CN114565732A

CN114565732A - Three-dimensional modeling method and device for occurrence layer of dendritic distribution soil

Info

Publication number: CN114565732A
Application number: CN202210201697.3A
Authority: CN
Inventors: 解宪丽; 黄键初; 李安波; 沈言根
Original assignee: Nanjing Normal University; Institute of Soil Science of CAS
Current assignee: Nanjing Normal University; Institute of Soil Science of CAS
Priority date: 2022-03-02
Filing date: 2022-03-02
Publication date: 2022-05-31
Anticipated expiration: 2042-03-02
Also published as: CN114565732B

Abstract

The invention discloses a three-dimensional modeling method and a three-dimensional modeling device for a generation layer of dendritic distribution soil, wherein the method comprises the following steps: acquiring a dendritic pattern spot surface, area grid data and an actually measured soil profile data set RP; extracting linear water systems in the range of the pattern spot surface to form a skeleton line set SKL; generating a dendritic pattern spot profile virtual section sampling line according to the boundary line profile SP and SKL of the pattern spot surface, and storing the sampling line in a sampling line set SOL; searching a sampling line closest to each section in RP in the SOL, storing the sampling line into a set SL _ A, and forming a set SL _ B by using the remaining lines and the SP; layering soil occurrence layers of each sampling point of each sampling line in the SL _ A, SL _ B respectively, estimating the thicknesses of the occurrence layers, and correspondingly forming a virtual soil profile set SP _ A, SP _ B according to the SL _ A, SL _ B; and combining the SP _ A, SP _ B and the RP, constructing a triangular surface according to the elevations of all the generation layers containing the soil profiles of the same generation layer in the combined set, and generating a three-dimensional model of the soil generation layer. The method is suitable for large-range rough three-dimensional modeling of the generation layer.

Description

Three-dimensional modeling method and device for occurrence layer of dendritic distribution soil

Technical Field

The invention relates to a geographic information technology, in particular to a three-dimensional modeling method and a three-dimensional modeling device for a generation layer of dendritic distribution soil.

Background

The dendritic distribution is a common spatial distribution pattern of soil. In a valley development area, water systems mostly extend in a tree shape, similar soil distribution rules are repeated among branches of each water system from a hill top to a valley bottom, and the distribution of the soil distribution is represented by pattern spots on a soil map, namely dendritic 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, due to various reasons such as difficulty in field and field investigations and limited expenses, the acquired soil profile data is limited, and it is difficult to efficiently perform three-dimensional modeling of a soil occurrence layer.

Disclosure of Invention

The purpose of the invention is as follows: aiming at the problems in the prior art, the invention provides a large-range rough three-dimensional modeling method and device for a generation layer of dendritic distribution soil.

The technical scheme is as follows: the three-dimensional modeling method for the occurrence layer of the dendritic distribution soil comprises the following steps:

(1) loading a digital soil map, regional DEM data and actually measured soil profile data to obtain a tree-like map patch surface SoilPolygon, a regional grid data set GeoDEM and an actually measured soil profile data set RP;

(2) on the basis of a regional raster data set GeoDEM, extracting a linear water system in a SoilPolygon range of a dendritic pattern patch surface to serve as a skeleton line of SoilPolygon, and storing the skeleton line into a skeleton line set SKL;

(3) generating a dendritic pattern spot outline virtual section sampling line according to the boundary line outline SP of the SoilPolygon and the skeleton line set SKL, and storing the sampling line in a sampling line set SOL;

(4) searching a sampling line closest to each actually measured soil profile in the actually measured soil profile data set RP in the sampling line set SOL, storing the sampling line set SL _ A, and forming other sampling line sets SL _ B by the rest sampling lines and SP;

(5) for each sampling line in the sampling line set SL _ A, extracting all folding points on the line as sampling points, layering soil occurrence layers for each sampling point, presuming the thickness of each occurrence layer of each sampling point, then constructing a virtual soil profile by adopting all sampling points of the sampling line, and storing the virtual soil profile into a virtual soil profile set SP _ A;

(6) for each sampling line in the sampling line set SL _ B, extracting all folding points on the line 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 a 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 SoilProfile, selecting any occurrence layer gh, and constructing an irregular triangulation network TIN data set according to the elevations of the occurrence layers gh of all soil profiles containing the occurrence layer gh in the SoilProfile;

(8) converting the irregular triangular network TIN data set into a triangular surface to generate a three-dimensional model of a soil occurrence layer gh;

(9) and (5) circularly executing the steps (7) - (8) until all the occurrence layers are traversed to obtain three-dimensional models of all the soil occurrence layers.

