Transparent dike construction method and system based on digital twinning

Abstract

The application discloses a transparent embankment construction method and system based on digital twinning, which firstly constructs an embankment three-dimensional geological model based on geological data to realize embankment information transparency; solves a seepage field in combination with actual water level working conditions and drainage conditions to obtain total water head, pore water pressure and a safety factor, and realizes embankment calculation transparency; collects infrared thermal images by using a drone, identifies hidden dangers such as embankment leakage and piping in combination with an improved YOLOv8 model, constructs an inspection risk index, and realizes embankment inspection transparency; further comprehensively evaluates the embankment according to the safety factor and the inspection risk index, determines a comprehensive risk grade, and realizes embankment evaluation transparency; and finally realizes multi-source data fusion, risk tracing, working condition simulation and scheme deduction on the transparent platform. The application realizes the full-process connection of transparent geology, transparent calculation, transparent inspection, transparent evaluation and a transparent platform, and can improve the embankment fine management and intelligent operation and maintenance level.

Description

Technical Field

[0001] This invention relates to the field of dike safety management and digital twin technology in water conservancy projects, specifically to a method and system for constructing transparent dikes based on digital twins. Background Technology

[0002] As crucial flood control and water security projects, dikes are influenced by multiple factors, including geological structure, seepage conditions, external water levels, and the evolution of apparent defects. Current dike safety management methods primarily rely on separate approaches such as geological exploration, seepage calculations, manual inspections, and individual monitoring. This results in problems like scattered, heterogeneous data from multiple sources, a disconnect between model results and inspection findings, and an incomplete risk identification and assessment chain, hindering a unified understanding of both internal dike stability and external hazard characteristics. Existing technologies often rely on static representations of geological models, making it difficult to establish a unified link with subsequent seepage analysis, inspection identification, and risk assessment. Seepage calculation results are frequently limited to specialized analysis, lacking a direct correlation with apparent hazards such as leakage and piping discovered during inspections. Inspection results are often presented independently as image recognition or manual records, lacking a mechanism for integrating them with internal dike stability calculations. Consequently, geological information, calculation results, inspection results, and evaluation results are difficult to integrate, hindering the provision of collaborative decision-making support for refined dike management, risk warning, and scheme development. Therefore, a transparent dike construction method and system based on digital twins is proposed to achieve transparency in dike geological information, calculation process, inspection process, evaluation process, and platform support, thereby improving the systematicness, accuracy, and intelligence of dike safety management. Summary of the Invention

[0003] The technical problem to be solved by this invention is to provide a method and system for constructing transparent dikes based on digital twins, addressing the aforementioned problems in the prior art. This invention aims to integrate multi-source data, realize the dynamic updating of dike digital twins, improve the visualization and analysis capabilities of dike geological modeling, achieve true transparency of dike geological information, and provide a solid foundation for the refined management and intelligent operation and maintenance of dikes.

[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A method for constructing a transparent dike based on digital twins includes the following steps: S101, Based on the geological exploration data of the dike, digital twin geological modeling is performed to generate a three-dimensional geological model of the dike, including: constructing a stratigraphic scalar sequence for boreholes based on the geological exploration data of the dike. ,in ~ For the 1st to Each stratum, As the lowest layer of the base, The uppermost stratum is defined as follows: the dip and dip angle of each stratigraphic control point at the geological interface are converted into normal vectors; a potential stratigraphic field is constructed based on the interpolation of the normal vectors of each stratigraphic control point, and the geological surfaces of each geological interface in the stratigraphic scalar sequence are obtained based on the potential stratigraphic field; the three-dimensional spatial region of the dike is discretized into a three-dimensional grid, and for each grid node, the coordinates and normal vector of the grid node are substituted into the potential stratigraphic field to obtain the value of the grid node in the potential stratigraphic field; the stratigraphic attribute values ​​of each grid node are determined based on the values ​​of each grid node in the potential stratigraphic field; the stratigraphic attribute values ​​of each grid node are smoothed using the moving average method; the smoothed stratigraphic attribute values ​​of each grid node are output as the generated three-dimensional geological model of the dike. S102, Based on the actual water level and drainage conditions, and combined with the stratigraphic attribute values ​​of each stratum in the three-dimensional geological model, the seepage field is modeled and solved to obtain the water head and pore water pressure of the embankment section, and the safety factor of the embankment is further calculated. S103 utilizes drones for dike inspection, collects infrared thermal image data, inputs the infrared thermal image data into a pre-trained neural network model to identify potential hazards such as piping and seepage, obtains the location, category and confidence level of the potential hazards, and constructs an inspection risk index based on the results of the potential hazard identification. S104. Based on the safety factor and the inspection risk index, a comprehensive evaluation is conducted on the target embankment section or hidden danger point to determine the comprehensive risk level, and a corresponding risk warning is triggered according to the preset threshold.

[0005] Optionally, the function expression for converting the dip and dip angle of each stratigraphic control point at the geological interface into a normal vector in step S101 is as follows: ; in, , and The unit normal vector is at directional components, and These represent the dip and dip angle of the stratigraphic control point at the geological interface.

