Anti-seepage wall modeling method and device and electronic equipment

Abstract

The invention provides a diaphragm wall modeling method and device and electronic equipment. The method comprises the following steps: in response to a modeling instruction, obtaining a physical and mechanical parameter set corresponding to a target diaphragm wall from a target database; using a processor to construct an initial diaphragm wall model according to the size parameters and the plurality of electromagnetic wave data; constructing a coefficient correlation model according to the plurality of second resistance coefficients and the initial permeability coefficient corresponding to each piece of electromagnetic wave data; and according to the coefficient correlation model and the initial diaphragm wall model, generating a target diaphragm wall model.

Description

Technical Field

[0001] This application relates to the field of water conservancy projects, and more specifically, to a method, device, electronic device, medium, and program product for impervious wall modeling. Background Art

[0002] An impervious wall is an underground continuous wall built in a loose pervious layer or an earth-rock dam to play an anti-seepage role, and it is an important underground structure constituting the anti-seepage system of a water conservancy project. However, since the impervious wall belongs to an underground hidden project, it is difficult to monitor the subsequent quality of the impervious wall after construction.

[0003] Currently, in the quality monitoring of impervious walls, the concrete core sample testing method is mainly used. However, this method can only monitor the quality of the cored area, cannot determine the quality of deep concrete, and takes a long time. It cannot scientifically and accurately monitor the quality of each part of the impervious wall, which is not conducive to the safety guarantee of the anti-seepage system of water conservancy projects. Summary of the Invention

[0004] In view of this, this application provides a method, device, electronic device, medium, and program product for impervious wall modeling.

[0005] One aspect of this application provides a method for impervious wall modeling, including:

[0006] In response to a modeling instruction, obtain a set of physical and mechanical parameters corresponding to a target impervious wall from a target database, where the set of physical and mechanical parameters includes dimension parameters, electromagnetic wave data corresponding to different first points on the target impervious wall, and an initial permeability coefficient;

[0007] Use a processor to construct an initial impervious wall model based on the dimension parameters and multiple electromagnetic wave data, where each second point on the initial impervious wall model has a first resistance coefficient corresponding to the second point;

[0008] Construct a coefficient correlation model based on multiple second resistance coefficients and the initial permeability coefficient corresponding to each electromagnetic wave data, where the multiple second resistance coefficients are calculated based on the multiple electromagnetic wave data;

[0009] Generate a target impervious wall model based on the coefficient correlation model and the initial impervious wall model, where each model grid on the target impervious wall model has a corresponding target permeability coefficient.

[0010] According to an embodiment of this application, the dimension parameters include the length parameter, depth parameter, and thickness parameter of the target impervious wall;

[0011] Among them, using a processor to construct an initial impervious wall model based on the dimension parameters and multiple electromagnetic wave data includes:

[0012] Based on the high-density electrical method, according to the above length parameters, the above depth parameters, and multiple pieces of the above electromagnetic wave data, generate resistivity cloud map data, where the above resistivity cloud map data includes a first resistivity coefficient corresponding to each of the above second positions;

[0013] According to the above resistivity cloud map data and the above thickness parameters, generate the above initial anti-seepage wall model.

[0014] According to an embodiment of the present application, generating the above initial anti-seepage wall model according to the above resistivity cloud map data and the above thickness parameters includes:

[0015] Based on a resistivity threshold, determine a wall defect area from multiple of the above first resistivity coefficients on the above resistivity cloud map data, where the probability of water seepage in the above wall defect area is greater than the probability of water seepage in other areas;

[0016] Perform edge detection processing on the above wall defect area to obtain edge contour data of the above wall defect area;

[0017] According to the above dimension parameters, the above edge contour data, and the above resistivity cloud map data, construct the above initial anti-seepage wall model.

[0018] According to an embodiment of the present application, the above electromagnetic wave data and the initial permeability coefficient are generated in the following manner:

[0019] Based on a point selection table, according to the geological parameters of the area where the above target anti-seepage wall is located and the material parameters of the above target anti-seepage wall, determine a point layout map, where the above material parameters represent the usage amounts of different types of construction materials used to construct the above target anti-seepage wall;

[0020] According to the above point layout map and the above dimension parameters, determine multiple of the above first positions on the above target anti-seepage wall;

[0021] For each of the above first positions, perform a core drilling test at the above first position to obtain the initial permeability coefficient at different depths on the sampled core;

[0022] Perform the above high-density electrical method test on the boreholes formed after the above core drilling test to obtain electromagnetic wave data corresponding to different depths.

[0023] According to an embodiment of the present application, constructing a coefficient correlation model according to multiple second resistivity coefficients and the initial permeability coefficient corresponding to each of the above electromagnetic wave data includes:

[0024] Based on the high-density method, according to multiple of the above electromagnetic wave data, generate a resistivity corresponding to each of the above electromagnetic wave data;

[0025] Generate a correlation graph based on multiple resistivity values and the initial permeability coefficients corresponding to each resistivity value;

[0026] Perform regression fitting analysis on the correlation graph to obtain the coefficient correlation model.

[0027] According to an embodiment of the present application, generate a target cut-off wall model based on the coefficient correlation model and the initial cut-off wall model, including:

[0028] Determine the target permeability coefficient corresponding to each first resistance coefficient according to the coefficient correlation model and multiple first resistance coefficients;

[0029] Generate the target cut-off wall model according to multiple target permeability coefficients and the initial cut-off wall model.

[0030] According to an embodiment of the present application, generate the target cut-off wall model according to multiple target permeability coefficients and the initial cut-off wall model, including:

[0031] Perform mesh division processing on the initial cut-off wall model to obtain multiple initial model meshes;

[0032] For each initial model mesh, perform parameter assignment processing on the initial model mesh using the target permeability coefficient corresponding to the initial model mesh to obtain a target model mesh;

[0033] Generate the target cut-off wall model according to multiple target model meshes.

[0034] According to an embodiment of the present application, before constructing the initial cut-off wall model, it further includes:

[0035] Preprocess the target data to obtain preprocessed size parameters and multiple preprocessed initial permeability coefficients to generate a new set of physical and mechanical parameters, where the target data includes at least one of the size parameters, the initial permeability coefficients, and the resistance cloud map data.