Further, the step (1) specifically comprises:

(1-1) loading digital soil map data, and extracting a dendritic pattern patch surface SoilPolygon in a branch shape from the digital soil map data;

(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 the actually measured soil profile data to generate an actually measured soil profile r_i(X_i,Y_i,sl_i) And storing the measured soil profile data set RP ═ r_i1,2, …, m, where r is_iRepresents the ith measured soil profile, X_iAnd Y_iCoordinate information of the ith measured profile is shown, m is the number of measured soil profiles, sl is_iInformation of soil formation layer, 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,jDenotes the ith soil section, the jth soil occurrence layer, n_iNumber of soil-growing layers of i-th soil section, gc_jThe generation layer coding symbols of the jth generation layer are represented, and the coding rules of the coding symbols are shown in the following table from top to bottom according to the generation depth; zUp_jDenotes the top depth of the jth generation layer, zDown_jDenotes the bottom depth, h, of the jth generation layer_jRepresents the thickness of the jth generation layer;

surface soil stratum code rule

(1-4) for each section in the actually measured soil section data set RP, searching whether all occurrence layer coding symbols of O, A, AB, B, BC, C and R are contained, if the occurrence layer coding symbols are lacked, adding corresponding occurrence layers according to the occurrence depth from top to bottom, 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 and R.

Further, the step (2) specifically comprises:

(2-1) generating linear water system data based on the region 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 the SoilPolygon on the dendritic pattern patch surface;

(2-3) extracting a linear water system positioned on the SoilPolygon on the dendritic pattern spot surface after cutting, taking the linear water system as a skeleton line of the SoilPolygon, and storing the skeleton line set SKL (SKL) ═ in a skeleton line set_x1,2, …, lx, wherein skl_xThe x-th skeleton line is shown, and lx represents the number of skeleton lines.

Further, the step (3) specifically comprises:

(3-1) extracting the boundary contour line SP of the SoilPolygon, extracting break points on the SP, and storing the break points in a boundary contour point set CP ═ CP_u1,2, …, U }, where cp_uRepresenting the U-th contour point, wherein 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 g 1;

(3-3) extracting all break points of all skeleton lines in the skeleton line set SKL, creating an attribute field Value for the break points, giving a Value g2, wherein g1 is not equal to g2, and storing all break points into a skeleton line break 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 the attribute field Value by using a natural neighborhood interpolation method;

(3-5) according to the distribution rule of the dendritic soil, extracting an attribute Value contour line parallel to the boundary contour line outwards along the skeleton line;

(3-6) storing the generated contour lines as the virtual cross-section sampling lines of the dendritic speckle contour into a sampling line set SOL ═ gl ═ g_v1,2, …, V }, where gl_vThe V-th sampling line is shown, and V represents the number of sampling lines.

Further, the step (4) specifically comprises:

(4-1) reading any measured soil profile r from the measured soil profile data set RP_iFinding the distance r within the set SOL of sampling lines_iThe nearest sampling line is stored in a sampling line set SL _ A;

(4-2) the step (4-1) is circulated until all the actually measured soil profiles in the actually measured soil profile data set RP are traversed to obtain a complete sampling line set SL _ A closest to the actually measured soil profiles;

and (4-3) removing the sampling lines in the sampling line set SL _ A from the sampling line set SOL, storing the rest sampling lines into other sampling line sets SL _ B, and adding the boundary contour line SP into the other sampling line sets SL _ B.