[0006] Optionally, step S101 involves constructing a potential stratigraphic field based on the normal vector interpolation of each stratigraphic control point, and obtaining the geological surfaces of each geological interface in the stratigraphic scalar sequence based on the potential stratigraphic field, including: S201, construct the potential field of the stratigraphic reference surface and the potential field of vertical thickness based on the interpolation of the normal vectors of each stratigraphic control point: ; ; in, Potential field for stratigraphic base level, Let the potential vertical thickness of the k-th stratum be denoted as . This is a radial basis interpolation function used to interpolate discrete formation control points into a continuous reference surface potential field; Here is the interpolation function used for spatially continuous interpolation of formation thickness. The coordinates of the spatial node. It is a set of stratigraphic control points. Let i be the coordinates of the i-th stratigraphic control point. This is the set of normal vectors for the stratigraphic control points. Let be the normal vector of the i-th stratigraphic control point; It is a collection of strata thickness. The thickness of the k-th stratum; S202, construct the potential field of the stratigraphic interface based on the potential field of the stratigraphic base level and the potential field of the vertical thickness: ; in, Let k be the potential stratigraphic interface of the k-th stratum. The potential vertical thickness of the i-th stratum is used to determine the isosurface constants of the basement stratum and each stratum, based on the potential field of the stratigraphic interface. The potential fault field is constructed using the normal vector interpolation of each stratigraphic control point. ; in, For spatial nodes The potential field of the strata, This is an interpolation function used to interpolate discrete stratigraphic control points into a continuous potential field; It is a set of stratigraphic control points. Let i be the coordinates of the i-th stratigraphic control point. This is the set of normal vectors for the stratigraphic control points. Let be the normal vector of the i-th stratigraphic control point; if the stratigraphic scalar sequence If a fault exists, the potential geological field on both sides of the fault is corrected: ; in, and Faults , Potential geological fields on both sides For spatial nodes The potential field of the strata, and Faults , The potential field offset of the strata on both sides; Representing spatial nodes Located on a fault side, Representing spatial nodes Located on a fault side; S203, extract stratigraphic scalar sequences based on the potential field of stratigraphic interfaces and the potential field of stratigraphy, respectively. Three-dimensional isosurfaces of geological interfaces of various strata were obtained, thus yielding a stratigraphic scalar sequence. Geological surfaces of geological interfaces of various strata in the middle; S204, for stratigraphic scalar sequences The geological surfaces of each stratigraphic interface were subjected to continuity checks and sequence consistency corrections to ensure that the geological surfaces of each stratigraphic interface met the stratigraphic scalar sequence. The hierarchical order of the middle level.

[0007] Optionally, the function expression for determining the formation attribute values ​​of the grid nodes based on the values ​​of each grid node in the formation potential field in step S101 is as follows: ; in, For the first grid nodes The stratigraphic attribute values, For the first grid nodes The potential field values ​​of the strata, For the first The coordinates of each grid node, The stratigraphic property values ​​of the basement strata. Let k be the stratigraphic attribute value of the k-th stratum. The stratigraphic property value is the value of the loose layer overlying the uppermost stratum. The isosurface constant of the basement strata, and These are the isosurface constants of the k-th and k+1-th strata, respectively. The isosurface constant of the uppermost stratum; the function expression for smoothing the stratum attribute values ​​of each grid node using the moving average method in step S101 is: ; in, For smoothing the first grid nodes The stratigraphic attribute values, This is the smoothing window size for the moving average method. For the first The formation attribute value of the nth grid node, the th grid nodes Indicates the first grid nodes Offset in the x, y, and z directions respectively The resulting grid nodes.

[0008] Optionally, step S102 includes: S301. Based on the stratum attribute values ​​of each grid node in the three-dimensional geological model of the dike, assign corresponding permeability coefficients to each stratum of the dike. Combine the permeability coefficients of each stratum with the actual water level conditions and drainage conditions to complete the seepage field modeling and obtain the three-dimensional seepage computable model of the dike. S302 solves the three-dimensional seepage computable model of the embankment to obtain the total head distribution of each grid node in the embankment cross section, and calculates the pore water pressure based on the total head distribution, thus obtaining the total head and pore water pressure of the embankment. S303. Based on the total head distribution and pore water pressure, as well as the location of the seepage line, the location of the seepage outlet, and the distribution range of the saturated zone of the dike, the potential slip zone is determined. The seepage line is the line formed by interpolation of the points on the calculated cross-section of the dike where the pore water pressure is equal to zero. The seepage outlet is the intersection of the seepage line and the outer boundary of the back slope or the free drainage boundary. The distribution range of the saturated zone is the area composed of grid nodes or grid cells where the pore water pressure is greater than zero. S304, candidate slip surfaces are generated within the potential slip zone, and the safety factor of each candidate slip surface is calculated using the simplified Bishop method; S305, the candidate slip surfaces are screened for effectiveness, and the effective candidate slip surface with the smallest safety factor is taken as the critical slip surface, and its corresponding safety factor is taken as the safety factor of the dike.

[0009] Optionally, step S304 includes: S401, within the potential slip zone, the center position and radius of the arc slip surface are sampled according to a preset scanning step size to generate multiple candidate slip surfaces; S402, for each generated candidate slip surface, the corresponding slip body is discretized into several vertical soil strips, and the safety factor is calculated using the simplified Bishop method based on the pore water pressure at the bottom of each vertical soil strip: ; ; in, For safety reasons, The number of vertical soil strips discretized from the candidate slip surface. For the auxiliary calculation item of the j-th vertical soil strip, Let the cohesion be the bottom surface of the j-th vertical soil strip. and Let be the weight and width of the j-th vertical soil strip, respectively. Let be the pore water pressure at the bottom of the j-th vertical soil strip. Let be the effective internal friction angle of the j-th vertical soil strip bottom surface. Let be the angle between the tangent to the bottom surface of the j-th vertical soil strip and the horizontal plane.

[0010] Optionally, after obtaining the location, category, and confidence level of the potential hazard target in step S103, the method includes mapping the location of the potential hazard target to latitude and longitude geographic locations; the neural network model is an improved YOLOv8 model, which is based on the backbone network, neck network, and detection head of the original YOLOv8 model, with a convolutional block attention module added at the end of the backbone network; the construction of the inspection risk index based on the result of potential hazard target identification includes: S501, calculate the single-target risk score for each potential hazard target. : ; in, The risk coefficient for the k-th hidden danger target is preset according to the hidden danger category, which includes piping and leakage; Let be the detection confidence level for the k-th potential hazard target, with a value range of [0,1]. Let be the normalized value of the spatial range of the k-th hidden danger target, with a value range of [0,1]. The location distribution risk coefficient of the kth hidden danger target is determined by classifying and assigning values ​​according to the spatial relationship between the hidden danger target and key risk areas. The multi-temporal consistency coefficient of the kth hidden danger target is determined based on the repeated occurrence of the same location in continuous inspection phases. Let be the weighting coefficients, and satisfy: ; S502, calculate the initial impact value for each potential hazard target. : ; in, , , The weighting coefficients used in the initial influence value calculation satisfy: ; S503 sets the initial impact value of a potential hazard target. The influence weights of potential hazards are obtained by normalization. : ; in, For the first Normalized impact weights of individual potential hazards; The number of potential hazards; For the first The initial impact value of each potential hazard target; This is the sum of the initial impact values ​​of all potential hazards. S504, according to Calculate the inspection risk index of potential hazards .