[0036] Another aspect of the present application provides a cut-off wall modeling device, including:

[0037] An acquisition module for obtaining a set of physical and mechanical parameters corresponding to a target cut-off wall from a target database in response to a modeling instruction, where the set of physical and mechanical parameters includes size parameters, electromagnetic wave data corresponding to different first points on the target cut-off wall, and initial permeability coefficients;

[0038] A first construction module for constructing an initial anti-seepage wall model by using a processor according to the above-mentioned size parameters and a plurality of the above-mentioned electromagnetic wave data, wherein each second point position on the above-mentioned initial anti-seepage wall model has a first resistivity coefficient corresponding to the above-mentioned second point position;

[0039] A second construction module for constructing a coefficient correlation model according to a plurality of second resistivity coefficients and an initial permeability coefficient corresponding to each of the above-mentioned electromagnetic wave data, wherein the plurality of the above-mentioned second resistivity coefficients are calculated according to the plurality of the above-mentioned electromagnetic wave data;

[0040] A generation module for generating a target anti-seepage wall model according to the above-mentioned coefficient correlation model and the above-mentioned initial anti-seepage wall model, wherein each of the above-mentioned model grids on the above-mentioned target anti-seepage wall model has a corresponding target permeability coefficient.

[0041] Another aspect of the present application provides an electronic device, including:

[0042] One or more processors;

[0043] A memory for storing one or more programs,

[0044] wherein when the above-mentioned one or more programs are executed by the above-mentioned one or more processors, the above-mentioned one or more processors implement the method according to any one of claims 1 to 8.

[0045] Another aspect of the present application provides a computer-readable storage medium storing computer-executable instructions, and the above-mentioned instructions are used to implement the method as described above when executed.

[0046] Another aspect of the present application provides a computer program product, and the above-mentioned computer program product includes computer-executable instructions, and the above-mentioned instructions are used to implement the method as described above when executed.

[0047] According to the embodiments of the present application, by obtaining a set of physical and mechanical parameters corresponding to a target anti-seepage wall from a target database, constructing an initial anti-seepage wall model according to size parameters and a plurality of electromagnetic wave data, and constructing a coefficient correlation model according to a plurality of second resistivity coefficients and an initial permeability coefficient corresponding to each electromagnetic wave data, the permeability coefficient of each position of the target anti-seepage wall can be calculated quickly and accurately. Furthermore, the anti-seepage performance of the anti-seepage wall can be accurately evaluated, the accuracy of model numerical analysis is improved, which has an important impact on the seepage analysis of the target anti-seepage wall. According to the above-mentioned coefficient correlation model and the above-mentioned initial anti-seepage wall model, a target anti-seepage wall model is generated, which can accurately identify the abnormal areas of the target anti-seepage wall, improve the accuracy and reliability of modeling, reduce the difficulty of daily quality monitoring of the target anti-seepage wall, and enhance the safety of the anti-seepage system of water conservancy projects. Description of the Drawings

[0048] Through the following description of the embodiments of the present application with reference to the accompanying drawings, the above and other objects, features, and advantages of the present application will become clearer. In the drawings:

[0049] Figure 1 A scene diagram of the application of the impervious wall modeling method according to an embodiment of the present application is shown;

[0050] Figure 2 A flowchart of the impervious wall modeling method according to an embodiment of the present application is shown;

[0051] Figure 3 A schematic diagram of the resistance contour map data according to an embodiment of the present application is shown;

[0052] Figure 4 A flowchart for constructing the coefficient correlation model according to an embodiment of the present application is shown;

[0053] Figure 5 A structural block diagram of the impervious wall modeling device according to an embodiment of the present application is shown; and

[0054] Figure 6 A block diagram of the electronic device for the impervious wall modeling method according to an embodiment of the present application is shown. Detailed Embodiments

[0055] Hereinafter, embodiments of the present application will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present application. In the following detailed description, for the sake of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present application. However, obviously, one or more embodiments can also be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessarily confusing the concepts of the present application.

[0056] The terms used herein are merely for describing specific embodiments and are not intended to limit the present application. The terms "including", "comprising", etc. used herein indicate the presence of the described features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0057] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0058] In the case of using expressions such as "at least one of A, B, and C", generally, it should be interpreted according to the meaning that those skilled in the art usually understand this expression (for example, "a system having at least one of A, B, and C" should include but not be limited to a system having only A, only B, only C, having A and B, having A and C, having B and C, and / or having A, B, and C, etc.).

[0059] An impervious wall is a diaphragm wall constructed in a loose permeable layer or an earth-rock dam for seepage prevention, and it is an important underground structure constituting the seepage prevention system of a water conservancy project. Currently, the impervious wall modeling method mainly relies on the previous geological exploration data and design drawings, and there are the following problems: First, the impervious wall belongs to an underground hidden project, and the construction quality is difficult to control, and construction defects are inevitable. The existing impervious wall modeling methods mainly rely on design drawings, and cannot accurately depict the shape and local construction quality defects of the actual impervious wall in terms of spatial position and geometric boundary; Second, the physical and mechanical parameters of the impervious wall depend on borehole detection, taking points instead of the whole. However, due to the limited number of borehole detections, the physical and mechanical properties of the entire impervious wall cannot be accurately reflected, especially the permeability coefficient of the quality defect part that has an important impact on seepage analysis.

[0060] In view of this, embodiments of the present application provide an impervious wall modeling method, device, and electronic device. The method includes: in response to a modeling instruction, obtaining a set of physical and mechanical parameters corresponding to the target impervious wall from the target database, using a processor to construct an initial impervious wall model according to size parameters and multiple electromagnetic wave data, then constructing a coefficient correlation model according to multiple second resistivity coefficients and the initial permeability coefficient corresponding to each electromagnetic wave data, and finally generating a target impervious wall model according to the above coefficient correlation model and the above initial impervious wall model.

[0061] Figure 1 The application scenario diagram of the impervious wall modeling method according to the embodiment of the present application is shown. It should be noted that Figure 1 The shown is only an example of the system architecture to which the embodiments of the present application can be applied, to help those skilled in the art understand the technical content of the present application, but it does not mean that the embodiments of the present application cannot be used in other devices, systems, environments, or scenarios.

