Surface karst identification method, device and equipment

Abstract

The application provides a karst identification method, device and equipment, and relates to the technical field of terrain identification. The method comprises the following steps: obtaining a digital elevation model for a target area; obtaining a mountain shadow map and a runoff network of the target area according to the digital elevation model; generating a surface runoff model of the target area according to the mountain shadow map and the runoff network; and identifying the surface karst of the target area according to the surface runoff model to obtain the distribution positions of a plurality of preset surface karsts in the target area. The method provided by the application can obtain the surface runoff model of the target area according to the digital elevation model, identify the distribution positions of the preset surface karsts according to the surface runoff model, avoid the limitation of identifying the distribution positions of the preset surface karsts from the two-dimensional morphological features, and improve the accuracy and efficiency of identifying the karst positions.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of terrain recognition, in particular to a surface karst identification method, device and equipment. BACKGROUND

[0002] Carbonate rocks are widely distributed in the southwest of China, and most of them are karst landforms. In these areas, engineering construction may encounter karst collapse, karst leakage, slope stability and other engineering geological problems. Therefore, identifying the location of karst is one of the important tasks in engineering geological investigation in karst areas.

[0003] The existing surface karst identification method is mainly remote sensing image identification. The remote sensing image identification method mainly identifies based on morphological features on two-dimensional optical images. For some areas where the surface morphological features are not obvious, there may be a problem of missing karst. In addition, in areas with dense vegetation, due to the obstruction of vegetation, it is also impossible to identify the karst under the vegetation using two-dimensional optical images. SUMMARY

[0004] The purpose of the present application is to provide a surface karst identification method, device and equipment to solve the problems in the prior art.

[0005] To achieve the above purpose, the technical solutions adopted by the embodiments of the present application are as follows:

[0006] In a first aspect, the embodiments of the present application provide a surface karst identification method, comprising:

[0007] obtaining a digital elevation model for a target area;

[0008] obtaining a mountain shadow map and a runoff network of the target area according to the digital elevation model;

[0009] generating a surface runoff model of the target area according to the mountain shadow map and the runoff network;

[0010] identifying the surface karst of the target area according to the surface runoff model to obtain the distribution positions of a plurality of preset surface karsts in the target area.

[0011] In a possible implementation example, the obtaining of the mountain shadow map and the runoff network of the target area according to the digital elevation model comprises:

[0012] performing light rendering according to the digital elevation model to generate the mountain shadow map;

[0013] performing catchment analysis according to the digital elevation model to generate the runoff network.

[0014] In a possible implementation example, the step of performing water catchment analysis based on the digital elevation model to generate the runoff network includes:

[0015] Flow direction analysis is performed based on the digital elevation model to obtain a flow direction grid map within the target area; the flow direction data of each grid in the flow direction grid map is used to characterize the water flow direction within the range corresponding to each grid in the target area;

[0016] Flow analysis is performed based on the flow direction grid to obtain the flow grid of the target area. The flow data of each grid in the flow grid is used to characterize the flow rate of the range corresponding to each grid in the target area.

[0017] The flow path network is obtained by extracting the flow path based on the flow grid diagram.

[0018] In a possible implementation example, generating a surface runoff model for the target area based on the hill shadow map and the runoff network includes:

[0019] The runoff network is rasterized and vectorized to obtain the target runoff network;

[0020] The target runoff network and the mountain shadow map are overlaid to generate a surface runoff model for the target area.

[0021] In a possible implementation example, the method further includes:

[0022] In the surface runoff model, the locations of the multiple preset surface karst sites in the target area are marked to generate a surface karst distribution map of the target area.

[0023] In a possible implementation example, the step of identifying surface karst in the target area based on the surface runoff model to obtain the distribution locations of multiple preset surface karst in the target area includes:

[0024] Based on the texture information of the surface runoff model and the distribution characteristics of the preset runoff network of the multiple preset surface karsts, the surface karsts in the target area are identified to obtain the distribution locations of the multiple preset surface karsts in the target area.

[0025] In a possible implementation example, the step of performing flow direction analysis based on the digital elevation model to obtain a flow direction raster map within the target area includes:

[0026] The target area is rasterized according to the digital elevation model to obtain multiple grids of the target area;

[0027] Centered on each grid cell, the water flow direction of each grid cell is determined based on the slope between each grid cell and its multiple adjacent grid cells.