[0011] Optionally, step S104 may also include converting the safety factor into a risk index. : ; in, For safety reasons, The threshold for high-risk safety factor The safety factor threshold for a safe state is given, and The risk index Inspection risk index To integrate and construct comprehensive risk assessment indicators : ; in, These are weighting coefficients; based on comprehensive risk assessment indicators. The value of is used to determine the overall risk level. : ; in, and A preset comprehensive risk level threshold is set; when the comprehensive risk level reaches the preset warning conditions, corresponding risk warning information is automatically generated, and the safety factor is adjusted accordingly. Risk Index Comprehensive risk assessment indicators and overall risk level Send to the transparent platform.

[0012] Optionally, after step S104, the transparent platform may also, based on the received risk warning information and the safety factor, [further details are needed]. Risk Index Comprehensive risk assessment indicators and overall risk level The system generates a comprehensive evaluation report and triggers corresponding risk warnings based on preset thresholds, as well as presenting, conducting source analysis, and performing operational condition simulations and scheme deductions, some or all of which are involved. The presentation includes the three-dimensional geological model, seepage field solution results, and safety factors. Distribution of potential hazards, location of potential hazards, risk index Distribution and overall risk level The data is overlaid and displayed; the source tracing analysis includes, after selecting a risk point, displaying the corresponding geological structure, seepage status, hazard details, inspection results, and historical inspection data; the working condition simulation and scheme deduction includes, for different water level working conditions and / or rainfall working conditions, calling the seepage calculation and comprehensive evaluation functions to perform simulation prediction, and after inputting the parameters of the engineering reinforcement or emergency treatment scheme, comparing and analyzing the treatment effects of different schemes and outputting treatment suggestions.

[0013] Furthermore, the present invention also provides a transparent dike construction system based on digital twins, including an interconnected microprocessor and a memory, wherein the microprocessor is programmed or configured to execute the transparent dike construction method based on digital twins.

[0014] Compared with existing technologies, this invention has the following beneficial effects: By integrating geological exploration data, water level conditions, drainage conditions, UAV inspection data, and intelligent recognition results, this invention forms a closed-loop process of "transparent geology—transparent calculation—transparent inspection—transparent evaluation—transparent platform"; it achieves transparency of levee geological information through the construction of a three-dimensional geological model, transparency of the levee calculation process through seepage field solution and safety factor calculation, transparency of the levee inspection process through UAV inspection and intelligent recognition, transparency of the levee evaluation process through comprehensive evaluation of safety factor and inspection risk index, and transparency of the levee evaluation process through the transparent platform, thereby achieving multi-source data fusion display, risk warning, risk tracing, working condition simulation, and scheme deduction. This solves the problems of static levee geological models, disconnect between calculation results and inspection results, and lack of unified correlation in risk assessment, thus improving the level of refined management and intelligent operation and maintenance of levees. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the basic process of the method in an embodiment of the present invention.

[0016] Figure 2 This is a detailed flowchart illustrating the method of an embodiment of the present invention. Detailed Implementation

[0017] The technical problem this invention aims to solve is: addressing the low degree of fusion of multi-source heterogeneous data for dikes in existing technologies, the lack of effective connection between geological models, seepage calculations, inspection identification, and risk assessment, and the difficulty in achieving unified correlation and collaborative decision support for geological information, calculation results, inspection results, and evaluation results. This invention provides a transparent dike construction method and system based on digital twins to achieve transparency in dike geological information, calculation processes, inspection processes, evaluation processes, and platform support, thereby improving the level of refined management and intelligent operation and maintenance of dikes. To enable those skilled in the art to better understand the technical solution of this invention, the following will provide a more detailed description of the technical solution in conjunction with the accompanying drawings of the embodiments of this invention.

[0018] like Figure 1 As shown, the transparent levee construction method based on digital twins in this embodiment includes the following steps: S101, Based on the geological exploration data of the dike, digital twin geological modeling is performed to generate a three-dimensional geological model of the dike, including: constructing a stratigraphic scalar sequence for boreholes based on the geological exploration data of the dike. ,in ~ For the 1st to Each stratum, As the lowest layer of the base, The uppermost stratum is defined as follows: the dip and dip angle of each stratigraphic control point at the geological interface are converted into normal vectors; a potential stratigraphic field is constructed based on the interpolation of the normal vectors of each stratigraphic control point, and the geological surfaces of each geological interface in the stratigraphic scalar sequence are obtained based on the potential stratigraphic field; the three-dimensional spatial region of the dike is discretized into a three-dimensional grid, and for each grid node, the coordinates and normal vector of the grid node are substituted into the potential stratigraphic field to obtain the value of the grid node in the potential stratigraphic field; the stratigraphic attribute values ​​of each grid node are determined based on the values ​​of each grid node in the potential stratigraphic field; the stratigraphic attribute values ​​of each grid node are smoothed using the moving average method; the smoothed stratigraphic attribute values ​​of each grid node are output as the generated three-dimensional geological model of the dike. S102, Based on the actual water level and drainage conditions, and combined with the stratigraphic attribute values ​​of each stratum in the three-dimensional geological model, the seepage field is modeled and solved to obtain the water head and pore water pressure of the embankment section, and the safety factor of the embankment is further calculated. S103 utilizes drones for dike inspection, collects infrared thermal image data, inputs the infrared thermal image data into a pre-trained neural network model to identify potential hazards such as piping and seepage, obtains the location, category and confidence level of the potential hazards, and constructs an inspection risk index based on the results of the potential hazard identification. S104. Based on the safety factor and the inspection risk index, a comprehensive evaluation is conducted on the target embankment section or hidden danger point to determine the comprehensive risk level, and a corresponding risk warning is triggered according to the preset threshold.

