Intelligent decision-making system and method for excavation and filling parameters of reservoir area of pumped storage power station

Abstract

The invention relates to the technical field of terrain information processing, in particular to an intelligent decision-making system and method for excavation and filling parameters of a reservoir area of a pumped storage power station. The method comprises the following steps: acquiring topographic data of a reservoir area; determining elevation distribution characteristics according to the topographic data of the reservoir area; performing excavation and filling boundary analysis based on the elevation distribution characteristics to obtain excavation and filling boundary data; performing earth-rock volume calculation according to the excavation and filling boundary data to obtain earth-rock balance data; identifying the section excavation and filling unbalance degree based on the earth-rock balance data to obtain excavation and filling deviation data; performing slope stability analysis according to the excavation and filling deviation data to obtain slope stability data; performing leakage risk prediction according to the slope stability data to obtain leakage risk data; and identifying a seepage path according to the seepage risk data. According to the method, excavation and filling parameter optimization is realized based on a topographic information processing technology, and the reservoir area construction decision efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of terrain information processing technology, and in particular to an intelligent decision-making system and method for excavation and filling parameters in the reservoir area of ​​a pumped storage power station. Background Technology

[0002] Pumped storage power stations, as crucial infrastructure for peak shaving and energy storage in power systems, are experiencing increasingly larger reservoir construction scales, involving massive excavation and filling works and complex terrain conditions. The excavation and filling processes directly impact the operational safety and long-term stability of both upper and lower reservoirs. With increasing engineering standards and precision requirements, traditional excavation and filling design methods have revealed several shortcomings: existing design methods largely rely on manual experience or single-topography elevation analysis, lacking a multi-dimensional comprehensive assessment of terrain undulations, earthwork balance, slope stability, and leakage risks. This can easily lead to imbalances in the excavation and filling areas, increased secondary transport volumes, and higher construction energy consumption. Current reservoir slope treatments often rely on static safety factors, failing to fully consider differences in fill materials, groundwater seepage, and dynamic stability changes caused by multi-stage construction disturbances, resulting in potential slippage or leakage hazards in local slopes. During operation, some reservoirs also experience unclear seepage paths, localized failure of the impermeable layer, and sudden increases in leakage. Existing methods lack targeted prediction and optimization mechanisms, making it difficult to identify high-risk sections and adjust parameters in a timely manner. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide an intelligent decision-making system and method for excavation and filling parameters in the reservoir area of ​​a pumped storage power station, in order to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, an intelligent decision-making method for excavation and filling parameters in the reservoir area of ​​a pumped storage power station includes the following steps: Step S1: Obtain reservoir area topographic data; determine elevation distribution characteristics based on reservoir area topographic data; perform cut-fill boundary analysis based on elevation distribution characteristics to obtain cut-fill boundary data; Step S2: Calculate the earthwork volume based on the cut-fill boundary data to obtain earthwork balance data; identify the degree of cut-fill imbalance in the section based on the earthwork balance data to obtain cut-fill deviation data. Step S3: Perform slope stability analysis based on cut-fill deviation data to obtain slope stability data; predict seepage risk based on slope stability data to obtain seepage risk data; identify seepage paths based on seepage risk data. Step S4: Optimize seepage prevention measures based on the seepage path to obtain seepage prevention data; adjust the filling structure based on the seepage prevention data to obtain filling structure data; iteratively optimize the cut and fill parameters based on the filling structure data to obtain cut and fill optimization parameters.

[0005] This invention achieves dynamic perception and scientific optimization of the entire process of excavation and filling in the reservoir area of ​​a pumped storage power station by introducing high-precision topographic data analysis and intelligent parameter decision-making mechanisms. By acquiring reservoir area topographic data and analyzing elevation distribution characteristics, the invention can comprehensively identify topographic undulations and the distribution patterns of high and low elevation areas in the early stages, providing a precise spatial basis for subsequent excavation and filling boundary delineation, thereby effectively avoiding the boundary ambiguity and positioning errors caused by traditional manual judgment. Excavation and filling boundary analysis based on elevation characteristics can accurately determine the boundary range between excavation and filling, significantly improving the spatial rationality of excavation and filling planning and the accuracy of construction guidance. Furthermore, through earthwork volume calculation and balance analysis, quantitative comparison of excavation and filling volumes across the entire reservoir area can be achieved, automatically identifying the degree of excavation and filling imbalance in each section, thereby effectively reducing secondary transportation and repetitive operations caused by excavation and filling volume errors, and reducing energy consumption and construction costs in earthwork allocation. On this basis, slope stability analysis is introduced, which can comprehensively assess the slope stress state and potential instability risk by combining excavation and filling deviations with topographic elevation differences, avoiding the shortcomings of the traditional static safety factor method in reflecting dynamic stress changes, and improving the reliability of slope safety prediction. By further conducting leakage risk prediction and seepage path identification, potential seepage channels in the reservoir area can be identified and spatially located in advance, thereby preventing leakage hazards during the design phase. The seepage prevention measure optimization steps can achieve targeted densification and local structural reinforcement based on seepage paths, significantly improving the overall continuity and safety margin of the seepage prevention system. Through the adjustment and iterative optimization of the filling structure, dynamic updates and multi-round feedback corrections of excavation and filling parameters can be achieved, fully considering material type, compaction degree, and structural layer thickness, ultimately achieving an optimal balance between safety, economy, and sustainability in the reservoir area's excavation and filling project. This invention achieves a closed-loop control across the entire chain, from terrain feature extraction, earthwork balance assessment, stability and seepage prevention performance coupling analysis to intelligent optimization of excavation and filling parameters, significantly improving the scientific nature of pumped storage power station reservoir design and the safety and reliability during operation.

[0006] Preferably, step S1 specifically includes: Step S11: Obtain the topographic data of the reservoir area and extract the original elevation point cloud to obtain elevation point cloud data; Step S12: Perform terrain gridding processing on the elevation point cloud data to obtain terrain grid data; Step S13: Calculate the elevation gradient based on the terrain grid data; Step S14: Identify high and low elevation zones based on the elevation gradient to obtain high and low elevation zone division data; Step S15: Determine the elevation distribution characteristics based on the high and low position zone division data; Step S16: Perform cut-fill boundary analysis based on elevation distribution characteristics to obtain cut-fill boundary data.

[0007] This invention extracts and meshes the original elevation point cloud of reservoir area topographic data, enabling comprehensive and accurate acquisition of three-dimensional spatial information of the reservoir area in the early stages, providing a reliable digital foundation for cut-and-fill design. Based on the topographic grid, it calculates elevation gradients and divides high and low elevation zones, accurately identifying topographic undulations and high / low elevation distribution characteristics, providing a scientific basis for delineating cut-and-fill boundaries, and avoiding boundary ambiguity and positioning deviations caused by manual experience or two-dimensional analysis. The elevation distribution characteristics determined by the high / low elevation zone division effectively guide cut-and-fill boundary analysis, achieving reasonable layout and spatial optimization of cut and fill areas, improving the accuracy and efficiency of construction planning, reducing unnecessary secondary transportation and construction energy consumption, and laying a solid data foundation for subsequent earthwork volume calculation, slope stability assessment, and leakage risk prediction, thereby comprehensively improving the scientific, safe, and economical aspects of reservoir area cut-and-fill engineering.

[0008] Preferably, step S16 specifically includes: Step S161: Calculate the terrain slope based on the elevation distribution characteristics to obtain slope distribution data; Step S162: Identify slope aspect based on slope distribution data to obtain slope aspect data; Step S163: Perform topographic flow direction analysis based on slope aspect data to obtain surface flow direction data; Step S164: Identify water catchment and drainage zones based on surface flow direction data to obtain watershed zoning data; Step S165: Perform cut-and-fill trend analysis based on watershed zoning data to obtain cut-and-fill trend data; Step S166: Extract the boundary lines based on the cut-and-fill trend data, and use the boundary lines to perform spatial fitting to obtain the cut-and-fill boundary data.

[0009] This invention, through slope calculation and aspect identification based on elevation distribution characteristics, can accurately reveal the spatial undulation patterns and slope directions of the reservoir area's topography, providing a scientific basis for water flow collection and discharge paths. Based on aspect data, topographic flow direction analysis can accurately simulate surface runoff direction and hydrodynamic trends, providing reliable data support for water catchment and drainage zone identification. Through water catchment and drainage zone identification and watershed zoning data analysis, the excavation and filling trends of different areas can be rationally delineated, achieving scientific and spatial optimization of excavation and filling layout. Utilizing excavation and filling trends for boundary line extraction and spatial fitting can accurately define excavation and filling boundaries, avoiding boundary deviations caused by empirical judgment or two-dimensional analysis, thereby improving the accuracy and feasibility of reservoir excavation and filling design. Simultaneously, it provides a comprehensive data foundation for subsequent earthwork volume calculation, slope stability assessment, and leakage risk prediction, significantly enhancing the safety, economy, and construction efficiency of the reservoir area.

[0010] Preferably, step S164 specifically includes: Slope runoff paths are extracted based on surface flow direction data to obtain runoff path data; Water collection nodes are identified based on confluence path data to obtain water collection node data; The catchment area is defined based on the data from the water collection nodes; Watershed lines are identified based on the catchment area to obtain watershed data; Drainage channels are identified based on watershed data to obtain drainage channel data. Regional coding is performed based on drainage channel data to obtain watershed zoning data.