[0028] The flow direction grid map is generated based on the water flow direction of the multiple grids.

[0029] In a possible implementation example, the step of performing flow analysis based on the flow direction grid to obtain the confluence grid of the target area includes:

[0030] Based on the data of each grid in the flow direction grid diagram and the data of each grid's adjacent grids, determine the sink flow data of each grid;

[0031] The flow raster map is generated based on the flow data of each raster.

[0032] Secondly, embodiments of this application provide a surface karst identification device, comprising:

[0033] The acquisition module is used to acquire a digital elevation model for the target area.

[0034] The extraction module is used to obtain the mountain shadow map and runoff network of the target area based on the digital elevation model;

[0035] The model generation module is used to generate a surface runoff model for the target area based on the mountain shadow map and the runoff network.

[0036] The identification module is used to identify the surface karst in the target area according to the surface runoff model, and obtain the distribution locations of multiple preset surface karst in the target area.

[0037] Thirdly, embodiments of this application provide a computer device, including: a processor, a storage medium, and a bus. The storage medium stores program instructions executable by the processor. When the computer device is running, the processor communicates with the storage medium via the bus, and the processor executes the program instructions to perform the steps of the surface karst identification method provided in the above embodiments.

[0038] Fourthly, embodiments of this application also provide a storage medium storing a computer program, which, when run by a processor, executes the steps of the surface karst identification method provided in the above embodiments.

[0039] The beneficial effects of this application are as follows: This application provides a method for identifying surface karst. The method includes acquiring a digital elevation model (DEM) for a target area; obtaining a mountain shadow map and runoff network for the target area based on the DEM; generating a surface runoff model for the target area based on the mountain shadow map and runoff network; and finally identifying the surface karst in the target area based on the surface runoff model to obtain the distribution locations of multiple preset surface karst features in the target area. The method provided by this application can obtain a surface runoff model for the target area based on the DEM. Since this surface runoff model includes a mountain shadow map and a runoff network, and the location of surface karst is related to the runoff network, using this surface runoff model, the distribution locations of multiple preset surface karst features in the target area can be directly obtained based on the runoff network, and the distribution locations can be correlated with the mountain shadow map. This avoids the limitations of identification based on two-dimensional morphological features and improves the accuracy and efficiency of karst location identification. Attached Figure Description

[0040] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is one of the flowcharts illustrating a method for identifying surface karst according to an embodiment of this application;

[0042] Figure 2 A schematic diagram of a mountain shadow map provided in an embodiment of this application;

[0043] Figure 3 A schematic diagram of the runoff diagram of water flow provided in one embodiment of this application;

[0044] Figure 4 A schematic diagram of a surface runoff model provided in an embodiment of this application;

[0045] Figure 5 A schematic diagram illustrating the distribution location of a preset surface karst in an embodiment of this application;

[0046] Figure 6 A schematic diagram of a karst depression provided in an embodiment of this application;

[0047] Figure 7 A schematic diagram of a karst funnel provided in an embodiment of this application;

[0048] Figure 8 A second schematic flowchart of a surface karst identification method provided in an embodiment of this application;

[0049] Figure 9 A flowchart illustrating a method for generating a runoff network based on a digital elevation model is provided for one embodiment of this application;

[0050] Figure 10 This is a schematic diagram of the D8 algorithm encoding.

[0051] Figure 11 This is a schematic diagram of the flow grid provided in this embodiment;

[0052] Figure 12 This is a schematic diagram of the bus grid provided in this embodiment;

[0053] Figure 13 A schematic flowchart illustrating a method for generating a surface runoff model according to an embodiment of this application;

[0054] Figure 14 A schematic diagram showing the pre-defined flow path network distribution characteristics of a karst depression;

[0055] Figure 15 This is one of the schematic diagrams showing the pre-defined flow path network distribution characteristics of karst funnels;

[0056] Figure 16 The second schematic diagram shows the pre-defined flow path network distribution characteristics of karst funnels;

[0057] Figure 17 A schematic flowchart of a flow raster generation method provided in an embodiment of this application;

[0058] Figure 18 A schematic flowchart of a bus grid generation method provided in an embodiment of this application;

[0059] Figure 19 A schematic diagram of the structure of a surface karst identification device provided in an embodiment of this application;

[0060] Figure 20 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0062] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0063] In the description of this application, it should be noted that if the terms "upper", "lower", etc. appear to indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship that the product of this application is usually placed in, it is only for the convenience of describing this application and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application.