[0062] As Figure 1 shown, the system architecture 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used to provide a medium for communication links between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired and / or wireless communication links, etc.

[0063] Users can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 via the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103.

[0064] The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be various electronic devices with a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and so on.

[0065] The server 105 can be a server that provides various services. For example, it can be a background management server (only for example) that supports the websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server can analyze and process data such as user requests received, and feedback the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0066] It should be noted that the anti-seepage wall modeling method provided in the embodiments of the present application can generally be executed by the server 105. Correspondingly, the anti-seepage wall modeling device provided in the embodiments of the present application can generally be set in the server 105. The anti-seepage wall modeling method provided in the embodiments of the present application can also be executed by a server or a server cluster different from the server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or the server 105. Correspondingly, the anti-seepage wall modeling device provided in the embodiments of the present application can also be set in a server or a server cluster different from the server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or the server 105. Or, the anti-seepage wall modeling method provided in the embodiments of the present application can also be executed by the first terminal device 101, the second terminal device 102, or the third terminal device 103, or can also be executed by other terminal devices different from the first terminal device 101, the second terminal device 102, or the third terminal device 103. Correspondingly, the anti-seepage wall modeling device provided in the embodiments of the present application can also be set in the first terminal device 101, the second terminal device 102, or the third terminal device 103, or set in other terminal devices different from the first terminal device 101, the second terminal device 102, or the third terminal device 103.

[0067] It should be understood that Figure 1 the numbers of terminal devices, networks, and servers in

[0068] Figure 2 The flowchart of the impervious wall modeling method according to an embodiment of the present application is shown.

[0069] As Figure 2 shown, the impervious wall modeling method includes operations S201 to S204.

[0070] In operation S201, in response to a modeling instruction, a set of physical and mechanical parameters corresponding to the target impervious wall is obtained from the target database, where the set of physical and mechanical parameters includes dimension parameters, electromagnetic wave data corresponding to different first points on the target impervious wall, and an initial permeability coefficient.

[0071] In an embodiment of the present application, the modeling instruction is a basic operation command used to create and modify a model during the modeling process, which can be operated by an operator on electronic devices such as mobile phones and computers, and the electronic device generates the modeling instruction in response to the operation. The target database can store various physical and mechanical parameters of the target impervious wall. The dimension parameters in the physical and mechanical parameters can include depth parameters, thickness parameters, and length parameters of the target impervious wall. The first point can represent the specific position where a core drilling test is carried out on the impervious wall. The electromagnetic wave data can be used to calculate the resistivity magnitudes at different positions (including but not limited to the first points) on the target impervious wall and can be obtained through a high-density electrical method test. The initial permeability coefficient characterizes the permeability of the material at the core-taking location of the impervious wall to water flow and can be obtained through a drilling method.

[0072] In an embodiment of the present application, the depth parameter and thickness parameter of the target impervious wall can be obtained through a drilling method, which can be understood as: the depth of the target impervious wall is directly measured by drilling and taking cores in the depth direction and thickness direction of the target impervious wall respectively. The length parameter of the target impervious wall can be obtained through an excavation measuring method, which can be understood as: the length parameter of the target impervious wall is obtained by measuring the length of the excavation foundation pit.

[0073] In operation S202, the processor constructs an initial impervious wall model based on the dimension parameters and the plurality of electromagnetic wave data, where each second point on the initial impervious wall model has a first resistance coefficient corresponding to the second point.

[0074] In an embodiment of the present application, the second point can be the point on the initial impervious wall model corresponding to each area of the target impervious wall. The first resistance coefficient can be the resistivity value at each position of the target impervious wall.

[0075] In an embodiment of the present application, an initial impervious wall model is constructed based on the depth parameter, thickness parameter, length parameter, and electromagnetic wave data obtained through a high-density electrical method, where the initial impervious wall model can be constructed using graphic design software, such as software that can process DWG format.

[0076] In operation S203, a coefficient correlation model is constructed based on a plurality of second resistivity coefficients and the initial permeability coefficients corresponding to each of the above electromagnetic wave data, wherein the plurality of second resistivity coefficients are calculated based on the plurality of above electromagnetic wave data.

[0077] In an embodiment of the present application, the second resistivity coefficient may be the resistivity value at the core drilling location. The coefficient correlation model can describe the correlation between the resistivity and the permeability coefficient at any point on the target impervious wall.

[0078] According to an embodiment of the present application, a plurality of second resistivity coefficients are calculated from a plurality of electromagnetic wave data, and a correlation model between the second resistivity coefficient and the permeability coefficient of the impervious wall is constructed using the calculated plurality of second resistivity coefficients and the initial permeability coefficients.

[0079] In operation S204, a target impervious wall model is generated based on the above coefficient correlation model and the above initial impervious wall model, wherein each model grid on the target impervious wall model has a corresponding target permeability coefficient.

[0080] In an embodiment of the present application, the target permeability coefficient of the second point can be calculated based on the first resistivity coefficient of each second point through the coefficient correlation model, and the plurality of target permeability coefficients are respectively assigned to the specific model grids on the initial impervious wall model, thereby obtaining the target impervious wall model. From this target impervious wall model, the target permeability coefficient and the corresponding first resistivity coefficient of each model grid can be accurately known.

[0081] According to an embodiment of the present application, by obtaining a set of physical and mechanical parameters corresponding to the target impervious wall from the target database, constructing an initial impervious wall model based on the size parameters and a plurality of electromagnetic wave data, and constructing a coefficient correlation model based on a plurality of second resistivity coefficients and the initial permeability coefficients corresponding to each electromagnetic wave data, the permeability coefficient of each position of the target impervious wall can be calculated quickly and accurately. Furthermore, the impervious performance of the impervious wall can be accurately evaluated, the accuracy of the model numerical analysis is improved, which has an important impact on the seepage analysis of the target impervious wall. Based on the above coefficient correlation model and the above initial impervious wall model, a target impervious wall model is generated, which can accurately identify the abnormal areas of the target impervious wall, improve the accuracy and reliability of the modeling, reduce the difficulty of daily quality monitoring of the target impervious wall, and enhance the safety of the impervious system of the water conservancy project.

