Basin model generation method and generation device, electronic equipment, and storage medium

Abstract

The application discloses a basin model generation method and device, electronic equipment and a storage medium, and relates to the field of three-dimensional digital twinning, and the basin model generation method comprises the following steps: acquiring point cloud data and an image map of a specified basin, wherein the point cloud data at least contains a center point coordinate of a target model; extracting a feature parameter set of the target model based on the center point coordinate of the target model and a model feature parameter library corresponding to the target model; comparing feature parameters in the extracted feature parameter set to generate a three-dimensional contour parameter library of the target model; and generating the target model based on the point cloud data and the three-dimensional contour parameter library. The application solves the technical problem of low modeling efficiency caused by the lack of systematic modeling planning when a basin model is built in the related art.

Description

Technical Field

[0001] This invention relates to the field of three-dimensional digital twins, and more specifically, to a method and apparatus for generating a watershed model, an electronic device, and a storage medium. Background Technology

[0002] Currently, the construction of watershed land and water models typically involves manual modeling based on image data. With the increasing demand for detailed land models within watersheds, the speed of manual modeling is often increased by adding more personnel to shorten the model building time. However, this approach has significant drawbacks: increased communication costs, labor costs, and project development costs. Furthermore, the currently constructed watershed land models are either generated from oblique photogrammetry data (not detailed models) or manually modeled from image data. Both methods lack systematic planning and have low modeling efficiency, which not only raises the bar for watershed simulation but also creates a barrier to the application of 3D digital twins within watersheds.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This invention provides a method, apparatus, electronic device, and storage medium for generating watershed models, which at least solves the technical problem in related technologies where the lack of systematic modeling planning leads to low modeling efficiency when building watershed models.

[0005] According to one aspect of the present invention, a method for generating a watershed model is provided, comprising: acquiring point cloud data and image map of a specified watershed, wherein the point cloud data includes at least: the center point coordinates of a target model; extracting a set of feature parameters of the target model based on the center point coordinates of the target model and a model feature parameter library corresponding to the target model; comparing the feature parameters in the extracted set of feature parameters to generate a three-dimensional contour parameter library of the target model; and generating the target model based on the point cloud data and the three-dimensional contour parameter library.

[0006] Optionally, the step of obtaining an image map of a specified watershed includes: acquiring a map of a target surface area, extracting geographic area information and region contours indicated by the map of the target surface area; marking a portion of the target surface area associated with the specified watershed based on the geographic area information and region contours; deleting other background maps other than the portion of the area associated with the specified watershed, thereby obtaining an image map of the specified watershed.

[0007] Optionally, after acquiring point cloud data and image maps of a specified watershed, the method further includes: acquiring the coordinate system of the image map; and converting the coordinate system in the point cloud data into the same coordinate system as the coordinate system of the image map.

[0008] Optionally, after acquiring point cloud data and image maps of a specified watershed, the method further includes: acquiring labeling information when labeling the specified watershed, wherein the labeling information includes at least: latitude and longitude information, model identifier, and structural features of the target model; indexing the model identifier of the target model based on the latitude and longitude information; indexing the model type and geometric feature parameters of the target model based on the model identifier and the structural features, wherein the geometric feature parameters include at least: model length, model width, and model height; and determining the set of index parameters of the target model based on the model identifier, combining the latitude and longitude information, the model type, and the geometric feature parameters.

[0009] Optionally, after determining the set of index parameters of the target model, the method further includes: establishing a unique model identifier based on the model identifier in the set of index parameters and the latitude and longitude information of the target model; determining a model splitting strategy according to the model type; splitting the parameters in the geometric feature parameters using the model splitting strategy to obtain sub-models; extracting model feature parameters from each sub-model based on the structural features; and constructing a model feature parameter library corresponding to the target model by combining the model feature parameters in each sub-model using the unique model identifier as a basis.

[0010] Optionally, the feature parameters in the feature parameter set include at least one of the following: geographic coordinate parameters, polygon parameters, two-dimensional feature parameters, and three-dimensional feature parameters.

[0011] Optionally, the extraction of the geographic coordinate parameters includes: extracting the latitude and longitude information and contour parameters of the target model based on the model's unique identifier number to obtain the geographic coordinate parameters.

[0012] Optionally, extracting the polygon parameters includes: matching the corresponding point cloud data based on the geographic coordinate parameters; and extracting polygon parameters within a specified range of the geometric feature parameters of the target model based on the matched point cloud data. The polygon parameters include: parameters of the contour edge of the target model at the target angle point, parameters of the midline point of the line, length parameters from the center point of the line to each face, and length parameters from the center point of the model to the edge point of the line.

[0013] Optionally, the extraction of the two-dimensional feature parameters includes: extracting planar feature parameters within the polygon area from a two-dimensional orthogonal projection perspective based on the polygon parameters to obtain the two-dimensional feature parameters, wherein the two-dimensional feature parameters include: parameters of the contour edge of the target model at the target angle point, parameters of the midline point of the line to the center point of the polygon, and point parameters of the model center point to each edge line segment.

[0014] Optionally, the extraction of the three-dimensional feature parameters includes: extracting a first point parameter from each face of the target model to the center point of the model; cutting the three-dimensional face of the target model and extracting a second point parameter from the cutting line to the center point of the model to obtain the three-dimensional feature parameters.

[0015] Optionally, after extracting the feature parameter set of the target model, the method further includes: verifying the three-dimensional feature parameters based on the polygon parameters and the two-dimensional feature parameters to obtain a verification result, wherein the verification result is used to confirm whether there are any parameters in the feature parameter set of the target model that do not meet the model feature requirements.

[0016] Optionally, the step of generating a three-dimensional contour parameter library of the target model by comparing the feature parameters in the extracted feature parameter set includes: comparing the polygon parameters, the two-dimensional feature parameters, and the three-dimensional feature parameters in the feature parameter set to determine the three-dimensional key point parameters of the target model; outlining the model lines of the target model based on the three-dimensional key point parameters; generating an overall contour parameter model of the target model based on the three-dimensional key point parameters and the model lines, and determining the parameter library of the overall contour parameter model; and representing the parameter library of the overall contour parameter model as the three-dimensional contour parameter library of the target model.

[0017] Optionally, the step of generating the target model based on the point cloud data and the three-dimensional contour parameter library includes: matching the three-dimensional contour parameter library with the point cloud data; and, if a matching point cloud data is found, filling the adjacent points in the three-dimensional contour parameter library that are associated with the points in the point cloud data with pixels to generate the target model.

[0018] Optionally, after generating the target model, the method further includes: acquiring oblique photographic images of the specified watershed and extracting the model corresponding to the latitude and longitude of the target model from the oblique photographic images to obtain the model to be evaluated; comparing the contour color similarity of the generated target model and the model to be evaluated to obtain a comparison similarity; if the comparison similarity is lower than a preset similarity threshold, confirming that the target model has a model defect and generating an error message.

[0019] According to another aspect of the present invention, a watershed model generation apparatus is also provided, comprising: an acquisition unit for acquiring point cloud data and image maps of a specified watershed, wherein the point cloud data includes at least: center point coordinates of a target model; an extraction unit for extracting a set of feature parameters of the target model based on the center point coordinates of the target model and a model feature parameter library corresponding to the target model; a comparison unit for comparing the feature parameters in the extracted set of feature parameters to generate a three-dimensional contour parameter library of the target model; and a generation unit for generating the target model based on the point cloud data and the three-dimensional contour parameter library.