Further, the step (5) specifically comprises:

(5-1) reading any sampling line gl from the set SL _ A of sampling lines_v；

(5-2) extraction of sampling line gl_vAll the break points are taken as sampling points and stored into a sampling point set SampPointA ═ sp_v,w1,2, …, W, where sp is_v,wDenotes the v thThe W sampling point of each sampling line, wherein W represents the number of sampling points on the v sampling line;

(5-3) adding all soil occurrence layers to each sampling point in the sampling point set SampPointA from top to bottom according to the occurrence depth;

(5-4) searching and sampling line gl from actually measured soil profile data RP_vDeducing the thickness of each soil generation layer of all sampling points in the sampling point set SampPointA according to the actually measured soil profile, constructing a virtual soil profile by adopting the sampling point set SampPointA, and storing the virtual soil profile into a virtual soil profile set SP _ A;

and (5-5) circularly executing the steps (5-1) - (5-4) until all sampling lines in the set SL _ A are traversed to obtain a complete virtual soil profile set SP _ A.

Further, the step (5-4) specifically comprises:

(5-4-1) each actually measured soil profile corresponds to a nearest sampling line, and all nearest sampling lines are searched for gl from actually measured soil profile data RP_vAnd a temporary profile set TP ═ r is stored in the measured soil profile of (a)_v,z1, 2., Z }, where r is_v,zIndicating the distance sampling line gl_vThe nearest Z-th actually measured soil profile, wherein Z represents the number of the profiles; if Z is 1, performing step (5-4-2); if Z is>1, executing the step (5-4-3);

(5-4-2) assigning the thickness of each occurrence layer of all sampling points in the sampling point set SampPoint A to the thickness of the corresponding occurrence layer of the found actually-measured soil profile, and executing the step (5-4-6);

(5-4-3) for each profile in the temporary set of profiles TP, find the sampling line gl_vThe sampling point closest to the profile is selected, the generation layer thickness of the sampling point is assigned to be the corresponding generation layer thickness of the profile, and a temporary sampling point set TSP ═ sp is stored_z|z＝1,2,...,Z}；

(5-4-4) along the sampling line gl_vDirection, reading any two adjacent sampling points sp from the temporary sampling point set TSP_z1And sp_z2Finding a sampling point sp from the set of sampling points sampPoint A_z1And sp_z2And calculating the thickness of each occurrence layer of the sampling points according to the following formula:

h_u＝Min(h_z1,h_z2)+u*(|h_z1-h_z2|)/N

wherein h is_uIndicates a certain occurrence of layer thickness, h, at the u-th sampling point_z1And h_z2Are respectively sampling points sp_z1And sp_z2Corresponding generation layer thickness, N denotes sp_z1And sp_z2The number of all sampling points in the interval;

(5-4-5) circulating the step (5-4-4) until the value of the generation layer of all sampling points of the sampling point set is assigned;

(5-4-6) generating a virtual soil profile by adopting the sampling point set SampPoint added with the thickness of the generating layer, and storing the virtual soil profile into the virtual soil profile set SP _ A.

Further, the step (6) specifically comprises:

(6-1) reading any sampling line gl from the other sampling line set SL _ B_v；

(6-2) extraction of sampling line gl_vAll the folding points are used as sampling points and stored into a sampling point set SampPointB, and all soil occurrence layers are added to each sampling point from top to bottom according to the occurrence depth;

(6-3) deducing the thickness of each occurrence layer of all sampling points of the sampling point set SampPointB 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 SampPointB, and storing the virtual soil profile into the virtual soil profile set SP _ B;

(6-4) executing the steps (6-1) - (6-3) circularly until all sampling lines in the SL _ B are traversed;

(6-5) reading the boundary contour line SP from the other sampling line set SL _ B, and finding the actually measured soil profile r closest to the SP from the actually measured soil profile data RP_c；

(6-6) extracting all broken points on ConLine, storing the broken points as sampling points into a sampling point set SampPointC, and adding all soil occurrence layers to each sampling point from top to bottom according to occurrence depth;