[0064] Furthermore, the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Additionally, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0065] It should be noted that, where there is no conflict, the features in the embodiments of this application can be combined with each other.

[0066] Existing methods for identifying surface karst mainly rely on remote sensing image recognition. These methods primarily identify karst based on morphological features in two-dimensional optical images. However, in areas with indistinct surface morphology, karst can easily be missed. Furthermore, in densely vegetated areas, karst beneath the vegetation cannot be identified. Therefore, this application provides a method, apparatus, and device for identifying surface karst. Using this method avoids the limitations of identification based on two-dimensional morphological features, improving the accuracy and efficiency of karst location identification.

[0067] It should be noted that this surface karst identification method can be generated by any computer device that integrates a preset surface karst identification algorithm. The computer device can be, for example, a terminal-facing computer device or a backend server.

[0068] The following examples, in conjunction with several accompanying figures, provide specific illustrations of the surface karst identification method provided in the embodiments of this application.

[0069] Figure 1 This is one of the flowcharts illustrating the surface karst identification method provided in the embodiments of this application, such as... Figure 1 As shown, the method includes:

[0070] S101. Obtain the digital elevation model for the target area.

[0071] This embodiment mainly uses digital elevation models to obtain the distribution location of surface karst. Therefore, in specific operations, the geographic coordinates of the target area can be obtained first, and the digital elevation model of the target area can be obtained from the preset elevation database based on the geographic coordinates.

[0072] The target area is a preset area to be identified. In practical applications, the area to be identified is a user-specified area, which the user can select according to actual needs. There is no limitation here. The preset elevation database can be, for example, a geographic database, in which the digital elevation model of the target area can be directly obtained.

[0073] S102. Based on the digital elevation model, obtain the mountain shadow map and runoff network of the target area.

[0074] After obtaining the digital elevation model according to step S101, the digital elevation model can be imported into ArcGIS software. ArcGIS software can obtain the mountain shadow map and runoff network of the target area according to the digital elevation model through preset methods.

[0075] Among them, the mountain shadow map can be, for example, as follows: Figure 2 The hillside shadow map of the target area shown can be represented by, for example, a runoff network such as... Figure 3 The runoff map of the target area shown ( Figure 3 The black lines in the diagram represent water flow. The preset method can be any method, as long as it can obtain the mountain shadow map and runoff network of the target area based on the digital elevation model and the preset method.

[0076] S103. Generate a surface runoff model for the target area based on the mountain shadow map and runoff network.

[0077] After obtaining the mountain shadow map and runoff network in step S102, ArcGIS software can generate a surface runoff model for the target area based on the mountain shadow map and runoff network. The surface runoff model can be, for example, as shown in... Figure 4 As shown ( Figure 4 (Includes a mountain shadow map and runoff network of the target area).

[0078] S104. Identify the surface karst in the target area based on the surface runoff model to obtain the distribution locations of multiple preset surface karsts in the target area.

[0079] After generating the surface runoff model, ArcGIS software can also identify surface karst in the target area based on the surface runoff model, obtaining the distribution locations of multiple preset surface karst features in the target area. For example, the distribution locations of these preset surface karst features can be obtained as follows: Figure 5 As shown.

[0080] It should be noted that surface karst is generally divided into karst depressions and karst funnels. Karst depressions are characterized by negative topography, surrounded by mountains or hills, without surface drainage outlets, and range in diameter from a few meters to several hundred meters (e.g., Figure 6 (As shown); karst sinkholes are closed depressions in the shape of a funnel or a dish, with a diameter of less than 100m. They are formed by the continuous dissolution of surface water along joints and fissures, accompanied by collapse, subsidence, infiltration, and filtration (as shown). Figure 7 As shown in the figure, therefore, in this embodiment, the preset surface karst can be, for example, a karst depression and a karst funnel.

[0081] In summary, this embodiment provides a method for identifying surface karst. The method includes acquiring a digital elevation model (DEM) of a target area; obtaining a mountain shadow map and runoff network for the target area based on the DEM; generating a surface runoff model for the target area based on the mountain shadow map and runoff network; and finally identifying the surface karst in the target area based on the surface runoff model to obtain the distribution locations of multiple preset surface karst features within the target area. The method provided in this embodiment can obtain a surface runoff model for the target area based on the DEM. Since this surface runoff model includes a mountain shadow map and a runoff network, and the location of surface karst is related to the runoff network, using this surface runoff model can directly obtain the distribution locations of multiple preset surface karst features in the target area based on the runoff network, and correlate these locations with the mountain shadow map. This avoids the limitations of identification based on two-dimensional morphological features and improves the accuracy and efficiency of karst location identification.

