CN114565732B - Three-dimensional modeling method and device for generation layer of branch-shaped distribution soil - Google Patents
Three-dimensional modeling method and device for generation layer of branch-shaped distribution soil Download PDFInfo
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Abstract
The invention discloses a three-dimensional modeling method and device for a generation layer of branch-shaped distribution soil, wherein the method comprises the following steps: acquiring a dendritic pattern spot surface, regional raster data and an actual measurement soil profile data set RP; extracting linear water in the area of the image spot surface to form a skeleton line set SKL; generating a dendritic pattern spot contour virtual section sampling line according to boundary line contours SP and SKL of the pattern spot surface, and storing the dendritic pattern spot contour virtual section sampling line into a sampling line set SOL; searching a sampling line closest to each section in the RP in the SOL, storing the sampling line in a set SL_A, and forming a set SL_B by the residual line and the SP; respectively layering soil occurrence layers of each sampling point of each sampling line in SL_ A, SL _B, estimating the thickness of the occurrence layers, and correspondingly forming a virtual soil profile set SP_ A, SP _B according to SL_ A, SL _B; SP_ A, SP _B and RP are combined, and a three-dimensional model of the soil occurrence layer is generated according to the high Cheng Goujian triangular surfaces of all occurrence layers containing the soil profile of the same occurrence layer in the combined set. The method is suitable for large-range and rough three-dimensional modeling of the generation layer.
Description
Technical Field
The invention relates to a geographic information technology, in particular to a three-dimensional modeling method and device for generation layers of branch-shaped distribution soil.
Background
The branch distribution is a relatively common soil spatial distribution pattern. In the valley development area, the water system is stretched in a dendritic mode, and similar soil distribution rules repeatedly appear from the tops of the hills to the bottoms of the valleys among branches of the water system, and pattern spots of the distribution appear on a soil map, namely branch pattern spots. The traditional method for constructing the three-dimensional model of the soil occurrence layer needs to use a large amount of soil profile data, and is suitable for small-range and high-precision three-dimensional modeling of the soil occurrence layer. However, in many cases, the obtained soil profile data is limited due to various reasons such as difficulty in field investigation and limited cost, and it is difficult to effectively perform three-dimensional modeling of the soil formation layer.
Disclosure of Invention
The invention aims to: aiming at the problems existing in the prior art, the invention provides a three-dimensional modeling method and device for a generation layer of large-range and rough branch-shaped distribution soil.
The technical scheme is as follows: the three-dimensional modeling method for the generation layer of the branch-shaped distribution soil comprises the following steps:
(1) Loading digital soil map, regional DEM data and measured soil profile data to obtain dendritic map spot surface SoilPolygon, regional grid data set GeoDEM and measured soil profile data set RP;
(2) Based on a regional raster data set GeoDEM, extracting a linear water system in the range of a dendritic pattern speckle surface SoilPolygon, and storing the linear water system as a skeleton line of the SoilPolygon into a skeleton line set SKL;
(3) Generating a dendritic pattern spot contour virtual section sampling line according to the boundary line contour SP of SoilPolygon and the skeleton line set SKL, and storing the dendritic pattern spot contour virtual section sampling line into the sampling line set SOL;
(4) Searching a sampling line closest to each measured soil profile in the measured soil profile data set RP in the sampling line set SOL, storing the sampling line into the sampling line set SL_A, and forming other sampling line sets SL_B by the residual sampling lines and SP;
(5) For each sampling line in the sampling line set SL_A, extracting all break points on the line as sampling points, layering a soil generation layer for each sampling point, presuming the thickness of each generation layer of each sampling point, then constructing a virtual soil profile by adopting all the sampling points of the sampling line, and storing the virtual soil profile into the virtual soil profile set SP_A;
(6) Extracting all break points on each sampling line in the sampling line set SL_B as sampling points, layering soil occurrence layers for each sampling point, estimating the thickness of each occurrence layer of each sampling point, constructing a virtual soil profile by adopting all sampling points of the sampling line, and storing the virtual soil profile into the virtual soil profile set SP_B;
(7) Combining the virtual soil profile data SP_ A, SP _B and the actually measured soil profile data RP into a soil profile set SoilProfiles, selecting any occurrence layer gh, and constructing an irregular triangular network TIN data set according to the elevation of the occurrence layer gh of all the soil profiles containing the occurrence layer gh in the SoilProfiles;
(8) Converting the irregular triangular net TIN data set into a triangular surface to generate a three-dimensional model of a soil generation layer gh;
(9) And (5) circularly executing the steps (7) - (8) until all the occurrence layers are traversed, and obtaining the three-dimensional model of all the soil occurrence layers.
Further, the step (1) specifically includes:
(1-1) loading digital soil map data, and extracting dendritic pattern spot surfaces SoilPolygon in a branch shape;
(1-2) loading regional DEM data, extracting regional raster data from the regional DEM data, and storing the regional raster data into a regional raster data set GeoDEM;
(1-3) loading measured soil profile data to generate a measured soil profile r i (X i ,Y i ,sl i ) And stores the measured soil profile data set rp= { r i I=1, 2, …, m }, where r i Represents the ith measured soil profile, X i And Y i Coordinate information indicating the ith measured section, m indicates the number of measured soil sections, sl i Soil formation information, sl, representing the ith measured profile i ＝{gh i,j (gc j ,zUp j ,zDown j ,h j )|j＝1,2,…,n i }，gh i,j Represents the ith soil profile and the jth soil horizon, n i Number of layers of soil formation layer, gc, representing ith soil profile j The code symbol of the generation layer which represents the j generation layer, the code rule of the code symbol is shown in the following table from top to bottom according to the generation depth; zUp j Represents the top depth, zDown, of the jth generation layer j Represents the bottom depth of the jth generation layer, h j Represents the thickness of the jth generation layer;
coding rule of surface soil generation layer
(1-4) for each section in the measured soil section data set RP, searching whether all occurrence layer coding symbols are contained O, A, AB, B, BC, C, R, if the occurrence layer coding symbols are missing, adding corresponding occurrence layers in sequence from top to bottom according to the occurrence depth, and setting the thickness of the occurrence layer to be 0 until each section contains all occurrence layers corresponding to O, A, AB, B, BC, C, R.