[0011] This invention extracts slope confluence paths and identifies water collection nodes from surface flow direction data, accurately revealing the water flow convergence patterns and key water collection point locations on various slopes within the reservoir area, providing a scientific basis for subsequent drainage and seepage prevention design. By delineating the catchment area based on water collection nodes and identifying watershed lines, it can rationally define different water system units and flow division boundaries, thereby optimizing the reservoir area's excavation and filling layout and hydrological control strategies. Based on watershed data, it identifies drainage channels and encodes regions, systematically describing the reservoir area's drainage network and watershed zoning information, ensuring that water flow management and earthwork layout in different areas are targeted and scientific. Overall, the combined application of these steps improves the accuracy and feasibility of reservoir area excavation and filling design, reduces the potential impact of water flow on slope stability and seepage risk, and provides a reliable data foundation for earthwork volume calculation, slope stability analysis, and seepage prevention measure optimization, thus significantly enhancing the safety, construction efficiency, and economic efficiency of reservoir construction.

[0012] Preferably, step S2 specifically includes: Step S21: Based on the cut-fill boundary data, create a three-dimensional surface model to obtain three-dimensional terrain model data; Step S22: Calculate the volume of each zone based on the three-dimensional terrain model data to obtain the earthwork balance data; Step S23: Calculate the actual excavation and filling volume based on the earthwork balance data to obtain the actual excavation and filling volume data; Step S24: Based on the comparison between the actual excavation and filling volume data and the preset excavation and filling volume, the section excavation and filling difference value is obtained; Step S25: Identify the degree of imbalance between cut and fill in a section based on the difference between cut and fill values, and obtain the cut and fill deviation data.

[0013] This invention, through three-dimensional surface modeling and zonal volume calculation, can accurately obtain the topographic information and earthwork volume of each zone in the reservoir area, providing an accurate data foundation for excavation and filling volume control and construction organization. Comparison of actual excavation and filling volumes with preset volumes can promptly identify deviations in excavation and filling sections, quantifying the degree of imbalance, thus providing a scientific basis for subsequent excavation and filling adjustments and optimizations. The overall application of these steps can effectively improve the balance of earthwork layout, reduce secondary transportation and construction energy consumption, and mitigate safety hazards caused by excessive slope excavation or filling. Simultaneously, it provides reliable data support for slope stability analysis, leakage risk assessment, and seepage prevention measure design, significantly enhancing the safety, construction efficiency, and economic efficiency of reservoir area excavation and filling projects.

[0014] Preferably, step S3 specifically includes: Step S31: Calculate the slope elevation difference based on the cut-fill deviation data to obtain the slope elevation difference data; Step S32: Based on the slope elevation difference data, assess the soil bearing capacity to obtain soil bearing capacity data; Step S33: Determine the type of slope fill material based on the soil bearing capacity data; Step S34: Conduct compaction testing based on the slope fill type to obtain compaction data; Step S35: Perform slope stability analysis based on compaction data to obtain slope stability data; Step S36: Based on the slope stability data, predict the leakage risk and obtain leakage risk data; Step S37: Identify the seepage path based on the leakage risk data.

[0015] This invention, through slope elevation difference calculation and soil bearing capacity assessment, can accurately grasp the mechanical bearing capacity and potential risks of each slope in the reservoir area, providing a scientific basis for selecting appropriate fill material types. The determination of fill material type and compaction degree testing ensure that slope materials possess sufficient strength and stability, reducing the risk of slope deformation and slippage during construction and operation. Slope stability analysis, combined with compaction degree and soil characteristics, can accurately predict potentially unstable areas and quantify risk levels. Further leakage risk prediction and seepage path identification can identify possible seepage channels in advance, guiding seepage prevention design and measures, significantly improving the safety of reservoir slopes, seepage prevention reliability, and overall stability during construction and operation. Simultaneously, it provides data support for optimizing excavation and filling parameters, reducing unnecessary transportation and construction energy consumption.

[0016] Preferably, step S36 specifically includes: Step S361: Divide the unstable area according to the slope stability data to obtain the unstable area data; Step S362: Simulate the groundwater flow field based on the unstable region data to obtain groundwater flow distribution data; Step S363: Identify seepage channels based on groundwater flow distribution data to obtain potential seepage channel data; Step S364: Calculate the osmotic pressure based on the potential seepage channel data; Step S365: Perform leakage trend analysis based on osmotic pressure to obtain leakage risk data.

[0017] This invention, through the division of unstable zones, can accurately identify potential slippage or collapse areas on reservoir slopes, providing clear guidance for risk management and construction control; groundwater flow field simulation can reflect the distribution and flow patterns of groundwater in the reservoir area, providing a scientific basis for assessing the impact of water pressure on slope stability; seepage channel identification can identify potential concentrated water flow paths in advance, guiding seepage prevention design and construction layout; seepage pressure calculation and leakage trend analysis can quantify the leakage risk level in different areas, predict possible leakage points and flow changes in advance, and provide reliable data support for optimizing seepage prevention measures, slope reinforcement, and adjusting excavation and filling parameters, thereby effectively improving the safety of reservoir slopes, the reliability of seepage prevention, and the scientific and economic aspects of the overall excavation and filling project.

[0018] Preferably, step S37 specifically includes: Step S371: Identify high-risk leakage areas based on leakage risk data; Step S372: Calculate the seepage potential energy gradient based on the high leakage risk area to obtain the potential energy gradient data; Step S373: Simulate the seepage direction based on the potential energy gradient data to obtain the seepage direction data; Step S374: Perform connectivity tracing analysis based on the seepage direction data to obtain seepage path data.

[0019] This invention identifies high-risk leakage areas, enabling precise location of critical leakage zones within the reservoir area, providing targeted references for subsequent seepage prevention and reinforcement measures. Seepage potential energy gradient calculation quantifies the intensity and direction of seepage under hydraulic drive, providing a scientific basis for assessing groundwater movement and potential seepage pressure. Seepage direction simulation clarifies the flow direction of water in slopes and fill bodies, assisting in optimizing drainage systems and seepage prevention structure layouts. Connectivity tracking analysis of seepage paths reveals the continuity of the seepage network and potential confluence channels, providing data support for seepage prevention design, slope stability reinforcement, and adjustment of excavation and filling parameters, thereby effectively improving the reliability and safety of seepage prevention in the reservoir area and the scientific management level of excavation and filling projects.

[0020] Preferably, step S4 specifically includes: Step S41: Identify weak points in the seepage prevention system based on the seepage path and obtain data on these weak points; Step S42: Based on the data of weak points in the seepage prevention, perform local backfilling and densification to obtain backfilling and densification data; Step S43: Thicken the concrete seepage barrier layer based on the data of weak seepage points to obtain the seepage barrier layer thickening data; Step S44: Integrate the backfill encryption data and the seepage prevention layer thickening data to obtain seepage prevention data; Step S45: Adjust the structural layer thickness based on the seepage prevention data to obtain structural layer thickness data; check the stability of the fill based on the structural layer thickness data to adjust the fill structure and obtain fill structure data. Step S46: Iteratively optimize the cut and fill parameters based on the fill structure data to obtain the optimized cut and fill parameters.

[0021] This invention identifies weak points in the seepage prevention system, accurately pinpointing the most vulnerable points in the reservoir area, providing a basis for targeted reinforcement. Localized backfill densification increases the compactness of the fill, enhances the soil's impermeability, and reduces the risk of leakage under hydraulic action. Thickening the concrete seepage prevention layer effectively strengthens its overall strength and continuity, improving the overall reliability of the reservoir's seepage prevention. The integration of seepage prevention data and adjustment of structural layer thickness optimize the filling scheme while balancing seepage prevention and structural stability, ensuring the long-term stability of slopes and the fill. Iterative optimization of excavation and filling parameters based on fill structure data enables dynamic adjustment and optimization of the reservoir's excavation and filling scheme, reducing secondary transportation and construction energy consumption while ensuring safety, improving project management efficiency and construction accuracy, thereby comprehensively enhancing the safety, reliability, and economic efficiency of the pumped storage power station reservoir area.

[0022] Preferably, this specification also provides an intelligent decision-making system for excavation and filling parameters of a pumped storage power station reservoir area, used to execute the intelligent decision-making system for excavation and filling parameters of a pumped storage power station reservoir area as described above. This intelligent decision-making system for excavation and filling parameters of a pumped storage power station reservoir area includes: The cut-fill boundary analysis module is used to acquire reservoir area topographic data; determine elevation distribution characteristics based on reservoir area topographic data; and perform cut-fill boundary analysis based on elevation distribution characteristics to obtain cut-fill boundary data. The cut-fill deviation analysis module is used to calculate the volume of earthwork based on the cut-fill boundary data to obtain earthwork balance data; and to identify the degree of cut-fill imbalance in a section based on the earthwork balance data to obtain cut-fill deviation data. The leakage prediction module is used to perform slope stability analysis based on cut-fill deviation data to obtain slope stability data; to predict leakage risk based on slope stability data to obtain leakage risk data; and to identify seepage paths based on leakage risk data. The cut-and-fill parameter iteration module is used to optimize seepage prevention measures based on the seepage path to obtain seepage prevention data; adjust the filling structure based on the seepage prevention data to obtain filling structure data; and perform iterative optimization of cut-and-fill parameters based on the filling structure data to obtain cut-and-fill optimization parameters. Attached Figure Description

[0023] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a flowchart illustrating the steps of an intelligent decision-making method for excavation and filling parameters in a pumped storage power station reservoir area according to the present invention. Figure 2 This is a detailed flowchart of step S1 in the present invention; Figure 3 This is a detailed flowchart of step S16 in the present invention; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0024] The technical method of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this invention.