[0019] like Figure 2 As shown, in this embodiment, steps S101 to S103 are respectively transparent geology, transparent calculation, transparent inspection, transparent evaluation, and transparent platform. Step S101 includes: constructing a stratigraphic scalar sequence for boreholes based on the geological exploration data of the dike. ,in ~ For the 1st to Each stratum, As the lowest layer of the base, The uppermost stratum; the strike of each stratum control point at the geological interface. ,tendency and tilt angle Convert to normal vectors; construct the stratigraphic potential field based on the normal vector interpolation of each stratigraphic control point, and obtain the stratigraphic scalar sequence based on the stratigraphic potential field. Geological surfaces at various geological interfaces of different strata; the three-dimensional spatial region of the dike. The data is discretized into a three-dimensional mesh. For each mesh node, its coordinates and normal vector are substituted into the potential geological field to obtain the value of the mesh node's coordinates within the potential field. Based on the values ​​of each mesh node in the potential field, the geological attribute values ​​of the mesh nodes are determined. These geological attribute values ​​are then smoothed using a moving average method. The smoothed geological attribute values ​​of each mesh node are output as the generated three-dimensional geological model of the levee. In this embodiment, the levee body serves as the calculation carrier (object) for seepage and stability measurement, while the levee itself is the overall engineering object.

[0020] In step S101 of this embodiment, the function expression for converting the dip and dip angle of each stratigraphic control point at the geological interface into a normal vector is as follows: ; in, , and The unit normal vector is at directional components, and These represent the dip and dip angle of the stratigraphic control points at the geological interface. The strike of each stratigraphic control point at the geological interface is also shown. ,tendency and tilt angle These three are the three elements of stratigraphic attitude, among which strike With tendency In a spatially vertical relationship ( In this embodiment, the normal vector calculation only involves the tendency. With tilt angle .

[0021] In this embodiment, step S101 involves constructing a potential stratigraphic field based on the normal vector interpolation of each stratigraphic control point, and obtaining the geological surfaces of each geological interface in the stratigraphic scalar sequence based on the potential stratigraphic field, including: S201, construct the potential field of the stratigraphic reference surface and the potential field of vertical thickness based on the interpolation of the normal vectors of each stratigraphic control point: ; ; in, Potential field for stratigraphic base level, Let the potential vertical thickness of the k-th stratum be denoted as . This is a radial basis interpolation function used to interpolate discrete formation control points into a continuous reference surface potential field; Here is the interpolation function used for spatially continuous interpolation of formation thickness. The coordinates of the spatial node. It is a set of stratigraphic control points. Let i be the coordinates of the i-th stratigraphic control point. This is the set of normal vectors for the stratigraphic control points. Let be the normal vector of the i-th stratigraphic control point; It is a collection of strata thickness. The thickness of the k-th stratum; S202, construct the potential field of the stratigraphic interface based on the potential field of the stratigraphic base level and the potential field of the vertical thickness: ; in, Let k be the potential stratigraphic interface of the k-th stratum. The potential vertical thickness of the i-th stratum is used to determine the isosurface constants of the basement stratum and each stratum, based on the potential field of the stratigraphic interface. The potential fault field is constructed using the normal vector interpolation of each stratigraphic control point. ; in, For spatial nodes The potential field of the strata, This is an interpolation function used to interpolate discrete stratigraphic control points into a continuous potential field; It is a set of stratigraphic control points. Let i be the coordinates of the i-th stratigraphic control point. This is the set of normal vectors for the stratigraphic control points. Let be the normal vector of the i-th stratigraphic control point; if the stratigraphic scalar sequence If a fault exists, the potential geological field on both sides of the fault is corrected: ; in, and Faults , Potential geological fields on both sides For spatial nodes The potential field of the strata, and Faults , The potential field offset of the strata on both sides; Representing spatial nodes Located on a fault side, Representing spatial nodes Located on a fault side; S203, extract stratigraphic scalar sequences based on the potential field of stratigraphic interfaces and the potential field of stratigraphy, respectively. Three-dimensional isosurfaces of geological interfaces of various strata were obtained, thus yielding a stratigraphic scalar sequence. Geological surfaces of geological interfaces of various strata in the middle; S204, for stratigraphic scalar sequences The geological surfaces of each stratigraphic interface were subjected to continuity checks and sequence consistency corrections to ensure that the geological surfaces of each stratigraphic interface met the stratigraphic scalar sequence. The hierarchical order of the middle level.

[0022] In step S101 of this embodiment, the three-dimensional spatial region of the dike is... When discretized into a three-dimensional mesh, the set of mesh nodes in the three-dimensional mesh can be represented as: ;in, Let m be the three-dimensional coordinates of the m-th grid node. This represents the total number of grid nodes.

[0023] After calculating the value of each grid node in the potential field of the formation, a corresponding formation attribute value is assigned to each node according to the correspondence between the potential field value and the isosurface constant. In this embodiment, the function expression for determining the formation attribute value of each grid node based on its value in the potential field is as follows: ; in, For the first grid nodes The stratigraphic attribute values, For the first grid nodes The potential field values ​​of the strata, For the first The coordinates of each grid node, The stratigraphic property values ​​of the basement strata. Let k be the stratigraphic attribute value of the k-th stratum. The stratigraphic property value is the value of the loose layer overlying the uppermost stratum. The isosurface constant of the basement strata, and These are the isosurface constants of the k-th and k+1-th strata, respectively. The isosurface constant of the uppermost stratum; the function expression for smoothing the stratum attribute values ​​of each grid node using the moving average method in step S101 is: ; in, For smoothing the first grid nodes The stratigraphic attribute values, This is the smoothing window size for the moving average method. For the first The formation attribute value of the nth grid node, the th grid nodes Indicates the first grid nodes Offset in the x, y, and z directions respectively The resulting mesh nodes. The smoothing window size N takes a positive integer of 1 or 2. This represents the total number of nodes within the smoothing window. After smoothing, the final 3D geological digital twin model of the dike (the 3D geological model of the dike body) is output.