Further, the step (2) specifically includes:
(2-1) generating linear water system data based on the regional raster data set GeoDEM, and storing the linear water system data in the linear water system set WT;
(2-2) cutting each linear water system in the linear water system set WT by using a dendritic patch surface soildigon;
(2-3) extracting a linear water system positioned on the dendritic pattern spot surface SoilPolygon after cutting, taking the linear water system as a skeleton line of SoilPolygon, and storing the skeleton line into a skeleton line set SKL= { SKL x |x=1, 2, …, lx }, where skl x The x-th skeleton line is represented, and lx represents the number of skeleton lines.
Further, the step (3) specifically includes:
(3-1) extracting a boundary contour line SP of SoilPolygon, extracting a break point on the SP, and storing the break point into a boundary contour point set CP= { CP u |u=1, 2, …, U }, where cp u The U-th contour point is represented, and U represents the number of contour points;
(3-2) creating an attribute field Value for all points in the boundary contour point set CP and giving a Value g1;
(3-3) extracting all folding points of all the skeleton lines in the skeleton line set SKL, creating an attribute field Value for the folding points, giving a Value g2, g1 not equal to g2, and storing all the folding points into the skeleton line folding point set SKLP;
(3-4) merging the boundary contour point set CP and the skeleton line break point set SKLP to obtain all point sets AP, and constructing raster data TempRaster according to an attribute field Value by using a natural neighborhood interpolation method;
(3-5) extracting attribute Value contour lines parallel to the boundary contour lines along the skeleton lines according to the distribution rule of the branch-shaped soil;
(3-6) storing the generated contour line as a dendritic patch profile virtual section sampling line into the sampling lineSet sol= { gl v V=1, 2, …, V }, where gl v Represents the V-th sampling line, and V represents the number of sampling lines.
Further, the step (4) specifically includes:
(4-1) reading any measured soil profile r from the measured soil profile data set RP i Within the sampling line set SOL, a distance r is found i The nearest sampling line and storing the sampling line set SL_A;
(4-2) cycling the step (4-1) until all measured soil profiles in the measured soil profile data set RP are traversed to obtain a complete sampling line set SL_A closest to the measured soil profile;
(4-3) removing the sampling lines in the sampling line set sl_a from the sampling line set SOL, storing the remaining sampling lines in the other sampling line set sl_b, and adding the boundary contour line SP to the other sampling line set sl_b.
Further, the step (5) specifically includes:
(5-1) reading any one of the sampling lines gl from the sampling line set SL_A v ；
(5-2) extracting the sampling line gl v All the folding points are taken as sampling points and stored into a sampling point set sampointa= { sp v,w |w=1, 2, …, W }, where sp v,w A W-th sampling point representing a v-th sampling line, W representing the number of sampling points on the v-th sampling line;
(5-3) adding all soil occurrence layers for each sampling point in the sampling point set sampointa according to the occurrence depth from top to bottom;
(5-4) searching and sampling line gl from measured soil profile data RP v Deducing the thickness of each soil occurrence layer of all sampling points in the sampling point set sampointa according to the actually measured soil profile, constructing a virtual soil profile by adopting the sampling point set sampointa, and storing the virtual soil profile into a virtual soil profile set sp_a;
(5-5) steps (5-1) - (5-4) are cyclically performed until all sampling lines within set sl_a are traversed, resulting in a complete set of virtual soil profiles sp_a.
Further, the step (5-4) specifically includes:
(5-4-1) each measured soil profile corresponds to a nearest sampling line, and from the measured soil profile data RP, all nearest sampling lines are searched for gl v Is stored in a temporary profile set tp= { r v,z Z=1, 2,..z }, where r v,z Representing distance sampling line gl v The nearest Z-th measured soil profile, Z representing the number of profiles; if z=1, then step (5-4-2) is performed; if Z>1, executing the step (5-4-3);
(5-4-2) assigning each occurrence layer thickness of all sampling points in the sampling point set sampPoint A to be the corresponding occurrence layer thickness of the found measured soil profile, and executing the step (5-4-6);
(5-4-3) for each section in the temporary section set TP, a sampling line gl is found v And (3) adding a sampling point closest to the profile, assigning each occurrence layer thickness of the sampling point to be the corresponding occurrence layer thickness of the profile, and storing a temporary sampling point set TSP= { sp z |z＝1,2,...,Z}；
(5-4-4) along the sampling line gl v Direction, reading any two adjacent sampling points sp from the temporary sampling point set TSP z1 And sp (sp) z2 Searching sampling point sp from sampling point set sampPoint A z1 And sp (sp) z2 All sampling points in the middle, and the thickness of each generation layer of the sampling points is calculated according to the following formula:
h u ＝Min(h z1 ,h z2 )+u*(|h z1 -h z2 |)/N
wherein h is u A certain occurrence layer thickness h representing the ith sampling point z1 And h z2 Respectively are sampling points sp z1 And sp (sp) z2 Corresponding thickness of the generating layer, N represents sp z1 And sp (sp) z2 The number of all sampling points within the interval;
(5-4-5) looping step (5-4-4) until all sample points of the set of sample points are assigned values for the occurrence layer;
(5-4-6) generating a virtual soil profile by using the sampling point set sampPoint added with the thickness of the generation layer, and storing the virtual soil profile set SP_A.