[0025] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0026] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0027] To achieve the above objectives, please refer to Figures 1 to 3This invention provides an intelligent decision-making method for excavation and filling parameters in the reservoir area of ​​a pumped storage power station, the method comprising the following steps: Step S1: Obtain reservoir area topographic data; determine elevation distribution characteristics based on reservoir area topographic data; perform cut-fill boundary analysis based on elevation distribution characteristics to obtain cut-fill boundary data; In this embodiment, the reservoir area topographic data is acquired using a high-precision laser scanner and GPS measuring equipment. The laser scanning accuracy is ±2 mm, and the GPS positioning accuracy is ±5 cm. The acquired point cloud data density is no less than 200 points per square meter. After acquisition, the original point cloud is denoised using point cloud data processing software to remove noise from reflection anomalies and occluded areas. Outliers are removed using statistical filtering methods, and a point distance threshold of twice the average spacing of the point cloud is set to eliminate points exceeding this threshold. The denoised point cloud is then used to establish a three-dimensional coordinate matrix according to a spatial coordinate system. The elevation distribution is calculated based on the Z-value of each point, and the elevation is divided into elevation bands spaced 1 meter apart, forming an elevation distribution matrix. Based on the elevation distribution matrix, cut-fill boundary analysis is performed: First, the upper limit of the cut-fill slope is set to 35° and the lower limit to 2°. Areas with an elevation difference of more than 20 meters are marked as key cut-fill areas. At the same time, combined with the maximum water level of the reservoir (normal water level is 500 meters and dead water level is 480 meters) and the safety clearance of the slope, the cut-fill boundary is generated along the elevation contour lines. The boundary lines are then smoothed with a smoothing curvature radius of not less than 5 meters to ensure that mechanical excavation and transportation equipment can operate smoothly, thus forming cut-fill boundary data.

[0028] Step S2: Calculate the earthwork volume based on the cut-fill boundary data to obtain earthwork balance data; identify the degree of cut-fill imbalance in the section based on the earthwork balance data to obtain cut-fill deviation data. In this embodiment, the cut-fill boundary data is imported into the three-dimensional earthwork calculation system. A partitioned grid is generated based on the boundaries, with a grid spacing of 2 meters × 2 meters to ensure calculation accuracy and engineering feasibility. The earthwork volume is calculated using the triangular mesh method (TIN method), calculating the volume of each grid cell according to the elevation difference between the cut and fill sections. The earthwork density is set as follows: the fill soil density is 1.9 t / m³. 3 The density of the excavated soil is 2.2 t / m³. 3 After the calculation is completed, the earthwork balance data for each section is obtained by comparing the excavation volume with the fill volume in sections. Furthermore, a cut-fill imbalance threshold is defined as ±5%. If the difference between the fill volume and the excavation volume in a section exceeds this range, the section is marked as having a cut-fill deviation. The specific deviation amount is calculated and the direction of the deviation (excessive excavation or insufficient fill) is recorded, generating cut-fill deviation data for subsequent slope stability analysis.

[0029] Step S3: Perform slope stability analysis based on cut-fill deviation data to obtain slope stability data; predict seepage risk based on slope stability data to obtain seepage risk data; identify seepage paths based on seepage risk data. In this embodiment, slope stability analysis is performed based on cut-and-fill deviation data. First, the slope elevation difference data is calculated. The slope height H is determined according to the actual elevation value after cut-and-fill, in meters. The slope soil parameters are determined according to the physical and mechanical properties in the engineering geotechnical investigation report: cohesion c is taken as 50kPa–150kPa, internal friction angle φ as 25°–35°, and unit weight γ as 18kN / m. 3 The slope stability calculation employed the limit equilibrium method, setting the sliding surface type as a circular arc sliding surface, dividing it into 50 sub-segments, and calculating the sliding safety factor SF, with a safety factor threshold set at 1.3. Simultaneously, combining groundwater level data and pore water pressure, Darcy's law was used to calculate the slope seepage pressure, with the permeability coefficient K taken as... – m / s, simulating the seepage field under continuous rainfall or rising water levels to predict leakage trends. Through leakage risk calculation, potential leakage paths are extracted along the slope, with a path node spacing of 1 meter, forming continuous seepage path data to provide input for seepage prevention design.

[0030] Step S4: Optimize seepage prevention measures based on the seepage path to obtain seepage prevention data; adjust the filling structure based on the seepage prevention data to obtain filling structure data; iteratively optimize the cut and fill parameters based on the filling structure data to obtain cut and fill optimization parameters.

[0031] In this embodiment, weak areas in the seepage prevention system are identified based on the seepage path data, and the criteria for determining weak areas are set as seepage flow rate > 0.01 m³ / s. 3 / s or seepage pressure >50kPa. For weak areas, local backfilling and densification measures are adopted. Well-graded sand is selected for backfilling, with a moisture content controlled at 12%–18%, a compaction degree of ≥95%, and a layer thickness controlled at 30cm. Nuclear density is measured after each layer compaction to ensure compliance. For the thickening of the concrete anti-seepage layer in weak areas, C35 reinforced concrete is used, with a thickness increased by 50–100mm, determined based on seepage pressure calculations. After integrating the backfilling and anti-seepage layer thickening data, the thickness of the fill structure layer is adjusted, and the slope is recalculated to ensure the structural thickness is not less than the design specifications (minimum thickness 500mm, slope protection layer thickness 1000mm). Finally, based on the updated structural layer thickness and soil volume information, iterative calculations of cut and fill parameters are performed, including cut depth, fill height, earthwork transportation path, and construction sequence. The iteration step size is set to 0.5 meters until the cut and fill deviation for the entire reservoir area is less than 5%, generating a complete set of optimized cut and fill parameters.

[0032] Preferably, step S1 specifically includes: Step S11: Obtain the topographic data of the reservoir area and extract the original elevation point cloud to obtain elevation point cloud data; In this embodiment, the reservoir area topographic data acquisition was completed using a high-precision laser scanner and RTK-GPS combined measurement. The laser scanner resolution was set to 1 mm, the scanning range covered the entire reservoir area, and the scanning interval did not exceed 2 meters. The generated point cloud data contained X, Y, and Z three-dimensional coordinate information. RTK-GPS was used to provide ground control point coordinates, with coordinate accuracy controlled within ±2 cm. The control point spacing was set to 50 meters according to the terrain complexity, and increased to 20 meters in complex terrain areas to improve accuracy. The acquired raw point cloud was denoised using a 3D point cloud processing tool. Outliers were removed using statistical filtering, with the filtering radius set to 1.5 times the average point spacing, and points with deviations greater than 3σ were deleted. Multiple scans were performed to supplement obstructed or blind spots to ensure that the point cloud coverage was greater than 99.5%. After processing, the Z value of each point was extracted to form elevation point cloud data, and an elevation matrix was generated according to a resolution of per square meter, with each elevation point accurate to the millimeter level, forming the basic data for subsequent gridding and gradient calculation.

[0033] Step S12: Perform terrain gridding processing on the elevation point cloud data to obtain terrain grid data; In this embodiment, the elevation point cloud data is gridded, dividing the point cloud into equally spaced grid cells according to the two-dimensional coordinate axes. The grid spacing is set to 2 meters × 2 meters. The Z-value of the grid cell is obtained through interpolation, using the nearest neighbor weighted average method. The weight coefficient is determined based on the inverse square of the distance from the point to the grid center, with points more than 3 meters away having a weight of 0. Anomalies in elevation are corrected using bidirectional average filtering to ensure the continuity and smoothness of the grid elevation. After gridding, a triangular mesh (TIN) is constructed, connecting the three vertices of each grid cell to form a triangle. The maximum side length of the triangle does not exceed 3 meters to ensure the accuracy of slope calculation. A grid number and spatial index are also generated to facilitate subsequent elevation gradient calculation and high / low elevation zone division. During processing, the average elevation, elevation extreme values, and triangular mesh normal vector of each grid cell are recorded for slope and aspect analysis.

[0034] Step S13: Calculate the elevation gradient based on the terrain grid data; In this embodiment, the elevation gradient is calculated based on terrain grid data. The gradient calculation uses the three-dimensional central difference method. For each grid cell, the gradient... The formula for calculation along the X direction is: , along The formula for direction calculation is: , and Both are 2 meters. After calculating the gradient components, the formula is used... The total gradient value for each grid cell is obtained. The gradient threshold is set from 0.05 (low slope) to 0.6 (steep slope). Regions with gradients greater than 0.6 are marked as steep slope regions and used for cut-fill boundary constraints. The gradient information for each grid cell includes the gradient magnitude and orientation angle (angle along the positive X-axis), with the orientation angle accurate to 0.1°. The data is stored in the gradient matrix and a spatial index is established for easy retrieval and subsequent region partitioning operations.