[0024] In this embodiment, when completing the computable modeling of seepage in the dike, four types of boundary conditions are automatically identified and set based on the dike topographic features, real-time water level data, and the location of the computational domain boundary: ① Water-facing boundary: When the computational grid node is located on the water-facing slope and its elevation is lower than the real-time water level, a constant head boundary condition is assigned, with the head value equal to the real-time water level; ② Backwater boundary: When the computational grid node is located on the backwater slope and its elevation is lower than the backwater water level, a free drainage boundary condition is initially assigned; ③ Dike crest boundary: Surface nodes located in the dike crest area are assigned an impermeable boundary condition; ④ Lateral and bottom boundaries: The bottom and lateral boundaries of the computational domain are assigned an impermeable boundary condition by default. After the initial boundary conditions are set, an adaptive iterative correction is performed on the backwater seepage surface. The iterative process is as follows: ① Complete the solution of the seepage control equation once to obtain the total head of each grid node; ② Perform seepage surface discrimination; ③ Repeat the above solution and boundary correction steps until the maximum change in head between two adjacent iterations is less than the preset convergence threshold. The total head distribution of each grid node in the levee cross-section is obtained by solving the three-dimensional seepage field model of the levee. Calculate the pore water pressure at the cross-section of the embankment to obtain the total head and pore water pressure at the embankment cross-section; where... For spatial nodes pore water pressure, The density of water, For the total water head, For spatial nodes The elevation of the seepage is determined. When solving the constructed computable seepage model, typical calculation sections are extracted based on the three-dimensional geological model of the levee. Two-dimensional steady-state seepage control equations are then constructed on these typical calculation sections according to Darcy's law and the mass conservation equation. The control equations are discretized using the five-point finite difference method, establishing a system of linear algebraic equations. The discretized equations for grid nodes (i,j) are as follows: ; in, For grid nodes With the original geological model points The Euclidean distance. Take the Euclidean distance. The smallest original geological model point is used as the matching point to complete the assignment of permeability coefficient.

[0025] In this embodiment, step S102 includes: S301. Based on the stratum attribute values ​​of each grid node in the three-dimensional geological model of the dike, assign corresponding permeability coefficients to each stratum of the dike. Combine the permeability coefficients of each stratum with the actual water level conditions and drainage conditions to complete the seepage field modeling and obtain the three-dimensional seepage computable model of the dike. S302 solves the three-dimensional seepage computable model of the embankment to obtain the total head distribution of each grid node in the embankment cross section, and calculates the pore water pressure based on the total head distribution, thus obtaining the total head and pore water pressure of the embankment. S303. Based on the total head distribution and pore water pressure, as well as the location of the seepage line, the location of the seepage outlet, and the distribution range of the saturated zone of the dike, the potential slip zone is determined. The seepage line is the line formed by interpolation of the points on the calculated cross-section of the dike where the pore water pressure is equal to zero. The seepage outlet is the intersection of the seepage line and the outer boundary of the back slope or the free drainage boundary. The distribution range of the saturated zone is the area composed of grid nodes or grid cells where the pore water pressure is greater than zero. S304, candidate slip surfaces are generated within the potential slip zone, and the safety factor of each candidate slip surface is calculated using the simplified Bishop method; S305, the candidate slip surfaces are screened for effectiveness, and the effective candidate slip surface with the smallest safety factor is taken as the critical slip surface, and its corresponding safety factor is taken as the safety factor of the dike.

[0026] In this embodiment, step S304 includes: S401, within the potential slip zone, the center position and radius of the arc slip surface are sampled according to a preset scanning step size to generate multiple candidate slip surfaces; S402, for each generated candidate slip surface, the corresponding slip body is discretized into several vertical soil strips, and the safety factor is calculated using the simplified Bishop method based on the pore water pressure at the bottom of each vertical soil strip: ; ; in, For safety reasons, The number of vertical soil strips discretized from the candidate slip surface. For the auxiliary calculation item of the j-th vertical soil strip, Let the cohesion be the bottom surface of the j-th vertical soil strip. and Let be the weight and width of the j-th vertical soil strip, respectively. Let be the pore water pressure at the bottom of the j-th vertical soil strip. Let be the effective internal friction angle of the j-th vertical soil strip bottom surface. Let be the angle between the bottom tangent of the j-th vertical soil strip and the horizontal plane. Based on the following conditions—that the candidate slip surface intersects the outer contour of the levee at only two points, does not intersect the levee foundation boundary, does not self-intersect, and the area of ​​the sliding body enclosed by the candidate slip surface is greater than a preset minimum area threshold—invalid candidate slip surfaces are eliminated, and the valid candidate slip surface with the smallest safety factor is determined as the critical slip surface. Finally, all candidate slip surfaces are screened for validity, eliminating invalid solutions with abnormal intersections with the terrain or unreasonable geometric shapes. The calculation results of the remaining valid slip surfaces are automatically compared, and the critical slip surface with the smallest safety factor is finally identified as the controlling indicator of levee stability under this condition.

[0027] To achieve automated, high-precision identification and dynamic early warning of potential seepage and piping hazards in dikes based on drone inspections and improved deep learning models, and to provide front-end sensing data for dike safety management, such as... Figure 2As shown, in this embodiment, step S103 includes using a drone to inspect the dike, collecting infrared thermal image data, and inputting the inspection data into a pre-trained neural network model to identify potential piping and seepage hazards. This process obtains the bounding box location, category, and confidence level of the hazard target, maps the bounding box location to the latitude and longitude geographic location of the hazard target, and outputs a structured recognition result. In this embodiment, the drone inspection data is infrared thermal image data; after obtaining the location, category, and confidence level of the hazard target in step S103, the process includes mapping the location of the hazard target to latitude and longitude geographic location; the neural network model is an improved YOLOv8 model. The improved YOLOv8 model is based on the original YOLOv8 model's backbone, neck, and head, with a Convolutional Block Attention Module (CBAM) added to the end of the backbone. The CBAM integrates channel attention branches and spatial attention branches, which can enhance the feature response of the target area, suppress background interference, and improve the identification accuracy of dike seepage and piping hazards.

[0028] The training of the improved YOLOv8 model includes: first, constructing a dataset of infrared images of dikes covering different scenarios and varying degrees of seepage; after data augmentation to increase sample diversity, dividing it into training, validation, and test sets in an 8:1:1 ratio; building a training environment for the improved YOLOv8 model based on the PyTorch framework, configuring appropriate hyperparameters, and using a transfer learning strategy to load pre-trained weights to start training; during training, monitoring core metrics such as loss value and mAP through the validation set, and using an early stopping mechanism to avoid overfitting; after training, performing multi-dimensional performance evaluation through the test set, optimizing and adjusting for issues such as missed detections and false detections, ultimately obtaining an improved YOLOv8 model adapted for dike seepage identification. Using the improved YOLOv8 model for dike piping and seepage identification includes: using a drone equipped with a high-sensitivity infrared thermal imager to collect infrared image data of key areas of the dike with geographic location markers and timestamps along a preset flight path; preprocessing the collected raw infrared data, and combining it with a geographic information system for coordinate mapping and multi-temporal correlation analysis, ultimately generating an inspection risk index and its visualization results.