[0082] Figure 3 A schematic diagram showing the resistivity cloud map data according to an embodiment of the present application is shown.

[0083] In an embodiment of the present application, a processor is used to construct an initial anti-seepage wall model according to the above size parameters and a plurality of the above electromagnetic wave data, including: based on a high-density electrical method test, generating resistivity cloud map data according to the length parameter, depth parameter of the anti-seepage wall and a plurality of electromagnetic wave data, wherein the resistivity cloud map data includes a first resistivity coefficient corresponding to each second point position, and generating an initial anti-seepage wall model according to the resistivity cloud map data and the thickness parameter.

[0084] As Figure 3 shown, the left and right boundaries of the resistivity cloud map data are established based on the depth parameter of the target anti-seepage wall, characterizing the change in the resistivity value of the target anti-seepage wall in the depth direction, and the upper and lower boundaries of the resistivity cloud map data are established based on the length parameter, characterizing the change in the resistivity value of the target anti-seepage wall in the length direction, obtaining an initial image, calculating the first resistivity coefficient corresponding to the plurality of electromagnetic wave data by using the plurality of the above electromagnetic wave data, and assigning values to the initial image based on the position of each second point position on the target anti-seepage wall and the first resistivity coefficient corresponding to each second point position, and differentiating with different colors.

[0085] Different color blocks on the resistivity cloud map data can characterize that the resistivity values of different positions of the target anti-seepage wall are different. For example, dark blue can characterize an area with a low resistivity value, and yellow can characterize an area with a high resistivity value, that is, different resistivity intervals are characterized by different colors, so that the resistivity size of each area (constituted by a plurality of first point positions) on the target anti-seepage wall can be intuitively known from the resistivity cloud map data and whether there is a defective area can be judged based on the resistivity.

[0086] In an embodiment of the present application, generating the above initial anti-seepage wall model according to the above resistivity cloud map data and the above thickness parameter includes: determining a wall defective area from a plurality of first resistivity coefficients on the resistivity cloud map data based on a resistivity threshold, wherein the probability of water seepage in the wall defective area is greater than the probability of water seepage in other areas, performing edge detection processing on the wall defective area to obtain edge contour data of the wall defective area, and constructing an initial anti-seepage wall model according to the size parameter, the edge contour data and the resistivity cloud map data.

[0087] In an embodiment of the present application, data analysis software can be used to process the resistivity cloud map data to obtain edge contour data of the wall defective area. For example, software that can process MAT format and CSV format can be used.

[0088] In the embodiments of the present application, the resistance threshold can be specifically set according to actual requirements. For example, it can be 0.9 Ω·m. The resistance threshold can be manually set by an operator or automatically generated by software. Based on the set resistance threshold, the area corresponding to the resistivity greater than the resistance threshold is the non-wall defect area, and the area corresponding to the resistivity smaller than the resistance threshold is the wall defect area. For example, the resistivity range of the non-wall defect area is 0.9 Ω·m - 1.5 Ω·m.

[0089] In the embodiments of the present application, the wall defect area can also be determined by color differentiation. For example, the area corresponding to dark blue represents the area with low resistivity, which can be understood as: the area corresponding to dark blue is the wall defect area. The areas corresponding to other colors represent the areas with high resistivity, which can be understood as: the other areas are non-wall defect areas.

[0090] In the embodiments of the present application, edge detection algorithms such as the Canny algorithm can be used to perform edge detection processing on the wall defect area to identify the edge contour of the wall defect area, extract the boundary line data of the wall defect area, and further obtain the edge contour data of the wall defect area.

[0091] According to the embodiments of the present application, by setting the resistance threshold or color differentiation, the anti-seepage wall defect area can be scientifically and accurately determined. By performing edge detection processing on the wall defect area, a smooth, clear, and continuous boundary line of the wall defect area can be obtained, improving the accuracy and reliability of the initial anti-seepage wall model and the target anti-seepage wall model, and enhancing the safety guarantee of the water conservancy project.

[0092] In the embodiments of the present application, the above electromagnetic wave data and initial permeability coefficient are generated in the following manner: Based on the point selection table, according to the geological parameters of the area where the target anti-seepage wall is located and the material parameters of the target anti-seepage wall, a point layout diagram is determined, where the material parameters represent the usage amounts of different types of construction materials used to build the target anti-seepage wall. According to the point layout diagram and the dimension parameters, a plurality of first points are determined on the target anti-seepage wall. Core drilling tests are performed at each first point to obtain the initial permeability coefficients at different depths on the sampled core. After the core drilling test, high-density electrical method tests are performed on the formed pore channels to obtain the electromagnetic wave data corresponding to different depths.

[0093] In the embodiments of the present application, the geological parameters can include lithology, geological structure, seismic activity, hydrogeological conditions, etc. The material parameters can include the usage amounts of materials such as concrete, cement, sand, gravel, soil powder, and water reducer. Concrete can be divided into different grades, such as C30, C40, and C50.

[0094] In the embodiment of the present application, according to the different geological parameters of the location of the target cut-off wall and the different material parameters of the target cut-off wall, the corresponding point selection basis in the point selection table is also different. For example, if the geological parameter is a loose permeable layer and the material parameter is 135 tons of C40 concrete, the point selection basis in the point selection table is to select a point every 2m. For example, if the geological parameter is an earth-rock dam and the material parameter is 135 tons of C40 concrete, the point selection basis in the point selection table is to select a point every 4m.

[0095] In an embodiment of the present application, the point layout diagram can be determined based on the point selection table according to the comprehensive consideration of the size parameters, material parameters and geological parameters of the target cut-off wall where the target cut-off wall is located. For example, the depth parameter and length parameter of the target cut-off wall are 10m×10m respectively, the geological parameter is a loose permeable layer, and the material parameter is 135 tons of C40 concrete. The point selection basis in the point selection table is to select a point every 2m, so the operator can select a point every 2m in the length direction and the depth direction respectively to obtain a point layout diagram.