[0035] Step S14: Identify high and low elevation zones based on the elevation gradient to obtain high and low elevation zone division data; In this embodiment, high and low elevation zones are identified based on elevation gradient and absolute elevation threshold. A high elevation zone is defined as an area with an elevation greater than the average elevation +10 meters, and a low elevation zone is defined as an area with an elevation less than the average elevation -10 meters. Edge areas with a slope greater than 0.05 are classified as transition zones. Each grid cell is classified, and the classification results are coded: high elevation zone is coded as 1, low elevation zone as 2, and transition zone as 3. Adjacent grid cells of the same type are clustered using connected component analysis, with a minimum area threshold of 50m². 2 Isolated grids smaller than this area are processed by merging neighboring regions. Boundary contour lines are generated for each high and low elevation zone, with the boundary smoothing curvature radius set to 5 meters to ensure the operation of subsequent excavation and filling machinery. The average elevation, slope range, and area of ​​each zone are recorded for elevation distribution characteristic analysis.

[0036] Step S15: Determine the elevation distribution characteristics based on the high and low position zone division data; In this embodiment, the elevation distribution characteristics are determined based on the high and low elevation zone division data. First, the elevation statistical characteristics of each zone are calculated, including the maximum, minimum, average, and standard deviation. The relative elevation difference between the high and low elevation zones is then marked by comparing the average elevation with the reservoir's designed water level (normal water level 500 meters, dead water level 480 meters). , Sections with an average elevation gradient greater than 0.3 are further marked as key cut-and-fill areas. Simultaneously, the elevation gradient distribution within the area is calculated, and sections with an average gradient greater than 0.3 are marked as steep slope areas. A regional feature table is generated by combining area and connectivity data, including area number, area, average elevation, maximum elevation, minimum elevation, gradient range, and slope orientation, providing parameter support for subsequent cut-and-fill boundary analysis.

[0037] Step S16: Perform cut-fill boundary analysis based on elevation distribution characteristics to obtain cut-fill boundary data.

[0038] In this embodiment, cut-fill boundary analysis is performed based on elevation distribution characteristics. First, contour lines are extracted along the boundaries of the high and low elevation zones, with each contour line spaced 1 meter apart. The boundary extension range is limited based on a maximum slope of 35°, and mechanical excavation and transportation restrictions are set for areas with slopes greater than 35°. A spatial fitting curve is constructed using the boundary point coordinates, and cubic spline interpolation is used to ensure curve continuity and a radius of curvature of not less than 5 meters. The high and low elevation differences within the cut-fill zone are then analyzed. For a given area, cut-fill boundary lines are generated based on the slope safety clearance and slope stability requirements. The boundary lines are then aligned with the terrain grid to form boundary unit data, including boundary coordinates, slope constraints, construction sequence numbers, and boundary block areas. The elevation, slope, and area data of each boundary block are recorded in the database for subsequent earthwork volume calculations and slope stability analysis.

[0039] Preferably, step S16 specifically includes: Step S161: Calculate the terrain slope based on the elevation distribution characteristics to obtain slope distribution data; In this embodiment, terrain slope is calculated based on elevation distribution characteristics. First, the elevation grid data is arranged in a two-dimensional coordinate system, with each grid cell having a side length of 2 meters and the grid elevation accuracy controlled within ±1 centimeter. The slope calculation uses the triangular mesh method (TIN method). For each triangular cell, the slope is calculated based on the elevation values ​​Z1, Z2, and Z3 of the three vertices and the horizontal distance. , Calculate the normal vector ,in , , .slope The calculation accuracy is controlled to 0.1°, and the slope value of each triangular unit is assigned to the corresponding grid unit. Iterative calculations are performed on each grid unit in the entire reservoir area, and the maximum and average slopes are recorded simultaneously. Slope classification thresholds are set: gentle slopes ≤ 5°, medium slopes 5°~20°, and steep slopes ≥ 20°. The classification results are stored as a slope distribution matrix for subsequent slope aspect and flow direction analysis.

[0040] Step S162: Identify slope aspect based on slope distribution data to obtain slope aspect data; In this embodiment, slope aspect is identified based on slope distribution data. The gradient component method is used to calculate the slope components along the X and Y directions for each grid cell. The formula is as follows: , , meters. The formula for calculating slope aspect α is: The direction angle is accurate to 0.1°, ranging from 0° to 360°, with 0° representing true north. The aspect matrix is ​​filtered using a 3×3 grid neighborhood weighted average to remove outlier directions; the weights are determined by the inverse square of the distance from the center point to its neighbors. An aspect data matrix is ​​generated, with each grid cell recording the aspect value and corresponding slope value for flow direction and water catchment analysis. Simultaneously, the spatial distribution and continuity of various aspect regions are recorded, providing fundamental parameters for cut-fill boundary constraints.

[0041] Step S163: Perform topographic flow direction analysis based on slope aspect data to obtain surface flow direction data; In this embodiment, the D8 algorithm is used for topographic flow direction analysis. Based on slope aspect and gradient data, the flow direction of water in each grid cell is calculated. For each grid cell, the elevation difference ΔZ between its eight neighboring cells is compared to determine the direction of maximum downward flow, and the flow direction is labeled with a number (1~8, corresponding to east, southeast, south, southwest, west, northwest, north, and northeast, respectively). If multiple directions have the same ΔZ, the direction closest to the direction of maximum slope is selected. Each grid cell records the flow direction number and slope gradient value. Iterative calculations are performed on the entire reservoir area to generate a surface flow direction matrix and record the flow direction connectivity. For special low-lying isolated grids (area <10m²), 2 The flow direction is corrected by compensating for the average flow direction of neighboring units, ensuring that the flow direction is continuous and conforms to the natural hydraulic flow direction. The generated data is used for catchment area delineation and drainage analysis.

[0042] Step S164: Identify water catchment and drainage zones based on surface flow direction data to obtain watershed zoning data; In this embodiment, water catchment and drainage zones are identified based on surface flow direction data, and a watershed accumulation algorithm is used to calculate the upstream catchment area for each grid cell. Initially, the water accumulation area of ​​each cell is set to 4m². 2 The total water accumulation area is calculated by iteratively accumulating the upstream inflow unit area. A threshold is set: when the water accumulation area > 1000m², the total water accumulation area is calculated. 2 At that time, it was designated as the main catchment area; the water accumulation area was 100~1000m². 2 Designated as a secondary catchment area with a water accumulation area of ​​<100m² 2 The area was designated as a scattered catchment area. Watershed unit identifiers were generated based on the connectivity of the catchment units and the watershed boundaries. Each watershed unit included a number, total watershed area, boundary grid coordinates, and watershed slope range. By polygonizing the watershed boundaries, a complete watershed zoning data table was generated, recording the attribute parameters of each watershed, including watershed area, maximum and minimum elevation, average slope, and boundary contour coordinates.

[0043] Step S165: Perform cut-and-fill trend analysis based on watershed zoning data to obtain cut-and-fill trend data; In this embodiment, the excavation and filling trend analysis is performed based on watershed zoning data, and the elevation difference of each watershed unit is calculated. And determine the direction of filling or cutting by combining the average slope θ. If the slope is greater than 0.05 meters and the average slope is greater than 0.05, it is marked as a major excavation area; if If the average slope is greater than 0.05, it is marked as a major fill area; The area within ±10 meters and with an average slope ≤0.05 is considered a gentle zone and serves as a secondary adjustment zone. A cut-fill trend matrix is ​​generated for each watershed unit, with each unit recording the trend direction and trend intensity (according to...). The calculation process includes corresponding slope and flow direction information. A trend statistics table is generated, containing watershed number, cut-and-fill type, cut-and-fill height range, average slope, area, and flow direction connectivity, providing a basis for boundary line extraction.

[0044] Step S166: Extract the boundary lines based on the cut-and-fill trend data, and use the boundary lines to perform spatial fitting to obtain the cut-and-fill boundary data.

[0045] In this embodiment, boundary lines are extracted based on cut-and-fill trend data. First, boundary points are extracted along the cut-and-fill trend change points of each watershed unit, with a boundary point spacing of 2 meters. Points with slope changes >5° or cut-and-fill direction changes >30° must be included as boundary points. The boundary point coordinates are fitted using cubic spline interpolation to form a continuous boundary curve, with the curve smoothness controlled to a radius of curvature of not less than 5 meters. Spatial calibration is performed on the boundary curves, aligning the boundary lines with the original terrain grid, and the boundary height value is taken as the average elevation of the corresponding grid. Attribute data is generated for each boundary line, including boundary length, start and end coordinates, slope constraint, radius of curvature, watershed number, and cut-and-fill type. The final output boundary data is saved in vector polygon format for easy subsequent earthwork calculations and slope stability analysis.

