CN110956696B - Submarine topography simulation method based on multi-scale chart data - Google Patents
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Abstract
The invention relates to a submarine topography simulation method based on multi-scale chart data, which is mainly technically characterized by estimating an optimal scale of the multi-scale chart data; calculating associated chart data according to the geographical space range to be queried; extracting water depth points and land areas; interpolation of the seafloor topography regular grid and sea Liu Pinjie; and constructing a sea-land stereo rendering chart by using a color band color matching method. The invention has reasonable design, realizes the submarine topography simulation based on the chart data, and is simpler in the aspect of data acquisition; through the combination of the multi-scale data, irregular characteristics of sea chart framing can be flexibly adapted; the best balance can be obtained in the aspects of effect and efficiency through the best scale estimation; after the submarine topography and the land topography are spliced, the uniform color band is used for rendering, so that the method has attractive and efficient effects.
Description
Technical Field
The invention belongs to the technical field of ocean mapping, and particularly relates to a submarine topography simulation method based on multi-scale chart data.
Background
The submarine topography simulation has great significance for ocean research, development and utilization. For confidentiality reasons, raw data of water depth measurement are difficult to obtain, and the internationally disclosed submarine topography rule grid data have defects in resolution and precision, so that the requirements of diversified applications cannot be met. Compared with the topographic map data, the electronic chart data has irregularities in terms of longitude and latitude ranges, scales and the like, and is comprehensively determined according to factors such as purposes (generally including overview, general, offshore, coastal, harbor, anchoring), navigation areas, data grasping conditions and the like. The electronic chart of the same purpose relates to a plurality of different scales, has a certain scale interval range and can have a spatial overlapping gland between different data; except for "overview" and "general" uses, the electronic chart of the same use cannot achieve spatially continuous coverage. How to realize the submarine topography simulation function by using the existing chart data is needed to solve the following two problems:
1. the user demand area is covered. The user's demand scope to submarine topography data often covers a plurality of chart data, and probably needs the chart data of a plurality of different uses to cover completely, must extract associated data through certain tactics, both needs to satisfy the user demand, can not have overlapping data yet.
2. Balance of effect and efficiency. The user wants to improve the fineness of the three-dimensional simulation, but the finer the water depth point which is required to participate in calculation, the higher the requirement on the calculation capacity, and the longer the response time.
Patent document (publication number CN103456041 a) discloses a three-dimensional terrain and radar terrain generating method based on S-57 electronic chart data, which extracts chart contour lines as onshore DEM data processing and manufacturing sources, and the simulation accuracy is not high. Another document, "electronic chart three-dimensional visualization based on S57 standard", can build a chart data three-dimensional visualization model, but it does not consider the relationship between multi-scale data, and thus its processing efficiency is not high.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a submarine topography simulation method based on multi-scale chart data, which breaks through the two core problems of 'optimal scale estimation' and 'associated data extraction' by utilizing the multi-scale chart data, achieves the effects of 'user demand coverage' and 'effect and efficiency balance', and solves the problem of insufficient submarine topography simulation fineness.
The invention solves the technical problems by adopting the following technical scheme:
a submarine topography simulation method based on multi-scale chart data comprises the following steps:
step 2, calculating associated chart data according to the geographical space range to be queried;
step 3, extracting water depth points and land areas;
step 4, interpolation of a submarine topography regular grid and sea Liu Pinjie;
and 5, constructing a sea-land stereo rendering chart by using a color band color matching method.
Further, the specific method of the step 1 comprises the following steps:
the method comprises the steps that a user inputs a geographic space range TargetRact to be queried;
calculating the field area T of the targetlect, and estimating the water depth point processing limit to be MaxCount and the water depth interval ensemble average estimated value D;
thirdly, estimating the average occupied area A of a single water depth point, wherein A=T/MaxCount;
the optimal scale denominator for the estimation is M, m=sqrt (a)/D.
Further, the specific processing method of the step 2 comprises the following steps:
filtering all chart data overlapped with the TargetRact according to a geographical space range TargetRact to be queried, and sequencing from small to large according to a scale denominator to form an MList to form a chart information list ChartList;
secondly, if the MList and the ChartList are empty, turning to step-red; otherwise, turning to the step;
searching whether an optimal scale denominator M exists in MList or not, if so, searching corresponding chart data in ChartList, and recording as TargetChart; if not, finding the scale denominator value M positioned on two sides of M in MList 1 And M 2 Taking a value TargetM closest to M from the two, searching corresponding chart data in a ChartList, and marking the chart data as TargetChart;
inserting TargetChart into the end of TargetChartList, removing TargetM from MList, and removing TargetChart from ChartList;
fifthly, judging that the effective data range in the ChartList is the same as or contained in the chart data of the TargetBuoundly according to the polygonal TargetBuoundly corresponding to the effective range of the TargetChart data, removing the chart data from the ChartList, and removing corresponding items in the MList;
performing polygon combination on all data effective ranges in the TargetChartList, marking the result as UnionBoundry, and ending the process if the UnionBoundry is identical to or completely contains the TargetRact; otherwise, turning to the step.