[0082] Figure 8 This is a second schematic flowchart of a surface karst identification method provided in an embodiment of this application, as shown below. Figure 8 As shown, in step S102, based on the digital elevation model, the mountain shadow map and runoff network of the target area are obtained, which may include:

[0083] S201. Perform lighting rendering based on the digital elevation model to generate a mountain shadow map.

[0084] After obtaining the digital elevation model, ArcGIS software can perform lighting rendering on the digital elevation model to generate a mountain shadow map. Rendering can be done using sunlight, for example, to ensure that the generated mountain shadow map reflects real natural light, making it more consistent with the actual mountain environment.

[0085] S202. Perform water catchment analysis based on the digital elevation model to generate a runoff network.

[0086] After obtaining the digital elevation model (DEM), ArcGIS software can perform runoff analysis based on the elevation data in the DEM to generate a runoff network. The elevation data consists of the height of mountains at various locations in the target area (the elevation data is the height difference between the mountains and sea level at each location, which can be directly obtained from the DEM). Due to gravity, water flows from high-altitude areas to low-altitude areas; therefore, a runoff network can be generated based on the elevation data.

[0087] In this embodiment, lighting rendering is performed based on the digital elevation model to generate a mountain shadow map, which makes the generated mountain shadow map more similar to the real mountain environment. Water runoff analysis is performed based on the elevation data in the digital elevation model to generate a runoff network. Compared with the use of two-dimensional optical images in the prior art, the generation of the runoff network is not affected by surface morphology features and obstructions, thereby improving the accuracy and efficiency of identifying karst locations based on the runoff network.

[0088] One embodiment of this application provides a method for generating a runoff network based on a digital elevation model. Figure 9 A flowchart illustrating a method for generating a runoff network based on a digital elevation model is provided for one embodiment of this application, as follows: Figure 9 As shown, in step S202 of the above embodiment, water catchment analysis is performed based on the digital elevation model to generate a runoff network, including:

[0089] S301. Perform flow direction analysis based on the digital elevation model to obtain a flow direction raster map within the target area.

[0090] This embodiment uses the D8 algorithm to analyze the flow direction of water within the target area. First, the D8 algorithm will be explained:

[0091] The D8 algorithm works as follows: Assuming that water flow in each grid cell can only flow into its 8 adjacent grid cells, the steepest slope method is used to determine the direction of water flow. Specifically, on a 3×3 DEM grid, the distance weighted drop between the central grid cell X and each adjacent grid cell is calculated (i.e., the drop between the center grid cell and the center points of each grid cell divided by the distance between the center grid cell and the center points of each grid cell; the drop refers to the elevation difference). The grid cell with the largest distance weighted drop is selected as the grid cell indicating the direction of water flow from the central grid cell. The specific formula is:

[0092]

[0093] Where P is the distance difference between two grids, ΔH is the altitude difference between two grids, and L is the distance between the centers of two grids.

[0094] Figure 10 A schematic diagram of the D8 algorithm, as shown below. Figure 10 As shown, the eight neighboring grid cells adjacent to the central grid are encoded. When water flows from the central grid to grid cells in the eastward direction, the encoding is specified as 2 to the power of 0 (i.e.,...). Figure 10 The "1" in the grid is rotated clockwise, and the exponent of 2 increases by 1 for each grid rotation. That is, 1, 2, 4, 8, 16, 32, 64, and 128 are used to represent the eight directions of water flow (1 represents east, 2 represents southeast, 4 represents south, 8 represents southwest, 16 represents west, 32 represents northwest, 64 represents north, and 128 represents northeast).

[0095] Figure 11 This is a schematic diagram of the flow raster provided in this embodiment. The application of the D8 algorithm in this embodiment can be, for example, as follows: Figure 11 As shown, Figure 11 In the left-hand image, the data in each grid cell represents the elevation of that cell. Based on the elevation differences between each grid cell and its adjacent cells, as well as the distance between the grid center points, the flow direction of the water in each grid cell can be determined. This flow direction data is then displayed in... Figure 11 The diagram on the right is marked.