[0020] Optionally, the acquisition unit includes: a first acquisition module, used to acquire a map of the target surface area and extract the geographic area information and region outline indicated by the map of the target surface area; a first marking module, used to mark a portion of the target surface area associated with the specified watershed based on the geographic area information and region outline; and a first deletion module, used to delete other background maps other than the portion of the area associated with the specified watershed to obtain an image map of the specified watershed.

[0021] Optionally, the watershed model generation device further includes: a first acquisition module, used to acquire the coordinate system of the image map after acquiring point cloud data and image map of a specified watershed; and a first transformation module, used to convert the coordinate system in the point cloud data into the same coordinate system as the coordinate system of the image map.

[0022] Optionally, the watershed model generation device further includes: a second acquisition module, configured to acquire marking information when marking the specified watershed after acquiring point cloud data and image maps of the specified watershed, wherein the marking information includes at least: latitude and longitude information, model identifier, and structural features of the target model; a first indexing module, configured to index the model identifier of the target model based on the latitude and longitude information; a second indexing module, configured to index the model type and geometric feature parameters of the target model based on the model identifier and the structural features, wherein the geometric feature parameters include at least: model length, model width, and model height; and a first determining module, configured to determine the set of index parameters of the target model based on the model identifier and by integrating the latitude and longitude information, the model type, and the geometric feature parameters.

[0023] Optionally, the watershed model generation device further includes: a first establishment module, configured to establish a unique model identifier number based on the model identifier in the index parameter set and the latitude and longitude information of the target model after determining the index parameter set of the target model; a second determination module, configured to determine a model splitting strategy according to the model type; a first splitting module, configured to split the parameters in the geometric feature parameters using the model splitting strategy to obtain split sub-models; a first extraction module, configured to extract model feature parameters in each split sub-model based on the structural features; and a second establishment module, configured to construct a model feature parameter library corresponding to the target model by integrating the model feature parameters in each split sub-model using the unique model identifier number as a basis.

[0024] Optionally, the feature parameters in the feature parameter set include at least one of the following: geographic coordinate parameters, polygon parameters, two-dimensional feature parameters, and three-dimensional feature parameters.

[0025] Optionally, the extraction unit includes a second extraction module, used to extract the latitude and longitude information and contour parameters of the target model based on the model unique identifier number of the target model, to obtain the geographic coordinate parameters.

[0026] Optionally, the extraction unit includes: a first matching module, used to match corresponding point cloud data based on the geographic coordinate parameters; and a third extraction module, used to extract polygon parameters within a specified range of geometric feature parameters of the target model based on the matched point cloud data, wherein the polygon parameters include: parameters of the contour edge of the target model at the target angle point, parameters of the midline point of the line, length parameters from the center point of the line to each face, and length parameters from the center point of the model to the edge point of the line.

[0027] Optionally, the extraction unit includes: a fourth extraction module, used to extract planar feature parameters within the polygon area from a two-dimensional orthogonal projection perspective based on the polygon parameters, to obtain the two-dimensional feature parameters, wherein the two-dimensional feature parameters include: parameters of the contour edge of the target model at the target angle point, parameters from the midline point of the line to the center point of the polygon, and point parameters from the center point of the model to each edge line segment.

[0028] Optionally, the extraction unit includes: a fifth extraction module, used to extract the first point parameter from each face of the target model to the center point of the model; and a sixth extraction module, used to cut the three-dimensional face of the target model and extract the second point parameter from the cutting line to the center point of the model to obtain the three-dimensional feature parameters.

[0029] Optionally, the watershed model generation device further includes: a verification module, used to verify the three-dimensional feature parameters based on the polygon parameters and the two-dimensional feature parameters after extracting the feature parameter set of the target model, and obtain a verification result, wherein the verification result is used to confirm whether there are any parameters in the feature parameter set of the target model that do not meet the model feature requirements.

[0030] Optionally, the comparison unit includes: a comparison module, used to compare the polygon parameters, the two-dimensional feature parameters, and the three-dimensional feature parameters in the feature parameter set to determine the three-dimensional key point parameters of the target model; a delineation module, used to delineate the model lines of the target model based on the three-dimensional key point parameters; a first determination submodule, used to generate an overall contour parameter model of the target model based on the three-dimensional key point parameters and the model lines, and determine the parameter library of the overall contour parameter model; and a second determination submodule, used to represent the parameter library of the overall contour parameter model as the three-dimensional contour parameter library of the target model.

[0031] Optionally, the generation unit includes: a first generation module for the second matching module, used to match the three-dimensional contour parameter library with the point cloud data; and a filling module, used to fill the adjacent points in the three-dimensional contour parameter library that are associated with the points in the point cloud data when a corresponding point cloud data is matched, thereby generating the target model.

[0032] Optionally, the watershed model generation device further includes: a second acquisition module, used to acquire oblique photographic images of the specified watershed after generating the target model, and extract the model corresponding to the latitude and longitude of the target model from the oblique photographic images to obtain the model to be evaluated; a comparison module, used to compare the contour color similarity of the generated target model and the model to be evaluated to obtain a comparison similarity; and a confirmation module, used to confirm that the target model has a model defect and generate an error message if the comparison similarity is lower than a preset similarity threshold.

[0033] According to another aspect of the present invention, an electronic device is also provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the watershed model generation method described in any one of the preceding embodiments via executing the executable instructions.

[0034] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the watershed model generation method described in any one of the above embodiments.

[0035] In this disclosure, point cloud data and image maps of a specified watershed are first obtained. The point cloud data includes at least the coordinates of the center point of the target model. Based on the coordinates of the center point of the target model and the model feature parameter library corresponding to the target model, a set of feature parameters of the target model is extracted. The feature parameters in the extracted set of feature parameters are compared to generate a three-dimensional contour parameter library of the target model. Based on the point cloud data and the three-dimensional contour parameter library, the target model is generated.

[0036] In this disclosure, point cloud data and image maps of a specified watershed can be obtained, and the set of feature parameters of the model can be automatically extracted to automatically generate the target model. This not only provides a standardized process management for modeling, but also greatly improves modeling efficiency. This solves the technical problem of low modeling efficiency caused by the lack of systematic modeling planning when building watershed models in related technologies.

[0037] In this disclosure, three types of image data can be collected: point cloud data, image maps (e.g., satellite imagery), and oblique photogrammetry data (e.g., data collected from multiple angles via a flight platform). After obtaining these three types of data, the coordinate systems of the three types of data are unified to facilitate coordinate planning and data conversion during model building.

[0038] In this disclosure, the target model to be generated in a certain area can be selected and marked on an image map, and the feature contour of the target model can be confirmed. This can accurately determine the positioning coordinates, geographical location and contour of the target model to be generated, thereby improving the model generation efficiency.

[0039] In this disclosure, the feature contours of the target model are selected, and then the features of the target model are confirmed. The computer continuously identifies and enriches the feature contour parameters of the model. The feature contours of the model are extracted based on point cloud data to form a three-dimensional contour parameter library of the model. Based on the three-dimensional contour parameter library, the corresponding three-dimensional target model is generated by pixel filling through a computer program. Finally, the model is optimized and adjusted by similarity evaluation based on oblique photogrammetry data to obtain the final usable three-dimensional target model. Attached Figure Description

[0040] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0041] Figure 1 This is a flowchart of an optional watershed model generation method according to an embodiment of the present invention;

[0042] Figure 2 This is a schematic diagram of an image map obtained by means of an embodiment of the present invention;

[0043] Figure 3 This is a schematic diagram of an image map after deleting a background map according to an embodiment of the present invention;

[0044] Figure 4 This is a schematic diagram of an optional method for generating a watershed water-land model according to an embodiment of the present invention;

[0045] Figure 5 This is a schematic diagram of an optional watershed model generation device according to an embodiment of the present invention;

[0046] Figure 6 This is a hardware structure block diagram of an electronic device (or mobile device) for a watershed model generation method according to an embodiment of the present invention. Detailed Implementation

[0047] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0048] It should be noted that the terms "first," "second," etc., 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 the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, 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.

