Multi-beam sounding data quality real-time evaluation method based on Delaunay triangulation network model

Abstract

The invention relates to a multi-beam sounding data quality real-time evaluation method based on a Delaunay triangulation network model, and the method comprises the steps: carrying out the thinning of an original discrete data set, generating a thinned data set, and constructing a Delaunay triangulation network; performing fragmentation processing on the original data set according to the Delaunay triangulation network, and extracting an accurate data range of the fragmented data set; and respectively carrying out gridding processing on each fragmented data set to generate fragmented grids, and carrying out multi-beam sounding data quality real-time evaluation. According to the method, the gridding processing of mass and large-area discrete point data of the multi-beam sounding data can be quickly and accurately realized, the problems of real-time quality evaluation of the mass multi-beam sounding data and the like are solved, and the method can be widely applied to the field of multi-beam sounding data processing.

Description

Technical Field

[0001] This invention belongs to the field of marine surveying technology, and in particular to a method for real-time evaluation of the quality of multibeam bathymetry data based on the Deloni triangulation model. Background Technology

[0002] Gridding is the foundation for two-dimensional (three-dimensional) visualization, spatial analysis, trend analysis, and quality assessment of multibeam bathymetry data. With the development of modern marine exploration and sensor technologies, and the continuous expansion of research areas and scales, multibeam bathymetry data generally exhibit characteristics of large areas and massive volumes, reaching 10,000 km. 2 The full coverage measurement of the sea area generates approximately 1 billion effective depth points. For this scale of discrete data, mainstream commercial data processing software and conventional methods are insufficient for data gridding, thus hindering data quality assessment. While data can be divided into blocks for gridding, existing algorithms involve cumbersome processes for handling block rules and grid stitching, requiring manual data segmentation and stitching. In practical multibeam bathymetry data gridding, the conventional method of manually segmenting and stitching data is no longer sufficient to meet the real-world requirements for real-time quality assessment of multibeam bathymetry data. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art and provide a real-time evaluation method for the quality of multibeam bathymetry data based on the Deloni triangulation model that is reasonably designed, has a fast processing speed, and is highly efficient.

[0004] The present invention solves the existing technical problems by adopting the following technical solution: A real-time quality assessment method for multibeam bathymetry data based on the Deloni triangulation model includes the following steps: Step 1: Thin the original dataset to generate a thinned dataset and construct a Delonix triangulation. Step 2: Based on the Deloni triangulation, extract the precise data range and outer rectangle of the original dataset; Step 3: Based on the precise data range and bounding rectangle of the original dataset, the original dataset is segmented. Edge segments with fewer discrete points are merged to obtain the segmented dataset and precise data range. Step 4: Based on the segmented dataset and the precise data range, perform gridding processing on each segment to generate segmented grids, and conduct real-time evaluation of the multibeam bathymetry data quality.

[0005] Moreover, the specific implementation method of step 1 is as follows: according to the measurement scale or mapping scale, the original dataset is thinned by the spacing on the map, and the thinning result is used to form a thinned dataset and saved.

[0006] Moreover, the specific implementation method of step 2 is as follows: the maximum side length of the constructed Deloni triangulation is selected within the range of 2 times the spacing between sparse discrete points and 3 times the spacing between sparse discrete points.

[0007] Moreover, the specific implementation method of step 4 is as follows: using the precise data range of the original dataset and the range of each rectangular segment to perform polygon intersection processing, extracting the precise data range of each segment dataset, forming a segment grid, and on this basis, performing real-time evaluation of the multibeam bathymetry data quality.

[0008] The advantages and positive effects of this invention are: 1. This invention generates a thinned dataset by thinning the original discrete dataset and constructing a Delonix triangulation network. Based on the Delonix triangulation network, the original dataset is then segmented to extract the precise data range of each segment. Each segment is then gridded to generate a segmented grid, and real-time quality assessment of the multibeam bathymetry data is performed. This invention can quickly and accurately perform gridding of massive, large-area discrete point data in multibeam bathymetry, solving the complexity and tediousness of gridding multibeam bathymetry data and enabling quality assessment.

[0009] 2. This invention can establish a processing model using common data processing software such as Matlab, and can quickly and accurately realize the gridding processing of massive and large-area discrete point data of multibeam bathymetry data. It solves the problem of real-time quality assessment of massive multibeam bathymetry data and can be widely applied in the field of multibeam bathymetry data processing. Detailed Implementation

[0010] A real-time quality assessment method for multibeam bathymetry data based on the Deloni triangulation model includes the following steps: Step 1: Thin the original dataset to generate a thinned dataset.

[0011] Based on the measurement scale or map scale, the original dataset is thinned at 1cm intervals on the map. When the measurement scale is 1:10000, the actual distance between the thinned discrete points is 0.01×10000=100m. The thinning result forms a thinned dataset and is saved in the process file. The data volume of the thinned dataset is reduced to about 1 / 1000 of the original dataset.