(6-7) based on the measured soilSoil profile r_cThe generation layers of all the sampling points in the sampling point set SampPointC are given to r_cAnd generating a virtual soil profile by adopting the sampling point set SampPointC according to 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 comprises:

(6-3-1) according to the sampling line gl_vValue of, find all and gl_vSampling lines with the same Value are screened out according to the principle of proximity, and the sampling line gl which is closest to the sampling line and has assigned Value of the thickness of the generating layer is screened out_f；

(6-3-2) extraction of sampling line gl_fTo form a sampling point set SampPoint_fAnd calculating a sampling point set SampPoint_fEvaluating the average value of the thickness of each generation layer of all the sampling points in the sampling point set SampPointB to the corresponding generation layer of all the sampling points;

(6-3-3) generating a virtual soil profile by adopting the sampling point set SampPointB, and storing the virtual soil profile into a virtual soil profile set SP _ B.

Further, the step (7) specifically comprises:

(7-1) combining the virtual soil profile set SP _ A, SP _ B and the actually measured soil profile data RP to obtain a soil profile set SoilProfiles;

(7-2) for any occurrence layer gh, extracting all soil profiles containing the occurrence layer gh in the set SoilProfiles, and storing the soil profiles into a set MP;

(7-3) according to the regional grid data set GeoDEM and the coordinates of the point positions of the soil profiles, obtaining 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 heights zUp of the occurrence layers gh of all the soil profiles in the set MP into the upper surface irregular triangular net TIN data set, and storing the bottom depth heights zDown of the occurrence layers gh of all the soil profiles in the set MP into the lower surface irregular triangular net TIN data set.

The three-dimensional modeling device for the generation layer of the dendritic distribution soil 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.

Has the advantages that: compared with the prior art, the invention has the following remarkable advantages: the method can be suitable for large-range and rough three-dimensional modeling of the 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 as used in this example;

FIG. 2 is regional DEM data as employed in the present embodiment;

FIG. 3 is a flow chart of a three-dimensional modeling method for a generation layer of a dendritic distribution soil provided by the invention;

FIG. 4 is a dendrogram spot extracted in the present example;

FIG. 5 shows skeleton lines extracted in the present example;

FIG. 6 is the grid data interpolated from the contour and the skeleton point set in the present embodiment;

FIG. 7 is a set of sampling lines parallel to the boundary of the contour in the present embodiment;

FIG. 8 is a schematic diagram illustrating the thickness inference of the occurrence layer of the virtual soil profile in the present embodiment;

fig. 9 shows a three-dimensional model of a soil formation layer created in the present example ((a) a global three-dimensional model (a box is a local enlargement area), (b) a local enlargement effect).

Detailed Description

In the following, the technical solution of the present invention is further described in detail, and in this embodiment, 1: 100 ten thousand Jiangxi soil maps, 30m resolution Jiangxi DEM data and a soil profile description record obtained by field sampling and literature data arrangement are taken as experimental data, and are shown in figures 1 and 2. The following further description is provided by describing a specific embodiment in conjunction with the accompanying drawings.

As shown in fig. 3, the present embodiment provides a three-dimensional modeling method for a generation layer of a dendritic distribution soil, including:

(1) and loading the digital soil map, the regional DEM data and the actually measured soil profile data to obtain a SoilPolygon surface of the tree-shaped map, 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 a dendritic pattern patch surface SoilPolygon in a branch shape from the digital soil map data, as shown in a figure 4;

(1-3) loading the actually measured soil profile data to generate an actually measured soil profile r_i(X_i,Y_i,sl_i) And storing the measured soil profile data set RP ═ r_i1,2, …, m, as shown in table 1, where r is_iRepresents the ith measured soil profile, X_iAnd Y_iCoordinate information of the ith measured profile is shown, m is the number of measured soil profiles, sl is_iInformation of soil formation layer showing the ith measured section, sl, as shown in Table 2_i＝{gh_i,j(gc_j,zUp_j,zDown_j,h_j)|j＝1,2,…,n_i}，gh_i,jDenotes the ith soil section, the jth soil occurrence layer, n_iNumber of soil-growing layers of i-th soil section, gc_jThe generation layer coding symbol of the jth generation layer is represented, and the coding rule of the coding symbol is shown in a table 3 from top to bottom according to the generation depth; zUp_jDenotes the top depth of the jth generation layer, zDown_jDenotes the bottom depth, h, of the jth generation layer_jRepresents the thickness of the jth generation layer;

