemented slope reinforcement measures are adjusted to respond to the changing trend of the sliding risk level and the spatial migration characteristics.

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