[0012] Step 2: Construct the Delauney triangulation based on the sparse dataset.

[0013] Based on the thinned dataset generated in step 1, construct a Delauney triangulation. The maximum side length of the constructed Delauney triangulation is set to 2.5 times the spacing between the thinned discrete points. When the measurement scale is 1:10,000, the maximum side length of the triangulation is 0.01×10000×2.5=250m.

[0014] Step 3: Based on the Delauney triangulation, extract the precise data range and outer rectangle of the original dataset.

[0015] Step 4: Based on the precise data range and bounding rectangle of the original dataset, the original dataset is segmented, and edge segments containing fewer discrete points are merged.

[0016] Based on the precise data range and bounding rectangle of the original dataset extracted in step 3, the original dataset is geographically segmented using the bounding rectangle to ensure that the maximum number of discrete points contained in each segment is less than the processing limit of the computer, and edge segments containing fewer discrete points are merged.

[0017] Step 5: Based on the results of geographic sharding and merging, expand the edges of the precise data range of the sharded dataset.

[0018] Based on the geographic sharding and merging results of step 4, polygon intersection processing is performed using the precise data range of the original dataset and the range of each rectangular shard to extract the precise data range of each shard dataset. Then, the actual data range polygon of each shard dataset is buffered and expanded by two grid spacings. For example, when the grid resolution is 25m, the polygon is expanded by 50m.

[0019] Step 6: Based on the fragmented dataset and the precise data range, perform gridding processing on each fragment to generate each fragmented grid.

[0020] Step 7: Perform splicing and polishing on the segmented mesh to generate the final large-area, seamless mesh.

[0021] By following steps 1 to 7, the function of rapid gridding of massive, large-area discrete marine environmental data can be completed.

[0022] Based on the above-mentioned real-time evaluation method for multibeam bathymetry data quality based on the Deloni triangulation model, the effectiveness of the present invention is verified through testing and data calculation.

[0023] Intermediate results generated during the processing of this method, such as sparse datasets, Delauney triangulations, pieced datasets, and pieced meshes, can be deleted after processing or retained permanently to facilitate the generation of mesh data that meets different needs using various interpolation methods.

[0024] Furthermore, testing has shown that this processing method can utilize commercial data processing software such as Matlab. Through simple programming, a gridded processing model for massive, large-area discrete multibeam bathymetry data can be established, automating the gridded processing of such data. During the gridded processing of massive discrete data, the following can be dynamically controlled: (1) The thinning interval parameter of the discrete data can be adjusted according to actual needs to ensure that the amount of data in the thinned dataset is suitable for quickly constructing the Delauney triangulation; (2) Adjust the grid (horizontal) resolution parameters to generate grid data of different resolutions.

[0025] It should be emphasized that the embodiments described in this invention are illustrative rather than limiting. Therefore, this invention includes, but is not limited to, the embodiments described in the specific implementation. Any other implementations derived by those skilled in the art based on the technical solutions of this invention are also within the scope of protection of this invention.

Claims

1. A real-time evaluation method for the quality of multibeam bathymetry data based on the Deloni triangulation model, characterized in that, Includes the following steps: Step 1: Thin the original dataset to generate a thinned dataset and construct a Delonix triangulation. Step 2: Based on the Deloni triangulation, extract the precise data range and outer rectangle of the original dataset; Step 3: Based on the precise data range and bounding rectangle of the original dataset, the original dataset is segmented. Edge segments with fewer discrete points are merged to obtain the segmented dataset and precise data range. Step 4: Based on the segmented dataset and the precise data range, perform gridding processing on each segment to generate segmented grids, and conduct real-time evaluation of the multibeam bathymetry data quality.

2. The real-time evaluation method for multibeam bathymetry data quality based on the Deloni triangulation model according to claim 1, characterized in that: The specific implementation method for generating the thinned dataset in step 1 is as follows: based on the measurement scale or mapping scale, the original dataset is thinned according to the interval on the map, and the thinning result is used to form a thinned dataset and saved.

3. The method for gridding massive discrete data based on the Deloni triangulation model according to claim 1, characterized in that: The maximum side length of the Deloni triangulation constructed in step 1 is selected within the range of 2 times the spacing between sparse discrete points and 3 times the spacing between sparse discrete points.

4. The real-time evaluation method for multibeam bathymetry data quality based on the Deloni triangulation model according to claim 1, characterized in that: The specific implementation method of step 4 is as follows: using the precise data range of the original dataset and the range of each rectangular segment, polygon intersection processing is performed to extract the precise data range of each segment dataset, forming a segment grid, and on this basis, real-time evaluation of the multibeam bathymetry data quality is performed.