[0096] In an embodiment of the present application, based on a point selection table, a point layout diagram is determined according to geological parameters of the area where the target cut-off wall is located and material parameters of the target cut-off wall, and multiple first points are determined on the target cut-off wall based on the point layout diagram.

[0097] like Figure 3 As shown, ZK1, ZK2, ZK3, ... ZK12 represent multiple first points. According to an embodiment of the present application, the multiple first points may include drilling point No. 1, drilling point No. 2, and so on. ZK1 represents drilling point No. 1, ZK2 represents drilling point No. 2, ... ZK12 represents drilling point No. 12.

[0098] It should be noted that Figure 3 Only 12 first points are shown for example, and it is not limited to setting only 12 first points. The number of first points can be calculated according to demand.

[0099] In an embodiment of the present application, a coring test is performed at each drilling point, the initial permeability coefficient of the sampled core is calculated, and a high-density electrical method experiment is performed on the pores formed after the coring test to obtain electromagnetic wave data corresponding to different depths.

[0100] In an embodiment of the present application, high-density electrical method is a physical exploration method, which arranges a large number of electrodes in the channel and uses multi-channel data acquisition equipment to measure the electromagnetic wave data of the anti-seepage wall. Based on the inversion algorithm, the electromagnetic wave data, the length parameters and depth parameters of the target anti-seepage wall are used to generate resistance cloud map data.

[0101] In the embodiments of the present application, an operator can use a high-density electrical method measurement system, a multi-functional direct current electrical method instrument, and an electrode converter to conduct a high-density electrical method test. Data analysis software is used to analyze and calculate multiple electromagnetic wave data and resistance cloud map data.

[0102] Figure 4 The flowchart for constructing the coefficient correlation model according to the embodiments of the present application is shown.

[0103] As Figure 4 shown, a coefficient correlation model is constructed based on multiple second resistance coefficients and the initial permeability coefficients corresponding to each of the above electromagnetic wave data, including operations S401 to S403.

[0104] In operation S401, based on the high-density method, resistivity corresponding to each of the above electromagnetic wave data is generated according to the multiple above electromagnetic wave data.

[0105] In the embodiments of the present application, data analysis software is used to transform and calculate multiple electromagnetic wave data to generate resistivity corresponding to each electromagnetic wave data.

[0106] In operation S402, a correlation relationship diagram is generated according to the multiple above resistivities and the initial permeability coefficients corresponding to each of the above resistivities.

[0107] In operation S403, regression fitting analysis is performed on the above correlation relationship diagram to obtain the above coefficient correlation model.

[0108] In the embodiments of the present application, the high-density method is the High-Density Resistivity Method, which is a geophysical exploration method that uses a multi-electrode system to measure the underground resistivity distribution with high resolution by densely arranging electrodes and multi-channel data acquisition equipment. The core idea of the high-density electrical method is to obtain underground electrical structure information more quickly and accurately through large-scale dense measurement and various electrode arrangement methods.

[0109] In the embodiments of the present application, regression fitting analysis may include linear regression analysis, polynomial regression analysis, and non-linear regression analysis, etc. The coefficient correlation model characterizes the relationship between the resistivity and the permeability coefficient at each point of the target impervious wall. The coefficient correlation model can be constructed using correlation coefficients such as Pearson and Spearman.

[0110] According to the embodiments of the present application, a correlation relationship diagram is obtained based on multiple above resistivity data points and the initial permeability coefficient data points corresponding to the resistivity, regression fitting analysis is performed on the correlation relationship diagram, and a coefficient correlation model is constructed using correlation coefficients such as Pearson and Spearman.

[0111] According to an embodiment of the present application, based on the above coefficient correlation model and the above initial cutoff wall model, a target cutoff wall model is generated, including: determining a target permeability coefficient corresponding to each first resistance coefficient according to the coefficient correlation model and a plurality of first resistance coefficients, and generating a target cutoff wall model according to the plurality of target permeability coefficients and the initial cutoff wall model.

[0112] In an embodiment of the present application, electromagnetic wave data of the cutoff wall is measured through a high-density electrical method test, and through transformation and calculation of the electromagnetic wave data, a plurality of first resistance coefficients corresponding to a plurality of second point electromagnetic wave data are generated. The target permeability coefficient is obtained based on the coefficient correlation model and the plurality of first resistance coefficients.

[0113] According to an embodiment of the present application, since the permeability coefficient of the wall defect area is calculated based on the coefficient correlation model, the anti-seepage performance of the wall defect area of the cutoff wall can be quickly and accurately evaluated, improving the accuracy of the numerical analysis of the model, which has an important impact on the seepage analysis of the cutoff wall.

[0114] In an embodiment of the present application, generating the above target cutoff wall model according to the plurality of above target permeability coefficients and the above initial cutoff wall model includes: performing mesh division processing on the initial cutoff wall model to obtain a plurality of initial model meshes; for each initial model mesh, performing parameter assignment processing on the initial model mesh by using the target permeability coefficient corresponding to the initial model mesh to obtain a target model mesh, and generating the above target cutoff wall model according to the plurality of above target model meshes.

[0115] In an embodiment of the present application, the initial cutoff wall model can be meshed according to the distance. For example, each grid is 50 cm × 50 cm. The initial cutoff wall can also be meshed according to the color of the resistance cloud map. For example, a dark blue area is one grid, and a light blue area is one grid. Parameter assignment processing is performed on each grid based on the target permeability coefficient to obtain a target model grid corresponding to each grid, and a target cutoff wall model is obtained according to the target model grid.

[0116] In an embodiment of the present application, before constructing the initial cutoff wall model, it further includes: preprocessing the target data to obtain preprocessed dimension parameters and a plurality of preprocessed initial permeability coefficients to generate a new set of physical and mechanical parameters, where the target data includes at least one of dimension parameters, initial permeability coefficients, and resistance cloud map data.

[0117] In an embodiment of the present application, preprocessing the target data may include performing outlier removal and invalid data processing on the cutoff wall depth parameter, width parameter, initial permeability coefficient, and resistivity tomography data. For example, removing a very small range of color anomaly regions at the four perimeters of the resistivity tomography data to obtain a smooth and color-continuous resistivity tomography; smoothing the boundary line data at the wall defect and removing noise and redundant information to extract a clear and continuous boundary line, obtaining the edge contour data of the wall defect region; removing a very small range of color anomaly regions at the cutoff wall defect, where the very small range of color anomaly regions may be the holes left by the core drilling test and have no analytical value.

