Reservoir dam body stability monitoring method, equipment and medium

Abstract

The invention discloses a reservoir dam body stability monitoring method and device and a medium, and belongs to the technical field of dam monitoring. The method comprises the steps that basic size parameters and layered soil material parameters of a dam body in a design drawing are obtained; processing the data based on an infiltration analysis model to generate a dam body cross-section diagram and a layered soil structure model; processing the layered soil structure model through an infiltration line generation algorithm, and generating an early warning infiltration line; marking a real-time water level point in the dam body cross-section diagram according to the dam body water level data, and generating a real-time infiltration line; comparing the real-time infiltration line with the early-warning infiltration line to calculate a deviation distance between the real-time infiltration line and the early-warning infiltration line; if the deviation distance exceeds the safety threshold value, an alarm signal is triggered, and a dam body stability evaluation report is generated and transmitted to a preset terminal. Through the method, whether the dam is stable or not can be judged according to simple data.

Description

Technical Field

[0001] The present application relates to the technical field of dam monitoring, and in particular to a method, equipment and medium for monitoring the stability of a reservoir dam. Background Art

[0002] Monitoring the stability of reservoir dams is a key step in ensuring the safe operation of water conservancy facilities. Currently, dam monitoring primarily relies on a combination of basic data collection and empirical judgment, obtaining dam structural parameters and environmental information for preliminary analysis. Existing methods generally suffer from complex data processing processes and inefficient model building. Furthermore, their ability to integrate multi-source heterogeneous data is limited, making it difficult to achieve real-time dynamic monitoring. In the context of small reservoirs, due to large differences in historical design standards and limited monitoring resources, traditional technical solutions often fail to meet the needs of rapid response and accurate early warning, limiting the timeliness and accuracy of risk assessments.

[0003] However, existing technologies still have significant defects: first, traditional methods are highly dependent on manual experience and scattered data sources, making it difficult to automate and intelligentize the monitoring process, resulting in low efficiency and easy introduction of subjective errors; second, most solutions lack systematic integration of dam body layered material parameters and seepage characteristics, and the accuracy of model construction is limited, making it difficult to accurately predict the position of the infiltration line under critical conditions; especially for small reservoirs with lower design standards, existing technologies are difficult to meet their low-cost and high-efficiency monitoring needs, exacerbating potential risks in safety management.

[0004] Therefore, how to determine whether a dam is stable through simple data has become a technical problem that needs to be solved urgently. Summary of the Invention

[0005] The embodiments of the present application provide a reservoir dam stability monitoring method, device and medium to solve the following technical problem: how to determine whether the dam is stable through simple data.

[0006] In a first aspect, an embodiment of the present application provides a reservoir dam stability monitoring method, which is applied to a small reservoir dam, and a front-end monitoring device is provided on the small reservoir dam. The method comprises: obtaining basic dimensional parameters and layered soil material parameters of the dam body in the design drawings of the small reservoir dam; wherein the basic dimensional parameters of the dam body include dam height, dam top width, and dam bottom width, and the layered soil material parameters include permeability coefficient and porosity of each layer of soil; processing the basic dimensional parameters and layered soil material parameters based on a preset infiltration analysis model to generate a dam cross-section diagram according to the basic dimensional parameters of the dam body, and constructing a layered soil structure model according to the layered soil material parameters; and generating a layered soil structure model according to the layered soil material parameters through a preset infiltration line generation algorithm. Process the layered soil structure model to generate an early warning infiltration line corresponding to the dam cross-section diagram; the early warning infiltration line is the infiltration boundary of the dam under a critical state; collect dam water level data through front-end monitoring equipment, and mark the real-time water level point in the dam cross-section diagram according to the dam water level data; based on the real-time water level point, generate a real-time infiltration line through a preset dynamic interpolation algorithm; perform spatial superposition and comparison between the real-time infiltration line and the early warning infiltration line, and calculate the deviation distance between the real-time infiltration line and the early warning infiltration line; if the deviation distance exceeds the preset safety threshold, trigger an alarm signal and generate a dam stability assessment report; transmit the alarm signal and the dam stability assessment report to the preset terminal through the preset interface.

[0007] In one implementation of the present application, basic dimensional parameters of the dam body and layered soil material parameters in the design drawings of a small reservoir dam are obtained, specifically including: parsing the vector layer of the design drawings to extract the coordinate point set of the dam body's contour line; calculating the dam height, dam top width and dam bottom width based on the coordinate point set; identifying the layered soil area boundaries marked in the design drawings to extract the distribution range and material identifiers of each layer of soil; and comparing the preset soil parameter database based on the material identifiers to obtain the permeability coefficient and porosity of each layer of soil.

[0008] In one implementation of the present application, basic size parameters and layered soil material parameters are processed based on a preset infiltration analysis model to generate a dam cross-section diagram based on the basic size parameters of the dam body, and a layered soil structure model is constructed based on the layered soil material parameters, specifically including: generating a two-dimensional dam cross-section diagram based on the dam height, dam top width and dam bottom width; dividing the dam cross-section diagram into multiple vertically layered soil areas based on the permeability coefficient and porosity in the layered soil material parameters; assigning permeability coefficient and porosity to each soil area based on the layered soil material parameters, and generating a discretized layered soil structure model through a preset finite element mesh division algorithm; and aligning the spatial coordinates of the layered soil structure model with the dam cross-section diagram.

[0009] In one implementation of the present application, a layered soil structure model is processed by a preset infiltration line generation algorithm to generate a warning infiltration line corresponding to the dam cross-section diagram, specifically including: based on the layered soil structure model, simulating the seepage field distribution under different water level conditions, and calculating the pore water pressure value of each grid node in the dam cross-section diagram; obtaining a shear pressure table; wherein the shear pressure table records the correlation between the pore water pressure value and the shear strength of the soil; determining the boundary point set where the pore water pressure exceeds the shear strength according to the shear pressure table; processing the boundary point set based on a preset contour line tracking algorithm to generate a continuous critical infiltration line boundary coordinate sequence; mapping the critical infiltration line boundary coordinates to the dam cross-section diagram, and smoothing it through a spline interpolation algorithm to output a warning infiltration line.

[0010] In one implementation of the present application, dam body water level data is collected by front-end monitoring equipment, and the real-time water point is marked in the dam body cross-sectional diagram based on the dam body water level data, specifically including: receiving the real-time water level elevation data transmitted by the front-end monitoring equipment, and associating the cross-sectional position identifier corresponding to the water level elevation data; determining the horizontal coordinates of the real-time water point in the dam body cross-sectional diagram based on the cross-sectional position identifier; calculating the vertical coordinates of the real-time water point based on the water level elevation data and the elevation reference plane of the cross-sectional diagram; marking the real-time water point as a vector mark with a timestamp, and storing it in a dynamic water level database.

[0011] In one implementation of the present application, a real-time infiltration line is generated based on the real-time water point through a preset dynamic interpolation algorithm, specifically including: extracting the coordinate data of the real-time water point to construct a discrete water point set; performing spatial interpolation on the discrete water point through a preset Kriging interpolation algorithm to generate a continuous water level distribution surface; determining the boundary of the real-time infiltration line based on the relationship between the water level distribution surface and the permeability gradient of the layered soil structure model; superimposing the real-time infiltration line on the dam cross-section diagram, and visually comparing it with the early warning infiltration line.

[0012] In one implementation of the present application, the real-time infiltration line and the warning infiltration line are spatially superimposed and compared, and the deviation distance between the real-time infiltration line and the warning infiltration line is calculated, specifically including: converting the real-time infiltration line and the warning infiltration line into vector line data of the same coordinate system; sampling the elevation values ​​of the two infiltration lines at preset intervals along the vertical profile direction of the dam cross-section; calculating the horizontal distance difference and vertical elevation difference of each sampling point to generate a deviation distance matrix; performing statistical analysis on the deviation distance matrix to extract the maximum deviation value, average deviation value and the proportion of areas where the deviation exceeds the threshold; if the maximum deviation value or the average deviation value exceeds the safety threshold, it is determined that the dam stability is abnormal.