[0046] Preferably, step S164 specifically includes: Slope runoff paths are extracted based on surface flow direction data to obtain runoff path data; In this embodiment, slope runoff paths are extracted based on surface flow direction data. The surface flow direction data is organized in a grid, with each grid cell having a side length of 2 meters and an elevation accuracy of ±1 centimeter. Using the D8 method, the elevation difference ΔZ between the eight neighboring cells of each grid cell is calculated to determine the direction of maximum water flow descent. The grid cells are numbered 1-8, corresponding to east, southeast, south, southwest, west, northwest, north, and northeast, respectively. The entire reservoir area grid cells are traversed, and the flow directions are connected sequentially to form paths. The starting point, ending point, and length of each path are recorded. Each path extends continuously along the direction of maximum descent, terminating when it reaches a low-lying catchment area or crosses the boundary. The cumulative water flow area along the path is calculated using a threshold setting; when the cumulative area > 50m², the path is considered complete. 2The paths are marked as the main confluence paths. The path length, average slope, and slope aspect variation of each path are recorded for subsequent water collection node identification. Paths shorter than 5 meters or with a slope of <1° are removed to eliminate isolated or discontinuous paths.

[0047] Water collection nodes are identified based on confluence path data to obtain water collection node data; In this embodiment, water collection node identification is performed based on confluence path data. First, all major confluence paths are traversed, and the coordinates of path confluence points and flow bifurcation points are extracted, recording their elevations, confluence areas, and adjacent path slopes. A threshold is set: when the confluence area is ≥200m²... 2 If the slope change at a bifurcation point is greater than 10°, the point is identified as a catchment node. Spatial clustering is performed on adjacent catchment nodes using the minimum spacing method. If the node spacing is less than 5 meters, they are merged into a single node. The coordinates are taken as the weighted average of the confluence points, and the elevation is taken as the average. For each catchment node, the number of inflow paths, total catchment area, node elevation, adjacent slope, and aspect are recorded to provide node boundary references for catchment area delineation.

[0048] The catchment area is defined based on the data from the water collection nodes; In this embodiment, the catchment area is divided based on the data from the water collection nodes. Each water collection node serves as the center of the catchment area. A flow-tracking method is used to trace each upstream grid cell back to its corresponding node along the flow direction, accumulating these traces to form a set of grid cells for the catchment area. Each grid cell records its associated water collection node number and cumulative inflow area. Overlapping areas are processed: if a grid cell is traced by two water collection nodes, it is assigned to the node with the lower elevation to ensure catchment connectivity. A boundary polygon for each catchment area is generated, recording its area, boundary length, highest and lowest elevations, average slope, and aspect distribution, providing boundary constraints for watershed line identification.

[0049] Watershed lines are identified based on the catchment area to obtain watershed data; In this embodiment, watershed lines are identified based on the catchment area, and the boundary lines between every two adjacent catchment areas are extracted as candidate watershed lines. Each candidate line is traced along the direction of the maximum elevation gradient of the boundary grid cells, with a step size of 2 meters, continuously generating a sequence of watershed points. For points with curvature changes greater than 30°, a smooth watershed line is generated through quadratic spline fitting, ensuring the curve is continuous and the radius of curvature is ≥5 meters. Each watershed line records its start and end coordinates, length, elevation range, average slope, and curvature information. Multiple watershed lines within a closed area are topologically sorted to ensure no intersections or repetitions, and a watershed line attribute table is generated to facilitate drainage channel identification and watershed zoning labeling.

[0050] Drainage channels are identified based on watershed data to obtain drainage channel data. In this embodiment, drainage channels are identified based on watershed data. The lowest elevation point between each watershed line is used as the starting point of the drainage channel, which is traced along the flow direction to the reservoir outlet or low-lying catchment area. The drainage channel path is iteratively extended along the direction of maximum slope decrease, with a step size of 2 meters. At each step, the slope and flow direction of the grid cell are calculated. If the slope is <1° or the path length is <5 meters, the extension stops. The length, average slope, lowest elevation point, elevation change, slope aspect, and adjacent watershed number of each drainage channel are recorded. Drainage channels with approximately overlapping paths or a spacing of <3 meters are merged, and the intermediate coordinates are used to generate the final channel line, ensuring the integrity and continuity of the channel network.

[0051] Regional coding is performed based on drainage channel data to obtain watershed zoning data.

[0052] In this embodiment, regional coding is performed based on drainage channel data, forming closed watershed boundaries together with the drainage channels and watershed lines. Each closed watershed area is assigned a unique code, with the coding rules following the reservoir area from west to east and from north to south. The numbering format is "B + two-digit serial number + W + two-digit sequence number", such as B01W01 representing the first watershed unit. For each watershed unit, the watershed area, boundary coordinates, maximum and minimum elevations, average slope, catchment node number, drainage channel number, and neighboring watershed codes are recorded. A complete watershed zoning table and vector graphics file are generated for all watershed units, used for subsequent earthwork calculations and slope stability analysis.

[0053] Preferably, step S2 specifically includes: Step S21: Based on the cut-fill boundary data, create a three-dimensional surface model to obtain three-dimensional terrain model data; In this embodiment, a 3D surface model of the reservoir area is created based on the cut-and-fill boundary data. First, the cut-and-fill boundary data is organized into a 3D coordinate point set, with each point containing XY coordinates and an elevation value, with elevation accuracy controlled within ±1 cm. A triangular mesh is generated based on the boundary points, and the surface inside the boundary is covered by dividing it into triangular units. Each triangle vertex is taken from either a boundary point or an internal interpolation point. The internal elevation value is calculated using a distance-weighted averaging method, with the weight determined by the reciprocal of the square of the horizontal distance to the triangle vertex. During mesh generation, the side length of each triangle is ensured to not exceed 3 meters to guarantee elevation continuity and match the terrain complexity. In areas with terrain protrusions or depressions, auxiliary elevation points are added for local subdivision, forming a denser mesh. The generated 3D terrain model records the coordinates of each triangle vertex, the topological relationships of the facets, and elevation attributes, forming a 3D data file that can be used for volume calculation and visualization. The entire terrain model maintains spatial consistency and elevation accuracy.

[0054] Step S22: Calculate the volume of each zone based on the three-dimensional terrain model data to obtain the earthwork balance data; In this embodiment, the volume of each zone is calculated based on a 3D terrain model. The reservoir area is divided into basic grid units, each with a side length of 10 meters, and triangular faces corresponding to each unit are extracted. The earthwork volume of each face is calculated based on the face elevation and a preset benchmark cut-and-fill elevation. The earthwork volumes of the faces are then summed to obtain the volume of the unit grid. During the earthwork volume summation process, cut and fill are counted separately. The cut volume is the portion of the face below the benchmark elevation, and the fill volume is the portion above the benchmark elevation. Within each grid, the grid number, number of faces, cut volume, fill volume, and average elevation are recorded to ensure that the accuracy of the zoned volume reaches ±0.01 cubic meters. The entire reservoir area is then summarized to form a zoned volume data table, providing basic data for calculating the actual cut-and-fill volume.

[0055] Step S23: Calculate the actual excavation and filling volume based on the earthwork balance data to obtain the actual excavation and filling volume data; In this embodiment, the actual excavation and filling volume is calculated using the partitioned volume data. The excavation volume of each grid cell is multiplied by the rock loosening coefficient, and the filling volume is multiplied by the soil compaction coefficient to obtain the actual earthwork volume. The rock loosening coefficient is set to 0.9, and the soil compaction coefficient is set to 1.25 to ensure that the corrected earthwork volume reflects the actual construction conditions. The actual excavation and filling volumes of each partition are summed to form the overall excavation and filling volumes, and the data table records the partition number, actual excavation volume, actual filling volume, average elevation, and slope information for each partition. Blocks exceeding 1000 cubic meters are marked, and statistical data is generated, including the maximum volume, minimum volume, and volume difference range, to facilitate subsequent excavation and filling deviation analysis.

[0056] Step S24: Based on the comparison between the actual excavation and filling volume data and the preset excavation and filling volume, the section excavation and filling difference value is obtained; In this embodiment, the actual excavation and filling volume data is compared with the designed excavation and filling volume. The actual volume is subtracted from the designed volume by the design volume corresponding to each grid, using the zoning number, to obtain the section's excavation and filling difference. Positive and negative differences indicate over-excavation / over-filling and under-excavation / filling, respectively. The difference data is summarized by gridded area to generate a difference statistics table, including zoning number, total difference, percentage difference, location of the grid with the largest difference, and average difference. A difference threshold of ±5 cubic meters per grid is set, and the number and percentage of grids exceeding the threshold are counted. The coordinates of high-difference grids are recorded to ensure clear spatial positioning of the difference data, providing a basis for deviation level classification.

[0057] Step S25: Identify the degree of imbalance between cut and fill in a section based on the difference between cut and fill values, and obtain the cut and fill deviation data.

[0058] In this embodiment, the degree of cut-fill imbalance is identified based on the cut-fill difference between sections. The difference of each grid is proportionalized to the unit grid volume to obtain a deviation coefficient. Based on the deviation coefficient, the blocks are divided into low-deviation, medium-deviation, and high-deviation levels, with a low deviation coefficient ≤ 0.05, a medium deviation coefficient between 0.05 and 0.15, and a high deviation coefficient > 0.15. The number, proportion, location of the grid with the largest deviation, and the average deviation value of each partition are statistically analyzed to form a complete cut-fill deviation data table. High-deviation blocks are visualized using contour lines and color coding to display the deviation intensity, facilitating spatial positioning and subsequent slope stability analysis.