[0096] by Figure 11 In the left-hand diagram, taking "78" in the first row and first column as an example, among the adjacent grid cells, "67" in the second row and second column has the largest distance weight difference. Therefore, the water flow from grid cell "78" to grid cell "67", consistent with the D8 algorithm. Figure 10 As can be seen from the comparison, the water flows in a southeast direction, represented by "2". Figure 11 In the image on the right, the number in the first cell of the first row and first column is 2, and so on for the other cells.

[0097] therefore, Figure 11 The diagram on the right is a flow direction grid diagram. In this embodiment, the flow direction data of each grid in the flow direction grid diagram is used to characterize the water flow direction within the range corresponding to each grid in the target area.

[0098] S302. Perform flow analysis based on the flow direction raster to obtain the confluence raster of the target area.

[0099] After obtaining the flow direction raster map according to step S301, flow analysis can be performed based on the flow direction raster map to obtain the confluence raster map of the target area.

[0100] Figure 12 This is a schematic diagram of the bus grid provided in this embodiment. Figure 12 The diagram on the left is the flow direction grid diagram obtained in step S301. As stated in step S301 above, the data in each grid of the flow direction grid diagram represents the water flow direction within the corresponding range of each grid. Therefore, assuming that each grid carries a portion of the water flow, the cumulative flow in each grid can be calculated... Figure 12 As shown in the diagram on the right, the cumulative flow of a grid cell in the flow grid represents the water flow rate of that cell (the value of a grid cell in the flow grid is the cumulative flow of that cell; the larger the cumulative flow value, the larger the water flow rate in that cell).

[0101] by Figure 12 In the diagram on the left, taking the number "2" in the second row and second column as an example, in the grid cells adjacent to "2", the "2" in the first row and first column indicates that its water flow direction is southeast, the "2" in the first row and second column indicates that its water flow direction is southeast, and the "2" in the second row and first column indicates that its water flow direction is southeast. Therefore, in the grid cells adjacent to the cell in the second row and second column, only the water in the first row and first column flows to the second row and second column. Thus, the number in the cell in the second row and second column is 1, indicating that in the grid cells adjacent to it, only one grid cell's water flows to the grid cell in the second row and second column. The other grid cells follow the same pattern.

[0102] therefore, Figure 12 The diagram on the right is the flow grid diagram. In this embodiment, the flow data of each grid in the flow grid diagram is used to characterize the flow rate of each grid within the target area.

[0103] It should be noted that surface runoff only forms when the accumulated runoff reaches a certain value. Therefore, a flow threshold can be set through flow direction analysis and runoff accumulation calculation. All grids with runoff exceeding this threshold are potential flow paths, and the network formed by these flow paths constitutes the runoff network (i.e., a flow threshold is set, and a grid is considered to have flow only if the accumulated runoff in the grid exceeds this threshold; for example, if the flow threshold is 30, a grid with an accumulated runoff of 25 is not considered to have flow). The setting of the flow threshold affects the density of the runoff network, and its value should be set in conjunction with the actual terrain and data accuracy. No specific value for the flow threshold is specified here.

[0104] S303. Extract the flow path based on the flow grid diagram to obtain the flow path network.

[0105] After obtaining the runoff grid map of the target area according to step S302, flow paths can be extracted based on the runoff grid map to obtain the flow path network (the flow path network can be as follows). Figure 3 (As shown).

[0106] In this embodiment, flow direction analysis is performed based on the digital elevation model to obtain a flow direction raster map within the target area; then, flow rate analysis is performed based on the flow direction raster map to obtain a runoff raster map of the target area; finally, flow path extraction is performed based on the runoff raster map to obtain a flow path network. This ensures that the generated flow path network is obtained from the real digital elevation model, guaranteeing the authenticity of the surface runoff model obtained from the flow path network, thereby ensuring a high degree of fit between the distribution location of surface karst obtained from the surface runoff model and the real target area.

[0107] One embodiment of this application provides a possible implementation of a method for generating a surface runoff model. Figure 13 A schematic flowchart illustrating a method for generating a surface runoff model according to an embodiment of this application is shown below. Figure 13 As shown, in step S103, a surface runoff model for the target area is generated based on the mountain shadow map and runoff network, including:

[0108] S401. Perform raster vectorization on the runoff network to obtain the target runoff network.