[0013] In one implementation of the present application, if the deviation distance exceeds a preset safety threshold, an alarm signal is triggered and a dam stability assessment report is generated, specifically including: determining the starting and ending points of the potential risk section of the dam based on the location of the area where the deviation exceeds the threshold; analyzing the seepage velocity and soil bearing capacity attenuation trend of the risk section by combining the layered soil structure model and real-time water level data; generating an assessment report containing the risk level, risk location coordinates, and recommended reinforcement measures, with an attached timestamp and cross-sectional screenshot; and sending differentiated alarm signals to different preset management terminals according to the risk level.

[0014] In a second aspect, an embodiment of the present application further provides a reservoir dam stability monitoring device, the device comprising: at least one processor; and a memory in communication with the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so as to enable the at least one processor to: obtain basic dimensional parameters and layered soil material parameters of the dam body in the design drawings of the small reservoir dam; wherein the basic dimensional parameters of the dam body include dam height, dam top width, and dam bottom width, and the layered soil material parameters include permeability coefficient and porosity of each layer of soil; based on a preset infiltration analysis model, the basic dimensional parameters and layered soil material parameters are processed to generate a dam cross-section diagram according to the basic dimensional parameters of the dam body, and a layered soil material parameter is generated according to the layered soil material parameter. The layered soil structure model is constructed based on the parameters; the layered soil structure model is processed by the preset infiltration line generation algorithm to generate the early warning infiltration line corresponding to the dam cross-section diagram; wherein the early warning infiltration line is the infiltration boundary of the dam body in the critical state; the dam body water level data is collected by the front-end monitoring equipment, and the real-time water position point is marked in the dam cross-section diagram according to the dam body water level data; based on the real-time water position point, the real-time infiltration line is generated by the preset dynamic interpolation algorithm; the real-time infiltration line and the early warning infiltration line are spatially superimposed and compared, and the deviation distance between the real-time infiltration line and the early warning infiltration line is calculated; if the deviation distance exceeds the preset safety threshold, an alarm signal is triggered and a dam body stability assessment report is generated; the alarm signal and the dam body stability assessment report are transmitted to the preset terminal through the preset interface.

[0015] In a third aspect, the embodiment of the present application further provides a non-volatile computer storage medium for monitoring the stability of a reservoir dam body, which stores computer executable instructions, and the computer executable instructions are configured to: obtain basic dimensional parameters and layered soil material parameters of the dam body in the design drawings of a small reservoir dam; wherein the basic dimensional parameters of the dam body include dam height, dam top width, and dam bottom width, and the layered soil material parameters include permeability coefficient and porosity of each layer of soil; process the basic dimensional parameters and layered soil material parameters based on a preset infiltration analysis model to generate a dam body cross-section diagram according to the basic dimensional parameters of the dam body, and construct a layered soil structure model according to the layered soil material parameters; generate an algorithm through a preset infiltration line The method processes the layered soil structure model and generates an early warning infiltration line corresponding to the dam cross-section diagram; the early warning infiltration line is the infiltration boundary of the dam body in a critical state; the dam body water level data is collected through the front-end monitoring equipment, and the real-time water level position is marked in the dam cross-section diagram according to the dam body water level data; based on the real-time water level position, a real-time infiltration line is generated through a preset dynamic interpolation algorithm; the real-time infiltration line and the early warning infiltration line are spatially superimposed and compared, and the deviation distance between the real-time infiltration line and the early warning infiltration line is calculated; if the deviation distance exceeds the preset safety threshold, an alarm signal is triggered and a dam body stability assessment report is generated; the alarm signal and the dam body stability assessment report are transmitted to a preset terminal through a preset interface.

[0016] The embodiments of the present application provide a reservoir dam stability monitoring method, device, and medium, which have at least the following technical effects: By automatically parsing the vector data of the design drawings and associating them with the soil parameter database, the basic dimensions of the dam body and the layered soil material parameters can be quickly extracted, reducing errors in manual measurement and parameter input and improving model construction efficiency. Based on the finite element meshing algorithm and the layered soil structure model, a precise match between the dam cross-section diagram and the seepage field analysis is achieved, enhancing the credibility of the model.

[0017] By simulating the seepage field distribution under different water level conditions and combining the correlation between pore water pressure and soil shear strength, the early warning infiltration line under the critical state is accurately generated, providing a scientific basis for judging the stability of the dam body.

[0018] Water level data is collected in real time using front-end monitoring equipment, and a real-time infiltration line is generated through a dynamic interpolation algorithm to dynamically reflect changes in the seepage state of the dam body, ensuring the timeliness and continuity of the monitoring data.

[0019] By comparing the real-time infiltration line and the warning infiltration line through spatial superposition, the deviation distance is automatically calculated and a graded alarm signal is triggered, which significantly shortens the risk response time and avoids the risk of dam failure caused by delayed manual judgment to a certain extent.

[0020] The generated dam stability assessment report combines the location of risk areas, seepage trend analysis and reinforcement recommendations, providing managers with an intuitive and multi-dimensional decision-making basis, effectively supporting the formulation and implementation of emergency measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 A flow chart of a reservoir dam stability monitoring method provided in an embodiment of the present application; Figure 2 A schematic diagram of the internal structure of a reservoir dam stability monitoring device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] To make the purpose, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the specific embodiments of this application and the corresponding drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0023] The embodiments of the present application provide a reservoir dam stability monitoring method, device and medium to solve the following technical problem: how to determine whether the dam is stable through simple data.

[0024] The technical solutions proposed in the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0025] Figure 1 This is a flow chart for monitoring the stability of a reservoir dam provided in the embodiment of the present application. Figure 1 As shown, a reservoir dam stability monitoring method provided in an embodiment of the present application specifically includes the following steps: Step 1. Obtain the basic dimensional parameters of the dam body and the layered soil material parameters from the design drawings of the small reservoir dam; the basic dimensional parameters of the dam body include the dam height, dam top width, and dam bottom width, and the layered soil material parameters include the permeability coefficient and porosity of each layer of soil.

[0026] By analyzing the dam design drawings, key geometric parameters and layered soil physical properties are extracted to provide a data basis for subsequent model construction.

[0027] Step 1.1: Analyze the vector layer of the design drawing to extract the coordinate point set of the dam body's contour line.

[0028] The vector layer is a digital layer that stores geometric shapes (such as lines and polygons) in the design drawings. By analyzing this layer, the precise coordinate data of the dam outline can be obtained.

[0029] In the specific example of this step: Use CAD parsing software (such as AutoCAD or a GIS tool) to read the vector file (e.g., DWG or DXF format) of the design drawing and identify and extract the geometric coordinate points of the dam contour. The contour includes the dam crest line, dam bottom line, and side slope lines, forming a closed polygon. During the parsing process, ensure that the coordinate system is consistent, for example, using a geodetic coordinate system (e.g., WGS-84) or an independent project coordinate system.

[0030] Step 1.2: Calculate the dam height, dam top width, and dam bottom width based on the coordinate point set.

[0031] The key dimensional parameters of the dam body are calculated through the geometric relationship of the contour line coordinates.

[0032] In the specific example of this step: Dam height calculation: Select the elevation difference between the highest point of the dam crest line and the lowest point of the dam bottom line, and determine the dam height in combination with the vertical projection distance.

[0033] Calculation of dam crest width: extract the horizontal distance between the turning points of the slope on the left and right sides of the dam crest line.