Further, the specific processing method of the step 3 comprises the following steps:
the method comprises the steps that a cutting polygon is made to be a main boundry=targetrect, a total set of water depth points is allSounds, and a total set of land areas is allLandAreas;
secondly, if the TargetChartList is empty, ending the process; otherwise, selecting first chart data from the TargetChartList, and marking the first chart data as TargetChart;
extracting all water depth elements from TargetChart, marking the water depth elements as a set Soundings, extracting land area elements as a set LandAreas, cutting the two element sets by using Remain Boundry, reserving elements positioned in the Remain Boundry, and respectively merging the elements into Allsoundings and AllLandAreas;
fourthly, cutting the remaininBoundry by using the data effective range TargetBuoundly of TargetChart, so that remaininBoundry=remaininBoundry-TargetBuundry;
fifthly, if the remalnrect is empty, ending the process; otherwise, the TargetChart is removed from the TargetChartList, and the step is changed.
Further, the specific processing method of the step 4 comprises the following steps:
the method comprises the steps of combining and recombining all polygons in all land areas to construct a new land area total set NewAllLandAreas;
secondly, constructing a Delaunay constraint triangle network by taking AllSounds as discrete points and taking boundaries of all polygons in NewAllLandAreas as 0-meter constraint lines;
thirdly, the known targetselect Width is Width, height, area is Rectreaea, the total number of discrete points is soundsCount, the calculated grid lateral spacing is GridCellX=Sqrt (Rectreaea/soundsCount) ×Sqrt (Width/Height)/N, the grid longitudinal spacing is GridCellY=Sqrt (Rectreaea/soundsCount) ×Sqrt (Height/Width)/N, N is a positive integer and can be adjusted in real time according to user needs and display effects;
fourth, setting invalid for all grid points in the newallrandarea polygon in the targetlect;
fifthly, for non-invalid grid points, generating a height value of each grid point by utilizing an interpolation algorithm;
the method comprises the steps of (1) for invalid grid points, recalculating a height value through a land DEM interpolation algorithm.
Further, the step of generating a negative value for the height value of each grid point; step six recalculation of the height value of (2) is positive.
Further, the color ribbon in the step 5 includes: the method comprises the steps of firstly, enabling a blue-graded seabed part and secondly enabling a green-graded land part, a brown-graded land part and a white-graded land part to be jointly graded.
The invention has the advantages and positive effects that:
the invention realizes the submarine topography simulation based on the chart data, and is simpler in the aspect of data acquisition; through the combination of the multi-scale data, irregular characteristics of sea chart framing can be flexibly adapted; the best balance can be obtained in the aspects of effect and efficiency through the best scale estimation; after the submarine topography and the land topography are spliced, rendering is carried out by using a unified color band, so that the method has attractive and efficient effects; by splicing the submarine topography and the land topography, the integrated sea-land simulation function is realized, and the method has the characteristics of flexibility, rapidness, strong self-adaption and the like.
Drawings
FIG. 1 is a rhombic distribution of water depth derived from US5NC17 M.000;
FIG. 2 is a schematic diagram of an associated chart query process;
FIG. 3 is a schematic view of a first crop;
FIG. 4 is a schematic diagram of a second crop;
FIG. 5 is a schematic diagram of a terrain rendering ink ribbon;
FIG. 6 is a diagram of the effect of rendering the simulated seafloor terrain of the present invention.
Detailed Description
Embodiments of the present invention are described in further detail below with reference to the accompanying drawings.
A submarine topography simulation method based on multi-scale chart data comprises the following steps:
the method comprises the steps that a user inputs a geographic space range (rectangle) TargetRect to be queried;
the actual area T (unit: m) of TargetRect is calculated 2 ) The water depth point process quota estimate is MaxCount, and the water depth interval ensemble average estimate D (unit: m).
Third step, the average occupied area A (unit: m) of a single water depth point is estimated 2 )，A＝T/MaxCount。
The optimal scale denominator for the estimation is M, m=sqrt (a)/D.