Further, the step (6) specifically includes:
(6-1) reading any one of the sampling lines gl from the other sampling line set SL_B v ；
(6-2) extracting the sampling line gl v All break points are stored as sampling points into a sampling point set sampPointB, and all soil generation layers are added for each sampling point according to the sequence from top to bottom of the generation depth;
(6-3) deducing the thickness of each occurrence layer of all sampling points of the sampling point set sampointb according to the actually measured soil profile data RP and the virtual soil profile set sp_a, generating a virtual soil profile by adopting the sampling point set sampointb, and storing the virtual soil profile into the virtual soil profile set sp_b;
(6-4) cyclically performing steps (6-1) - (6-3) until all sample lines within sl_b have been traversed;
(6-5) reading the boundary contour line SP from the other sampling line set SL_B, and finding the measured soil profile r closest to the SP from the measured soil profile data RP c ；
(6-6) extracting all break points on Conline, storing the break points as sampling points into a sampling point set sampPointC, and adding all soil occurrence layers for each sampling point according to the occurrence depth from top to bottom;
(6-7) according to the measured soil profile r c The generation layer information of all sampling points in the sampling point set sampointc is assigned with r c And generating a virtual soil profile by adopting the sampling point set sampointc with the same generating layer thickness, and storing the virtual soil profile into a virtual soil profile set sp_b.
Further, the step (6-3) specifically includes:
(6-3-1) according to the sampling line gl v Attribute field Value of (1), find all and gl v Sampling lines with the same Value are selected according to the nearest sampling line gl with assigned occurrence layer thickness f ；
(6-3-2) extracting the sampling line gl f Form sampling point set sampPoint f And calculates sampling point set sampPoint f Average value of thickness of each occurrence layer of all sampling points in the arrayAssigning values to all sampling points in the sampling point set sampointb corresponding to the generation layer;
(6-3-3) generating a virtual soil profile using the sampling point set sampointb, and storing the virtual soil profile set sp_b.
Further, the step (7) specifically includes:
(7-1) merging the virtual soil profile set SP_ A, SP _B and the measured soil profile data RP to obtain a soil profile set SoilProfiles;
(7-2) extracting all soil sections containing the occurrence layer gh from the set SoilProfiles for any occurrence layer gh, and storing the soil sections into the set MP;
(7-3) according to the regional grid data set GeoDEM and the point coordinates of the soil profile points, obtaining the elevation information of each soil profile in the set MP;
(7-4) calculating the top depth elevation zUp and the bottom depth elevation zDown of the occurrence layer gh of each soil profile according to the elevation information of each soil profile and the thickness of each occurrence layer;
(7-5) storing the top depth elevation zUp of the occurrence layer gh of all the soil profiles in the set MP in the upper surface irregular triangle network TIN data set, and storing the bottom depth elevation zDown of the occurrence layer gh of all the soil profiles in the set MP in the lower surface irregular triangle network TIN data set.
The invention relates to a three-dimensional modeling device for a generation layer of branch-shaped distribution soil, which comprises a processor and a computer program which is stored on a memory and can run on the processor, wherein the processor realizes the method when executing the program.
The beneficial effects are that: compared with the prior art, the invention has the remarkable advantages that: the method can be suitable for three-dimensional modeling of a large-scale rough soil occurrence layer under the condition of limited soil profile data.
Drawings
FIG. 1 is a plot of regional soil and measured soil profile data employed in this example;
fig. 2 is regional DEM data employed in the present embodiment;
FIG. 3 is a flow chart of a three-dimensional modeling method for an occurrence layer of a branch-shaped distribution soil provided by the invention;
FIG. 4 is a dendritic image spot extracted in the present embodiment;
fig. 5 is a skeleton line extracted in the present embodiment;
FIG. 6 is raster data interpolated from a contour and a set of skeleton points in the present embodiment;
FIG. 7 is a set of sampling lines parallel to the contour boundary in this embodiment;
FIG. 8 is a schematic view showing the estimation of the thickness of the layer of the virtual soil profile generation layer according to the present embodiment;
fig. 9 shows a three-dimensional model of a soil formation layer ((a) a global three-dimensional model (box is a locally enlarged region), (b) a locally enlarged effect) generated in the present embodiment.
Detailed Description
The technical scheme of the invention is described in further detail below, and 1 is selected in this embodiment: 100 ten thousand Jiangxi soil map, 30m resolution Jiangxi DEM data and soil profile description records obtained by field sampling and literature data arrangement are used as experimental data, and are shown in FIG. 1 and FIG. 2. Further description will be provided by describing a specific embodiment with reference to the accompanying drawings.
As shown in fig. 3, the present embodiment provides a three-dimensional modeling method for a generation layer of a branched distribution soil, including:
(1) And loading the digitized soil map, the regional DEM data and the actually measured soil profile data to obtain a dendritic map spot surface SoilPolygon, a regional grid data set GeoDEM and an actually measured soil profile data set RP.