[0034] Dam bottom width calculation: determined based on the horizontal projection distance between the left and right end points of the dam bottom line.

[0035] For example: If the dam top line is composed of coordinate points A(0, 100) and B(50, 100), and the dam bottom line is composed of coordinate points C(0, 0) and D(80, 0), then the dam top width is 50 meters (the distance between points B and A), the dam bottom width is 80 meters (the distance between points D and C), and the dam height is 100 meters (the elevation difference between the dam top and dam bottom).

[0036] Step 1.3: Identify the boundaries of the layered soil areas marked in the design drawings to extract the distribution range and material identifiers of each layer of soil.

[0037] Different soil layers are distinguished in design drawings by color, fill pattern or text annotation. Their boundaries need to be identified and associated with material properties.

[0038] In the specific example of this step: Boundary recognition: Use image recognition algorithms (such as edge detection or semantic segmentation) to extract the boundary coordinates of different fill areas in the drawing.

[0039] Material identifier extraction: Identify text labels (such as "clay layer" or "gravel layer") or symbol codes in the annotation layer and bind them to the layered area.

[0040] Data association: Store the coordinate range of the layered area and the material identifier into a structured database to form a "area-material" mapping table.

[0041] Step 1.4: Compare the preset soil parameter database based on the material identifier to obtain the permeability and porosity of each layer of soil.

[0042] The preset database is queried through the material identifier to obtain the physical property parameters of the corresponding soil.

[0043] In the specific example of this step: Database construction: A soil parameter database is pre-established, including the standard permeability coefficient (unit: m / s) and porosity (percentage) of common materials (such as clay, sand, and gravel).

[0044] Parameter matching: Match the material identifier (e.g., "CL-01") in step 1.3 with the records in the database. If multiple sets of data exist, take the average.

[0045] Exception handling: If the material identifier is not found in the database, a manual review process is triggered, and the engineer supplements the parameters or selects an approximate material as an alternative.

[0046] In a specific case The design drawings of a small reservoir dam are stored in DWG format.

[0047] The vector layer of the drawing was parsed using AutoCAD software to extract the coordinate point set of the dam contour line, including the dam top line coordinates (X1, Y1) to (X2, Y2) and the dam bottom line coordinates (X3, Y3) to (X4, Y4).

[0048] Based on the coordinate calculation, the dam height is 25 meters (dam top elevation 150 meters, dam bottom elevation 125 meters), the dam top width is 8 meters, and the dam bottom width is 60 meters.

[0049] Identify the 3 soil layers marked on the drawing: Surface area (identifier "CL-01"): clay layer, boundary coordinate range (X5, Y5) to (X6, Y6).

[0050] Middle layer (identifier "SM-02"): sandy clay layer, with boundary coordinates ranging from (X7, Y7) to (X8, Y8).

[0051] Bottom layer (identifier "GW-03"): gravel layer, with boundary coordinates ranging from (x9, y9) to (x10, y10).

[0052] Query the soil parameter database to obtain the parameters of each layer: "CL-01": Permeability coefficient 1×10 -7m / s, porosity 30%.

[0053] "SM-02": Permeability coefficient 5×10 -6 m / s, porosity 25%.

[0054] "GW-03": Permeability coefficient 1×10 -3 m / s, porosity 20%.

[0055] Step 2: Process the basic size parameters and layered soil material parameters based on the preset infiltration analysis model to generate a dam cross-section diagram based on the basic size parameters of the dam body and construct a layered soil structure model based on the layered soil material parameters.

[0056] The geometric parameters of the dam body are combined with the physical properties of the soil to construct a numerical model for seepage analysis, providing a basis for the calculation of the seepage line.

[0057] Step 2.1: Generate a two-dimensional dam cross-section based on the dam height, dam top width, and dam bottom width.

[0058] The two-dimensional cross-section diagram is a geometric expression of the vertical section of the dam body, reflecting the shape of the dam top, dam bottom and slope.

[0059] In the specific example of this step: Geometric modeling: Use CAD software (such as AutoCAD or Civil3D) to input the dam height (H), dam crest width (W1) and dam bottom width (W2), and generate cross-sectional drawings using parametric modeling tools.

[0060] Dam top line: horizontal line segment with a length of W1.

[0061] Slope line: The inclined line segment connecting the two ends of the dam crest to the bottom line of the dam. The slope is determined according to the design drawings or standard specifications (for example, 1:2.5).

[0062] Dam bottom line: horizontal line segment with a length of W2.

[0063] Coordinate system setting: The center of the dam top is the origin (0, 0), the vertical downward direction is the positive direction of the Y axis, and the horizontal direction is the X axis to ensure the spatial consistency of the model and subsequent analysis.

[0064] Step 2.2: Based on the permeability coefficient and porosity in the layered soil material parameters, the dam cross section is divided into multiple vertically layered soil areas.

[0065] According to the difference in permeability of soil materials, the dam section is divided into several vertical layers, each layer corresponding to specific material parameters.

[0066] In the specific example of this step: Stratification basis: High permeability layer (such as gravel layer): permeability coefficient ≥ 1×10 -4 m / s, porosity ≥25%.

[0067] Medium permeability layer (such as sandy soil layer): permeability coefficient 1×10 -6 ~1×10 -4 m / s, porosity 20%~25%.

[0068] Low permeability layer (such as clay layer): permeability coefficient ≤ 1×10 -6 m / s, porosity ≤20%.

[0069] Division method: In the cross-sectional view, the area is divided along the vertical direction (Y axis) according to the layer height marked on the design drawing.

[0070] The boundary lines of each layer are parallel to the horizontal plane to ensure that the layer thickness matches the material parameters.

[0071] Step 2.3: Assign permeability coefficient and porosity to each soil region based on the layered soil material parameters, and generate a discretized layered soil structure model using a preset finite element meshing algorithm.

[0072] The continuous layered region is discretized into finite element grids to provide calculation units for numerical simulation of seepage field.

[0073] In the specific example of this step: Parameter assignment: For each soil layer, bind the permeability coefficient (k) and porosity (n) matched in step 1.4.

[0074] For example, the clay layer area allocation k=1×10 -7 m / s, n=30%.

[0075] Meshing: Using finite element software (such as COMSOL or ANSYS), select a triangular or quadrilateral mesh type.

[0076] Set the grid density: use a coarser grid (cell size 0.5m×0.5m) for the high permeability layer and a finer grid (cell size 0.2m×0.2m) for the low permeability layer to balance calculation accuracy and efficiency.

[0077] Model export: Generate a hierarchical soil structure model file (such as INP or MSH format) containing node coordinates, element connection relationships and material properties.

[0078] Step 2.4: Align the spatial coordinates of the layered soil structure model with the dam cross-section diagram.

[0079] Ensure that the geometric position of the finite element model exactly matches the cross-section diagram to avoid spatial errors in seepage analysis.

[0080] In the specific example of this step: Coordinate transformation: adjust the global coordinate system of the finite element model to be consistent with the cross section Figure 1 The local coordinate system is consistent with the dam top center.

[0081] Verify the degree of coincidence between the model boundary line and the dam top line, slope line and dam bottom line of the cross-section diagram. If the deviation exceeds 1 cm, grid reconstruction will be triggered.

[0082] Use a visual comparison tool (such as Paraview) to overlay the model and cross-section image and manually confirm the alignment accuracy.

[0083] In a specific case Taking a small reservoir dam as an example, the parameters are as follows: Basic dimensions: dam height 20 meters, dam top width 10 meters, dam bottom width 70 meters.

[0084] Layered soil parameters: Surface layer (0-5m depth): clay layer (k=1×10 -7 m / s, n=30%).

[0085] Middle layer (5-15m depth): sandy clay layer (k=5×10 -6 m / s, n=25%).