This step will be described below taking the diamond-shaped distribution of water depth from us5nc17m.000 as an example given in fig. 1. The estimated optimal scale is used to avoid processing excessive data beyond the system load capacity. In order to correctly reflect the seabed topography, the pattern of sea chart water depth points is generally in diamond distribution. To simplify the calculation, the average footprint of each water depth point may be calculated approximately as a square.
Assuming that the user inputs the geospatial range to be queried, targetect, as a rectangle, the field area T of the targetect queries the field area T (unit: m) corresponding to the rectangle targetect 2 ) The estimated water depth point process limit estimate is MaxCount, and the water depth interval (paper diagram) ensemble average estimate D (unit: m). Then, the average maximum occupied area A (unit: m 2 ) The estimation can be performed in terms of "rectangular area/accommodation point number", i.e.: a=t/MaxCount; the optimal scale denominator is M, which can be estimated according to the actual length/the length on the graph, namely: m=sqrt (a)/D.
Since most sea chart data has land elements (no water depth points), the water depth points extracted by the scale denominator tend to be smaller than MaxCount. By using a statistical method, the approximate relationship between the water depth interval and the depth interval can be constructed, as shown in the following table:
for ease of calculation, an intermediate value of 0.0015m may be chosen as the initial value of D. In the actual calculation process, if the extracted water depth point is found to be too small, the D value can be properly increased; otherwise, the D value is reduced appropriately.
Step 2, calculating associated chart data, wherein the specific method comprises the following steps:
according to the method, all chart data overlapped with the TargetRact are filtered out according to the geographical space range TargetRact to be queried, and are sorted from small to large according to the scale denominator to form an MList, so that a chart information list ChartList is formed.
Secondly, if the MList and the ChartList are empty, turning to step-red; otherwise, turning to the step.
Searching whether an optimal scale denominator exists in MList or not, if so, searching corresponding chart data in ChartList, and recording as TargetChart; if not, finding the scale denominator value M positioned on two sides of M in MList 1 And M 2 And taking a value TargetM closest to M from the two values, and searching corresponding chart data in the ChartList, and marking the chart data as TargetChart.
Inserting TargetChart into the end of TargetChartList, removing TargetM from MList, and removing TargetChart from ChartList.
And step five, judging that the effective data range in the ChartList is the same as or contained in the chart data of the TargetBuoundly according to the polygonal TargetBuoundly corresponding to the effective range of the TargetChart data, removing the chart data from the ChartList, and removing corresponding items in the MList.
Performing polygon combination on all data effective ranges in the TargetChartList, marking the result as UnionBoundry, and ending the process if the UnionBoundry is identical to or completely contains the TargetRact; otherwise, turning to the step.
The following describes a method for associating chart data with a certain scenario of chart data query shown in fig. 2: in the figure, the dotted line is the query area targetlect, and the candidate chart list MList is obtained by filtering, including: sea chart A (1:5 ten thousand), sea chart B (1:10 ten thousand), sea chart C (1:2 ten thousand), sea chart D (1:20 ten thousand). Assuming that the optimal scale denominator M is 6 ten thousand, the scale denominators positioned at two sides of the M are 2 ten thousand and 5 ten thousand respectively. The proximity of the scale denominator can be calculated using three strategies:
the tradeoff strategy: min (Max (M1, M)/Min (M1, M), max (M2, M)/Min (M2, M))
The positive strategy is as follows: selecting Min (M1, M2) with larger scale;
negative strategies: smaller scale, max (M1, M2);
here, a compromise strategy is used to describe Min (Max (20000, 60000)/Min (20000, 60000), max (50000, 60000)/Min (50000, 60000)), and as a result, 50000, i.e., chart a should be selected. The chart a cannot cover targetlect, so that related data needs to be continuously searched in the candidate chart list to obtain the chart B, and meanwhile, the chart C can be directly removed because the chart B contains the chart C. The union of the chart A and the chart B still cannot cover the targetlect, and the chart D is obtained by continuing to search, so that the searching of all the associated data is completed.
Step 3, extracting water depth points and land areas, wherein the specific method comprises the following steps of:
the method comprises the steps of enabling a clipping polygon to be remaininBoundry=TargetRact, wherein the total set of water depth points is AllSounds, and the total set of land areas is AllLandAreas.
Secondly, if the TargetChartList is empty, ending the process; otherwise, selecting the first chart data from the TargetChartList, and recording the first chart data as TargetChart.
Extracting all water depth elements from TargetChart, marking as a set Soundings, extracting land area elements, marking as a set LandAreas, cutting the two element sets by using Remain Boundry, reserving elements positioned in the Remain Boundry, and respectively merging the elements into Allsoundings and AllLandAreas.