[0109] After obtaining the runoff network according to step S102, the runoff network can be converted from raster data to vector data to obtain the target runoff network. The target runoff network is the runoff network map of the target area (e.g., ...). Figure 3 ).

[0110] S402. Overlay the target runoff network and the mountain shadow map to generate a surface runoff model for the target area.

[0111] After obtaining the target runoff network, the target runoff network ( Figure 3 ) and mountain shadow map ( Figure 2 By overlaying (overlaying refers to matching the target runoff network and the hill shadow map according to their corresponding positions and displaying them in the same image), a surface runoff model of the target area can be generated. Figure 4 ).

[0112] In this embodiment, the target runoff network and the mountain shadow map are overlaid to generate a surface runoff model of the target area. The surface karst in the target area can then be identified based on the generated surface runoff model to obtain the distribution locations of multiple preset surface karsts in the target area.

[0113] Based on the surface karst identification method provided in the above embodiments, a surface karst identification method provided in this application further includes: after identifying the surface karst in the target area according to the surface runoff model and obtaining the distribution locations of multiple preset surface karsts in the target area, marking the locations of the multiple preset surface karsts in the target area in the surface runoff model to generate a surface karst distribution map of the target area (e.g., Figure 5 In the process, different markers are used to mark karst depressions and karst funnels.

[0114] In this embodiment, multiple preset locations of surface karst in the target area are marked in the surface runoff model to generate a surface karst distribution map of the target area. Users can intuitively obtain the distribution location of various types of surface karst in the target area based on the surface karst distribution map, which facilitates the use of the surface karst distribution map by users.

[0115] One embodiment of this application also provides a possible implementation of a method for obtaining the distribution location of a preset surface karst based on a surface runoff model.

[0116] In step S104, the surface karst in the target area is identified according to the surface runoff model to obtain the distribution locations of multiple preset surface karsts in the target area. This may include: identifying the surface karst in the target area based on the texture information of the surface runoff model and the preset flow path network distribution characteristics of multiple preset surface karsts to obtain the distribution locations of multiple preset surface karsts in the target area.

[0117] The preset flow path network distribution characteristics represent the relationship between the flow direction of each preset surface karst and water flow. The preset flow path network distribution characteristics can be used to identify the type of each preset surface karst.

[0118] Taking multiple pre-defined surface karst as karst depressions and karst funnels as examples, the corresponding pre-defined flow path network distribution characteristics are as follows: karst depressions are generally located at the confluence of multiple flow paths, and their flow path network is radially distributed in all directions; karst funnels are generally located at both ends of a single or multiple flow paths.

[0119] For example, Figure 14 This is a schematic diagram showing the pre-defined flow path network distribution characteristics of a karst depression. Figure 15 This is one of the schematic diagrams showing the pre-defined flow path network distribution characteristics of karst funnels. Figure 16 This is the second schematic diagram illustrating the pre-defined flow path network distribution characteristics of karst funnels. Figure 14 In the middle, the confluence of multiple runoffs is a karst depression; in Figure 15 In the middle, the two ends of a single runoff are karst funnels; in Figure 16 In the middle, the ends of multiple runoffs are karst funnels.

[0120] In this embodiment, the preset flow path network distribution characteristics are used to identify the types of preset surface karst. Since the preset flow path network distribution characteristics are set according to the relationship between each preset surface karst and water flow, the method provided in this embodiment can make the obtained preset surface karst types match the actual water flow conditions in the target area, thereby improving the accuracy of the obtained preset surface karst types.

[0121] Figure 17 This is a schematic flowchart of a flow raster generation method provided in an embodiment of this application, as shown below. Figure 17 As shown, in step S301, flow direction analysis is performed based on the digital elevation model to obtain a flow direction raster map within the target area, including:

[0122] S501. The target area is rasterized according to the digital elevation model to obtain multiple grids of the target area.

[0123] After obtaining the digital elevation model, the target area can be rasterized based on its geographical coordinates in the digital elevation model, resulting in multiple raster cells of the target area.

[0124] S502. Taking each grid cell as the center, determine the water flow direction of each grid cell based on the slope between each grid cell and multiple adjacent grid cells.

[0125] After obtaining multiple grids according to step S501, the water flow direction of each grid can be determined based on the slope between each grid and its adjacent grids, using each grid as the center (the water flow direction is represented by...). Figure 11 The values ​​in the graph on the right are used to represent this.