[0086] Bottom layer (15-20m depth): gravel layer (k=1×10 -3 m / s, n=20%).

[0087] Input the dam height, dam top and dam bottom width into AutoCAD to generate the cross-section drawing, and set the slope gradient to 1:2.5.

[0088] The area is divided into three layers according to depth, and the boundary lines of each layer are Y=0m (dam top), Y=5m, Y=15m, and Y=20m (dam bottom).

[0089] Import the cross-section into COMSOL, assign material parameters to each layer, divide the triangular mesh (the mesh size of the surface layer is 0.2m and that of the bottom layer is 0.5m), and derive the layered soil structure model.

[0090] Set the origin of the model coordinate system to the center of the dam crest and check that the slope line coincides with the model boundary.

[0091] Step 3: Process the layered soil structure model using a preset infiltration line generation algorithm to generate an early warning infiltration line corresponding to the dam cross-section diagram; wherein the early warning infiltration line is the infiltration boundary of the dam body in a critical state.

[0092] Through seepage field simulation and shear strength analysis, the position of the infiltration line of the dam under critical state is determined, providing a benchmark for stability early warning.

[0093] Step 3.1: Based on the layered soil structure model, simulate the seepage field distribution under different water level conditions and calculate the pore water pressure value of each grid node in the dam cross-section diagram.

[0094] The seepage field simulation uses numerical methods to solve the movement law of water in the dam body. The pore water pressure is the key parameter for judging the shear failure of soil.

[0095] In the specific example of this step: Working condition setting: Set multiple groups of water level conditions (such as normal water level, flood level, extreme low water level) to simulate steady-state or transient seepage under different working conditions.

[0096] Solver configuration: Use finite element analysis software (such as SEEP / W or PLAXIS), input the layered soil structure model, and set the boundary conditions: The upstream water level is the constant head boundary.

[0097] The downstream slope is the free seepage boundary.

[0098] The bottom is an impermeable boundary.

[0099] Calculation results: Output the pore water pressure (unit: kPa) of each grid node and store it as a pressure distribution matrix.

[0100] Step 3.2: Obtain a shear pressure gauge; wherein the shear pressure gauge records the correlation between the pore water pressure value and the soil shear strength.

[0101] Shear pressure tables determine the shear strength thresholds for different soil types at specific pore water pressures, either experimentally or through specifications.

[0102] In the specific example of this step: Data source: Laboratory test: The shear strength (τ) of soil at different moisture contents is obtained through direct shear test or triaxial test.

[0103] Specification reference: Based on the empirical formula in the Geotechnical Testing Code or regional standards (such as τ=σ·tanφ+c, where σ is the effective stress, φ is the internal friction angle, and c is the cohesion).

[0104] Table construction: Store the corresponding relationship between pore water pressure (u) and shear strength (τ) by soil type (such as clay, sand), and generate a shear pressure table.

[0105] Step 3.3: Determine the boundary point set where the pore water pressure exceeds the shear strength based on the shear pressure table.

[0106] When the pore water pressure exceeds the shear strength of the soil, the soil may undergo shear failure, and the collection of such nodes constitutes the potential boundary of the critical seepage line.

[0107] In the specific example of this step: Threshold comparison: Traverse each grid node and query the shear strength threshold (τ-threshold) of the corresponding soil type in the shear pressure table. If the pore water pressure (u) satisfies u ≥ τ-threshold, it is marked as a critical point.

[0108] Coordinate extraction: Collect the coordinates (X, Y) of all critical nodes to form a discrete boundary point set.

[0109] Step 3.4: Process the boundary point set based on the preset contour line tracking algorithm to generate a continuous critical infiltration line boundary coordinate sequence.

[0110] The contour line tracing algorithm connects discrete critical points into continuous boundary lines, reflecting the spatial distribution of infiltration lines.

[0111] In the specific example of this step: Algorithm selection: Use the MarchingCubes algorithm or Delaunay triangulation method to build topological connections based on critical point density.

[0112] Line generation: trace adjacent critical points along the contour line direction to form a boundary line coordinate sequence, which is stored in clockwise or counterclockwise order.

[0113] Step 3.5: Map the boundary coordinates of the critical infiltration line to the dam cross-section diagram, smooth it using the spline interpolation algorithm, and output the warning infiltration line.

[0114] Smoothing eliminates the jagged fluctuations of discrete point sets and generates continuous curves that meet the requirements of engineering analysis.

[0115] In the specific example of this step: Coordinate mapping: Convert the critical seepage line coordinates to the local coordinate system of the dam section to ensure alignment with the section geometry.

[0116] Spline interpolation: Use the cubic spline interpolation algorithm to fit discrete coordinate points to generate a smooth curve.

[0117] Output format: Save the warning infiltration line as vector line data (such as SHP or GeoJSON format) and overlay it on the cross-section visualization interface.

[0118] In a specific example: taking a small reservoir dam as an example, the parameters are as follows: Layered soil structure model: including clay layer (k=1×10 -7 m / s), sand layer (k=5×10 -6 m / s), gravel layer (k=1×10 -3 m / s).

[0119] Shear pressure table: τ-threshold value for clay layer is 50kPa, τ-threshold value for sand layer is 30kPa, and τ-threshold value for gravel layer is 10kPa.

[0120] The flood level condition was simulated in PLAXIS, and the pore water pressure at each node was calculated (e.g., u=55kPa for the clay layer node and u=35kPa for the sand layer node).

[0121] Call the pre-stored shear pressure table to match the τ-threshold value of each layer of soil.

[0122] Mark the nodes (X1, Y1) to (Xn, Yn) where u ≥ 50 kPa in the clay layer, and the nodes (Xa, Ya) to (Xb, Yb) where u ≥ 30 kPa in the sand layer.

[0123] The critical infiltration line boundary coordinate sequence is generated by the marching cube algorithm, which contains 200 discrete points.

[0124] The coordinates are mapped to the cross-section diagram, and a smooth warning infiltration line is output after cubic spline interpolation.

[0125] Step 4: Collect dam body water level data through front-end monitoring equipment, and mark the real-time water level point on the dam body cross-section diagram based on the dam body water level data.

[0126] The dam water level information is acquired in real time and accurately mapped to the cross-sectional diagram, providing data support for the dynamic generation of infiltration lines.

[0127] Step 4.1: Receive the real-time water level elevation data transmitted by the front-end monitoring equipment, and associate the real-time water level elevation data with the section location identifier corresponding to the data.

[0128] The data collected by front-end monitoring equipment (such as piezometers and ultrasonic water level meters) must be associated with specific locations in the cross-sectional diagram to ensure spatial correspondence of the data.

[0129] In the specific example of this step: Equipment deployment: Piezometers are installed on the upstream slope, downstream slope and key sections of the dam body. Each device has a built-in unique identifier (such as "S-01" or "S-02").

[0130] Data transmission: Real-time water level elevation data is transmitted to the central server via LoRa or 4G network. The data packet contains the device identifier, collection timestamp and water level value.

[0131] Identifier mapping: Query the preset section position mapping table based on the device identifier to determine the section position identifier corresponding to the device (such as "Section A-upstream" and "Section B-downstream").

[0132] Step 4.2: Determine the horizontal coordinates of the real-time water level point in the dam cross-section diagram based on the cross-section position identifier.

[0133] The horizontal coordinate (X-axis) represents the horizontal position of the water point in the cross-section diagram and needs to be matched with the preset coordinates through the identifier.

[0134] In the specific example of this step: Coordinate mapping table: The correspondence between the cross-section position identifier and the horizontal coordinate is pre-established in the system. For example: "Section A-Upstream" corresponds to X=0m (center of dam crest).

[0135] "Section B-Midstream" corresponds to X=30 meters.