The method specifically comprises the following steps:
(1-1) loading digital soil map data, and extracting dendritic pattern spot faces SoilPolygon in a branch shape from the digital soil map data, wherein the digital soil map data are shown in fig. 4;
(1-2) loading regional DEM data, extracting regional raster data from the regional DEM data, and storing the regional raster data into a regional raster data set GeoDEM;
(1-3) loading measured soil profile data to generate a measured soil profile r i (X i ,Y i ,sl i ) And stores the measured soil profile data set rp= { r i |i＝1,2,…,m}，As shown in Table 1, wherein r i Represents the ith measured soil profile, X i And Y i Coordinate information indicating the ith measured section, m indicates the number of measured soil sections, sl i Soil formation information indicating the ith measured profile, as shown in Table 2, sl i ＝{gh i,j (gc j ,zUp j ,zDown j ,h j )|j＝1,2,…,n i }，gh i,j Represents the ith soil profile and the jth soil horizon, n i Number of layers of soil formation layer, gc, representing ith soil profile j The code symbol of the generation layer which represents the j generation layer, the code rule of the code symbol is shown in table 3 from top to bottom according to the generation depth; zUp j Represents the top depth, zDown, of the jth generation layer j Represents the bottom depth of the jth generation layer, h j Represents the thickness of the jth generation layer;
table 1 actually measured soil profile data table
| Section numbering | Abscissa of the circle | Ordinate of the ordinate | Ground elevation/m | Depth of section/cm |
| SS001 | 404616.18 | 3286082.76 | 53 | 100 |
| SS002 | 399703.35 | 3271084.23 | 1069 | 100 |
| SS003 | 393518.19 | 3269374.15 | 75 | 80 |
| ... | ... | ... | ... | ... |
| SS230 | 403228.62 | 3272947.02 | 886 | 30 |
Table 2 actually measured soil profile generation layer information table
| Section numbering | Generation layer symbol | Top depth/cm | Depth of bottom/cm | Thickness/ | |
| SS001 | A | ||||
| 0 | 10 | 10 | |||
| SS001 | B | 10 | 75 | 65 | |
| SS001 | C | 75 | 100 | 25 | |
| | A | 0 | 16 | 16 | |
| SS002 | B | 16 | 100 | 84 | |
| ... | ... | ... | ... | ... | |
| SS230 | A | 0 | 5 | 5 | |
| SS230 | B | 5 | 30 | 25 |
TABLE 3 coding rules for soil horizon
(1-4) for each section in the measured soil section data set RP, searching whether all occurrence layer coding symbols are contained O, A, AB, B, BC, C, R, if the occurrence layer coding symbols are missing, adding corresponding occurrence layers in sequence from top to bottom according to the occurrence depth, and setting the thickness of the occurrence layer to be 0 until each section contains all occurrence layers corresponding to O, A, AB, B, BC, C, R.
For example, for section SS001 in table 2, which contains only A, B, C three generation layers, four generation layers are added corresponding to O, AB, BC, R, each generation layer having a thickness of 0. The measured soil profile data set RP after the treatment is shown in table 4.
Table 4 measured soil profile data after filling the formation
| Section numbering | Generation layer symbol | Top depth/cm | Depth of bottom/cm | Thickness/ | |
| SS001 | O | ||||
| 0 | 0 | 0 | |||
| | A | 0 | 10 | 10 | |
| SS001 | AB | 10 | 10 | 0 | |
| SS001 | B | 10 | 75 | 65 | |
| SS001 | BC | 75 | 75 | 0 | |
| SS001 | C | 75 | 100 | 25 | |
| SS001 | R | 100 | 100 | 0 | |
| ... | ... | ... | ... | ... |
(2) And extracting a linear water system in the range of SoilPolygon on the dendritic pattern spot surface based on the regional raster data set GeoDEM, and storing the linear water system as skeleton lines of the SoilPolygon into a skeleton line set SKL.
The method specifically comprises the following steps:
(2-1) generating linear water system data based on the regional raster data set GeoDEM, and storing the linear water system data in the linear water system set WT;
(2-2) cutting each linear water system in the linear water system set WT by using a dendritic patch surface soildigon;
(2-3) extracting a linear water system positioned on the dendritic pattern spot surface SoilPolygon after cutting, taking the linear water system as a skeleton line of SoilPolygon, and storing the skeleton line into a skeleton line set SKL= { SKL x |x=1, 2, …, lx }, where skl x The x-th skeleton line is represented, and lx represents the number of skeleton lines. As shown in fig. 5, in the present embodiment, lx=58.
(3) And generating a dendritic pattern spot contour virtual section sampling line according to the boundary line contour SP of SoilPolygon and the skeleton line set SKL, and storing the dendritic pattern spot contour virtual section sampling line into the sampling line set SOL.
The method specifically comprises the following steps:
(3-1) extracting a boundary contour line SP of SoilPolygon, extracting a break point on the SP, and storing the break point into a boundary contour point set CP= { CP u |u=1, 2, …, U }, where cp u The U-th contour point is represented, and U represents the number of contour points;
(3-2) creating an attribute field Value for all points in the boundary contour point set CP and giving a Value g1; in this embodiment, g1=20;
(3-3) extracting all folding points of all the skeleton lines in the skeleton line set SKL, creating an attribute field Value for the folding points, giving a Value g2, g1 not equal to g2, and storing all the folding points into the skeleton line folding point set SKLP; in this embodiment, g2=0;
(3-4) merging the boundary contour point set CP and the skeleton line break point set SKLP to obtain all point sets AP, and constructing raster data TempRaster according to the attribute field Value by using a natural neighborhood interpolation method, as shown in figure 6;
(3-5) extracting attribute Value contour lines parallel to the boundary contour lines along the skeleton lines according to the distribution rule of the branch-shaped soil; as shown in fig. 7, in the present embodiment, the contour interval takes a value of 5;
(3-6) storing the generated contour line as a dendritic patch contour virtual section sampling line into a sampling line set SOL= { gl v V=1, 2, …, V }, where gl v Represents the V-th sampling line, and V represents the number of sampling lines.