[0029] In this embodiment, the inspection risk index is constructed based on the results of hazard target identification, including: S501, calculate the single-target risk score for each potential hazard target. : ; in, The single-objective risk score for the k-th potential hazard target. The risk coefficient for the k-th hazard target is a preset value according to the hazard category; for example, when the hazard category is piping, ... When the hazard category is leakage, ; Let be the detection confidence level of the k-th potential hazard target, with a value range of [0,1]. It is the normalized value of the spatial range of the k-th hidden danger target, and its value range is [0,1]. The location distribution risk coefficient of the kth hidden danger target is determined by classifying the value according to the spatial relationship between the hidden danger target and the key risk area. It is 1.0 when it is located within the key risk area, 0.7 when it is adjacent to the key risk area, and 0.4 when it is in other areas. The multi-temporal consistency coefficient of the k-th hidden danger target is determined based on the recurrence of the same location in consecutive inspection phases. A value of 1.0 is obtained when the target is identified in three or more consecutive phases, 0.7 is obtained when the target is identified in two consecutive phases, and 0.4 is obtained when the target is identified in only a single phase. Let be the weighting coefficients, and satisfy: ; S502, calculate the initial impact value for each potential hazard target. : ; in, , , The weighting coefficients used in the initial influence value calculation satisfy: ; S503 sets the initial impact value of a potential hazard target. The influence weights of potential hazards are obtained by normalization. : ; in, For the first Normalized impact weights of individual potential hazards; The number of potential hazards; For the first The initial impact value of each potential hazard target; This is the sum of the initial impact values ​​of all potential hazards. S504, according to Calculate the inspection risk index of potential hazards .

[0030] In this embodiment, after obtaining the dike safety factor and inspection risk index, a comprehensive evaluation is conducted on the target dike section or potential hazard location. Specifically, the internal stability risk represented by the safety factor and the apparent hazard risk represented by the inspection risk index are uniformly mapped and fused for analysis to form a comprehensive risk evaluation index. Then, the comprehensive risk level of the corresponding dike section or potential hazard location is determined according to a preset comprehensive risk level threshold, and a corresponding risk warning is triggered when the comprehensive risk level reaches a preset condition. Through this comprehensive evaluation process, a unified judgment of the internal stability state of the dike and external inspection hazards is achieved.

[0031] Step S104 in this embodiment also includes converting the safety factor into a risk index. : ; in, For safety reasons, The threshold for high-risk safety factor The safety factor threshold for a safe state is given, and The risk index Inspection risk index To integrate and construct comprehensive risk assessment indicators : ; in, These are weighting coefficients; based on comprehensive risk assessment indicators. The value of is used to determine the overall risk level. : ; in, and A preset comprehensive risk level threshold is set; when the comprehensive risk level reaches the preset warning conditions, corresponding risk warning information is automatically generated, and the safety factor is adjusted accordingly. Risk Index Comprehensive risk assessment indicators and overall risk level Send to the transparent platform.

[0032] In this embodiment, after step S104, the transparent platform further includes adjusting the received risk warning information and security factor based on the information received. Risk Index Comprehensive risk assessment indicators and overall risk level The system generates a comprehensive evaluation report and triggers corresponding risk warnings based on preset thresholds, as well as presenting, conducting source analysis, and performing operational condition simulations and scheme deductions, some or all of which are involved. The presentation includes the three-dimensional geological model, seepage field solution results, and safety factors. Distribution of potential hazards, location of potential hazards, risk index Distribution and overall risk level The data is overlaid and displayed. The source tracing analysis includes, after selecting a risk point, displaying the corresponding geological structure, seepage status, hazard details, inspection results, and historical inspection data. The working condition simulation and scheme deduction include calling seepage calculation and comprehensive evaluation functions to perform simulation predictions under different water level conditions and / or rainfall conditions, and comparing and analyzing the treatment effects of different schemes after inputting engineering reinforcement or emergency treatment scheme parameters, and outputting treatment suggestions. To achieve collaborative visualization and decision support of geological models, calculation results, inspection results, and comprehensive evaluation results, this embodiment uses a transparent platform for unified display and application. The platform comprises four core functional modules: ① A transparent levee digital twin 3D scene: built on a WebGL 3D rendering engine, used to overlay and display, interactively query, and switch perspectives the 3D geological model, seepage field results, safety factor distribution, hazard point distribution, inspection risk index distribution, and comprehensive risk level results; ② A risk source analysis module, used to associate and display the corresponding geological structure, seepage status, hazard details, and historical inspection data after selecting a risk point; ③ A comprehensive evaluation and risk warning module, used to receive safety factor, inspection risk index, and comprehensive risk level results, generate a comprehensive evaluation report, and trigger corresponding risk warnings based on preset thresholds; ④ A working condition simulation and scheme deduction module, used to call the seepage calculation and comprehensive evaluation functions to perform simulation predictions under different water levels and / or rainfall conditions, and to compare and analyze the treatment effects of different schemes after inputting engineering reinforcement or emergency treatment scheme parameters and output treatment suggestions. In summary, this embodiment of the transparent dike construction method based on digital twins unfolds along the technical path of "transparent geology—transparent calculation—transparent inspection—transparent evaluation—transparent platform": a three-dimensional geological model of the dike is constructed using geological exploration data, achieving transparency of geological information; the calculation process is made transparent through seepage field solving and safety factor calculation; the inspection process is made transparent through UAV inspection and improved deep learning recognition; the evaluation process is made transparent by fusing safety factors and inspection risk indices to form a comprehensive risk level; and the platform is made transparent through multi-source data fusion display, risk warning, risk tracing, operational condition simulation, and scheme deduction. Therefore, this embodiment can solve the problems of traditional dike construction, such as difficulty in fusing multi-source heterogeneous data, static geological models, disconnect between calculation results and inspection results, and lack of unified correlation in risk assessment, thereby improving the level of refined management and intelligent operation and maintenance of dikes.