[0136] "Section C-Downstream" corresponds to X=60 meters.

[0137] Coordinate matching: Directly read the corresponding horizontal coordinate value based on the received section position identifier.

[0138] Step 4.3: Calculate the vertical coordinate of the real-time water level point based on the water level elevation data and the elevation datum of the cross-section.

[0139] The vertical coordinate (Y-axis) represents the vertical position of the water point relative to the elevation datum and needs to be calculated through the elevation difference.

[0140] In the specific example of this step: Reference plane setting: The elevation of the dam crest center is used as the reference plane (set to Y=0 meters), with downward being the positive direction of the Y axis.

[0141] Coordinate calculation: The difference between the real-time water level elevation (H-water) and the base elevation (H-base) is the vertical coordinate, that is, Y=H-base-H-water.

[0142] Example: If the datum elevation is 150 meters and the real-time water level elevation is 145 meters, then Y = 5 meters.

[0143] Step 4.4: Mark the real-time water level point as a vector mark with a timestamp and store it in the dynamic water level database.

[0144] Vector tags contain spatial coordinates and time information for subsequent dynamic analysis and historical tracing.

[0145] In the specific example of this step: Tag format: Stored in GeoJSON format, including the following fields: coordinates:[X, Y]; timestamp: "YYYY-MM-DDHH:MM:SS"; device-id: "S-01"; Database Design: Table structure: water point ID (primary key), coordinate X, coordinate Y, timestamp, device ID.

[0146] Index: Create a composite index based on timestamp and device ID to support fast query.

[0147] Data writing: real-time data is written in batches through SQL or NoSQL database interfaces (such as MySQL or MongoDB).

[0148] In a specific case Three piezometers (S-01 to S-03) are deployed on the dam of a small reservoir. The parameters are as follows: Datum elevation: 160 meters (center of dam crest).

[0149] Section position mapping table: S-01: Identifier "Section A-Upstream", horizontal coordinate X=0 meters.

[0150] S-02: Identifier "Section B-Midstream", horizontal coordinate X = 35 meters.

[0151] S-03: Identifier "Section C-Downstream", horizontal coordinate X=70 meters.

[0152] Implementation process: The piezometer S-02 transmits real-time water level elevation data (H-water = 155 m), which the system associates with the corresponding section location based on the identifier “Section B-Midstream”.

[0153] Query the mapping table and determine that the horizontal coordinate X = 35 meters.

[0154] Calculate the longitudinal coordinate Y = 160 meters - 155 meters = 5 meters, and get the coordinate point (35, 5).

[0155] Generate vector markers in GeoJSON format: "coordinates": [35, 5]; "timestamp": "2023-10-0514:30:00"; "device-id": "S-02"; Stored in the MySQL database.

[0156] Step 5: Based on the real-time water point, a real-time infiltration line is generated through a preset dynamic interpolation algorithm.

[0157] The discrete real-time water level data is converted into a continuous infiltration line to dynamically reflect the changes in the seepage state of the dam body.

[0158] Step 5.1: Extract the coordinate data of the real-time water point and construct a discrete water point set.

[0159] Filter the water level points within the last N minutes (e.g., 30 minutes) by timestamp, excluding historical data and outliers (e.g., values ​​outside the dam elevation range).

[0160] Convert the coordinate fields (X, Y) in the database into a two-dimensional array format, for example: [[x1,y1],[x2,y2],...,[xn,yn]]; The discrete point set is temporarily stored in the memory queue for real-time calling of the interpolation algorithm.

[0161] Step 5.2: Use the preset Kriging interpolation algorithm to perform spatial interpolation on discrete water level points to generate a continuous water level distribution surface.

[0162] Kriging interpolation is a statistical interpolation method based on spatial autocorrelation, which can generate a smooth water level distribution surface that conforms to physical laws.

[0163] In the specific example of this step: Parameter configuration: Variogram model: Select Gaussian model or exponential model to characterize the spatial correlation of water level data.

[0164] Search radius: Set it to 1.5 times the width of the dam section (e.g. 100 meters) to ensure that the interpolation range covers the entire section.

[0165] Interpolation execution: Call the geostatistical library (such as PyKrige or ArcGIS interpolation tools), input the discrete point set, and output the rasterized water level distribution surface (resolution 0.1m × 0.1m).

[0166] Surface optimization: Eliminate interpolation noise through smoothing filtering (such as Gaussian filtering) to ensure that the surface is continuous and has no sudden changes.

[0167] Step 5.3: Determine the boundary of the real-time infiltration line based on the relationship between the water level distribution surface and the infiltration gradient of the layered soil structure model.

[0168] The infiltration gradient reflects the movement trend of water in the soil, and combined with the water level distribution, the actual position of the infiltration line can be deduced.

[0169] In the specific example of this step: Gradient calculation: Based on the permeability coefficient (k) of the layered soil structure model, the hydraulic gradient of each layer is calculated (i=Δh / ΔL, Δh is the head difference, and ΔL is the seepage path length).

[0170] Boundary determination: On the water level distribution surface, search for the critical point where the hydraulic gradient suddenly changes along the vertical direction (Y axis) and mark it as the infiltration line boundary.

[0171] Example: If k=1×10-6m / s in a layer and its critical gradient ic=0.5, then it is determined to be a wetting boundary when the actual gradient i≥ic.

[0172] Coordinate extraction: Collect the coordinates of all boundary points to form a real-time infiltration line discrete point set.

[0173] Step 5.4: Superimpose the real-time infiltration line on the dam cross-section diagram and perform a visual comparison with the early warning infiltration line.

[0174] Through visual comparison, the deviation between the real-time seepage state and the critical state is intuitively displayed, assisting managers in making quick decisions.

[0175] In the specific example of this step: Overlay display: In the cross-section visualization interface (such as QGIS or a custom web platform), different colors are used to distinguish the real-time infiltration line (red) and the warning infiltration line (blue).

[0176] Dynamic update: Automatically refresh the real-time infiltration line every preset time (such as 5 minutes) to keep the data up to date.

[0177] Deviation marking: Add text markings (such as "Deviation +15%") to areas where the infiltration lines intersect or the deviation exceeds the limit to prompt attention.

[0178] In a specific case The real-time water level data of a small reservoir dam is as follows: Discrete point set: 10 water points with coordinate range of X=0~70m, Y=5~18m (base elevation 160m).

[0179] Layered soil permeability coefficient: surface clay layer k=1×10-7m / s, middle sand layer k=5×10-6m / s.

[0180] Implementation process: Extract the water level points in the last 30 minutes and construct a discrete point set points=[[0, 5], [20, 8], [35, 10], [50, 12], [70, 15]].

[0181] Configure the kriging interpolation parameters (Gaussian model, search radius of 100 meters) to generate a water level distribution surface with a grid resolution of 0.1 meters.

[0182] The critical gradient of the sand layer i c = 0.4 is calculated, the boundary points with gradient i ≥ 0.4 are marked, and a real-time infiltration line coordinate set is generated.

[0183] In QGIS, the real-time infiltration line (red) and the warning infiltration line (blue) are superimposed, and the maximum deviation area is marked as "X=40 meters, deviation +12%".

[0184] Step 6: Compare the real-time infiltration line and the early warning infiltration line by spatial overlay, and calculate the deviation distance between the real-time infiltration line and the early warning infiltration line.

[0185] By quantifying the spatial deviation of the two infiltration lines, the stability of the dam body can be dynamically evaluated to provide data support for risk warning.

[0186] Step 6.1: Convert the real-time infiltration line and the warning infiltration line into vector line data in the same coordinate system.

[0187] The coordinate system is the premise of spatial analysis and ensures the geometric consistency of deviation calculation.

[0188] In the specific example of this step: Data format conversion: Real-time infiltration line: read vector line data in GeoJSON format from a dynamic database.