[0118] Figure 5 The structural block diagram of a cutoff wall modeling device according to an embodiment of the present application is shown.

[0119] As Figure 5 shown, the cutoff wall modeling device 500 according to an embodiment of the present application includes an acquisition module 510, a first construction module 520, a second construction module 530, and a generation module 540.

[0120] The acquisition module 510 is configured to obtain a set of physical and mechanical parameters corresponding to the target cutoff wall from the target database in response to a modeling instruction, where the set of physical and mechanical parameters includes dimension parameters, electromagnetic wave data corresponding to different first points on the target cutoff wall, and an initial permeability coefficient.

[0121] The first construction module 520 is configured to construct an initial cutoff wall model by using a processor according to the dimension parameters and a plurality of the electromagnetic wave data, where each second point on the initial cutoff wall model has a first resistivity coefficient corresponding to the second point.

[0122] The second construction module 530 is configured to construct a coefficient correlation model according to a plurality of second resistivity coefficients and the initial permeability coefficient corresponding to each of the electromagnetic wave data, where the plurality of second resistivity coefficients are calculated according to the plurality of electromagnetic wave data.

[0123] The generation module 540 is configured to generate a target cutoff wall model according to the coefficient correlation model and the initial cutoff wall model, where each model grid on the target cutoff wall model has a corresponding target permeability coefficient.

[0124] According to an embodiment of the present application, by obtaining a set of physical and mechanical parameters corresponding to a target cut-off wall from a target database, constructing an initial cut-off wall model based on size parameters and multiple electromagnetic wave data, and constructing a coefficient correlation model according to multiple second resistivity coefficients and an initial permeability coefficient corresponding to each electromagnetic wave data, the permeability coefficient of each position of the target cut-off wall can be calculated quickly and accurately. Furthermore, the anti-seepage performance of the cut-off wall can be accurately evaluated, the accuracy of model numerical analysis is improved, which has an important impact on the seepage analysis of the target cut-off wall. According to the above coefficient correlation model and the above initial cut-off wall model, a target cut-off wall model is generated, which can accurately identify the abnormal area of the target cut-off wall, improve the accuracy and reliability of modeling, reduce the difficulty of daily quality monitoring of the target cut-off wall, and enhance the safety of the anti-seepage system of the water conservancy project.

[0125] According to an embodiment of the present application, the acquisition module 510 includes a first determination sub-module and a second determination sub-module.

[0126] The first determination sub-module is used to determine the length parameter, depth parameter, and thickness parameter of the target cut-off wall.

[0127] The second determination sub-module is used to determine electromagnetic wave data and an initial permeability coefficient.

[0128] According to an embodiment of the present application, the first construction module 520 includes a first generation sub-module and a second generation sub-module.

[0129] The first generation sub-module is used to generate resistivity cloud map data based on the high-density resistivity method, according to the above length parameter, the above depth parameter, and multiple above electromagnetic wave data, wherein the resistivity cloud map data includes a first resistivity coefficient corresponding to each of the above second points.

[0130] The second generation sub-module is used to generate the above initial cut-off wall model according to the above resistivity cloud map data and the above thickness parameter.

[0131] According to an embodiment of the present application, the second generation sub-module includes a first determination unit, a first processing unit, and a construction unit.

[0132] The first determination unit is used to determine a wall defect area from multiple above first resistivity coefficients on the above resistivity cloud map data based on a resistivity threshold, wherein the probability of water seepage in the above wall defect area is greater than the probability of water seepage in other areas.

[0133] The first processing unit is used to perform edge detection processing on the above wall defect area to obtain edge contour data of the above wall defect area.

[0134] The construction unit is used to construct the above initial cut-off wall model according to the above size parameters, the above edge contour data, and the resistivity cloud map data.

[0135] According to an embodiment of the present application, the second determination sub-module includes a second determination unit, a third determination unit, a fourth determination unit, and a fifth determination unit.

[0136] The second determination unit is configured to determine a point layout diagram based on the point selection table, according to the geological parameters of the area where the target cutoff wall is located and the material parameters of the target cutoff wall, wherein the material parameters represent the amounts of different types of construction materials used to construct the target cutoff wall.

[0137] The third determination unit is configured to determine a plurality of the first points on the target cutoff wall according to the point layout diagram and the dimension parameters.

[0138] The fourth determination unit is configured to perform a core drilling test at each of the first points to obtain the initial permeability coefficients at different depths on the sampled core.

[0139] The fifth determination unit is configured to perform the high-density resistivity method test on the holes formed after the core drilling test to obtain the electromagnetic wave data corresponding to different depths.

[0140] According to an embodiment of the present application, the second construction module 530 includes a third generation sub-module, a fourth generation sub-module, and an analysis module.

[0141] The third generation sub-module is configured to generate a resistivity corresponding to each of the electromagnetic wave data based on the high-density resistivity method and according to a plurality of the electromagnetic wave data.

[0142] The fourth generation sub-module is configured to generate a correlation relationship diagram according to a plurality of the resistivities and the initial permeability coefficients corresponding to each of the resistivities.

[0143] The analysis module is configured to perform a regression fitting analysis on the correlation relationship diagram to obtain the coefficient correlation model.

[0144] According to an embodiment of the present application, the generation module 540 includes a third determination sub-module and a fifth generation sub-module.

[0145] The third determination sub-module is configured to determine a target permeability coefficient corresponding to each of the first resistance coefficients according to the coefficient correlation model and a plurality of the first resistance coefficients.

[0146] The fifth generation sub-module is configured to generate the target cutoff wall model according to a plurality of the target permeability coefficients and the initial cutoff wall model.

[0147] According to an embodiment of the present application, the fifth generation sub-module includes a second processing unit, a third processing unit, and a generation unit.

[0148] The second processing unit is used to perform mesh division processing on the above initial anti-seepage wall model to obtain a plurality of initial model meshes.