(4) And searching a sampling line closest to each measured soil profile in the measured soil profile data set RP in the sampling line set SOL, storing the sampling line into the sampling line set SL_A, and forming other sampling line sets SL_B by the residual sampling lines and SP.
The method specifically comprises the following steps:
(4-1) reading any measured soil profile r from the measured soil profile data set RP i Within the sampling line set SOL, a distance r is found i Recent miningSample lines and storing a sample line set SL_A;
(4-2) cycling the step (4-1) until all measured soil profiles in the measured soil profile data set RP are traversed to obtain a complete sampling line set SL_A closest to the measured soil profile;
(4-3) removing the sampling lines in the sampling line set sl_a from the sampling line set SOL, storing the remaining sampling lines in the other sampling line set sl_b, and adding the boundary contour line SP to the other sampling line set sl_b.
(5) For each sampling line in the sampling line set SL_A, all folding points on the line are extracted as sampling points, soil occurrence layer layering is carried out for each sampling point, the thickness of each occurrence layer of each sampling point is estimated, then all sampling points of the sampling line are adopted to construct a virtual soil profile, and the virtual soil profile is stored in the virtual soil profile set SP_A.
The method specifically comprises the following steps:
(5-1) reading any one of the sampling lines gl from the sampling line set SL_A v ；
(5-2) extracting the sampling line gl v All the folding points are taken as sampling points and stored into a sampling point set sampointa= { sp v,w |w=1, 2, …, W }, where sp v,w A W-th sampling point representing a v-th sampling line, W representing the number of sampling points on the v-th sampling line;
(5-3) adding all soil generation layers for each sampling point in the sampling point set sampPointA in sequence from top to bottom according to the generation depth, namely adding the generation layers of O-A-AB-B-BC-C-R in sequence;
(5-4) searching and sampling line gl from measured soil profile data RP v Deducing the thickness of each soil occurrence layer of all sampling points in the sampling point set sampointa according to the actually measured soil profile, constructing a virtual soil profile by adopting the sampling point set sampointa, and storing the virtual soil profile into a virtual soil profile set sp_a;
(5-5) steps (5-1) - (5-4) are cyclically performed until all sampling lines within set sl_a are traversed, resulting in a complete set of virtual soil profiles sp_a.
The step (5-4) specifically comprises:
(5-4-1) each measured soil profile corresponds to a nearest sampling line, and from the measured soil profile data RP, all nearest sampling lines are searched for gl v Is stored in a temporary profile set tp= { r v,z Z=1, 2,..z }, where r v,z Representing distance sampling line gl v The nearest Z-th measured soil profile, Z representing the number of profiles; if z=1, then step (5-4-2) is performed; if Z>1, executing the step (5-4-3);
(5-4-2) assigning each occurrence layer thickness of all sampling points in the sampling point set sampPoint A to be the corresponding occurrence layer thickness of the found measured soil profile, and executing the step (5-4-6);
(5-4-3) for each section in the temporary section set TP, a sampling line gl is found v And (3) adding a sampling point closest to the profile, assigning each occurrence layer thickness of the sampling point to be the corresponding occurrence layer thickness of the profile, and storing a temporary sampling point set TSP= { sp z Z = 1,2, & Z; as shown in fig. 8;
(5-4-4) along the sampling line gl v Direction, reading any two adjacent sampling points sp from the temporary sampling point set TSP z1 And sp (sp) z2 Searching sampling point sp from sampling point set sampPoint A z1 And sp (sp) z2 All sampling points in the middle, and the thickness of each generation layer of the sampling points is calculated according to the following formula:
h u ＝Min(h z1 ,h z2 )+u*(|h z1 -h z2 |)/N
wherein h is u A certain occurrence layer thickness h representing the ith sampling point z1 And h z2 Respectively are sampling points sp z1 And sp (sp) z2 Corresponding thickness of the generating layer, N represents sp z1 And sp (sp) z2 The number of all sampling points within the interval;
(5-4-5) looping step (5-4-4) until all sample points of the set of sample points are assigned values for the occurrence layer;
(5-4-6) generating a virtual soil profile by using the sampling point set sampPoint added with the thickness of the generation layer, and storing the virtual soil profile set SP_A.
(6) And (3) extracting all folding points on each sampling line in the sampling line set SL_B to serve as sampling points, layering the soil occurrence layer for each sampling point, presuming the thickness of each occurrence layer of each sampling point, constructing a virtual soil profile by adopting all sampling points of the sampling line, and storing the virtual soil profile into the virtual soil profile set SP_B.
The method specifically comprises the following steps:
(6-1) reading any one of the sampling lines gl from the other sampling line set SL_B v ；
(6-2) extracting the sampling line gl v All break points are stored as sampling points into a sampling point set sampPointB, and all soil generation layers are added for each sampling point according to the sequence from top to bottom of the generation depth;
(6-3) deducing the thickness of each occurrence layer of all sampling points of the sampling point set sampointb according to the actually measured soil profile data RP and the virtual soil profile set sp_a, generating a virtual soil profile by adopting the sampling point set sampointb, and storing the virtual soil profile into the virtual soil profile set sp_b;
(6-4) cyclically performing steps (6-1) - (6-3) until all sample lines within sl_b have been traversed;
(6-5) reading the boundary contour line SP from the other sampling line set SL_B, and finding the measured soil profile r closest to the SP from the measured soil profile data RP c ；
(6-6) extracting all break points on Conline, storing the break points as sampling points into a sampling point set sampPointC, and adding all soil occurrence layers for each sampling point according to the occurrence depth from top to bottom;
(6-7) according to the measured soil profile r c The generation layer information of all sampling points in the sampling point set sampointc is assigned with r c And generating a virtual soil profile by adopting the sampling point set sampointc with the same generating layer thickness, and storing the virtual soil profile into a virtual soil profile set sp_b.