[0059] Preferably, step S3 specifically includes: Step S31: Calculate the slope elevation difference based on the cut-fill deviation data to obtain the slope elevation difference data; In this embodiment, slope elevation difference is calculated based on cut-fill deviation data. The reservoir area is divided into basic grid units, each with a side length of 5 meters. The cut-fill deviation value within each grid is used, combined with the actual elevation of that grid and the elevations of surrounding grids to calculate the difference, and the elevation difference data for each grid is recorded. The elevation difference calculation uses the difference between the maximum and minimum elevations to ensure an accuracy of ±0.01 meters. In steep slope areas, the grid is subdivided to 2 meters to improve the accuracy of elevation difference calculation. The elevation difference data table records the grid number, grid center coordinates, maximum elevation, minimum elevation, elevation difference value, and slope gradient. The slope gradient is obtained by the ratio of the elevation difference to the horizontal distance of the grid. Areas with elevation differences exceeding 3 meters are marked to provide accurate slope information for subsequent soil bearing capacity analysis. During data processing, all elevation difference data are ensured to be uniformly calibrated according to spatial coordinates to avoid deviations caused by different measurement benchmarks.

[0060] Step S32: Based on the slope elevation difference data, assess the soil bearing capacity to obtain soil bearing capacity data; In this embodiment, slope elevation difference data is used to assess soil bearing capacity, and the soil stress state is calculated based on the elevation difference and slope gradient information. For each grid cell, the slope length, slope angle, soil layer thickness, and filler type are recorded. Field soil sample parameters, including soil density (1.8–2.1 tons / m³), friction angle (28–35°), cohesion (20–50 kPa), and void ratio (0.35–0.5), are used, combined with slope gradient and elevation difference, to calculate the soil's safe bearing capacity level. Bearing capacity levels are divided into low (<100 kPa), medium (100–250 kPa), and high (>250 kPa). Each level is recorded in the data table with the grid number, bearing capacity value, slope angle, and soil layer thickness. Grids with slope gradients exceeding 45° are individually labeled. The influence of water level changes on soil bearing capacity is also considered, incorporating the buoyancy of groundwater on the slope soil and the pore water pressure coefficient into the calculation.

[0061] Step S33: Determine the type of slope fill material based on the soil bearing capacity data; In this embodiment, the slope fill type is determined based on soil bearing capacity data. High-strength fillers, such as graded gravel mixtures with a particle size range of 10–50 mm and a moisture content controlled at 12–16%, are used in low-bearing-capacity areas. Sand-soil mixtures with a particle size range of 5–30 mm and a moisture content controlled at 14–18% are used in medium-bearing-capacity areas. In high-bearing-capacity areas, undisturbed soil or lightly processed clay fillers can be used, with a moisture content controlled at 16–20%. The filler type allocation table records the grid number, bearing capacity level, slope angle, filler type, maximum particle size, minimum particle size, and target moisture content. For steep slopes or areas with elevation differences exceeding 4 meters, the thickness of the high-strength filler is increased to at least 0.5 meters; in areas with low elevation differences, the thickness is controlled at 0.3 meters. The distribution of slope filler types ensures continuity, and the difference between adjacent grids of filler type at the slope contact surface does not exceed one level.

[0062] Step S34: Conduct compaction testing based on the slope fill type to obtain compaction data; In this embodiment, compaction degree testing is conducted based on the slope fill material type. Each fill material zone is divided into grids, and measurements are taken for each grid using a field nuclear density meter or a lightweight compactor. Target compaction degrees are set according to fill material type: high-strength gravel fill material compaction degree ≥95%, sand-soil mixture compaction degree ≥90%, and clay fill material compaction degree ≥88%. At least three testing points are set up in each grid, with a spacing of no more than 3 meters between points. Testing data is recorded as grid number, testing point coordinates, measured dry density, target dry density, and compaction degree percentage. Grids with compaction degrees more than 5% below the target value are marked, and the location and deviation of the defect points are recorded for supplementary processing in subsequent slope stability analysis.

[0063] Step S35: Perform slope stability analysis based on compaction data to obtain slope stability data; In this embodiment, slope stability analysis is performed based on compaction data. The compaction degree, fill material type, slope gradient, and elevation difference data for each grid are input into a slope stability assessment table. Slope stability is classified into stable, basically stable, and unstable levels. A stable level requires that the fill material compaction degree be ≥ the target value, the slope gradient ≤ the allowable slope gradient, and the elevation difference ≤ 3 meters. A basically stable level requires that the compaction degree be slightly lower than the target value or the slope gradient be slightly higher than the allowable value. An unstable level requires that the compaction degree be more than 10% lower than the target value or the slope gradient be more than 10% higher than the allowable value. Each grid records the slope angle, fill material type, compaction degree, stability level, and location coordinates. For unstable areas, the locations of concentrated elevation differences and slope concentrations are recorded to facilitate leakage risk analysis.

[0064] Step S36: Based on the slope stability data, predict the leakage risk and obtain leakage risk data; In this embodiment, slope stability data is used to predict leakage risk. The stability level, compaction degree, slope gradient, and filler type of each grid are combined with on-site groundwater level data to calculate potential leakage risk. Groundwater level data is obtained through continuous well logging measurements with an accuracy of ±0.05 meters. Leakage risk levels are categorized as low, medium, and high. Low risk indicates a stable zone with the groundwater level 0.5 meters below the slope bottom; medium risk indicates a stable or basically stable zone with the groundwater level 0.2–0.5 meters below the slope bottom; and high risk indicates an unstable zone or a groundwater level ≤0.2 meters below the slope bottom. Each grid records the leakage risk level, stability level, compaction degree, slope gradient, and groundwater level.

[0065] Step S37: Identify the seepage path based on the leakage risk data.

[0066] In this embodiment, seepage paths are identified based on leakage risk data. Potential water flow paths are extracted through connectivity analysis using the leakage risk level and slope gradient of each high, medium, and high-risk grid. Path identification is based on the high-risk level and slope continuity of adjacent grids to ensure water flow can proceed from high to low elevations. Each path records the starting and ending coordinates, path length, slope changes along the route, and leakage risk distribution. A minimum path width of 1 meter is set to reflect the actual seepage impact range. The path distribution data table records the path number, starting and ending coordinates, length, average slope, leakage level of grids along the path, and total risk score.

[0067] Preferably, step S36 specifically includes: Step S361: Divide the unstable area according to the slope stability data to obtain the unstable area data; In this embodiment, based on slope stability data, the entire reservoir area is divided into several grid units, each with a side length of 5 meters. For steep slope areas, the grid is refined to 2 meters. Using slope stability level, slope angle, fill material type, and compaction degree data within the grid, unstable areas are divided according to the discontinuity of stability level, combining continuous unstable and basically stable grids to form unstable blocks. Each unstable block records the block number, start and end grid coordinates, area, average slope angle, slope elevation difference, and fill material type distribution. Grids with slope angles exceeding 45° or compaction degrees more than 10% below the target value are designated as initial markers for unstable areas. The unstable area division employs spatial connectivity analysis, expanding along continuous grids along the slope elevation until adjacent stable grids are reached. The division results are output as a grid number table and a spatial polygon file, containing block numbers, boundary point coordinates, and internal grid numbers within each block.

[0068] Step S362: Simulate the groundwater flow field based on the unstable region data to obtain groundwater flow distribution data; In this embodiment, groundwater flow field simulation is performed based on data from the unstable region. Groundwater monitoring points are first arranged within each unstable region, with a spacing of 3–5 meters, to collect data such as groundwater level, water temperature, and pore water pressure. The flow field simulation uses the finite difference method to discretize the soil layers within the unstable region, using the soil permeability coefficient and porosity of each grid as input parameters. The permeability coefficient range is... The porosity ranges from 0.35 to 0.50. For steep slopes with elevation differences exceeding 3 meters, the grid is further refined to 1 meter to improve the accuracy of groundwater flow. The simulation time step is set to 1 day, recording groundwater level and flow direction changes at each step. The groundwater flow velocity and direction are output as a two-dimensional vector field, with each vector recording the grid center coordinates, flow velocity (in m / d), flow direction angle (in °), and pore pressure value.

[0069] Step S363: Identify seepage channels based on groundwater flow distribution data to obtain potential seepage channel data; In this embodiment, seepage channels are identified based on groundwater flow distribution data. Grids with a flow velocity exceeding 0.05 m / d and continuous flow direction are designated as the starting points of potential seepage channels. Channel extraction is performed through grid connectivity analysis, tracing continuous high-velocity grids along the flow direction. The path width is set to a minimum of 2 meters to ensure the spatial influence range of potential seepage is recorded. For each potential seepage channel, the channel number, starting point coordinates, ending point coordinates, channel length, average and maximum flow velocities along the channel, pore pressure along the channel, and corresponding slope grid number are recorded. Channel intersections are marked, and the intersection grid number and confluence direction are recorded for use in subsequent hydraulic superposition analysis during seepage pressure calculations.