And fourthly, cutting the remarkBoundry by using the data effective range TargetBuundry of TargetChart, so that remarkBoundry=remarkBoundry-TargetBuundry.
Fifthly, if the remalnrect is empty, ending the process; otherwise, the TargetChart is removed from the TargetChartList, and the step is changed.
In the step, on the basis of fig. 2, when the first water depth point and land area data extraction is carried out, the targetlect is used for carrying out element cutting on the chart A to obtain the data of the horizontal line parts of fig. 3, and meanwhile, the cutting polygon is reduced to the oblique line parts of fig. 3; in the second data extraction, the clipping polygon completely comprises a chart B, so that all water depth points and land areas in the chart B are reserved, and the clipping polygon is reduced to be the oblique line part of the chart 4; and when the third data is extracted, the rest clipping polygons are contained by the chart D, diagonal parts in the chart D are reserved, and the clipping polygons are left empty.
The water depth element name in the sea chart data conforming to the IHO international standard is abbreviated as SOUNDG, the digital code is 129, and the sea chart data is displayed in black; the land area element name is abbreviated as LNDARE, the number is coded 71, and the color is shown as yellow.
Step 4, splicing the sea Liu Gewang, wherein the concrete method comprises the following steps:
the method comprises the steps of combining and recombining all polygons in all land areas, and constructing a new land area total set NewAllLandAreas.
And secondly, constructing a Delaunay constraint triangle network by taking AllSounds as discrete points and taking boundaries of all polygons in NewAllLandAreas as 0-meter constraint lines.
From the description below, it is known that targetselect is wide, high, area is RectArea, total number of discrete points is soundcount, calculated grid lateral distance is gridcellx=sqrt (RectArea/soundcount) ×sqrt (Width/Height)/N, grid longitudinal distance is gridcelly=sqrt (RectArea/soundcount) ×sqrt (Height/Width)/N, N is positive integer, and the method can be manually adjusted in real time according to user's needs and display effect.
Fourth, all grid points within the newallrandarea polygon within the targetlect are set to invalid.
For non-invalid grid points, the height value (negative value) of each grid point is generated by using an interpolation algorithm.
The height value (positive value) is recalculated for the invalid grid points through a land DEM interpolation algorithm.
There are many ways to construct the constrained triangle in this step, for example Jonathan Richard Shewchuk provides a complete set of methods and tools (see "a Two-Dimensional Quality Mesh Generator and Delaunay Triangulator").
The average area of the water depth points is RectArea/soundingsCount, and the average pitch is Sqrt (RectArea/soundingsCount). To equalize the regular grid horizontal pitch and vertical pitch of the subsequent construction, the initial horizontal pitch is set to gridcellx=sqrt (RectArea/soundscount) ×sqrt (Width/Height), and the initial vertical pitch is set to gridcelly=sqrt (RectArea/soundscount) ×sqrt (Height/Width). The mesh may be encrypted as needed, i.e. the pitch is at N.
In the interpolation calculation process, for the grid points in the triangular net, the triangles where the grid points are located are required to be searched, the triangles can be rapidly screened through the coordinate range, the accurate judgment is carried out by using the vector cross product, and finally the depth value of the points is calculated by using the inverse distance interpolation method; for single lattice points outside the triangular net, searching discrete water depth points to the periphery with the size of lattice spacing, if the number of the found points is less than 3, searching with the spacing of 2 times, 3 times or more until the found points are met, finding out 3 points closest to the lattice points from the found points, constructing a plane equation, and performing extrapolation calculation of the lattice depth values.
And 5, constructing a sea-land stereo rendering chart by a color band color matching method.
In the process of constructing the sea-land stereo rendering map, the construction method of the color bars is important content influencing the terrain rendering effect. As shown in fig. 5, the color band provided in this example includes two parts, one is a submarine, blue gradation is selected, and the other is a land, green, brown and white gradation is selected. The color bands are stored according to the pictures, and color matching can be achieved through texture pasting, so that a complete sea-land three-dimensional rendering chart is constructed, and the chart is shown in fig. 6.
The invention is applicable to the prior art where it is not described.
It should be emphasized that the examples described herein are illustrative rather than limiting, and therefore the invention includes, but is not limited to, the examples described in the detailed description, as other embodiments derived from the technical solutions of the invention by a person skilled in the art are equally within the scope of the invention.