[0189] Warning infiltration line: Read vector line data in SHP format from the model file.

[0190] Coordinate system conversion: Use GIS tools (such as the GDAL library) to transform the warning infiltration line into the same local coordinate system as the real-time infiltration line (such as WGS-84 or an engineering independent coordinate system).

[0191] Check the alignment accuracy of the coordinate origin (such as the center of the dam crest). If the deviation exceeds 0.1 meters, the Step 6.2: Sample the elevation values ​​of two infiltration lines at preset intervals along the vertical section direction of the dam cross-section.

[0192] The vertical section direction is the transverse cutting line along the X-axis of the dam cross-section diagram, and the elevation comparison data are obtained by sampling at equal intervals.

[0193] In the specific example of this step: Sampling interval setting: The default interval is 1 meter (X-axis direction), which can be adjusted according to the length of the dam (for example, if the dam is 70 meters long, 70 sampling points will be generated).

[0194] Sampling method: In the cross-section view, vertical section lines are generated from the center of the dam crest (X=0) to the end point of the dam bottom (X=70).

[0195] For each profile line, extract the Y value (elevation) of the real-time infiltration line and the warning infiltration line at the profile.

[0196] Step 6.3: Calculate the horizontal distance difference and vertical height difference of each sampling point to generate a deviation distance matrix.

[0197] The horizontal distance difference reflects the difference in the Y values ​​of the two lines at the same X coordinate, and the vertical height difference reflects the offset of the X coordinate at the same Y value.

[0198] In the specific example of this step: Horizontal distance difference calculation: For each sampling point i (Xi), calculate ΔY-i=Y-real-time(i)-Y-warning(i).

[0199] Vertical height difference calculation: If the real-time infiltration line is offset on the X-axis, calculate ΔX-i = X-real-time (i)-X-warning (i).

[0200] Matrix generation: Store the ΔY and ΔX values ​​of all sampling points to form a two-dimensional matrix: [[ΔX-0, ΔY-0], [ΔX-1, ΔY-1],..., [ΔX-n, ΔY-n]] Step 6.4: Perform statistical analysis on the deviation distance matrix to extract the maximum deviation value, average deviation value, and the proportion of areas where the deviation exceeds the threshold.

[0201] Statistical analysis was used to quantify the overall degree of bias and risk distribution.

[0202] In the specific example of this step: Maximum deviation value: traverse the ΔY column and take the ΔY-max with the largest absolute value.

[0203] Average Deviation: Calculates the mean of the absolute values ​​of the ΔY column, ΔY-avg.

[0204] Proportion of areas exceeding the threshold: Set a safety threshold (for example, ΔY-threshold = 0.5 meters) and count the percentage of sampling points where the absolute value of ΔY is ≥ ΔY-threshold.

[0205] Step 6.5: If the maximum deviation value or the average deviation value exceeds the safety threshold, it is determined that the dam stability is abnormal.

[0206] Safety thresholds are set based on engineering specifications or historical data to trigger graded alarms.

[0207] In the specific example of this step: Threshold setting: According to the Technical Specifications for Reservoir and Dam Safety Monitoring, ΔY-threshold = 0.5 m and ΔY-avg-threshold = 0.3 m are set.

[0208] Decision logic: If ΔY-max ≥ ΔY-threshold or ΔY-avg ≥ ΔY-avg-threshold, it is determined to be a stability abnormality.

[0209] Alarm classification: There are three levels of abnormality: Level 1 (ΔY-max ≥ 1.0 m): Immediately activate the emergency plan.

[0210] Level 2 (0.5 m ≤ ΔY-max < 1.0 m): Strengthen monitoring and prepare for reinforcement.

[0211] Level 3 (ΔY-avg ≥ 0.3 m): Continuous observation and report generation.

[0212] In a specific case The real-time and early warning infiltration line data of a small reservoir dam are as follows: Real-time infiltration line: 70 sampling points, ΔY range [-0.2 m, +0.6 m].

[0213] Warning infiltration line: 70 sampling points, ΔY-threshold = 0.5 m, ΔY-avg-threshold = 0.3 m.

[0214] Implementation process: The warning infiltration line was converted from SHP to GeoJSON format in QGIS, and the maximum deviation after coordinate system calibration was 0.05 meters.

[0215] Generate 70 vertical sections at 1-meter intervals along the X axis, and extract the Y value of each section.

[0216] The calculation shows that ΔY-max = 0.6 meters (at X = 35 meters), and ΔY-avg = 0.25 meters.

[0217] The proportion of super-threshold areas is 15% (there are 11 sampling points with ΔY ≥ 0.5 m).

[0218] Because ΔY-max = 0.6 meters exceeds ΔY-threshold = 0.5 meters, it is judged as a level 2 anomaly and the reinforcement preparation command is triggered.

[0219] Step 7: If the deviation distance exceeds the preset safety threshold, an alarm signal is triggered and a dam stability assessment report is generated.

[0220] Generate decision support reports through risk positioning and analysis, and trigger graded alarms to ensure timely and effective emergency response.

[0221] Step 7.1: Determine the starting and ending points of the potential risk section of the dam body based on the location of the area where the deviation exceeds the threshold.

[0222] The risk section refers to the spatial extent of continuous over-threshold areas, which needs to be accurately located to guide reinforcement measures.

[0223] In the specific example of this step: Regional clustering: adjacent over-threshold sampling points are merged into continuous areas. If the horizontal distance between two points is ≤ the sampling interval (such as 1 meter), they are considered to be the same segment.

[0224] Boundary extraction: The X coordinate of the leftmost sampling point of the segment is taken as the starting point (X-start), and the X coordinate of the rightmost sampling point is taken as the ending point (X-end).

[0225] Coordinate mapping: Convert X-start and X-end to the actual position of the dam (such as "20 meters to the left and 15 meters to the right of the center of the dam top").

[0226] Step 7.2: Combine the layered soil structure model and real-time water level data to analyze the seepage velocity and soil bearing capacity attenuation trend in the risk section.

[0227] The seepage velocity reflects the scouring effect of water on the soil, and the bearing capacity attenuation trend indicates potential landslide risk.

[0228] In the specific example of this step: Calculation of seepage velocity: Based on the permeability coefficient (k) and hydraulic gradient (i) of the layered soil, the seepage velocity v=k·i is calculated according to Darcy's law.

[0229] Bearing capacity analysis: By comparing historical data, the rate of decrease of soil bearing capacity in the risk section with the increase of seepage velocity is calculated (for example, the bearing capacity decreases by 5% for every 0.1m / s increase).

[0230] Trend prediction: Use a linear regression model to predict the load-bearing capacity attenuation within the next hour. If the predicted value exceeds the safety threshold (such as 20%), it is marked as high risk.

[0231] Step 7.3: Generate an assessment report containing the risk level, risk location coordinates, and recommended reinforcement measures, and attach a timestamp and a screenshot of the cross-section.

[0232] The report should provide actionable decision-making information and incorporate visual evidence to enhance readability.

[0233] In the specific example of this step: Report template: Risk levels: Level 1 (red), Level 2 (orange), Level 3 (yellow).

[0234] Risk location: starting point (X-start, Y), ending point (X-end, Y).

[0235] Reinforcement suggestions: For level one risk, emergency drainage + filter layer reinforcement is recommended; for level two risk, local grouting is recommended; for level three risk, enhanced monitoring is recommended.

[0236] Call the graphics rendering interface to intercept the risk section of the cross-section diagram, overlay the real-time and warning infiltration lines, add a timestamp watermark, fill the analysis results into a PDF template through a report generation tool (such as JasperReports), and save it to the specified directory.

[0237] Step 7.4: Send differentiated alarm signals to different preset management terminals according to risk levels.