Wherein, the step (6-3) specifically comprises:
(6-3-1) according to the sampling line gl v Attribute field Value of (1), find all and gl v Sampling lines with the same Value and screening according to the principle of nearbyThe sampling line gl closest to the thickness of the generation layer is assigned f ；
(6-3-2) extracting the sampling line gl f Form sampling point set sampPoint f And calculates sampling point set sampPoint f The average value of the thickness of each occurrence layer of all sampling points in the sampling point set sampointb is assigned to the corresponding occurrence layer of all sampling points in the sampling point set sampointb;
(6-3-3) generating a virtual soil profile using the sampling point set sampointb, and storing the virtual soil profile set sp_b.
(7) Combining the virtual soil profile data SP_ A, SP _B and the actual measured soil profile data RP into a soil profile set SoilProfiles, selecting any occurrence layer gh (the gh can be any value O, A, AB, B, BC, C, R), and constructing an irregular triangular network TIN data set according to the elevations of the occurrence layer gh of all the soil profiles containing the occurrence layer gh in the SoilProfiles.
The method specifically comprises the following steps:
(7-1) merging the virtual soil profile set SP_ A, SP _B and the measured soil profile data RP to obtain a soil profile set SoilProfiles;
(7-2) extracting all soil sections containing the occurrence layer gh from the set SoilProfiles for any occurrence layer gh, and storing the soil sections into the set MP;
(7-3) according to the regional grid data set GeoDEM and the point coordinates of the soil profile points, obtaining the elevation information of each soil profile in the set MP;
(7-4) calculating the top depth elevation zUp and the bottom depth elevation zDown of the occurrence layer gh of each soil profile according to the elevation information of each soil profile and the thickness of each occurrence layer;
(7-5) storing the top depth elevation zUp of the occurrence layer gh of all the soil profiles in the set MP in the upper surface irregular triangle network TIN data set, and storing the bottom depth elevation zDown of the occurrence layer gh of all the soil profiles in the set MP in the lower surface irregular triangle network TIN data set.
(8) And converting the irregular triangular net TIN data set into a triangular surface to generate a three-dimensional model of the soil generation layer gh.
The method specifically comprises the following steps:
(8-1) exporting the triangle net in the irregular triangle net data set TIN to the face element class Tri;
(8-2) constructing a field ID in the triangle face element class Tri attribute table, correlating with the boundary line number, and transmitting the boundary line attribute into the attribute table of the Tri;
(8-3) converting the triangle face element class Tri into a 3D model format file and a three-dimensional model of the soil generation layer gh.
(9) And (5) circularly executing the steps (7) - (8) until all the occurrence layers are traversed (namely O, A, AB, B, BC, C, R is traversed), and obtaining the three-dimensional model of all the soil occurrence layers. As shown in fig. 9.
In the embodiment of the invention, partial GIS operation is provided based on Arcgis Engine API, and related steps can also use APIs of software such as SuperMap, arcgis Object and the like to perform corresponding GIS operation.
The embodiment also provides a three-dimensional modeling device for the generation layer of the branch-shaped distribution soil, which comprises a processor and a computer program stored on a memory and capable of running on the processor, wherein the processor realizes the method when executing the program.
The above disclosure is only a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention, which is defined by the appended claims.
Claims (11)
1. A three-dimensional modeling method for a generation layer of branch-shaped distribution soil is characterized by comprising the following steps:
(1) Loading digital soil map, regional DEM data and measured soil profile data to obtain dendritic map spot surface SoilPolygon, regional grid data set GeoDEM and measured soil profile data set RP;
(2) Based on a regional raster data set GeoDEM, extracting a linear water system in the range of a dendritic pattern speckle surface SoilPolygon, and storing the linear water system as a skeleton line of the SoilPolygon into a skeleton line set SKL;
(3) Generating a dendritic pattern spot contour virtual section sampling line according to the boundary line contour SP of SoilPolygon and the skeleton line set SKL, and storing the dendritic pattern spot contour virtual section sampling line into the sampling line set SOL;
(4) Searching a sampling line closest to each measured soil profile in the measured soil profile data set RP in the sampling line set SOL, storing the sampling line into the sampling line set SL_A, and forming other sampling line sets SL_B by the residual sampling lines and SP;
(5) For each sampling line in the sampling line set SL_A, extracting all break points on the line as sampling points, layering a soil generation layer for each sampling point, presuming the thickness of each generation layer of each sampling point, then constructing a virtual soil profile by adopting all the sampling points of the sampling line, and storing the virtual soil profile into the virtual soil profile set SP_A;
(6) Extracting all break points on each sampling line in the sampling line set SL_B as sampling points, layering soil occurrence layers for each sampling point, estimating the thickness of each occurrence layer of each sampling point, constructing a virtual soil profile by adopting all sampling points of the sampling line, and storing the virtual soil profile into the virtual soil profile set SP_B;
(7) Combining the virtual soil profile data SP_ A, SP _B and the actually measured soil profile data RP into a soil profile set SoilProfiles, selecting any occurrence layer gh, and constructing an irregular triangular network TIN data set according to the elevation of the occurrence layer gh of all the soil profiles containing the occurrence layer gh in the SoilProfiles;
(8) Converting the irregular triangular net TIN data set into a triangular surface to generate a three-dimensional model of a soil generation layer gh;
(9) And (5) circularly executing the steps (7) - (8) until all the occurrence layers are traversed, and obtaining the three-dimensional model of all the soil occurrence layers.