[0049] This invention can be applied to the field of 3D digital twins to automate the construction of watershed models without increasing the number of construction personnel. It can also be applied to other related technical fields for building watershed models.

[0050] The watershed model generation method and device provided by this invention can be applied to various model building systems / software / products. The following embodiments use a fixed watershed land and water model as an example for illustrative purposes, and are equally applicable to other types of watershed model building systems / software / products.

[0051] The types of watershed models provided in this invention include, but are not limited to, bridges, hydrological stations, dams, reservoirs, hydropower stations, and sluice gates. Each type of watershed model contains a large number of model feature parameters. For example, for bridges, it includes bridge deck data, pier data, bridge body data, and bridge facility data. Bridge types include: large water bridges, highway bridges, and railway bridges.

[0052] The present invention provides a watershed model generation method and apparatus that uses point cloud data, image maps, and oblique photogrammetry data as basic data to generate a watershed model. The method involves marking the target model on the image map (i.e., marking the geometric contour of the target model) to establish the feature contour selection of the target model; then, by confirming the features of the target model, the computer continuously identifies and enriches the model's feature contour parameters; extracting the model's feature contours based on point cloud data to form a three-dimensional contour parameter library; generating the corresponding three-dimensional target model by pixel filling using a computer program based on the three-dimensional contour parameter library; and finally optimizing and adjusting the model through similarity evaluation based on oblique photogrammetry data to obtain the final usable three-dimensional target model.

[0053] The present invention will now be described in detail with reference to various embodiments.

[0054] Example 1

[0055] According to an embodiment of the present invention, a method for generating a watershed model is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0056] Figure 1 This is a flowchart of an optional watershed model generation method according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0057] Step S102: Obtain point cloud data and image map of the specified watershed, wherein the point cloud data includes at least: the coordinates of the center point of the target model;

[0058] Step S104: Based on the center point coordinates of the target model and the model feature parameter library corresponding to the target model, extract the feature parameter set of the target model;

[0059] Step S106: Compare the feature parameters in the extracted feature parameter set to generate a 3D contour parameter library for the target model;

[0060] Step S108: Generate the target model based on point cloud data and a 3D contour parameter library.

[0061] Through the above steps, point cloud data and image maps of a specified watershed can be obtained first. The point cloud data includes at least the coordinates of the center point of the target model. Based on the center point coordinates of the target model and a corresponding model feature parameter library, a set of feature parameters of the target model is extracted. The feature parameters in the extracted feature parameter set are compared to generate a 3D contour parameter library for the target model. Based on the point cloud data and the 3D contour parameter library, the target model is generated. In this embodiment, by acquiring point cloud data and image maps of a specified watershed, the set of feature parameters of the model can be automatically extracted, and the feature parameters in the extracted feature parameter set can be compared to generate a 3D contour parameter library for the target model. This automatically generates the target model, providing not only a standardized process management system for modeling but also significantly improving modeling efficiency. This addresses the technical problem in related technologies where the lack of systematic modeling planning leads to low modeling efficiency when building watershed models.

[0062] The embodiments of the present invention will be described in detail below with reference to the above implementation steps.

[0063] Step S102: Obtain point cloud data and image map of the specified watershed, wherein the point cloud data includes at least: the coordinates of the center point of the target model;

[0064] Optionally, the step of obtaining an image map of a specified watershed includes: acquiring a map of the target surface area, extracting the geographic area information and region outline indicated by the map of the target surface area; marking a portion of the target surface area associated with the specified watershed based on the geographic area information and region outline; deleting other background maps other than the portion of the area associated with the specified watershed, thereby obtaining an image map of the specified watershed.

[0065] In order to generate the target model, it is necessary to first collect the surface, hydrology, coordinates and other data of the watershed where the target model is located. At this time, it is necessary to first obtain a map of a large area of ​​the surface containing the target model, and then perform operations such as marking, cropping and deleting on the map to obtain an image map of the accurate range of the target model in the specified watershed.

[0066] It should be noted that the regional map, image map, or surface map mentioned in this embodiment can refer to real-time surface watershed images collected by satellites or high-altitude camera equipment. For example, various sensors on satellites are used to acquire comprehensive, accurate, and objective data reflecting surface features. This data is then processed using professional remote sensing technology to obtain image maps with high-precision geographic coordinate information.

[0067] Figure 2This is a schematic diagram of an image map obtained by means of an embodiment of the present invention, such as... Figure 2 As shown, in order to construct a target model of the bridge, it is necessary to first take images of the riverbed's surface watershed, which includes the riverbed and the irregular terrain on both sides of the river, as well as a section of highway. Figure 2 (Illustration of S8105 (highway) and the bridge spanning the river.)

[0068] The target models to be generated in a specified area are selected and marked using an image map. For example, in Figure 2 In this method, the outline of a portion of the watershed associated with a bridge in the surface area is marked by setting marker points and highlighting them with dashed boxes. In actual implementation, the marking method can also be used by highlighting, color, numbers or characters to highlight a portion of the target model to be generated. The specific marking method is not limited to the example.

[0069] In this embodiment, the area to be marked is not limited. For example, the area to be marked can be the river channel containing the bridge, the bridge piers on both sides, the main body of the bridge, etc., or other landmark buildings or hydrological stations containing the target model can be marked. Different types of areas can use different or the same marking methods.

[0070] Figure 3 This is a schematic diagram of an image map after deleting a background map according to an embodiment of the present invention, such as... Figure 3 As shown, relative to Figure 2 The associated river channel and the surface information on both sides were deleted, while the image map of the model to be built was retained.

[0071] Optionally, in this embodiment, after deleting the background map and obtaining the image map of the specified watershed, the image map is a color image. It is necessary to extract the color information (such as RGB parameters) of each point in the image map and perform grayscale processing on the image map.

[0072] In this embodiment, point cloud data can refer to a set of vectors in a three-dimensional coordinate system. The point cloud data source obtained by scanning is recorded in the form of points (a point cloud is data composed of many points). Each point may contain three-dimensional coordinate information (longitude, latitude, and altitude). Some points may contain color information (which can be obtained by analyzing the color information (RGB parameters) of corresponding locations in the image map and then assigning them to the corresponding points in the point cloud) or reflection intensity information (which can be determined by the echo intensity information collected by the laser scanner receiving device, which is obtained by correlating the echo duration of the laser with each point of the target model and the energy emitted by the instrument). The three-dimensional coordinate information can be represented in the form of (x, y, z), and this coordinate data will be used later.

[0073] When acquiring point cloud data, laser scanners are used to click on each point of the target model. By combining the light reflection duration and energy intensity, the coordinates of N points and a series of operation parameters are determined to generate point cloud data.

[0074] Optionally, after acquiring point cloud data and image maps of a specified watershed, the method further includes: acquiring the coordinate system of the image map; and converting the coordinate system in the point cloud data to the same coordinate system as the image map. That is, through data transformation, the coordinate system of the point cloud data (converting data from different coordinate systems to data in the same coordinate system of (longitude, latitude, and altitude)) is converted into the unified coordinate system of the image map data.