[0149] The third processing unit is used to perform parameter assignment processing on each of the above initial model meshes by using the above target permeability coefficient corresponding to the above initial model mesh to obtain target model meshes.

[0150] The generating unit is used to generate the above target anti-seepage wall model according to a plurality of the above target model meshes.

[0151] According to an embodiment of the present application, the first construction module 520 further includes a preprocessing sub-module, which is used to preprocess target data to obtain preprocessed dimension parameters and a plurality of preprocessed initial permeability coefficients, so as to generate a new set of the above physical and mechanical parameters, wherein the target data includes at least one of the above dimension parameters, the above initial permeability coefficient, and the above resistance cloud map data.

[0152] According to an embodiment of the present application, any plurality of modules, sub-modules, units, and sub-units, or at least part of the functions of any of them can be implemented in one module. Any one or more of the modules, sub-modules, units, and sub-units according to an embodiment of the present application can be split into multiple modules for implementation. Any one or more of the modules, sub-modules, units, and sub-units according to an embodiment of the present application can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by any other reasonable way of integrating or packaging circuits in hardware or firmware, or implemented in any one of the three implementation ways of software, hardware, and firmware or in any appropriate combination of several of them. Alternatively, one or more of the modules, sub-modules, units, and sub-units according to an embodiment of the present application can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding functions can be executed.

[0153] For example, any combination of the obtaining module 510, the first construction module 520, the second construction module 530, and the generating module 540 can be implemented in one module, or any one of these modules can be split into multiple modules. Alternatively, at least some functions of one or more of these modules can be combined with at least some functions of other modules and implemented in one module. According to an embodiment of the present application, at least one of the obtaining module 510, the first construction module 520, the second construction module 530, and the generating module 540 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or any other reasonable way of integrating or packaging circuits, etc., implemented by hardware or firmware, or implemented in any one of the three implementation manners of software, hardware, and firmware, or in an appropriate combination of any several of them. Alternatively, at least one of the obtaining module 510, the first construction module 520, the second construction module 530, and the generating module 540 can be at least partially implemented as a computer program module, and when the computer program module is run, it can execute the corresponding functions.

[0154] It should be noted that the part of the anti-seepage wall modeling device in the embodiment of the present application corresponds to the part of the anti-seepage wall modeling method in the embodiment of the present application. For the description of the part of the anti-seepage wall modeling device, please refer to the part of the anti-seepage wall modeling method for details, and will not be repeated here.

[0155] Figure 6 The block diagram of the electronic device of the anti-seepage wall modeling method according to the embodiment of the present application is shown. Figure 6 The shown electronic device is only an example and should not bring any limitation to the functions and usage scope of the embodiment of the present application.

[0156] As Figure 6 As shown, the electronic device 600 according to the embodiment of the present application includes a processor 601, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 602 or the program loaded from the storage part 608 into the random access memory (RAM) 603. The processor 601 can include, for example, a general microprocessor (such as a CPU), an instruction set processor, and / or a related chipset, and / or a dedicated microprocessor (such as an application specific integrated circuit (ASIC)), etc. The processor 601 can also include on-board memory for caching purposes. The processor 601 can include a single processing unit or multiple processing units for performing different actions of the method flow according to the embodiment of the present application.

[0157] In the RAM 603, various programs and data required for the operation of the electronic device 600 are stored. The processor 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. The processor 601 performs various operations of the method flow according to the embodiments of the present application by executing the programs in the ROM 602 and / or the RAM 603. It should be noted that the programs can also be stored in one or more memories other than the ROM 602 and the RAM 603. The processor 601 can also perform various operations of the method flow according to the embodiments of the present application by executing the programs stored in the one or more memories.

[0158] According to an embodiment of the present application, the electronic device 600 may further include an input / output (I / O) interface 605, and the input / output (I / O) interface 605 is also connected to the bus 604. The electronic device 600 may further include one or more of the following components connected to the input / output (I / O) interface 605: an input part 606 including a keyboard, a mouse, etc.; an output part 607 including, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker, etc.; a storage part 608 including a hard disk, etc.; and a communication part 609 including a network interface card such as a LAN card, a modem, etc. The communication part 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the input / output (I / O) interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is mounted on the drive 610 as needed so that a computer program read from it can be installed into the storage part 608 as needed.

[0159] According to an embodiment of the present application, the method flow according to the embodiments of the present application can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable storage medium, and the computer program includes program codes for performing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication part 609, and / or installed from the removable medium 611. When the computer program is executed by the processor 601, the above functions defined in the system according to the embodiments of the present application are executed. According to an embodiment of the present application, the above-described systems, devices, apparatuses, modules, units, etc. can be implemented by computer program modules.

[0160] The present application also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist separately without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiments of the present application is implemented.

[0161] According to an embodiment of the present application, the computer-readable storage medium may be a non-volatile computer-readable storage medium. For example, it may include but is not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In the present application, the computer-readable storage medium may be any tangible medium that contains or stores a program, and the program may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0162] For example, according to an embodiment of the present application, the computer-readable storage medium may include one or more memories other than the above-described ROM 602 and / or RAM 603 and / or ROM 602 and RAM 603.

[0163] An embodiment of the present application further includes a computer program product, which includes a computer program that contains program code for executing the method provided in the embodiments of the present application. When the computer program product runs on an electronic device, the program code is used to cause the electronic device to implement the method provided in the embodiments of the present application.

[0164] When the computer program is executed by the processor 601, the above functions defined in the system / apparatus of the embodiments of the present application are executed. According to an embodiment of the present application, the above-described systems, apparatuses, modules, units, etc. may be implemented by computer program modules.

[0165] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices and magnetic storage devices. In another embodiment, the computer program may also be transmitted and distributed in the form of a signal on a network medium, and be downloaded and installed through the communication part 609, and / or be installed from the removable medium 611. The program code included in the computer program may be transmitted by any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0166] In accordance with embodiments of the present application, program code for executing the computer programs provided by the embodiments of the present application can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. The programming languages include, but are not limited to, programming languages such as Java, C++, Python, the "C" language, or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device can be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or alternatively, can be connected to an external computing device (e.g., by connecting through the Internet using an Internet service provider).