2. The method for three-dimensional modeling of an occurrence layer of a branch distribution soil according to claim 1, wherein: the step (1) specifically comprises:
(1-1) loading digital soil map data, and extracting dendritic pattern spot surfaces SoilPolygon in a branch shape;
(1-2) loading regional DEM data, extracting regional raster data from the regional DEM data, and storing the regional raster data into a regional raster data set GeoDEM;
(1-3) Loading measured soilSoil profile data, generating measured soil profile r i (X i ,Y i ,sl i ) And stores the measured soil profile data set rp= { r i I=1, 2, …, m }, where r i Represents the ith measured soil profile, X i And Y i Coordinate information indicating the ith measured section, m indicates the number of measured soil sections, sl i Soil formation information, sl, representing the ith measured profile i ＝{gh i,j (gc j ,zUp j ,zDown j ,h j )|j＝1,2,…,n i }，gh i,j Represents the ith soil profile and the jth soil horizon, n i Number of layers of soil formation layer, gc, representing ith soil profile j The code symbol of the generation layer which represents the j generation layer, the code rule of the code symbol is shown in the following table from top to bottom according to the generation depth; zUp j Represents the top depth, zDown, of the jth generation layer j Represents the bottom depth of the jth generation layer, h j Represents the thickness of the jth generation layer;
surface soil generation layer coding rule
(1-4) for each section in the measured soil section data set RP, searching whether all occurrence layer coding symbols are contained O, A, AB, B, BC, C, R, if the occurrence layer coding symbols are missing, adding corresponding occurrence layers in sequence from top to bottom according to the occurrence depth, and setting the thickness of the occurrence layer to be 0 until each section contains all occurrence layers corresponding to O, A, AB, B, BC, C, R.
3. The method for three-dimensional modeling of an occurrence layer of a branch distribution soil according to claim 1, wherein: the step (2) specifically comprises:
(2-1) generating linear water system data based on the regional raster data set GeoDEM, and storing the linear water system data in the linear water system set WT;
(2-2) cutting each linear water system in the linear water system set WT by using a dendritic patch surface soildigon;
(2-3) extracting a linear water system positioned on the dendritic pattern spot surface SoilPolygon after cutting, taking the linear water system as a skeleton line of SoilPolygon, and storing the skeleton line into a skeleton line set SKL= { SKL x |x=1, 2, …, lx }, where skl x The x-th skeleton line is represented, and lx represents the number of skeleton lines.
4. The method for three-dimensional modeling of an occurrence layer of a branch distribution soil according to claim 1, wherein: the step (3) specifically comprises:
(3-1) extracting a boundary contour line SP of SoilPolygon, extracting a break point on the SP, and storing the break point into a boundary contour point set CP= { CP u |u=1, 2, …, U }, where cp u The U-th contour point is represented, and U represents the number of contour points;
(3-2) creating an attribute field Value for all points in the boundary contour point set CP and giving a Value g1;
(3-3) extracting all folding points of all the skeleton lines in the skeleton line set SKL, creating an attribute field Value for the folding points, giving a Value g2, g1 not equal to g2, and storing all the folding points into the skeleton line folding point set SKLP;
(3-4) merging the boundary contour point set CP and the skeleton line break point set SKLP to obtain all point sets AP, and constructing raster data TempRaster according to an attribute field Value by using a natural neighborhood interpolation method;
(3-5) extracting attribute Value contour lines parallel to the boundary contour lines along the skeleton lines according to the distribution rule of the branch-shaped soil;
(3-6) storing the generated contour line as a dendritic patch contour virtual section sampling line into a sampling line set SOL= { gl v V=1, 2, …, V }, where gl v Represents the V-th sampling line, and V represents the number of sampling lines.
5. The method for three-dimensional modeling of an occurrence layer of a branch distribution soil according to claim 1, wherein: the step (4) specifically comprises:
(4-1) reading any measured soil profile r from the measured soil profile data set RP i In the sampling line set SWithin OL, find distance r i The nearest sampling line and storing the sampling line set SL_A;
(4-2) cycling the step (4-1) until all measured soil profiles in the measured soil profile data set RP are traversed to obtain a complete sampling line set SL_A closest to the measured soil profile;
(4-3) removing the sampling lines in the sampling line set sl_a from the sampling line set SOL, storing the remaining sampling lines in the other sampling line set sl_b, and adding the boundary contour line SP to the other sampling line set sl_b.
6. The method for three-dimensional modeling of an occurrence layer of a branch distribution soil according to claim 1, wherein: the step (5) specifically comprises:
(5-1) reading any one of the sampling lines gl from the sampling line set SL_A v ；
(5-2) extracting the sampling line gl v All the folding points are taken as sampling points and stored into a sampling point set sampointa= { sp v,w |w=1, 2, …, W }, where sp v,w A W-th sampling point representing a v-th sampling line, W representing the number of sampling points on the v-th sampling line;
(5-3) adding all soil occurrence layers for each sampling point in the sampling point set sampointa according to the occurrence depth from top to bottom;
(5-4) searching and sampling line gl from measured soil profile data RP v Deducing the thickness of each soil occurrence layer of all sampling points in the sampling point set sampointa according to the actually measured soil profile, constructing a virtual soil profile by adopting the sampling point set sampointa, and storing the virtual soil profile into a virtual soil profile set sp_a;
(5-5) steps (5-1) - (5-4) are cyclically performed until all sampling lines within set sl_a are traversed, resulting in a complete set of virtual soil profiles sp_a.