[0075] As an optional implementation of this embodiment, after acquiring the point cloud data and image map of the specified watershed, the method further includes: acquiring the marking information when marking the specified watershed, wherein the marking information includes at least: the latitude and longitude information, model identifier, and structural features of the target model; indexing the model identifier of the target model based on the latitude and longitude information; indexing the model type and geometric feature parameters of the target model based on the model identifier and structural features, wherein the geometric feature parameters include at least: model length, model width, and model height; and determining the set of index parameters of the target model based on the model identifier, by comprehensively considering the latitude and longitude information, model type, and geometric feature parameters.

[0076] It should be noted that this embodiment does not limit the specific content of the model identifier and model type. For example, the model identifier can be a name, label, or other information expressed in the form of text, numbers, characters, or English letters. In this embodiment, the model name is used as the model identifier for illustrative purposes. The model type can be represented by information corresponding to different surface watersheds, as explained above, and will not be repeated here.

[0077] For a specified watershed, the model name and corresponding latitude and longitude information are selected, and the model parameters are indexed (for example, the corresponding model name is obtained through the latitude and longitude information of the marked model). Through image recognition of the image map and recognition of the target structure features, the length, width and height of the target model are indexed (the parameters (geometric feature parameters of the model (length, width, height)) and latitude and longitude information, model name, etc. are obtained through the information marked by satellite image, and the existing models are indexed in detail based on the parameters).

[0078] Optionally, after determining the set of index parameters for the target model, the process further includes: establishing a unique model identifier based on the model identifier in the set of index parameters and the latitude and longitude information of the target model; determining a model splitting strategy based on the model type; splitting the parameters in the geometric feature parameters using the model splitting strategy to obtain sub-models; extracting model feature parameters from each sub-model based on structural features; and constructing a model feature parameter library corresponding to the target model by combining the model feature parameters from each sub-model using the unique model identifier as a benchmark.

[0079] A model feature parameter library is established by indexing the parameter set. A unique identifier for the target model is created using its latitude and longitude coordinates and abbreviation. For example, for a bridge model, the distribution of geometric features such as length, width, and height is identified. The model's height is segmented to identify the length and width data of different models at different heights. The basic bridge structure is used to identify and match the model parameters, thus establishing the target model's feature parameter library (for land models like bridges: under the bridge: number of piers, length, width, and height of piers, spacing between piers; bridge deck: length, width, and height of the bridge deck; on the bridge: polygonal structure of the bridge's stabilizing facilities, polygon length, height, width, and angle parameters).

[0080] Step S104: Based on the center point coordinates of the target model and the model feature parameter library corresponding to the target model, extract the feature parameter set of the target model.

[0081] Optionally, the feature parameters in the feature parameter set provided in this embodiment include at least one of the following: geographic coordinate parameters, polygon parameters, two-dimensional feature parameters, and three-dimensional feature parameters.

[0082] The target model is located using latitude and longitude information, and the center point coordinates of the model in the point cloud data are located. Then, the point cloud data of the polygon boundary, two-dimensional feature parameters, and three-dimensional feature parameters of the target model are extracted using the center point coordinates and the corresponding model feature parameter library. The extracted data is used to establish the three-dimensional key point parameters of the target model and outline the lines of the target model. The three-dimensional target model is generated using this parameter information.

[0083] Optionally, the extraction of geographic coordinate parameters includes: extracting the latitude and longitude information and contour parameters of the target model based on its unique identifier, thus obtaining the geographic coordinate parameters. When obtaining geographic coordinate parameters, the latitude and longitude information and contour parameter information can be determined using the unique identifier of the target model.

[0084] Alternatively, when extracting polygon parameters, the following steps are included: matching the corresponding point cloud data based on geographic coordinate parameters; and extracting polygon parameters within a specified range of geometric feature parameters of the target model based on the matched point cloud data. The polygon parameters include: parameters of the outline edge of the target model at the target angle point, parameters of the midline point of the line, length parameters from the center point of the line to each face, and length parameters from the center point of the model to the edge point of the line.

[0085] In this embodiment, when extracting polygon parameters, the edge contour angle point parameters of the target model can be extracted. These parameters include target angle point parameters, line midline point parameters, and length parameters from the polygon center point to each face and line edge point.

[0086] Optionally, when extracting two-dimensional feature parameters, the method includes: extracting planar feature parameters within the polygon region from a two-dimensional orthogonal projection perspective based on the polygon parameters to obtain two-dimensional feature parameters, wherein the two-dimensional feature parameters include: parameters of the contour edge of the target model at the target angle point, parameters of the midline point of the line to the center point of the polygon, and point parameters of the model center point to each edge line segment.

[0087] In this embodiment, when extracting two-dimensional feature parameters, the model edge contour angle point parameters can be extracted, including target angle point parameters, parameters from the midline point of the line to the center point of the polygon, and point parameters from the center point to each edge line segment.

[0088] Optionally, when extracting the three-dimensional feature parameters, the following steps are included: extracting the first point parameter from each face of the target model to the center point of the model; cutting the solid face of the target model and extracting the second point parameter from the cutting line to the center point of the model to obtain the three-dimensional feature parameters.

[0089] In this embodiment, when extracting three-dimensional feature parameters, the parameters from each face of the target model to the center point can be extracted. The three-dimensional faces of the target model are split and calculated. Each face of the target model is divided into four equal parts, and the parameters from the cutting lines to the center point are extracted (for example, the six square faces of a cube are cut by connecting the midpoint of each square face to the midpoint of the edge of the square), thus forming three-dimensional feature parameters.

[0090] After obtaining the various types of feature parameters mentioned above, this embodiment requires feature parameter screening. Optionally, after extracting the feature parameter set of the target model, it further includes: verifying the three-dimensional feature parameters based on polygon parameters and two-dimensional feature parameters to obtain verification results, wherein the verification results are used to confirm whether there are any parameters in the feature parameter set of the target model that do not meet the model feature requirements.

[0091] When screening feature parameters, the extracted polygon parameters and two-dimensional feature parameters can be compared with the model's three-dimensional feature parameters to determine the uniqueness, correctness, and consistency of the data. For example, bridge parameters can be screened using polygon parameter two-dimensional feature contour data and three-dimensional height feature parameter height contour data. The correctness can be verified by checking the data size of bridge features such as pier parameters, bridge deck parameters, bridge body parameters, and bridge feature parameters (for example, verifying the data of bridge length and pier spacing, i.e., verifying data features such as bridge length of 1m and pier height of 800m that are not illogical).

[0092] Step S106: Compare the feature parameters in the extracted feature parameter set to generate a 3D contour parameter library for the target model.

[0093] Optionally, the step of generating a 3D contour parameter library for the target model by comparing the feature parameters in the extracted feature parameter set includes: comparing polygon parameters, two-dimensional feature parameters, and three-dimensional feature parameters in the feature parameter set to determine the 3D key point parameters of the target model; outlining the model lines of the target model based on the 3D key point parameters; generating an overall contour parameter model of the target model based on the 3D key point parameters and the model lines, and determining the parameter library of the overall contour parameter model; and representing the parameter library of the overall contour parameter model as the 3D contour parameter library of the target model.

[0094] After screening is completed, a three-dimensional contour parameter library can be generated. In this embodiment, when generating a three-dimensional contour parameter library for a specified watershed, the collected target model feature parameters are screened and compared to establish a three-dimensional contour parameter library that can generate models ((based on the bridge's feature parameters, two-dimensional parameters (length, width, height, angle, etc.) and three-dimensional parameters (solid parameters, such as the parameter data of the six faces of a cube) to generate the overall contour parameter model of the bridge, such as the bridge deck data, pier data, and bridge facility data)).