[0238] Tiered alerts ensure that response measures match the risk level and avoid wasting resources.

[0239] In the specific example of this step: Alarm rules: Level 1 risk: SMS notification + platform pop-up window + on-site sound and light alarm.

[0240] Secondary risk: platform pop-up window + email notification.

[0241] Level 3 risk: platform pop-up window + log record.

[0242] Terminal configuration: SMS Gateway: Connects to operator API and supports batch sending.

[0243] Mail server: Configure the SMTP protocol and bind the administrator's mailbox list.

[0244] Sound and light devices: Control on-site alarms via IoT protocols such as MQTT.

[0245] Step 8: Transmit the alarm signal and dam stability assessment report to the preset terminal through the preset interface.

[0246] Ensure that alarm information and reports are efficiently delivered to managers to support quick decision-making.

[0247] In the specific example of this step: Interface protocol: HTTP REST API: Transmits assessment report PDF files and alarm metadata (JSON format).

[0248] MQTT protocol: push alarm signals to mobile APP or monitoring screen in real time.

[0249] Terminal Type: Management platform: The web terminal receives reports and displays visual analysis results.

[0250] Mobile terminal: Receive alarm push and report summary through APP.

[0251] Database: Stores complete alarm records and report copies, supporting historical tracing.

[0252] In a specific case The monitoring data of a small reservoir dam are as follows: Risk section: X-start=25 meters, X-end=40 meters, risk level two.

[0253] Layered soil parameters: middle sand layer (k=5×10 -6 m / s, initial bearing capacity 100kPa).

[0254] Real-time seepage velocity: v=0.08m / s, predicted bearing capacity attenuation rate is 18%.

[0255] Implementation process: Cluster the sampling points exceeding the threshold and determine the risk section as X = 25 meters to X = 40 meters.

[0256] Calculate the seepage velocity v=5×10 -6 ×16=0.08m / s, and the bearing capacity is predicted to drop to 82kPa (a decrease of 18%) after 1 hour.

[0257] Generate a PDF report containing the risk level (orange), location coordinates (25m to 40m), recommended measures (local grouting), and an attached cross-section screenshot (time stamp "2023-10-0515:00:00").

[0258] Trigger a secondary alarm and send a notification to the platform pop-up window and the administrator's email address (admin@xxx.com).

[0259] The report is uploaded to the management platform through the HTTP interface, and the alarm signal is pushed to the on-site monitoring screen through MQTT.

[0260] The above is an embodiment of the method proposed in this application. Based on the same inventive concept, this application embodiment also provides a reservoir dam stability monitoring device, the structure of which is as follows: Figure 2 shown.

[0261] Figure 2 This is a schematic diagram of the internal structure of a reservoir dam stability monitoring device provided in an embodiment of the present application. Figure 2 As shown, the equipment includes: at least one processor 201; and, a memory 202 communicatively coupled to the at least one processor; The memory 202 stores instructions that can be executed by at least one processor, and the instructions are executed by the at least one processor 201 to enable the at least one processor 201 to: Obtain the basic dimensional parameters of the dam body and the layered soil material parameters in the design drawings of the small reservoir dam; wherein the basic dimensional parameters of the dam body include the dam height, dam crest width, and dam bottom width, and the layered soil material parameters include the permeability coefficient and porosity of each layer of soil; process the basic dimensional parameters and layered soil material parameters based on a preset infiltration analysis model to generate a dam cross-section diagram based on the basic dimensional parameters of the dam body, and construct a layered soil structure model based on the layered soil material parameters; process the layered soil structure model using a preset infiltration line generation algorithm to generate a warning infiltration line corresponding to the dam cross-section diagram; wherein The early warning infiltration line is the infiltration boundary of the dam body under the critical state; the dam body water level data is collected through the front-end monitoring equipment, and the real-time water level position is marked in the dam body cross-section diagram according to the dam body water level data; based on the real-time water level position, the real-time infiltration line is generated through the preset dynamic interpolation algorithm; the real-time infiltration line and the early warning infiltration line are spatially superimposed and compared, and the deviation distance between the real-time infiltration line and the early warning infiltration line is calculated; if the deviation distance exceeds the preset safety threshold, the alarm signal is triggered and a dam body stability assessment report is generated; the alarm signal and the dam body stability assessment report are transmitted to the preset terminal through the preset interface.

[0262] Some embodiments of the present application provide corresponding Figure 1 A non-volatile computer storage medium for monitoring reservoir dam stability stores computer executable instructions, wherein the computer executable instructions are set as follows: Obtain the basic dimensional parameters of the dam body and the layered soil material parameters in the design drawings of the small reservoir dam; wherein the basic dimensional parameters of the dam body include the dam height, dam crest width, and dam bottom width, and the layered soil material parameters include the permeability coefficient and porosity of each layer of soil; process the basic dimensional parameters and layered soil material parameters based on a preset infiltration analysis model to generate a dam cross-section diagram based on the basic dimensional parameters of the dam body, and construct a layered soil structure model based on the layered soil material parameters; process the layered soil structure model using a preset infiltration line generation algorithm to generate a warning infiltration line corresponding to the dam cross-section diagram; wherein The early warning infiltration line is the infiltration boundary of the dam body under the critical state; the dam body water level data is collected through the front-end monitoring equipment, and the real-time water level position is marked in the dam body cross-section diagram according to the dam body water level data; based on the real-time water level position, the real-time infiltration line is generated through the preset dynamic interpolation algorithm; the real-time infiltration line and the early warning infiltration line are spatially superimposed and compared, and the deviation distance between the real-time infiltration line and the early warning infiltration line is calculated; if the deviation distance exceeds the preset safety threshold, the alarm signal is triggered and a dam body stability assessment report is generated; the alarm signal and the dam body stability assessment report are transmitted to the preset terminal through the preset interface.

[0263] The various embodiments in this application are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other. Each embodiment focuses on the differences from the other embodiments. In particular, the IoT device and media embodiments are generally similar to the method embodiments, so their description is relatively simple. For relevant portions, refer to the description of the method embodiments.

[0264] The system and medium provided in the embodiments of the present application correspond one-to-one to the method. Therefore, the system and medium also have similar beneficial technical effects to their corresponding methods. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the system and medium will not be repeated here.

[0265] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0266] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0267] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0268] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0269] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0270] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0271] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0272] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0273] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A reservoir dam stability monitoring method, applied to a small reservoir dam, wherein the small reservoir dam is provided with a front-end monitoring device, characterized in that: The method comprises: Obtaining basic dimensional parameters of the dam body and layered soil material parameters from the design drawings of the small reservoir dam; wherein the basic dimensional parameters of the dam body include dam height, dam crest width, and dam bottom width; and the layered soil material parameters include permeability coefficient and porosity of each layer of soil; Processing the basic size parameters and layered soil material parameters based on a preset infiltration analysis model to generate a dam body cross-sectional view according to the basic size parameters of the dam body and constructing a layered soil structure model according to the layered soil material parameters; The layered soil structure model is processed using a preset infiltration line generation algorithm to generate an early warning infiltration line corresponding to the dam body cross-section diagram; wherein the early warning infiltration line is the infiltration boundary of the dam body in a critical state; Collecting dam body water level data through the front-end monitoring equipment, and marking the real-time water level point on the dam body cross-section diagram according to the dam body water level data; Based on the real-time water position point, a real-time infiltration line is generated by a preset dynamic interpolation algorithm; Comparing the real-time infiltration line with the early warning infiltration line in space, and calculating the deviation distance between the real-time infiltration line and the early warning infiltration line; If the deviation distance exceeds a preset safety threshold, an alarm signal is triggered and a dam stability assessment report is generated; The alarm signal and the dam stability assessment report are transmitted to a preset terminal through a preset interface.