TABLE 1 actually measured soil profile data sheet

Section numbering	Abscissa of the circle	Ordinate of the curve	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 symbols	Depth/cm of top	Depth of bottom/cm	Thickness/cm
					SS001	A
	0	10	10
				SS001	B	10	75	65
SS001	C	75	100						25
				SS002	A		0	16		16
SS002	B	16	100						84
				...	...	...	...	...
SS230	A	0	5						5
				SS230	B	5	30	25

TABLE 3 soil occurrence layer coding rules

(1-4) searching whether all generation layer coding symbols of O, A, AB, B, BC, C and R are contained in each section in the actually measured soil section data set RP, if the generation layer coding symbols are not contained, adding corresponding generation layers according to the generation depth from top to bottom, and setting the thickness of the generation layer to be 0 until each section contains all generation layers corresponding to O, A, AB, B, BC, C and R.

For example, in the section SS001 in table 2, only A, B, C three generation layers are included, and four generation layers of O, AB, BC, and R are added, and the thickness of each generation layer is set to 0. The set of measured soil profile data RP after treatment is shown in table 4.

TABLE 4 actual measurement soil profile data after filling of the occurrence layer

Section numbering	Generation layer symbols	Depth/cm of top	Depth/cm of bottom	Thickness/cm
					SS001	O
	0	0	0
				SS001	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) Based on the region grid data set GeoDEM, linear water systems in the range of SoilPolygon of the dendritic pattern patch surface are extracted and stored in a skeleton line set SKL as skeleton lines of SoilPolygon.

The method specifically comprises the following steps:

(2-3) extracting a linear water system positioned on the SoilPolygon on the dendritic pattern spot surface after cutting, taking the linear water system as a skeleton line of the SoilPolygon, and storing the skeleton line set SKL (SKL) ═ in a skeleton line set_x1,2, …, lx, wherein skl_xThe x-th skeleton line is shown, and lx represents the number of skeleton lines. As shown in fig. 5, in the present embodiment, lx is 58.

(3) And generating a dendritic pattern spot outline virtual section sampling line according to the boundary line outline SP of the SoilPolygon and the skeleton line set SKL, and storing the sampling line in a sampling line set SOL.

The method specifically comprises the following steps:

(3-2) creating an attribute field Value for all points in the boundary contour point set CP, and giving a Value g 1; in the present embodiment, g1 ═ 20;

(3-3) extracting all break points of all skeleton lines in the skeleton line set SKL, creating an attribute field Value for the break points, giving a Value g2, wherein g1 is not equal to g2, and storing all break points into a skeleton line break point set SKLP; in the present embodiment, g2 is 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 FIG. 6;

(3-5) according to the distribution rule of the dendritic soil, extracting an attribute Value contour line parallel to the boundary contour line outwards along the skeleton line; as shown in fig. 7, in the present embodiment, the contour interval is 5;

(4) And searching a sampling line closest to each actually measured soil profile in the actually measured soil profile data set RP in the sampling line set SOL, storing 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:

(4-3) removing the sampling lines in the sampling line set SL _ A from the sampling line set SOL, storing the rest sampling lines into other sampling line sets SL _ B, and adding the boundary contour line SP into other sampling line sets 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 on each sampling point, the thickness of each occurrence layer of each sampling point is presumed, then a virtual soil profile is constructed by adopting all sampling points of the sampling line, 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 sampling line gl from the set SL _ A of sampling lines_v；

(5-2) extraction of sampling line gl_vAll the folding points are taken as sampling points and stored into a sampling point set SampPointA ═ sp_v,w1,2, …, W, where sp is_v,wW represents sampling point of v sampling line, W represents sampling on v sampling lineThe number of sampling points;