[0070] Step S364: Calculate the osmotic pressure based on the potential seepage channel data; In this embodiment, seepage pressure is calculated based on potential seepage channel data. Sampling points are taken at equal intervals along the path of each channel, with the seepage channel location recorded every 1 meter. The seepage pressure calculation uses the pore water pressure value, soil thickness, slope angle, and filler type at each sampling point as input. The soil thickness ranges from 0.5 to 3 meters, and the pore water pressure is obtained from water level measurements with an accuracy of ±0.01 meters. For each sampling point, the pressure distribution along the channel direction is calculated, and the maximum seepage pressure, average seepage pressure, and pressure gradient are recorded. The seepage pressure table records the channel number, sampling point number, coordinates, elevation, pore water pressure, soil thickness, filler type, and the calculated maximum pressure and gradient values. Sampling points with pore pressure greater than 100 kPa are marked with high pressure for reference in subsequent leakage trend analysis.

[0071] Step S365: Perform leakage trend analysis based on osmotic pressure to obtain leakage risk data.

[0072] In this embodiment, leakage trend analysis is performed based on osmotic pressure. The osmotic pressure value of each channel is combined with the slope along the route, the slope stability level, and the compaction degree of the filler, and segmented analysis is conducted according to the pressure change trend. The leakage trend level is divided into low, medium, and high, with a high leakage trend characterized by a continuous increase in osmotic pressure exceeding 80 kPa and unstable or basically stable slope stability along the route. During the analysis, areas along the channel with osmotic pressure exceeding 40 kPa and a continuous length greater than 5 meters are marked continuously, and the leakage length, average slope, and average pore pressure along the route are calculated. The leakage risk data table records the channel number, the start and end coordinates of the leakage section, the length, the average pressure along the route, the pressure change rate, the slope, and the stability level. Finally, a spatial distribution map of leakage risk is generated to identify potentially high-risk leakage paths.

[0073] Preferably, step S37 specifically includes: Step S371: Identify high-risk leakage areas based on leakage risk data; In this embodiment, based on leakage risk data, the reservoir area is divided into 1m × 1m grids. For each grid, the maximum seepage pressure, seepage pressure gradient, slope stability level, filler type, and compaction degree along the seepage channel are recorded. A high leakage risk threshold is set as an average seepage pressure exceeding 80kPa across a continuous 5m grid, with the slope stability level being unstable or basically stable. Continuous grids meeting this condition are merged into high leakage risk areas. Each area is recorded with its ID, boundary grid coordinates, area, average seepage pressure along the line, maximum seepage pressure, and average slope. For steep slope areas, the grid is refined to 0.5m to ensure the accuracy of leakage risk identification. In area boundary identification, expansion is performed through the connectivity of adjacent grids until adjacent grids no longer meet the high leakage threshold. A high leakage risk area data table is output, including the ID, boundary point coordinates, grid IDs within the area, and the average and maximum pressures of the area.

[0074] Step S372: Calculate the seepage potential energy gradient based on the high leakage risk area to obtain the potential energy gradient data; In this embodiment, seepage potential energy gradient calculation is performed based on data from high-leakage-risk areas. First, the pore water pressure, ground elevation, and soil thickness recorded within each grid are used as input parameters. The soil thickness ranges from 0.5 to 3 meters, and the pore water pressure accuracy is ±0.01 MPa. The potential energy gradient is calculated using the pore water pressure and elevation differences between the four adjacent nodes of each grid. The gradient direction is from high potential energy to low potential energy, and the gradient magnitude is recorded as the pressure change per unit length (in MPa / m). For steep slope areas, a minimum grid spacing of 0.5 meters is used to capture local potential energy changes. The potential energy gradient data table records each grid number, coordinates, elevation, pore water pressure, potential energy gradient magnitude, and direction angle (in °). After the potential energy gradient calculation is completed, a complete grid gradient distribution map is output for subsequent seepage direction simulation.

[0075] Step S373: Simulate the seepage direction based on the potential energy gradient data to obtain the seepage direction data; In this embodiment, seepage direction simulation is performed based on potential energy gradient data. A flow direction vector is established along the potential energy gradient direction within each high-leakage-risk grid. The flow direction simulation records the direction at the grid center point, advancing every 0.5 meters along the potential energy direction, recording the coordinates, elevation, pressure, and slope along the advancement point. It continues to advance along the direction of decreasing potential energy until it encounters a grid boundary or a low potential energy point. Each simulated path records the path number, starting point coordinates, ending point coordinates, path length, average potential energy gradient, and slope along the path. For intersections of multiple paths, the coordinates of the intersection point and the confluence direction are recorded. A seepage direction data table is output, including the path number, grid number, potential energy gradient direction angle, and pressure distribution along the path, for further processing in connectivity tracing analysis.

[0076] Step S374: Perform connectivity tracing analysis based on the seepage direction data to obtain seepage path data.

[0077] In this embodiment, connectivity tracking analysis is performed based on seepage direction data. The connectivity of each seepage path is determined along a continuous grid of potential energy directions. The determination criteria are that the directional deviation between adjacent grids is less than 30° and the potential energy gradient along the path is greater than 0.02 MPa / m. The continuous grid is tracked along the path, recording the start point, end point, total length, average pore water pressure, and maximum pressure along each connected path. Confluence analysis is performed at path intersections to identify confluence nodes and label the main channel and tributary directions. For each seepage path, the path number, path grid number, elevation along the path, pore water pressure, and potential energy gradient value are recorded. Spatial data of the seepage path is output, including the path polygon coordinates and connectivity network diagram, for subsequent seepage prevention optimization and backfill structure adjustment. The path tracking process ensures a minimum resolution of 0.5 meters to capture localized minute changes in seepage connectivity.

[0078] Preferably, step S4 specifically includes: Step S41: Identify weak points in the seepage prevention system based on the seepage path and obtain data on these weak points; In this embodiment, based on the seepage path data, the reservoir area is divided into 1m × 1m equally spaced grids, and the seepage rate, pore water pressure, and path density of each grid are recorded. Within a continuous 3m range, if the average pore water pressure is greater than 75kPa, the seepage rate exceeds 0.03m / d, and the path density is greater than 0.6 paths / m... 2 If the grid area is identified as a weak point in the seepage prevention system, then the grid size is adjusted to 0.5m x 0.5m for steep slope areas to capture local weak points. The weak point's number, boundary coordinates, area, average pore water pressure, maximum seepage rate, and slope along the line are recorded. Finally, a data table of weak points in the seepage prevention system is generated, providing a data basis for localized intensified backfilling and thickening of the seepage prevention layer.

[0079] Step S42: Based on the data of weak points in the seepage prevention, perform local backfilling and densification to obtain backfilling and densification data; In this embodiment, localized backfilling and densification are carried out in the weak areas of the seepage barrier. The backfill grid size is 0.5 m × 0.5 m, and each grid increases the backfill thickness by 0.3–0.6 m, with the thickness determined based on pore water pressure and seepage rate. The backfill material is a soil-rock mixture with a moisture content controlled at 12%–18%, particle size distribution conforming to the C30 mixture standard, and a compaction degree of not less than 95%. Construction is carried out in layers, each layer being 0.15 m thick, and compacted layer by layer in two to four layers, using alternating vibratory and static compaction. Backfill densification data is recorded for the backfill thickness, compaction degree, soil density, and number of construction layers for each grid, clearly defining the start and end coordinates of construction, providing input data for thickening the seepage barrier layer and integrating seepage prevention.

[0080] Step S43: Thicken the concrete seepage barrier layer based on the data of weak seepage points to obtain the seepage barrier layer thickening data; In this embodiment, the concrete waterproofing layer is thickened in the weakest areas of the seepage barrier by 0.3–0.5 meters, based on the original design thickness. The thickness is determined according to the maximum pore water pressure and seepage rate at the weakest points. C35 high-performance waterproof concrete is used, with a moisture content controlled at 10%–12%, and 1.5% polymer waterproofing agent is added. Before construction, the surface of the original waterproofing layer is cleaned to ensure no loose material remains. The thickening is done in layers, each 0.15 meters thick, with each layer compacted by vibration to ensure no gaps. The thickening extends 0.5 meters along the slope direction to ensure coverage of the seepage path. Data on the thickening of the waterproofing layer is recorded for each grid, including thickness, elevation, concrete mix ratio, number of layers, and construction time, providing a data foundation for integrated seepage control.

[0081] Step S44: Integrate the backfill encryption data and the seepage prevention layer thickening data to obtain seepage prevention data; In this embodiment, local backfill densification data and concrete impermeable layer thickness data are integrated to generate complete impermeable data. During the integration process, the backfill thickness and impermeable layer thickness of each grid are superimposed to record the total impermeable thickness. The construction sequence, earthwork type, concrete grade, compaction degree, and pore water pressure level are also noted. For steep slope areas, the grid is refined to 0.25m × 0.25m to ensure coverage of local weak points. After integration, an impermeable data table is output, including grid number, boundary coordinates, total impermeable thickness, number of construction layers, material type, and construction control parameters, providing data input for adjusting the structural layer thickness.