2. A reservoir dam stability monitoring method according to claim 1, characterized in that: Obtain the basic dimensional parameters of the dam body and the layered soil material parameters from the design drawings of the small reservoir dam, specifically including: Parsing the vector layer of the design drawing to extract a coordinate point set of the contour line of the dam body; Calculating the dam height, dam top width and dam bottom width based on the coordinate point set; Identifying the boundaries of the layered soil areas marked in the design drawings to extract the distribution range and material identifiers of each layer of soil; The material identifier is compared with a preset soil parameter database to obtain the permeability coefficient and porosity of each layer of soil.

3. A reservoir dam stability monitoring method according to claim 1, characterized in that: The basic size parameters and layered soil material parameters are processed based on a preset infiltration analysis model to generate a dam cross-section diagram according to the basic size parameters of the dam body, and a layered soil structure model is constructed according to the layered soil material parameters, specifically including: generating a two-dimensional cross-sectional view of the dam body based on the dam height, dam crest width, and dam bottom width; Dividing the dam cross-section into a plurality of vertically layered soil regions based on the permeability coefficient and porosity in the layered soil material parameters; Assigning a permeability coefficient and a porosity to each soil region based on the layered soil material parameters, and generating a discretized layered soil structure model using a preset finite element meshing algorithm; The layered soil structure model is aligned with the dam cross-section diagram in spatial coordinates.

4. A reservoir dam stability monitoring method according to claim 1, characterized in that: The layered soil structure model is processed by a preset infiltration line generation algorithm to generate an early warning infiltration line corresponding to the dam cross-section diagram, specifically including: Based on the layered soil structure model, the seepage field distribution under different water level conditions is simulated, and the pore water pressure value of each grid node in the dam cross-section diagram is calculated; Obtaining a shear pressure gauge; wherein the shear pressure gauge records the correlation between the pore water pressure value and the soil shear strength; Determining a set of boundary points where the pore water pressure exceeds the shear strength according to the shear pressure table; Processing the boundary point set based on a preset contour line tracing algorithm to generate a continuous critical infiltration line boundary coordinate sequence; The boundary coordinates of the critical infiltration line are mapped to the dam cross-section diagram, and smoothed using a spline interpolation algorithm to output a warning infiltration line.

5. A reservoir dam stability monitoring method according to claim 1, characterized in that: Collecting dam body water level data through the front-end monitoring equipment and marking the real-time water level point on the dam body cross-section diagram according to the dam body water level data specifically includes: Receive the real-time water level elevation data transmitted by the front-end monitoring device, and associate the real-time water level elevation data with the section position identifier corresponding to the real-time water level elevation data; Determine the horizontal coordinates of the real-time water level point in the dam body cross-section diagram according to the cross-section position identifier; Calculate the longitudinal coordinate of the real-time water level point based on the water level elevation data and the elevation reference plane of the cross-section diagram; The real-time water level points are marked as vector tags with timestamps and stored in the dynamic water level database.

6. A reservoir dam stability monitoring method according to claim 1, characterized in that: Based on the real-time water position point, a real-time infiltration line is generated by a preset dynamic interpolation algorithm, specifically including: Extracting coordinate data of the real-time water point location and constructing a discrete water point set; The discrete water level points are spatially interpolated using the preset Kriging interpolation algorithm to generate a continuous water level distribution surface; determining the boundary of the real-time infiltration line according to the relationship between the water level distribution surface and the infiltration gradient of the layered soil structure model; The real-time infiltration line is superimposed on the dam cross-section diagram and visually compared with the early warning infiltration line.

7. A reservoir dam stability monitoring method according to claim 1, characterized in that: The real-time infiltration line and the early warning infiltration line are spatially superimposed and compared, and the deviation distance between the real-time infiltration line and the early warning infiltration line is calculated, specifically including: Convert the real-time infiltration line and the warning infiltration line into vector line data of the same coordinate system; Along the vertical section direction of the dam cross section, the elevation values ​​of two infiltration lines are sampled at preset intervals; Calculate the horizontal distance difference and vertical height difference of each sampling point to generate a deviation distance matrix; Perform statistical analysis on the deviation distance matrix to extract the maximum deviation value, average deviation value, and the proportion of areas where the deviation exceeds the threshold; If the maximum deviation value or the average deviation value exceeds the safety threshold, the dam stability is judged to be abnormal.

8. A reservoir dam stability monitoring method according to claim 1, characterized in that: If the deviation distance exceeds the preset safety threshold, an alarm signal is triggered and a dam stability assessment report is generated, specifically including: Determine the starting and ending points of the potential risk section of the dam body based on the location of the area where the deviation exceeds the threshold; Combined with the layered soil structure model and real-time water level data, the seepage velocity and soil bearing capacity attenuation trend in the risk section were analyzed; Generate an assessment report containing the risk level, risk location coordinates, and recommended reinforcement measures, with a timestamp and cross-section screenshots attached; Differentiated alarm signals are sent to different preset management terminals according to the risk level.

9. A reservoir dam stability monitoring device, characterized in that: The device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: Obtaining basic dimensional parameters of the dam body and layered soil material parameters from the design drawings of the small reservoir dam; wherein the basic dimensional parameters of the dam body include dam height, dam crest width, and dam bottom width; and the layered soil material parameters include permeability coefficient and porosity of each layer of soil; Processing the basic size parameters and layered soil material parameters based on a preset infiltration analysis model to generate a dam body cross-sectional view according to the basic size parameters of the dam body and constructing a layered soil structure model according to the layered soil material parameters; The layered soil structure model is processed using a preset infiltration line generation algorithm to generate an early warning infiltration line corresponding to the dam body cross-section diagram; wherein the early warning infiltration line is the infiltration boundary of the dam body in a critical state; Collecting dam body water level data through the front-end monitoring equipment, and marking the real-time water level point on the dam body cross-section diagram according to the dam body water level data; Based on the real-time water position point, a real-time infiltration line is generated by a preset dynamic interpolation algorithm; Comparing the real-time infiltration line with the early warning infiltration line in space, and calculating the deviation distance between the real-time infiltration line and the early warning infiltration line; If the deviation distance exceeds a preset safety threshold, an alarm signal is triggered and a dam stability assessment report is generated; The alarm signal and the dam stability assessment report are transmitted to a preset terminal through a preset interface.

10. A non-volatile computer storage medium for monitoring reservoir dam stability, storing computer-executable instructions, characterized in that: The computer executable instructions are configured to: Obtaining basic dimensional parameters of the dam body and layered soil material parameters from the design drawings of the small reservoir dam; wherein the basic dimensional parameters of the dam body include dam height, dam crest width, and dam bottom width; and the layered soil material parameters include permeability coefficient and porosity of each layer of soil; Processing the basic size parameters and layered soil material parameters based on a preset infiltration analysis model to generate a dam body cross-sectional view according to the basic size parameters of the dam body and constructing a layered soil structure model according to the layered soil material parameters; The layered soil structure model is processed using a preset infiltration line generation algorithm to generate an early warning infiltration line corresponding to the dam body cross-section diagram; wherein the early warning infiltration line is the infiltration boundary of the dam body in a critical state; Collecting dam body water level data through the front-end monitoring equipment, and marking the real-time water level point on the dam body cross-section diagram according to the dam body water level data; Based on the real-time water position point, a real-time infiltration line is generated by a preset dynamic interpolation algorithm; Comparing the real-time infiltration line with the early warning infiltration line in space, and calculating the deviation distance between the real-time infiltration line and the early warning infiltration line; If the deviation distance exceeds a preset safety threshold, an alarm signal is triggered and a dam stability assessment report is generated; The alarm signal and the dam stability assessment report are transmitted to a preset terminal through a preset interface.

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