7. The method for three-dimensional modeling of an occurrence layer of a dendrimer-distributed soil according to claim 6, wherein: the step (5-4) specifically comprises:
(5-4-1) each measured soil profileCorresponding to a nearest sampling line, searching all nearest sampling lines as gl from measured soil profile data RP v Is stored in a temporary profile set tp= { r v,z Z=1, 2,..z }, where r v,z Representing distance sampling line gl v The nearest Z-th measured soil profile, Z representing the number of profiles; if z=1, then step (5-4-2) is performed; if Z>1, executing the step (5-4-3);
(5-4-2) assigning each occurrence layer thickness of all sampling points in the sampling point set sampPoint A to be the corresponding occurrence layer thickness of the found measured soil profile, and executing the step (5-4-6);
(5-4-3) for each section in the temporary section set TP, a sampling line gl is found v And (3) adding a sampling point closest to the profile, assigning each occurrence layer thickness of the sampling point to be the corresponding occurrence layer thickness of the profile, and storing a temporary sampling point set TSP= { sp z |z＝1,2,...,Z}；
(5-4-4) along the sampling line gl v Direction, reading any two adjacent sampling points sp from the temporary sampling point set TSP z1 And sp (sp) z2 Searching sampling point sp from sampling point set sampPoint A z1 And sp (sp) z2 All sampling points in the middle, and the thickness of each generation layer of the sampling points is calculated according to the following formula:
h u ＝Min(h z1 ,h z2 )+u*(|h z1 -h z2 |)/N
wherein h is u A certain occurrence layer thickness h representing the ith sampling point z1 And h z2 Respectively are sampling points sp z1 And sp (sp) z2 Corresponding thickness of the generating layer, N represents sp z1 And sp (sp) z2 The number of all sampling points within the interval;
(5-4-5) looping step (5-4-4) until all sample points of the set of sample points are assigned values for the occurrence layer;
(5-4-6) generating a virtual soil profile by using the sampling point set sampPoint added with the thickness of the generation layer, and storing the virtual soil profile set SP_A.
8. The method for three-dimensional modeling of an occurrence layer of a branch distribution soil according to claim 1, wherein: the step (6) specifically comprises:
(6-1) reading any one of the sampling lines gl from the other sampling line set SL_B v ；
(6-2) extracting the sampling line gl v All break points are stored as sampling points into a sampling point set sampPointB, and all soil generation layers are added for each sampling point according to the sequence from top to bottom of the generation depth;
(6-3) deducing the thickness of each occurrence layer of all sampling points of the sampling point set sampointb according to the actually measured soil profile data RP and the virtual soil profile set sp_a, generating a virtual soil profile by adopting the sampling point set sampointb, and storing the virtual soil profile into the virtual soil profile set sp_b;
(6-4) cyclically performing steps (6-1) - (6-3) until all sample lines within sl_b have been traversed;
(6-5) reading the boundary contour line SP from the other sampling line set SL_B, and finding the measured soil profile r closest to the SP from the measured soil profile data RP c ；
(6-6) extracting all break points on Conline, storing the break points as sampling points into a sampling point set sampPointC, and adding all soil occurrence layers for each sampling point according to the occurrence depth from top to bottom;
(6-7) according to the measured soil profile r c The generation layer information of all sampling points in the sampling point set sampointc is assigned with r c And generating a virtual soil profile by adopting the sampling point set sampointc with the same generating layer thickness, and storing the virtual soil profile into a virtual soil profile set sp_b.
9. The method for three-dimensional modeling of an occurrence layer of a dendrimer-distributed soil according to claim 8, wherein: the step (6-3) specifically comprises:
(6-3-1) according to the sampling line gl v Attribute field Value of (1), find all and gl v Sampling lines with the same Value are selected according to the nearest sampling line gl with assigned occurrence layer thickness f ；
(6-3-2) extracting the sampling line gl f Form sampling point set sampPoint f And calculates sampling point set sampPoint f The average value of the thickness of each occurrence layer of all sampling points in the sampling point set sampointb is assigned to the corresponding occurrence layer of all sampling points in the sampling point set sampointb;
(6-3-3) generating a virtual soil profile using the sampling point set sampointb, and storing the virtual soil profile set sp_b.
10. The method for three-dimensional modeling of an occurrence layer of a branch distribution soil according to claim 1, wherein: the step (7) specifically comprises:
(7-1) merging the virtual soil profile set SP_ A, SP _B and the measured soil profile data RP to obtain a soil profile set SoilProfiles;
(7-2) extracting all soil sections containing the occurrence layer gh from the set SoilProfiles for any occurrence layer gh, and storing the soil sections into the set MP;
(7-3) according to the regional grid data set GeoDEM and the point coordinates of the soil profile points, obtaining the elevation information of each soil profile in the set MP;
(7-4) calculating the top depth elevation zUp and the bottom depth elevation zDown of the occurrence layer gh of each soil profile according to the elevation information of each soil profile and the thickness of each occurrence layer;
(7-5) storing the top depth elevation zUp of the occurrence layer gh of all the soil profiles in the set MP in the upper surface irregular triangle network TIN data set, and storing the bottom depth elevation zDown of the occurrence layer gh of all the soil profiles in the set MP in the lower surface irregular triangle network TIN data set.
11. A three-dimensional modeling apparatus for a generation layer of a gably distributed soil, comprising a processor and a computer program stored on a memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 10 when executing the program.
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