[0082] Step S45: Adjust the structural layer thickness based on the seepage prevention data to obtain structural layer thickness data; check the stability of the fill based on the structural layer thickness data to adjust the fill structure and obtain fill structure data. In this embodiment, the thickness of the structural layer is adjusted based on the seepage prevention data. The total thickness of each grid is no less than 1.2 times the thickness of the weakest point. For every 10° increase in slope, the structural layer thickness increases by 0.1 meters. After the thickness adjustment is completed, the stability of the fill is checked. The structural layer thickness, compaction degree, earthwork type, and slope height difference are used as input parameters. The shear stress safety factor is determined to be no less than 1.5, and the safety factor for areas with a slope height difference of less than 5 meters is set at 1.3. For grids that fail the check, the thickness is increased locally or the backfill layer is thickened until the safety standard is met. The fill structure data table records the structural layer thickness, compaction degree, earthwork type, slope, and safety factor for each grid, providing accurate input for the optimization of cut and fill parameters.

[0083] Step S46: Iteratively optimize the cut and fill parameters based on the fill structure data to obtain the optimized cut and fill parameters.

[0084] In this embodiment, iterative optimization of cut and fill parameters is performed based on the fill structure data. The backfill thickness, compaction degree, structural layer thickness, and slope safety factor of each grid are input into the iterative calculation table. The initial cut and fill parameters are compared with the safety factor and structural thickness. If the shear stress safety factor of a certain grid is less than the target value, the local backfill thickness is increased by 0.05–0.1 meters or the structural layer thickness is increased by 0.05 meters in the next iteration. The iterative process gradually updates the cut and fill sequence, layer thickness, compaction times, and construction material layout of each grid until the safety factor of all grids meets the design standards. Finally, the cut and fill optimization parameter table is output, including the backfill thickness, number of compaction layers, structural layer thickness, and construction sequence of each grid, providing accurate parameter basis for on-site implementation.

[0085] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0086] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A smart decision-making method for excavation and filling parameters in the reservoir area of ​​a pumped storage power station, characterized in that, Includes the following steps: Step S1: Obtain reservoir area topographic data; determine elevation distribution characteristics based on reservoir area topographic data; Based on the elevation distribution characteristics, cut-fill boundary analysis is performed to obtain cut-fill boundary data; Step S2: Calculate the earthwork volume based on the cut-fill boundary data to obtain earthwork balance data; identify the degree of cut-fill imbalance in the section based on the earthwork balance data to obtain cut-fill deviation data. Step S3: Perform slope stability analysis based on cut-fill deviation data to obtain slope stability data; predict leakage risk based on slope stability data to obtain leakage risk data. Identify penetration paths based on leakage risk data; Step S4: Optimize seepage prevention measures based on the seepage path to obtain seepage prevention data; adjust the filling structure based on the seepage prevention data to obtain filling structure data; iteratively optimize the cut and fill parameters based on the filling structure data to obtain cut and fill optimization parameters.

2. The intelligent decision-making method for excavation and filling parameters of pumped storage power station reservoir area according to claim 1, characterized in that, Step S1 is as follows: Step S11: Obtain the topographic data of the reservoir area and extract the original elevation point cloud to obtain elevation point cloud data; Step S12: Perform terrain gridding processing on the elevation point cloud data to obtain terrain grid data; Step S13: Calculate the elevation gradient based on the terrain grid data; Step S14: Identify high and low elevation zones based on the elevation gradient to obtain high and low elevation zone division data; Step S15: Determine the elevation distribution characteristics based on the high and low position zone division data; Step S16: Perform cut-fill boundary analysis based on elevation distribution characteristics to obtain cut-fill boundary data.

3. The intelligent decision-making method for excavation and filling parameters of pumped storage power station reservoir area according to claim 2, characterized in that, Step S16 is as follows: Step S161: Calculate the terrain slope based on the elevation distribution characteristics to obtain slope distribution data; Step S162: Identify slope aspect based on slope distribution data to obtain slope aspect data; Step S163: Perform topographic flow direction analysis based on slope aspect data to obtain surface flow direction data; Step S164: Identify water catchment and drainage zones based on surface flow direction data to obtain watershed zoning data; Step S165: Perform cut-and-fill trend analysis based on watershed zoning data to obtain cut-and-fill trend data; Step S166: Extract the boundary lines based on the cut-and-fill trend data, and use the boundary lines to perform spatial fitting to obtain the cut-and-fill boundary data.

4. The intelligent decision-making method for excavation and filling parameters of pumped storage power station reservoir area according to claim 3, characterized in that, Step S164 is as follows: Slope runoff paths are extracted based on surface flow direction data to obtain runoff path data; Water collection nodes are identified based on confluence path data to obtain water collection node data; The catchment area is defined based on the data from the water collection nodes; Watershed lines are identified based on the catchment area to obtain watershed data; Drainage channels are identified based on watershed data to obtain drainage channel data. Regional coding is performed based on drainage channel data to obtain watershed zoning data.

5. The intelligent decision-making method for excavation and filling parameters of pumped storage power station reservoir area according to claim 1, characterized in that, Step S2 is as follows: Step S21: Based on the cut-fill boundary data, create a three-dimensional surface model to obtain three-dimensional terrain model data; Step S22: Calculate the volume of each zone based on the three-dimensional terrain model data to obtain the earthwork balance data; Step S23: Calculate the actual excavation and filling volume based on the earthwork balance data to obtain the actual excavation and filling volume data; Step S24: Based on the comparison between the actual excavation and filling volume data and the preset excavation and filling volume, the section excavation and filling difference value is obtained; Step S25: Identify the degree of imbalance between cut and fill in a section based on the difference between cut and fill values, and obtain the cut and fill deviation data.

6. The intelligent decision-making method for excavation and filling parameters of pumped storage power station reservoir area according to claim 1, characterized in that, Step S3 is as follows: Step S31: Calculate the slope elevation difference based on the cut-fill deviation data to obtain the slope elevation difference data; Step S32: Based on the slope elevation difference data, assess the soil bearing capacity to obtain soil bearing capacity data; Step S33: Determine the type of slope fill material based on the soil bearing capacity data; Step S34: Conduct compaction testing based on the slope fill type to obtain compaction data; Step S35: Perform slope stability analysis based on compaction data to obtain slope stability data; Step S36: Based on the slope stability data, predict the leakage risk and obtain leakage risk data; Step S37: Identify the seepage path based on the leakage risk data.

7. The intelligent decision-making method for excavation and filling parameters of pumped storage power station reservoir area according to claim 6, characterized in that, Step S36 is as follows: Step S361: Divide the unstable area according to the slope stability data to obtain the unstable area data; Step S362: Simulate the groundwater flow field based on the unstable region data to obtain groundwater flow distribution data; Step S363: Identify seepage channels based on groundwater flow distribution data to obtain potential seepage channel data; Step S364: Calculate the osmotic pressure based on the potential seepage channel data; Step S365: Perform leakage trend analysis based on osmotic pressure to obtain leakage risk data.

8. The intelligent decision-making method for excavation and filling parameters of pumped storage power station reservoir area according to claim 6, characterized in that, Step S37 is as follows: Step S371: Identify high-risk leakage areas based on leakage risk data; Step S372: Calculate the seepage potential energy gradient based on the high leakage risk area to obtain the potential energy gradient data; Step S373: Simulate the seepage direction based on the potential energy gradient data to obtain the seepage direction data; Step S374: Perform connectivity tracing analysis based on the seepage direction data to obtain seepage path data.

9. The intelligent decision-making method for excavation and filling parameters of pumped storage power station reservoir area according to claim 1, characterized in that, Step S4 is as follows: Step S41: Identify weak points in the seepage prevention system based on the seepage path and obtain data on these weak points; Step S42: Based on the data of weak points in the seepage prevention, perform local backfilling and densification to obtain backfilling and densification data; Step S43: Thicken the concrete seepage barrier layer based on the data of weak seepage points to obtain the seepage barrier layer thickening data; Step S44: Integrate the backfill encryption data and the seepage prevention layer thickening data to obtain seepage prevention data; Step S45: Adjust the structural layer thickness based on the seepage prevention data to obtain the structural layer thickness data; The stability of the fill is checked based on the structural layer thickness data in order to adjust the fill structure and obtain the fill structure data. Step S46: Iteratively optimize the cut and fill parameters based on the fill structure data to obtain the optimized cut and fill parameters.

10. An intelligent decision-making system for excavation and filling parameters in the reservoir area of ​​a pumped storage power station, characterized in that, For executing the intelligent decision-making method for excavation and filling parameters in the pumped storage power station reservoir area as described in claim 1, the intelligent decision-making system for excavation and filling parameters in the pumped storage power station reservoir area includes: The cut-fill boundary analysis module is used to acquire reservoir area topographic data; determine elevation distribution characteristics based on reservoir area topographic data; and perform cut-fill boundary analysis based on elevation distribution characteristics to obtain cut-fill boundary data. The cut-fill deviation analysis module is used to calculate the volume of earthwork based on the cut-fill boundary data to obtain earthwork balance data; and to identify the degree of cut-fill imbalance in a section based on the earthwork balance data to obtain cut-fill deviation data. The leakage prediction module is used to perform slope stability analysis based on cut-fill deviation data to obtain slope stability data; to predict leakage risk based on slope stability data to obtain leakage risk data; and to identify seepage paths based on leakage risk data. The cut-and-fill parameter iteration module is used to optimize seepage prevention measures based on the seepage path to obtain seepage prevention data; adjust the filling structure based on the seepage prevention data to obtain filling structure data; and perform iterative optimization of cut-and-fill parameters based on the filling structure data to obtain cut-and-fill optimization parameters.

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