[0095] Step S108: Generate the target model based on point cloud data and a 3D contour parameter library.

[0096] Optionally, the step of generating a target model based on point cloud data and a 3D contour parameter library includes: matching the 3D contour parameter library with the point cloud data; and, if a matching point cloud data is found, filling the adjacent points in the 3D contour parameter library that are associated with the points in the point cloud data with pixels to generate the target model.

[0097] By performing feature contour matching on the model's 3D contour parameter library and point cloud data, and then filling adjacent pixels based on the geometric point data of the point cloud after matching the corresponding data, a 3D target model is generated through pixel filling. Taking a bridge model as an example, during pixel filling, the pixels of each face of the bridge are filled according to the corresponding bridge parameters. One pixel filling method is illustrated as follows: a face is divided into countless transparent small grids (i.e., the alpha channel parameter in RGBA is 0). If the RGB value of this face is (0, 0, 0), i.e., black, the pixel RGB value of all small grids will be automatically assigned to (0, 0, 0), and black color will be filled. The alpha channel parameter will be changed to a non-transparent parameter until each face is filled.

[0098] Optionally, after generating the target model, the process may also include: acquiring oblique photographic images of a specified watershed and extracting the model corresponding to the latitude and longitude of the target model from the oblique photographic images to obtain the model to be evaluated; comparing the contour color similarity between the generated target model and the model to be evaluated to obtain the comparison similarity; if the comparison similarity is lower than a preset similarity threshold, confirming that the target model has model defects and generating an error message.

[0099] In this embodiment, after generating the target model, a comparison check and model optimization can be performed on the target model. The model comparison check can be performed by comparing the contour and color similarity between the generated target model and the model in the oblique photogrammetry image at the corresponding latitude and longitude using point cloud data. When the system evaluates the similarity to a certain threshold (e.g., below 70%), it is considered that the generated target model has a large error (e.g., for a bridge, it should contain many beams, but the generated model is a surface or a wall without many beams). The reason for the failure to generate the target model is determined and the process is reported. When the similarity of the evaluated model and the target model in the oblique photogrammetry is higher than the similarity threshold, it is determined that the model generation is OK, and a model file corresponding to the target model is generated.

[0100] When acquiring oblique photogrammetry images, a flight platform (e.g., a drone) equipped with sensors (usually cameras) can collect data (photographs) from the air in five directions, including orthographic, front-back, left-right, and right-back views, of a scene. During shooting, images can be acquired simultaneously from five different perspectives (one vertical and four oblique), obtaining rich data on building rooftops and side views (image / photograph acquisition). After data processing, accurate and complete location information and texture data of vegetation, buildings, roads, etc., are obtained. This data uses three-dimensional structural data. (Oblique photogrammetry synthesizes images from top-down and side views into image data of a single area).

[0101] It should be noted that after acquiring oblique photogrammetric images, coordinate unification is also required. This involves converting the coordinate system of the oblique photogrammetric images / data into a unified coordinate system similar to that of the image map through data transformation.

[0102] If a significant error is confirmed in the model, the administrator needs to review the system's feature library or parameter library for any errors that might cause the similarity to fall below a certain threshold. The administrator will then notify the relevant technical personnel to review the parameters from the faces to the center points in the target model's 3D features to check for defects. If defects are found, the administrator will correct the target's feature parameters (correction criteria: based on the type of the target model, the administrator will correct the feature parameter errors). A new round of indexing and comparison will then be performed using the feature parameters, re-entering the model feature parameter extraction, comparison, and generation process. For example, the administrator might trace the data of significant differences in bridge data to check if the feature data (parameters) used in the data indexing stage have significant differences in the feature parameter index due to parameter differences in the measurement stage, thus creating significant parameter discrepancies. Oblique photogrammetry will be used as the data basis to correct these parameter differences and optimize the corresponding difference points (eliminating parameter differences from the measurement stage).

[0103] After optimizing the differences in the model, the final step is to check and optimize the type parameter feature data of the target model.

[0104] After optimizing the parameter feature data of the target model, a model file corresponding to the target model can be exported. The export formats of this file include, but are not limited to: OSGB, OBJ, and DAE.

[0105] Through the above embodiments, target models can be automatically generated, and model files corresponding to the target models can be exported, realizing systematic process management of model generation. At the same time, it can greatly reduce the professionalism of watershed modeling, simplify the monitoring process, reduce monitoring costs, and thus reduce project modeling costs.

[0106] The present invention will now be described in conjunction with another more detailed embodiment.

[0107] Example 2

[0108] Figure 4 This is a schematic diagram of an optional method for generating a watershed water-land model according to an embodiment of the present invention, as shown below. Figure 4 As shown, the generation method includes:

[0109] 1. Data Acquisition: Utilizing equipment to acquire point cloud data, oblique imagery (or oblique photography data), and satellite imagery of the target watershed.

[0110] Point cloud data refers to a set of points of a vector in a three-dimensional coordinate system. The point cloud data source obtained by scanning is recorded in the form of points. Each point contains three-dimensional coordinates (longitude, latitude, and altitude). Some points may contain color information (RGB) or reflectance information.

[0111] Oblique imagery: By simultaneously acquiring images from one vertical, four oblique, and five different perspectives, rich data on building rooftops and side views (photographic acquisition) is obtained. After data processing, accurate and complete location information and texture data of vegetation, buildings, roads, etc., are acquired. This data uses 3D structural data. Oblique imagery is created by synthesizing images from top-down and side views into image data of a single area, such as... Figure 2 The bridge.

[0112] Satellite imagery: Data that comprehensively, accurately, and objectively reflects the features of the Earth's surface is obtained through various sensors carried by satellites. This data is then processed using professional remote sensing technology to obtain images with high-precision geographic coordinate information.

[0113] 2. Geographic coordinate matching: Data from different coordinate systems, such as oblique images and point cloud data, are converted into the same coordinate system as satellite imagery to unify the coordinate system of these collected data, for example, to unify them into the EPSG4845 coordinate system.

[0114] 3. Target Model Labeling and Selection: Using satellite imagery data, the models to be generated within the target area are selected and labeled, such as... Figure 2 The outline is marked according to the corresponding image position, and marker points are set.

[0115] 4. Model Feature Parameter Index Acquisition: For the selected model name and corresponding latitude and longitude information of the target area, perform model parameter indexing (obtain the name of the corresponding model through the latitude and longitude information of the marked model). Through image recognition of satellite imagery and target structure recognition, perform parameter indexing on the length, width and height of the target model (obtain parameters (geometric feature parameters of the model (length, width, height, features (bridge type: Yellow River water bridge, highway bridge or railway bridge; geometric feature parameters of bridge frame), latitude and longitude information, model name) based on the information marked on the satellite imagery, and perform detailed parameter indexing on the existing model based on the parameters).

[0116] 5. Feature Parameter Library: A model parameter library is established based on the indexed feature parameters of the target model. A unique identifier for each model is created using its latitude, longitude, and short name. For example, for a bridge model, the library identifies the distribution of geometric features such as length, width, and height. The model's height is segmented to identify the length and width data of different models at different heights. Furthermore, the library uses the basic bridge structure to identify and match model parameters, thus establishing the target model's feature parameter library (for land models like bridges: under the bridge: number of piers, length, width, and height of piers, spacing between piers; bridge deck: length, width, and height of the bridge deck; on the bridge: polygonal structure of the bridge's stabilizing facilities, polygon length, height, width, and angle parameters).