Claims (6)
1. The submarine topography simulation method based on the multi-scale chart data is characterized by comprising the following steps of:
step 1, estimating an optimal scale of multi-scale chart data;
step 2, calculating associated chart data according to the geographical space range to be queried;
step 3, extracting water depth points and land areas;
step 4, interpolation of a submarine topography regular grid and sea Liu Pinjie;
step 5, constructing a sea-land stereo rendering chart by using a color band color matching method;
the specific method of the step 1 comprises the following steps:
the method comprises the steps that a user inputs a geographic space range TargetRact to be queried;
calculating the field area T of the targetlect, and estimating the water depth point processing limit to be MaxCount and the water depth interval ensemble average estimated value D;
thirdly, estimating the average occupied area A of a single water depth point, wherein A=T/MaxCount;
the optimal scale denominator for the estimation is M, m=sqrt (a)/D.
2. The seafloor terrain simulation method based on multi-scale chart data according to claim 1, wherein the method comprises the following steps of: the specific processing method of the step 2 comprises the following steps:
filtering all chart data overlapped with the TargetRact according to a geographical space range TargetRact to be queried, and sequencing from small to large according to a scale denominator to form an MList to form a chart information list ChartList;
secondly, if the MList and the ChartList are empty, turning to step-red; otherwise, turning to the step;
searching whether an optimal scale denominator M exists in MList or not, if so, searching corresponding chart data in ChartList, and recording as TargetChart; if not, finding the scale denominator value M positioned on two sides of M in MList 1 And M 2 Taking a value TargetM closest to M from the two, searching corresponding chart data in a ChartList, and marking the chart data as TargetChart;
inserting TargetChart into the end of TargetChartList, removing TargetM from MList, and removing TargetChart from ChartList;
fifthly, judging that the effective data range in the ChartList is the same as or contained in the chart data of the TargetBuoundly according to the polygonal TargetBuoundly corresponding to the effective range of the TargetChart data, removing the chart data from the ChartList, and removing corresponding items in the MList;
performing polygon combination on all data effective ranges in the TargetChartList, marking the result as UnionBoundry, and ending the process if the UnionBoundry is identical to or completely contains the TargetRact; otherwise, turning to the step.
3. The seafloor terrain simulation method based on multi-scale chart data according to claim 1, wherein the method comprises the following steps of: the specific processing method of the step 3 comprises the following steps:
the method comprises the steps that a cutting polygon is made to be a main boundry=targetrect, a total set of water depth points is allSounds, and a total set of land areas is allLandAreas;
secondly, if the TargetChartList is empty, ending the process; otherwise, selecting first chart data from the TargetChartList, and marking the first chart data as TargetChart;
extracting all water depth elements from TargetChart, marking the water depth elements as a set Soundings, extracting land area elements as a set LandAreas, cutting the two element sets by using Remain Boundry, reserving elements positioned in the Remain Boundry, and respectively merging the elements into Allsoundings and AllLandAreas;
fourthly, cutting the remaininBoundry by using the data effective range TargetBuoundly of TargetChart, so that remaininBoundry=remaininBoundry-TargetBuundry;
fifthly, if the remalnrect is empty, ending the process; otherwise, the TargetChart is removed from the TargetChartList, and the step is changed.
4. The seafloor terrain simulation method based on multi-scale chart data according to claim 1, wherein the method comprises the following steps of: the specific processing method of the step 4 comprises the following steps:
the method comprises the steps of combining and recombining all polygons in all land areas to construct a new land area total set NewAllLandAreas;
secondly, constructing a Delaunay constraint triangle network by taking AllSounds as discrete points and taking boundaries of all polygons in NewAllLandAreas as 0-meter constraint lines;
thirdly, the known targetselect Width is Width, height, area is Rectreaea, the total number of discrete points is soundsCount, the calculated grid lateral spacing is GridCellX=Sqrt (Rectreaea/soundsCount) ×Sqrt (Width/Height)/N, the grid longitudinal spacing is GridCellY=Sqrt (Rectreaea/soundsCount) ×Sqrt (Height/Width)/N, N is a positive integer and can be adjusted in real time according to user needs and display effects;
fourth, setting invalid for all grid points in the newallrandarea polygon in the targetlect;
fifthly, for non-invalid grid points, generating a height value of each grid point by utilizing an interpolation algorithm;
the method comprises the steps of (1) for invalid grid points, recalculating a height value through a land DEM interpolation algorithm.
5. The seafloor terrain simulation method based on multi-scale chart data according to claim 4, wherein the method comprises the following steps of: generating a negative value of the height value of each grid point; step six recalculation of the height value of (2) is positive.
6. The seafloor terrain simulation method based on multi-scale chart data according to claim 1, wherein the method comprises the following steps of: the color band in the step 5 comprises: the method comprises the steps of firstly, enabling a blue-graded seabed part and secondly enabling a green-graded land part, a brown-graded land part and a white-graded land part to be jointly graded.
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