[0033] Furthermore, this embodiment also provides a transparent dike construction system based on digital twins, including a microprocessor and a memory interconnected, wherein the microprocessor is programmed or configured to execute the transparent dike construction method based on digital twins.

[0034] Those skilled in the art will understand that the technical solutions provided by this invention may take the form of a method, system, or computer program product. Therefore, this invention may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this invention may take the form of a computer program product embodied on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, produce an implementation of the flowchart... Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0035] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for constructing a transparent dike based on digital twins, characterized in that, Includes the following steps: S101, Based on the geological exploration data of the dike, digital twin geological modeling is performed to generate a three-dimensional geological model of the dike, including: constructing a stratigraphic scalar sequence for boreholes based on the geological exploration data of the dike. ,in ~ For the 1st to Each stratum, As the lowest layer of the base, The uppermost stratum is defined as follows: the dip and dip angle of each stratigraphic control point at the geological interface are converted into normal vectors; a potential stratigraphic field is constructed based on the interpolation of the normal vectors of each stratigraphic control point, and the geological surfaces of each geological interface in the stratigraphic scalar sequence are obtained based on the potential stratigraphic field; the three-dimensional spatial region of the dike is discretized into a three-dimensional grid, and for each grid node, the coordinates and normal vector of the grid node are substituted into the potential stratigraphic field to obtain the value of the grid node in the potential stratigraphic field; the stratigraphic attribute values ​​of each grid node are determined based on the values ​​of each grid node in the potential stratigraphic field; the stratigraphic attribute values ​​of each grid node are smoothed using the moving average method; the smoothed stratigraphic attribute values ​​of each grid node are output as the generated three-dimensional geological model of the dike. S102, Based on the actual water level and drainage conditions, and combined with the stratigraphic attribute values ​​of each stratum in the three-dimensional geological model, the seepage field is modeled and solved to obtain the water head and pore water pressure of the embankment section, and the safety factor of the embankment is further calculated. S103 utilizes drones for dike inspection, collects infrared thermal image data, inputs the infrared thermal image data into a pre-trained neural network model to identify potential hazards such as piping and seepage, obtains the location, category and confidence level of the potential hazards, and constructs an inspection risk index based on the results of the potential hazard identification. S104. Based on the safety factor and the inspection risk index, a comprehensive evaluation is conducted on the target embankment section or hidden danger point to determine the comprehensive risk level, and a corresponding risk warning is triggered according to the preset threshold.

2. The method for constructing a transparent dike based on digital twins according to claim 1, characterized in that, In step S101, the function expression for converting the dip and dip angle of each stratigraphic control point at the geological interface into a normal vector is as follows: ;in, , and The unit normal vector is at directional components, and These represent the dip and dip angle of the stratigraphic control point at the geological interface.

3. The method for constructing a transparent dike based on digital twins according to claim 1, characterized in that, In step S101, the stratigraphic potential field is constructed based on the normal vector interpolation of each stratigraphic control point, and the geological surfaces of each geological interface in the stratigraphic scalar sequence are obtained based on the stratigraphic potential field, including: S201, construct the potential field of the stratigraphic reference surface and the potential field of vertical thickness based on the interpolation of the normal vectors of each stratigraphic control point: ； ； in, Potential field for stratigraphic base level, Let the potential vertical thickness of the k-th stratum be denoted as . This is a radial basis interpolation function used to interpolate discrete formation control points into a continuous reference surface potential field; Here is the interpolation function used for spatially continuous interpolation of formation thickness. The coordinates of the spatial node. It is a set of stratigraphic control points. Let i be the coordinates of the i-th stratigraphic control point. This is the set of normal vectors for the stratigraphic control points. Let be the normal vector of the i-th stratigraphic control point; It is a collection of strata thickness. The thickness of the k-th stratum; S202, construct the potential field of the stratigraphic interface based on the potential field of the stratigraphic base level and the potential field of the vertical thickness: ； in, Let k be the potential stratigraphic interface of the k-th stratum. Let the potential vertical thickness of the i-th stratum be defined, and then the isosurface constants of the basement stratum and each stratum be determined based on the potential field of the stratigraphic interface; the potential fault field is constructed based on the normal vector interpolation of each stratigraphic control point. ； in, For spatial nodes The potential field of the strata, This is an interpolation function used to interpolate discrete stratigraphic control points into a continuous potential field; It is a set of stratigraphic control points. Let i be the coordinates of the i-th stratigraphic control point. This is the set of normal vectors for the stratigraphic control points. Let be the normal vector of the i-th stratigraphic control point; if the stratigraphic scalar sequence If a fault exists, the potential geological field on both sides of the fault is corrected: ； in, and Faults , Potential geological fields on both sides For spatial nodes The potential field of the strata, and Faults , The potential field offset of the strata on both sides; Representing spatial nodes Located on a fault side, Representing spatial nodes Located on a fault side; S203, extract stratigraphic scalar sequences based on the potential field of stratigraphic interfaces and the potential field of stratigraphy, respectively. Three-dimensional isosurfaces of geological interfaces of various strata were obtained, thus yielding a stratigraphic scalar sequence. Geological surfaces of geological interfaces of various strata in the middle; S204, for stratigraphic scalar sequences The geological surfaces of each stratigraphic interface were subjected to continuity checks and sequence consistency corrections to ensure that the geological surfaces of each stratigraphic interface met the stratigraphic scalar sequence. The hierarchical order of the middle level.

4. The method for constructing a transparent dike based on digital twins according to claim 1, characterized in that, The function expression for determining the formation attribute values ​​of grid nodes based on the values ​​of each grid node in the formation potential field in step S101 is as follows: ； in, For the first grid nodes The stratigraphic attribute values, For the first grid nodes The potential field values ​​of the strata, For the first The coordinates of each grid node. The stratigraphic property values ​​of the basement strata. Let k be the stratigraphic attribute value of the k-th stratum. The stratigraphic property value is the value of the loose layer overlying the uppermost stratum. The isosurface constant of the basement strata, and These are the isosurface constants of the k-th and k+1-th strata, respectively. The isosurface constant of the uppermost stratum; the function expression for smoothing the stratum attribute values ​​of each grid node using the moving average method in step S101 is: ； in, For smoothing the first grid nodes The stratigraphic attribute values, This is the smoothing window size for the moving average method. For the first The formation attribute value of the nth grid node, the th grid nodes Indicates the first grid nodes Offset in the x, y, and z directions respectively The resulting grid nodes.