[0167] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagram can represent a module, a program segment, or a portion of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, and combinations of blocks in the block diagram or flowchart, can be implemented using a dedicated hardware-based system for performing the specified functions or operations, or can be implemented using a combination of dedicated hardware and computer instructions. Those skilled in the art can understand that the features described in the various embodiments of the present application can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in the present application. In particular, without departing from the spirit and teachings of the present application, the features described in the various embodiments of the present application can be combined and / or combined in various ways. All such combinations and / or combinations fall within the scope of the present application.

[0168] The above describes the embodiments of the present application. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present application. Although the embodiments are described separately above, this does not mean that the measures in each embodiment cannot be used advantageously in combination. Without departing from the scope of the present application, those skilled in the art can make various substitutions and modifications, and all such substitutions and modifications should fall within the scope of the present application.

Claims

1. A method for modeling an anti-seepage wall, characterized in that: include: In response to the modeling instruction, a set of physical and mechanical parameters corresponding to the target cut-off wall is obtained from a target database, wherein the set of physical and mechanical parameters includes size parameters, electromagnetic wave data corresponding to different first points on the target cut-off wall, and an initial permeability coefficient; Using a processor to construct an initial cut-off wall model according to the size parameter and the plurality of electromagnetic wave data, wherein each second point on the initial cut-off wall model has a first resistivity similar to that of the second point; constructing a coefficient correlation model according to a plurality of second resistivities and an initial permeability coefficient corresponding to each electromagnetic wave data, wherein the plurality of second resistivities are calculated according to the plurality of electromagnetic wave data; A target cut-off wall model is generated according to the coefficient correlation model and the initial cut-off wall model, wherein each model grid on the target cut-off wall model has a corresponding target permeability coefficient.

2. The method according to claim 1, characterized in that The size parameters include the length parameter, depth parameter and thickness parameter of the target cut-off wall; The process of constructing an initial cut-off wall model according to the size parameters and the plurality of electromagnetic wave data using a processor includes: Based on the high-density electrical method, generating resistance cloud map data according to the length parameter, the depth parameter and the plurality of electromagnetic wave data, wherein the resistance cloud map data includes a first resistivity corresponding to each of the second points; The initial anti-seepage wall model is generated according to the resistance cloud map data and the thickness parameter.

3. The method according to claim 2, characterized in that Generating the initial cut-off wall model according to the resistance cloud map data and the thickness parameter includes: Based on the resistance threshold, determining the wall defect area from the plurality of the first resistance coefficients on the resistance cloud map data, wherein the probability of water seepage in the wall defect area is greater than the probability of water seepage in other areas; Performing edge detection processing on the wall defect area to obtain edge contour data of the wall defect area; The initial anti-seepage wall model is constructed according to the size parameters, the edge contour data and the resistance cloud map data.

4. The method according to claim 2 or 3, characterized in that: The electromagnetic wave data and initial permeability coefficient are generated in the following manner: Based on the point selection table, a point layout diagram is determined according to geological parameters of the area where the target cut-off wall is located and material parameters of the target cut-off wall, wherein the material parameters represent the amount of different types of construction materials used to construct the target cut-off wall; Determine a plurality of the first points on the target cut-off wall according to the point layout diagram and the size parameters; For each of the first points, a drilling coring test is performed at the first point to obtain initial permeability coefficients at different depths on the sampling core; The high-density electrical method test is carried out in the hole formed after the drilling coring test to obtain electromagnetic wave data corresponding to different depths.

5. The method according to claim 1, characterized in that According to the plurality of second resistivities and the initial permeability coefficient corresponding to each electromagnetic wave data, a coefficient correlation model is constructed, comprising: Based on the high density method, generating the resistivity corresponding to each electromagnetic wave data according to the plurality of electromagnetic wave data; generating a correlation relationship graph according to the plurality of resistivities and the initial permeability coefficient corresponding to each of the resistivities; A regression fitting analysis is performed on the correlation relationship diagram to obtain the coefficient correlation model.

6. The method according to claim 1 or 5, characterized in that: Generating a target cut-off wall model according to the coefficient correlation model and the initial cut-off wall model includes: Determining a target permeability coefficient corresponding to each of the first resistivities according to the coefficient correlation model and the plurality of the first resistivities; The target cut-off wall model is generated according to the plurality of target permeability coefficients and the initial cut-off wall model.

7. The method according to claim 6, characterized in that Generating the target cut-off wall model according to the plurality of target permeability coefficients and the initial cut-off wall model comprises: Performing meshing processing on the initial cut-off wall model to obtain a plurality of initial model meshes; For each of the initial model grids, parameter assignment processing is performed on the initial model grid using the target permeability coefficient corresponding to the initial model grid to obtain a target model grid; The target cut-off wall model is generated according to the plurality of target model grids.

8. The method according to claim 2, characterized in that: Before constructing the initial cut-off wall model, the following steps are also included: The target data is preprocessed to obtain preprocessed size parameters and multiple preprocessed initial permeability coefficients to generate a new set of physical and mechanical parameters, wherein the target data includes at least one of the size parameters, the initial permeability coefficients and the resistance cloud map data.

9. An anti-seepage wall modeling device, comprising: An acquisition module, configured to acquire a set of physical and mechanical parameters corresponding to a target cut-off wall from a target database in response to a modeling instruction, wherein the set of physical and mechanical parameters includes a size parameter, electromagnetic wave data corresponding to different first points on the target cut-off wall, and an initial permeability coefficient; A first construction module is used to construct an initial cut-off wall model according to the size parameter and the plurality of electromagnetic wave data using a processor, wherein each second point on the initial cut-off wall model has a first resistivity equal to that of the second point; A second construction module is used to construct a coefficient correlation model according to a plurality of second resistivities and an initial permeability coefficient corresponding to each electromagnetic wave data, wherein the plurality of second resistivities are calculated according to the plurality of electromagnetic wave data; A generating module is used to generate a target cut-off wall model according to the coefficient correlation model and the initial cut-off wall model, wherein each model grid on the target cut-off wall model has a corresponding target permeability coefficient.

10. An electronic device, comprising: one or more processors; a memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 8.

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