[0126] S503. Generate a flow direction grid map based on the water flow direction of multiple grids.

[0127] After determining the water flow direction of each grid cell according to step S501, a flow direction grid map can be generated based on the water flow directions of multiple grid cells (the flow direction grid map can be as follows). Figure 11 (As shown in the diagram on the right).

[0128] Figure 18 This is a schematic flowchart of a bus grid generation method provided in an embodiment of this application, as shown below. Figure 18 As shown, in step S302, flow analysis is performed based on the flow direction grid to obtain the confluence grid of the target area, including:

[0129] S601. Determine the flow rate data for each grid cell based on the data of each grid cell in the flow direction grid diagram and the data of each grid cell's adjacent grid cells.

[0130] After obtaining the flow direction raster, the sink flow data for each raster can be determined based on the data in each raster and the data in each of its adjacent raster cells (sink flow data is used for...). Figure 12 The values ​​in the graph on the right are used to represent this.

[0131] S602. Generate a runoff raster map based on the runoff data of each raster.

[0132] After determining the flow rate data for each raster, a flow raster map can be generated based on the flow rate data from multiple raster maps (the flow direction raster map can be like this). Figure 12 (As shown in the diagram on the right).

[0133] One embodiment of this application also provides a surface karst identification device. Figure 19 A schematic diagram of the structure of a surface karst identification device provided in one embodiment of this application is shown below. Figure 19 As shown, the device includes:

[0134] The acquisition module 701 is used to acquire the digital elevation model for the target area.

[0135] Extraction module 702 is used to obtain the mountain shadow map and runoff network of the target area based on the digital elevation model.

[0136] The model generation module 703 is used to generate a surface runoff model for the target area based on the mountain shadow map and runoff network.

[0137] The identification module 704 is used to identify the surface karst in the target area based on the surface runoff model, and obtain the distribution locations of multiple preset surface karst in the target area.

[0138] In a possible implementation example, the extraction module 702 is further configured to perform lighting rendering based on the digital elevation model to generate the mountain shadow map; and to perform water catchment analysis based on the digital elevation model to generate the runoff network.

[0139] In a possible implementation example, the extraction module 702 is further configured to perform flow direction analysis based on the digital elevation model to obtain a flow direction grid map within the target area; the flow direction data of each grid in the flow direction grid map is used to characterize the water flow direction within the range corresponding to each grid in the target area; perform flow rate analysis based on the flow direction grid map to obtain a flow path grid map of the target area, the flow rate data of each grid in the flow path grid map is used to characterize the flow rate within the range corresponding to each grid in the target area; and perform flow path extraction based on the flow path grid map to obtain the flow path network.

[0140] In a possible implementation example, the model generation module 703 is also used to perform raster vectorization on the runoff network to obtain a target runoff network; and to overlay the target runoff network and the mountain shadow map to generate a surface runoff model of the target area.

[0141] In a possible implementation example, the surface karst identification device provided in this embodiment further includes a marking module, which is used to mark the locations of the multiple preset surface karst sites in the target area in the surface runoff model, and generate a surface karst distribution map of the target area.

[0142] In a possible implementation example, the identification module 704 is further configured to identify the surface karst in the target area based on the texture information of the surface runoff model and the distribution characteristics of the preset runoff network of the multiple preset surface karsts, so as to obtain the distribution location of the multiple preset surface karsts in the target area.

[0143] In a possible implementation example, the extraction module 702 is further configured to rasterize the target area according to the digital elevation model to obtain multiple grids of the target area; determine the water flow direction of each grid based on the slope between each grid and multiple adjacent grids, with each grid as the center; and generate the flow direction grid map based on the water flow direction of the multiple grids.

[0144] In a possible implementation example, the extraction module 702 is further configured to determine the flow rate data of each grid based on the data of each grid in the flow direction grid and the data of the adjacent grids of each grid; and generate the flow grid based on the flow rate data of the plurality of grids.

[0145] The above-described device is used to execute the method provided in the foregoing embodiments, and its implementation principle and technical effect are similar, so they will not be described again here.

[0146] These modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more microprocessors, or one or more Field Programmable Gate Arrays (FPGAs). Alternatively, when a module is implemented using processing element scheduler code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. Furthermore, these modules can be integrated together as a system-on-a-chip (SOC).