5. The method for constructing a transparent dike based on digital twins according to claim 1, characterized in that, Step S102 includes: S301. Based on the stratum attribute values ​​of each grid node in the three-dimensional geological model of the dike, assign corresponding permeability coefficients to each stratum of the dike. Combine the permeability coefficients of each stratum with the actual water level conditions and drainage conditions to complete the seepage field modeling and obtain the three-dimensional seepage computable model of the dike. S302 solves the three-dimensional seepage computable model of the embankment to obtain the total head distribution of each grid node in the embankment cross section, and calculates the pore water pressure based on the total head distribution, thus obtaining the total head and pore water pressure of the embankment. S303. Based on the total head distribution and pore water pressure, as well as the location of the seepage line, the location of the seepage outlet, and the distribution range of the saturated zone of the dike, the potential slip zone is determined. The seepage line is the line formed by interpolation of the points on the calculated cross-section of the dike where the pore water pressure is equal to zero. The seepage outlet is the intersection of the seepage line and the outer boundary of the back slope or the free drainage boundary. The distribution range of the saturated zone is the area composed of grid nodes or grid cells where the pore water pressure is greater than zero. S304, candidate slip surfaces are generated within the potential slip zone, and the safety factor of each candidate slip surface is calculated using the simplified Bishop method; S305, the candidate slip surfaces are screened for effectiveness, and the effective candidate slip surface with the smallest safety factor is taken as the critical slip surface, and its corresponding safety factor is taken as the safety factor of the dike.

6. The method for constructing a transparent dike based on digital twins according to claim 5, characterized in that, Step S304 includes: S401, within the potential slip zone, the center position and radius of the arc slip surface are sampled according to a preset scanning step size to generate multiple candidate slip surfaces; S402, for each generated candidate slip surface, the corresponding slip body is discretized into several vertical soil strips, and the safety factor is calculated using the simplified Bishop method based on the pore water pressure at the bottom of each vertical soil strip: ； ； in, For safety reasons, The number of vertical soil strips discretized from the candidate slip surface. For the auxiliary calculation item of the j-th vertical soil strip, Let the cohesion be the bottom surface of the j-th vertical soil strip. and Let be the weight and width of the j-th vertical soil strip, respectively. Let be the pore water pressure at the bottom of the j-th vertical soil strip. Let be the effective internal friction angle of the j-th vertical soil strip bottom surface. Let be the angle between the tangent to the bottom surface of the j-th vertical soil strip and the horizontal plane.

7. The method for constructing a transparent dike based on digital twins according to claim 1, characterized in that, After obtaining the location, category, and confidence level of the potential hazard target in step S103, the location of the potential hazard target is mapped to a latitude and longitude geographic location; the neural network model is an improved YOLOv8 model, which is based on the backbone network, neck network, and detection head of the original YOLOv8 model, with a convolutional block attention module added at the end of the backbone network; The inspection risk index constructed based on the results of hazard target identification includes: S501, calculate the single-target risk score for each potential hazard target. : ； in, The risk coefficient for the k-th hidden danger target is preset according to the hidden danger category, which includes piping and leakage; Let be the detection confidence level for the k-th potential hazard target, with a value range of [0,1]. Let be the normalized value of the spatial range of the k-th hidden danger target, with a value range of [0,1]. The location distribution risk coefficient of the kth hidden danger target is determined by classifying and assigning values ​​according to the spatial relationship between the hidden danger target and key risk areas. The multi-temporal consistency coefficient of the kth hidden danger target is determined based on the repeated occurrence of the same location in continuous inspection phases. Let be the weighting coefficients, and satisfy: ； S502, calculate the initial impact value for each potential hazard target. : ； in, , , The weighting coefficients used in the initial influence value calculation satisfy: ； S503 sets the initial impact value of a potential hazard target. The influence weights of potential hazards are obtained by normalization. : ； in, For the first Normalized impact weights of individual potential hazards; The number of potential hazards; For the first The initial impact value of each potential hazard target; This is the sum of the initial impact values ​​of all potential hazards. S504, according to Calculate the inspection risk index of potential hazards .

8. The method for constructing a transparent dike based on digital twins according to claim 1, characterized in that, Step S104 also includes converting the safety factor into a risk index. : ； in, For safety reasons, The threshold for high-risk safety factor The safety factor threshold for a safe state is given, and The risk index Inspection risk index To integrate and construct comprehensive risk assessment indicators : ； in, These are weighting coefficients; based on comprehensive risk assessment indicators. The value of is used to determine the overall risk level. : ； in, and A preset comprehensive risk level threshold is set; when the comprehensive risk level reaches the preset warning conditions, corresponding risk warning information is automatically generated, and the safety factor is adjusted accordingly. Risk Index Comprehensive risk assessment indicators and overall risk level Send to the transparent platform.

9. The method for constructing a transparent dike based on digital twins according to claim 8, characterized in that, Step S104 is followed by the transparent platform taking into account the received risk warning information and the security factor. Risk Index Comprehensive risk assessment indicators and overall risk level The system generates a comprehensive evaluation report and triggers corresponding risk warnings based on preset thresholds, as well as presenting, conducting source analysis, and performing operational condition simulations and scheme deductions, some or all of which are involved. The presentation includes the three-dimensional geological model, seepage field solution results, and safety factors. Distribution of potential hazards, location of potential hazards, risk index Distribution and overall risk level The data is overlaid and displayed; the source tracing analysis includes, after selecting a risk point, displaying the corresponding geological structure, seepage status, hazard details, inspection results, and historical inspection data; the working condition simulation and scheme deduction includes, for different water level working conditions and / or rainfall working conditions, calling the seepage calculation and comprehensive evaluation functions to perform simulation prediction, and after inputting the parameters of the engineering reinforcement or emergency treatment scheme, comparing and analyzing the treatment effects of different schemes and outputting treatment suggestions.

10. A transparent dike construction system based on digital twins, comprising interconnected microprocessors and memory, characterized in that, The microprocessor is programmed or configured to execute the transparent dike construction method based on digital twins as described in any one of claims 1 to 9.

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