[0117] 6. Extract feature parameter set: Locate the target model using latitude and longitude information, pinpoint the center point coordinates of the model in the point cloud data, and extract the geographic coordinate parameters of the polygon boundary, polygon data, two-dimensional feature parameters, and three-dimensional feature parameters of the target model using the center point coordinates and the corresponding model feature parameter library. Use the extracted data to establish the three-dimensional key point parameters of the target model and outline the lines of the target model, and use this parameter information to generate a three-dimensional target model.

[0118] Among them, geographic coordinate parameters: a unique identifier for the target model, containing latitude and longitude information and contour parameter information;

[0119] Polygon parameters: Extract the edge contour angle point parameters of the target model. These parameters include the target angle point parameters, the line midline point parameters, and the length parameters from the polygon center point to each face and line edge point (the specific extraction steps include: first step: matching the data location of the corresponding point cloud based on the geographical location; second step: extracting polygon data within the length, width, and height range from the extracted polygon parameter data based on the marker index parameters).

[0120] Two-dimensional feature parameters: Extract the angle point parameters of the model edge contour, including the target angle point parameters, the parameters from the midline point of the line to the center point of the polygon, and the point parameters from the center point to each edge line segment (for specific extraction steps, step 3: based on the obtained polygon data, extract the feature parameter data within the polygon area, and extract the planar feature parameters from the two-dimensional orthogonal projection perspective).

[0121] 3D feature parameters: Extract the parameters from each face of the target model to the center point, calculate the 3D surface of the target model, divide each face of the target model into 4 equal parts, and extract the parameters from the cutting line to the center point (for example, cut the 6 square faces of a cube by connecting the midpoint of each square face to the midpoint of the edge of the square), forming 3D feature parameters (specific extraction steps, step 4: extract the height of the 3D solid features based on the plane data and the angle of the tilted side).

[0122] 7. Feature Contour Parameter Screening: The extracted polygon parameters and two-dimensional feature parameters are compared and screened against the model's three-dimensional feature parameters to determine the uniqueness, correctness, and consistency of the data. Bridge parameters are screened using the polygon parameter two-dimensional feature contour and the three-dimensional height feature parameter height contour data. Correctness is verified by examining bridge features such as pier parameters, bridge deck parameters, bridge body parameters, and the magnitude of these bridge feature parameters (e.g., verifying the bridge length and pier spacing data, i.e., verifying data features such as a bridge length of 1m and pier height of 800m that seem illogical).

[0123] 8. Generate a 3D contour parameter library: After screening and comparing the collected target model parameters, establish a 3D contour parameter library that can generate models (for example, based on the bridge's feature parameters, two-dimensional parameters (length, width, height, angle, etc.) and three-dimensional parameters (solid parameters, such as the parameter data of the six faces of a cube), generate the overall contour parameter model of the bridge, such as the bridge deck data, pier data, and bridge facility data).

[0124] 9. Generate the target model: By performing feature contour matching on the model's 3D contour parameter library and point cloud data, and after matching the corresponding data, the adjacent pixel points are filled according to the geometric point data of the point cloud. The 3D model of the target is generated by filling the pixels. (For example, the pixels of each face of the bridge are filled according to the corresponding bridge parameters. The pixel filling method is as follows: a face is divided into countless transparent small grids (i.e., the alpha channel parameter in RGBA is 0). If the RGB value of this face is (0, 0, 0), i.e., black, the pixel RGB value of all small grids will be automatically assigned to (0, 0, 0), black color will be filled, and the alpha channel parameter will be changed to a non-transparent parameter until each face is filled.)

[0125] 10. Model Comparison Check: The target model generated from point cloud data is compared with the model in the oblique photography at the corresponding latitude and longitude to evaluate its contour and color similarity. If the system evaluates the similarity to be below 70%, the generated target model is considered to have a large error (i.e., obvious defects). The similarity for models with inconspicuous defects is 70%–100%. The reason for the failure to generate the target model is determined and the process is initiated. If the similarity of the evaluated model and the target model in the oblique photography parameters is above 70%, the model is considered to have passed the check, and the model file is generated. For example, obvious defects include significant differences in length, inconsistent numbers of piers, and large differences in the shape of bridge deck facilities.

[0126] 11. Report to the administrator: Review the parameter library or system parameter library for feature parameters that may contain errors that could cause the similarity to fall below a certain percentage (e.g., 60%).

[0127] When reviewing the target model parameters, the administrator notifies the relevant technical personnel to review the parameters from the faces to the center points in the 3D features of the target model to check for defects. If defects are found, the administrator will correct the target's feature parameters and then conduct a new round of index comparison using the feature parameters, re-entering the model feature parameter extraction, comparison, and generation process. For example, the administrator traces the data of obvious differences in bridge data to check whether the feature data (parameters) used in the data indexing stage have significant differences in feature parameter indexes due to parameter differences in the measurement stage, thus forming significant parameter differences. The administrator corrects the parameter differences based on oblique photography and optimizes the corresponding difference points (optimizing out the parameter differences in the measurement stage).

[0128] 12. Optimize target model parameter features: Check and optimize the type parameter feature data of the target model.

[0129] Model file export: Export model files in the corresponding formats (OSGB, OBJ, DAE).

[0130] The above embodiments provide a method for generating a fixed watershed land and water model based on point cloud data, satellite imagery, and oblique imagery. This invention involves marking the established target model on a satellite imagery map (i.e., marking the geometric contour of the target model), selecting the feature contours of the target model, confirming the target model features, and then continuously enriching the model feature contour parameters through computer identification. A three-dimensional contour parameter library of the model is formed by extracting the model feature contours based on point cloud data. Based on the three-dimensional contour parameter library, a computer program performs pixel filling to generate the corresponding three-dimensional target model. Finally, a usable three-dimensional model is generated through similarity evaluation based on oblique imagery.

[0131] Through the above embodiments, after collecting the corresponding watershed image maps, point cloud data, and oblique images, a target model for a specified watershed can be automatically generated, reducing the professionalism required for watershed modeling. At the same time, this embodiment can realize digital process management, standardize the modeling process, greatly simplify the monitoring process, and reduce project modeling costs.

[0132] The invention will now be described in conjunction with another alternative embodiment.

[0133] Example 3

[0134] This embodiment provides a watershed model generation device, wherein each implementation unit contained in the generation device corresponds to each implementation step in the above embodiment one.

[0135] Figure 5 This is a schematic diagram of an optional watershed model generation device according to an embodiment of the present invention, such as... Figure 5 As shown, the generating device may include: an acquisition unit 51, an extraction unit 53, a comparison unit 55, and a generating unit 57, wherein,

[0136] The acquisition unit 51 is used to acquire point cloud data and image map of a specified watershed, wherein the point cloud data includes at least the coordinates of the center point of the target model;

[0137] Extraction unit 53 is used to extract the set of feature parameters of the target model based on the center point coordinates of the target model and the model feature parameter library corresponding to the target model;

[0138] The comparison unit 55 is used to compare the feature parameters in the extracted feature parameter set and generate a three-dimensional contour parameter library of the target model.

[0139] Generation unit 57 is used to generate a target model based on point cloud data and a 3D contour parameter library.