[0147] Figure 20 This application provides a schematic diagram of the structure of a computer device, as shown in the embodiment of the present application. Figure 20 As shown in the embodiments of this application, a computer device is also provided, including: a processor 801, a storage medium 802 and a bus 803. The storage medium stores program instructions that can be executed by the processor. When the computer device is running, the processor communicates with the storage medium through the bus, and the processor executes the program instructions to perform the steps of the surface karst identification method provided in the above embodiments.

[0148] This application embodiment also provides a storage medium storing a computer program, which, when run by a processor, executes the steps of the surface karst identification method provided in the above embodiments.

[0149] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0150] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0151] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0152] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0153] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for identifying surface karst, characterized in that, include: Obtain a digital elevation model for the target area; Based on the digital elevation model, the mountain shadow map and runoff network of the target area are obtained; Based on the mountain shadow map and the runoff network, a surface runoff model for the target area is generated; The surface karst in the target area is identified based on the surface runoff model, and the distribution locations of multiple preset surface karsts in the target area are obtained. The step of generating a surface runoff model for the target area based on the mountain shadow map and the runoff network includes: The runoff network is rasterized and vectorized to obtain the target runoff network; The target runoff network and the mountain shadow map are overlaid to generate a surface runoff model for the target area.

2. The method according to claim 1, characterized in that, The step of obtaining the mountain shadow map and runoff network of the target area based on the digital elevation model includes: The mountain shadow map is generated by performing lighting rendering based on the digital elevation model. Based on the digital elevation model, water catchment analysis is performed to generate the runoff network.

3. The method according to claim 2, characterized in that, The step of performing water catchment analysis based on the digital elevation model to generate the runoff network includes: Flow direction analysis is performed based on the digital elevation model to obtain a flow direction grid map within the target area; the flow direction data of each grid in the flow direction grid map is used to characterize the water flow direction within the range corresponding to each grid in the target area; Flow analysis is performed based on the flow direction grid to obtain the flow grid of the target area. The flow data of each grid in the flow grid is used to characterize the flow rate of the range corresponding to each grid in the target area. The runoff network is obtained by extracting the runoff path based on the runoff grid diagram.

4. The method according to claim 1, characterized in that, The method further includes: In the surface runoff model, the locations of the multiple preset surface karst sites in the target area are marked to generate a surface karst distribution map of the target area.

5. The method according to claim 1, characterized in that, The step involves identifying surface karst in the target area based on the surface runoff model, thereby obtaining the distribution locations of multiple preset surface karst formations in the target area, including: Based on the texture information of the surface runoff model and the distribution characteristics of the preset runoff network of the multiple preset surface karsts, the surface karsts in the target area are identified to obtain the distribution locations of the multiple preset surface karsts in the target area.

6. The method according to claim 3, characterized in that, The step of performing flow direction analysis based on the digital elevation model to obtain a flow direction raster map within the target area includes: The target area is rasterized according to the digital elevation model to obtain multiple grids of the target area; Centered on each grid cell, the water flow direction of each grid cell is determined based on the slope between each grid cell and its multiple adjacent grid cells. The flow direction grid map is generated based on the water flow direction of the multiple grids.

7. The method according to claim 3, characterized in that, The step of performing flow analysis based on the flow direction grid to obtain the confluence grid of the target area includes: Based on the data of each grid in the flow direction grid diagram and the data of each grid's adjacent grids, determine the sink flow data of each grid; The flow raster map is generated based on the flow data of each raster.

8. A surface karst identification device, characterized in that, include: The acquisition module is used to acquire a digital elevation model for the target area. The extraction module is used to obtain the mountain shadow map and runoff network of the target area based on the digital elevation model; The model generation module is used to generate a surface runoff model for the target area based on the mountain shadow map and the runoff network. The identification module is used to identify the surface karst in the target area according to the surface runoff model, and obtain the distribution locations of multiple preset surface karst in the target area; The model generation module is also used to perform raster vectorization on the runoff network to obtain the target runoff network; and to overlay the target runoff network and the mountain shadow map to generate the surface runoff model of the target area.

9. A computer device, characterized in that, include: The system includes a processor, a storage medium, and a bus. The storage medium stores program instructions executable by the processor. When the computer device is running, the processor communicates with the storage medium via the bus, and the processor executes the program instructions to perform the steps of the surface karst identification method as described in any one of claims 1 to 7.

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