[0140] The aforementioned watershed model generation device can first acquire point cloud data and image maps of a specified watershed through acquisition unit 51. The point cloud data includes at least the coordinates of the center point of the target model. Then, extraction unit 53 extracts a set of feature parameters of the target model based on the center point coordinates and a corresponding model feature parameter library. Comparison unit 55 compares the feature parameters in the extracted feature parameter set to generate a 3D contour parameter library for the target model. Finally, generation unit 57 generates the target model based on the point cloud data and the 3D contour parameter library. In this embodiment, the device can automatically extract the model's feature parameter set from the acquired point cloud data and image maps of the specified watershed, compare the feature parameters in the extracted feature parameter set, and generate a 3D contour parameter library for the target model, thereby automatically generating the target model. This not only provides a standardized process management system for modeling but also significantly improves modeling efficiency. This addresses the technical problem in related technologies where the lack of systematic modeling planning leads to low modeling efficiency when building watershed models.

[0141] Optionally, the acquisition unit includes: a first acquisition module, used to acquire a map of the target surface area and extract the geographic area information and area outline indicated by the map of the target surface area; a first marking module, used to mark a portion of the target surface area associated with a specified watershed based on the geographic area information and area outline; and a first deletion module, used to delete other background maps other than the portion of the area associated with the specified watershed to obtain an image map of the specified watershed.

[0142] Optionally, the watershed model generation device further includes: a first acquisition module, used to acquire the coordinate system of the image map after acquiring point cloud data and image map of the specified watershed; and a first transformation module, used to convert the coordinate system in the point cloud data into the same coordinate system as the coordinate system of the image map.

[0143] Optionally, the watershed model generation device further includes: a second acquisition module, used to acquire marking information when marking the specified watershed after acquiring point cloud data and image maps of the specified watershed, wherein the marking information includes at least: latitude and longitude information, model identifier, and structural features of the target model; a first indexing module, used to index the model identifier of the target model based on latitude and longitude information; a second indexing module, used to index the model type and geometric feature parameters of the target model based on the model identifier and structural features, wherein the geometric feature parameters include at least: model length, model width, and model height; and a first determining module, used to determine the set of index parameters of the target model based on the model identifier and by integrating latitude and longitude information, model type, and geometric feature parameters.

[0144] Optionally, the watershed model generation device further includes: a first establishment module, used to establish a unique model identifier number based on the model identifier in the index parameter set and the latitude and longitude information of the target model after determining the index parameter set of the target model; a second determination module, used to determine the model splitting strategy according to the model type; a first splitting module, used to split the parameters in the geometric feature parameters using the model splitting strategy to obtain the split sub-models; a first extraction module, used to extract the model feature parameters in each split sub-model based on structural features; and a second establishment module, used to construct a model feature parameter library corresponding to the target model by integrating the model feature parameters in each split sub-model based on the unique model identifier number.

[0145] Optionally, the feature parameters in the feature parameter set include at least one of the following: geographic coordinate parameters, polygon parameters, two-dimensional feature parameters, and three-dimensional feature parameters.

[0146] Optionally, the extraction unit includes: a second extraction module, used to extract the latitude and longitude information and contour parameters of the target model based on the model's unique identifier number, to obtain geographic coordinate parameters.

[0147] Optionally, the extraction unit includes: a first matching module, used to match corresponding point cloud data based on geographic coordinate parameters; and a third extraction module, used to extract polygon parameters within a specified range of geometric feature parameters of the target model based on the matched point cloud data, wherein the polygon parameters include: parameters of the contour edge of the target model at the target angle point, parameters of the midline point of the line, length parameters from the center point of the line to each face, and length parameters from the center point of the model to the edge point of the line.

[0148] Optionally, the extraction unit includes: a fourth extraction module, used to extract planar feature parameters within the polygon area from a two-dimensional orthogonal projection perspective based on the polygon parameters, to obtain two-dimensional feature parameters, wherein the two-dimensional feature parameters include: parameters of the contour edge of the target model at the target angle point, parameters of the midline point of the line to the center point of the polygon, and point parameters of the model center point to each edge line segment.

[0149] Optionally, the extraction unit includes: a fifth extraction module, used to extract the first point parameter from each face of the target model to the center point of the model; and a sixth extraction module, used to cut the three-dimensional face of the target model and extract the second point parameter from the cutting line to the center point of the model to obtain the three-dimensional feature parameters.

[0150] Optionally, the watershed model generation device further includes: a verification module, used to verify the three-dimensional feature parameters based on polygon parameters and two-dimensional feature parameters after extracting the feature parameter set of the target model, and obtain verification results, wherein the verification results are used to confirm whether there are any parameters in the feature parameter set of the target model that do not meet the model feature requirements.

[0151] Optionally, the comparison unit includes: a comparison module, used to compare the polygon parameters, the two-dimensional feature parameters, and the three-dimensional feature parameters in the feature parameter set to determine the three-dimensional key point parameters of the target model; a delineation module, used to delineate the model lines of the target model based on the three-dimensional key point parameters; a first determination submodule, used to generate an overall contour parameter model of the target model based on the three-dimensional key point parameters and the model lines, and determine the parameter library of the overall contour parameter model; and a second determination submodule, used to represent the parameter library of the overall contour parameter model as the three-dimensional contour parameter library of the target model.

[0152] Optionally, the generation unit includes: a first generation module for the second matching module, used to match the 3D contour parameter library with the point cloud data; and a filling module, used to fill the adjacent points in the 3D contour parameter library that are associated with the points in the point cloud data with pixels when the corresponding point cloud data is matched, thereby generating a target model.

[0153] Optionally, the watershed model generation device further includes: a second acquisition module, used to acquire oblique photographic images of a specified watershed after generating the target model, and extract the model corresponding to the latitude and longitude of the target model from the oblique photographic images to obtain the model to be evaluated; a comparison module, used to compare the contour color similarity between the generated target model and the model to be evaluated to obtain the comparison similarity; and a confirmation module, used to confirm that the target model has model defects and generate an error message if the comparison similarity is lower than a preset similarity threshold.

[0154] The aforementioned watershed model generation device may also include a processor and a memory. The aforementioned acquisition unit 51, extraction unit 53, comparison unit 55, generation unit 57, etc., are all stored in the memory as program units, and the processor executes the aforementioned program units stored in the memory to realize the corresponding functions.

[0155] The aforementioned processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and by adjusting kernel parameters, a target model can be generated based on point cloud data and a 3D contour parameter library.

[0156] The aforementioned memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0157] According to another aspect of the present invention, an electronic device is also provided, including: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the watershed model generation method of any one of the above-mentioned methods by executing the executable instructions.

[0158] Figure 6 This is a hardware structure block diagram of an electronic device (or mobile device) for a watershed model generation method according to an embodiment of the present invention. Figure 6 As shown, the electronic device may include one or more processors 602 (shown as 602a, 602b, ..., 602n in the figure) 602 (processor 602 may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 604 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, a keyboard, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 6 The structure shown is for illustrative purposes only and does not limit the structure of the electronic device described above. For example, the electronic device may also include... Figure 6 The more or fewer components shown, or having the same Figure 6 The different configurations shown.

[0159] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored computer program, wherein, when the computer program is running, it controls the device where the computer-readable storage medium is located to execute the watershed model generation method of any of the above.

[0160] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program with the following method steps: acquiring point cloud data and image maps of a specified watershed, wherein the point cloud data includes at least: the center point coordinates of a target model; extracting a set of feature parameters of the target model based on the center point coordinates of the target model and a model feature parameter library corresponding to the target model; comparing the feature parameters in the extracted feature parameter set to generate a three-dimensional contour parameter library of the target model; and generating the target model based on the point cloud data and the three-dimensional contour parameter library.

[0161] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0162] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0163] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0164] 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 units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0165] 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 as a software functional unit.

[0166] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the 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, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0167] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for generating a watershed model, characterized in that, include: Acquire point cloud data and image map of a specified watershed, wherein the point cloud data includes at least: the coordinates of the center point of the target model; Based on the center point coordinates of the target model and the model feature parameter library corresponding to the target model, the feature parameter set of the target model is extracted. The feature parameters in the feature parameter set include: geographic coordinate parameters, polygon parameters, two-dimensional feature parameters, and three-dimensional feature parameters. By comparing the feature parameters in the extracted feature parameter set, a three-dimensional contour parameter library for the target model is generated; The target model is generated based on the point cloud data and the 3D contour parameter library; The polygon parameters include: parameters of the contour edge of the target model at the target angle point, parameters of the midline point of the line, length parameters from the center point of the line to each face, and length parameters from the center point of the model to the edge point of the line. The two-dimensional feature parameters include: the parameters of the contour edge of the target model at the target angle point, the parameters from the midline point of the line to the center point of the polygon, and the point parameters from the center point of the model to each edge line segment. The extraction of the three-dimensional feature parameters includes: extracting the first point parameter from each face of the target model to the center point of the model; cutting the three-dimensional face of the target model and extracting the second point parameter from the cutting line to the center point of the model to obtain the three-dimensional feature parameters; After extracting the feature parameter set of the target model, the method further includes: verifying the three-dimensional feature parameters based on the polygon parameters and the two-dimensional feature parameters to obtain a verification result, wherein the verification result is used to confirm whether there are any parameters in the feature parameter set of the target model that do not meet the model feature requirements.

2. The generation method according to claim 1, characterized in that, The steps for obtaining an image map of a specified watershed include: Collect a map of the target surface area and extract the geographic area information and area outline indicated by the map of the target surface area; Based on the geographic region information and the region outline, a portion of the target surface region associated with the specified watershed is marked; Delete other background maps outside the designated watershed area to obtain an image map of the designated watershed.

3. The generation method according to claim 1, characterized in that, After acquiring point cloud data and image maps of a specified watershed, the process also includes: Obtain the coordinate system of the image map; The coordinate system in the point cloud data is converted to the same coordinate system as that in the image map.

4. The generation method according to claim 2, characterized in that, After acquiring point cloud data and image maps of a specified watershed, the process also includes: Obtain the marking information when marking the specified watershed, wherein the marking information includes at least: the latitude and longitude information of the target model, model identifier, and structural features; Based on the latitude and longitude information, the model identifier of the target model is indexed; Based on the model identifier and the structural features, the model type and geometric feature parameters of the target model are indexed, wherein the geometric feature parameters include at least: model length, model width, and model height; Based on the model identifier, and by combining the latitude and longitude information, the model type, and the geometric feature parameters, the set of index parameters for the target model is determined.

5. The generation method according to claim 4, characterized in that, After determining the set of index parameters for the target model, the process also includes: Based on the model identifier and the latitude and longitude information of the target model in the index parameter set, a unique identifier sequence number for the model is established; Based on the model type, determine the model splitting strategy; The model splitting strategy is used to split the parameters in the geometric feature parameters to obtain split sub-models; Based on the structural features, extract the model feature parameters from each of the decomposed sub-models; Based on the unique identifier of the model, the model feature parameter library corresponding to the target model is constructed by integrating the model feature parameters in each of the sub-models.

6. The generation method according to claim 1, characterized in that, Extracting the geographic coordinate parameters includes: Based on the unique identifier of the target model, the latitude and longitude information and contour parameters of the target model are extracted to obtain the geographic coordinate parameters.

7. The generation method according to claim 1, characterized in that, Extracting the polygon parameters includes: Based on the geographic coordinate parameters, match the corresponding point cloud data; Based on the matched point cloud data, polygon parameters within a specified range of geometric feature parameters of the target model are extracted.

8. The generation method according to claim 1, characterized in that, Extracting the two-dimensional feature parameters includes: Based on the polygon parameters, planar feature parameters within the polygon region are extracted using a two-dimensional orthogonal projection perspective to obtain the two-dimensional feature parameters.

9. The generation method according to claim 1, characterized in that, The step of generating a 3D contour parameter library for the target model by comparing the feature parameters in the extracted feature parameter set includes: By comparing the polygon parameters, the two-dimensional feature parameters, and the three-dimensional feature parameters in the feature parameter set, the three-dimensional key point parameters of the target model are determined. Based on the three-dimensional key point parameters, the model lines of the target model are outlined; Based on the three-dimensional key point parameters and the model lines, generate the overall contour parameter model of the target model, and determine the parameter library of the overall contour parameter model; The parameter library of the overall contour parameter model is represented as the three-dimensional contour parameter library of the target model.

10. The generation method according to claim 1, characterized in that, The steps for generating the target model based on the point cloud data and the 3D contour parameter library include: Match the three-dimensional contour parameter library with the point cloud data; If the corresponding point cloud data is matched, the adjacent points in the three-dimensional contour parameter library that are associated with the points in the point cloud data are filled with pixels to generate the target model.

11. The generation method according to claim 1, characterized in that, After generating the target model, the process also includes: Oblique photographic images of the specified watershed are acquired, and the model corresponding to the latitude and longitude of the target model in the oblique photographic images is extracted to obtain the model to be evaluated. The generated target model is compared with the model to be evaluated by contour color similarity to obtain the comparison similarity. If the comparison similarity is lower than a preset similarity threshold, it is confirmed that the target model has a model defect, and an error message is generated.

12. A watershed model generation device, characterized in that, include: The acquisition unit is used to acquire point cloud data and image maps of a specified watershed, wherein the point cloud data includes at least the coordinates of the center point of the target model; The extraction unit is used to extract a set of feature parameters of the target model based on the center point coordinates of the target model and the model feature parameter library corresponding to the target model. The feature parameters in the set of feature parameters include: geographic coordinate parameters, polygon parameters, two-dimensional feature parameters, and three-dimensional feature parameters. The comparison unit is used to compare the feature parameters in the extracted feature parameter set and generate a three-dimensional contour parameter library of the target model. A generation unit is used to generate the target model based on the point cloud data and the three-dimensional contour parameter library; The polygon parameters include: parameters of the contour edge of the target model at the target angle point, parameters of the midline point of the line, length parameters from the center point of the line to each face, and length parameters from the center point of the model to the edge point of the line. The two-dimensional feature parameters include: the parameters of the contour edge of the target model at the target angle point, the parameters from the midline point of the line to the center point of the polygon, and the point parameters from the center point of the model to each edge line segment. The extraction unit includes: a fifth extraction module, used to extract the first point parameter from each face of the target model to the center point of the model; and a sixth extraction module, used to cut the three-dimensional face of the target model and extract the second point parameter from the cutting line to the center point of the model to obtain the three-dimensional feature parameters. The watershed model generation device further includes a verification module, which is used to verify the three-dimensional feature parameters based on the polygon parameters and the two-dimensional feature parameters after extracting the feature parameter set of the target model, and obtain a verification result, wherein the verification result is used to confirm whether there are any parameters in the feature parameter set of the target model that do not meet the model feature requirements.

13. An electronic device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to execute the watershed model generation method according to any one of claims 1 to 11 by executing the executable instructions.

14. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device on which the computer-readable storage medium is located to perform the watershed model generation method according to any one of claims 1 to 11.

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