(5-3) adding all soil occurrence layers to each sampling point in the sampling point set SampPointA from top to bottom according to the occurrence depth, namely sequentially adding the occurrence layers of O-A-AB-B-BC-C-R;

The step (5-4) specifically comprises the following steps:

(5-4-1) each actually measured soil profile corresponds to a nearest sampling line, and all nearest sampling lines are searched for gl from actually measured soil profile data RP_vThe measured soil profile of (a), and a temporary profile set TP ═ r is stored in the measured soil profile of (a)_v,z1, 2., Z }, where r is_v,zIndicating a distance sampling line gl_vThe nearest Z-th actually measured soil profile, wherein Z represents the number of the profiles; if Z is 1, performing step (5-4-2); if Z is>1, executing the step (5-4-3);

(5-4-3) for each profile in the temporary set of profiles TP, find the sampling line gl_vThe sampling point closest to the profile is selected, the generation layer thickness of the sampling point is assigned to be the corresponding generation layer thickness of the profile, and a temporary sampling point set TSP ═ sp is stored_z1, 2.., Z }; as shown in fig. 8;

(5-4-4) along the sampling line gl_vDirection, reading any two adjacent sampling points sp from the temporary sampling point set TSP_z1And sp_z2Finding a sampling point sp from the set of sampling points sampPoint A_z1And sp_z2All samples taken in betweenThe respective occurrence layer thicknesses of these sampling points are calculated according to the following formula:

h_u＝Min(h_z1,h_z2)+u*(|h_z1-h_z2|)/N

(5-4-5) the step (5-4-4) is circulated until the values of the generation layers of all sampling points of the sampling point set are assigned;

(6) And for each sampling line in the sampling line set SL _ B, extracting all folding points on the line 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.

The method specifically comprises the following steps:

(6-1) reading any sampling line gl from the other sampling line set SL _ B_v；

(6-5) reading the boundary contour line SP from the other sampling line set SL _ B, and finding the boundary contour line SP closest to the SP from the actually measured soil profile data RPActually measured soil profile r_c；

(6-7) measuring the soil profile r according to the measured soil profile_cThe generation layers of all the sampling points in the sampling point set SampPointC are given to r_cAnd generating a virtual soil profile by adopting the sampling point set SampPointC according to 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 the following steps:

(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 (the gh can be any value of O, A, AB, B, BC, C and R), and constructing an irregular triangular network TIN data set according to the elevations of the occurrence layer gh of all soil profiles containing the occurrence layer gh in the SoilProfiles.

The method specifically comprises the following steps:

(8) And converting the irregular triangular network TIN data set into a triangular surface to generate a three-dimensional model of the soil occurrence layer gh.

The method comprises the following steps:

(8-1) exporting the triangulation in the irregular triangulation network data set TIN to a surface element class Tri;

(8-2) constructing a field ID in the attribute table of the triangle surface element class Tri, associating the field ID with the boundary line number, and transmitting the boundary line attribute to the attribute table of the Tri;

(8-3) converting the triangular surface element Tri into a three-dimensional model of a 3D model format file and a soil occurrence 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 and R are traversed), and obtaining three-dimensional models of all the soil occurrence layers. As shown in fig. 9.

In the embodiment of the invention, partial GIS operation is provided based on the Arcgis Engine API, and related steps can also use the APIs of software such as SuperMap, Arcgis Object and the like to carry out corresponding GIS operation.

The embodiment also provides a three-dimensional modeling device for the occurrence layer of the dendriform 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 executes the program to realize the method.

While the invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not to be limited to the disclosed embodiment, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

Claims

1. A three-dimensional modeling method for a generation layer of dendritic distribution soil is characterized by comprising the following steps:

(5) for each sampling line in the sampling line set SL _ A, extracting all folding points on the line as sampling points, layering soil occurrence layers for each sampling point, presuming the thickness of each occurrence layer of each sampling point, then constructing a virtual soil profile by adopting all sampling points of the sampling line, and storing the virtual soil profile set SP _ A;

(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 elevations of the occurrence layers gh of all soil profiles containing the occurrence layer gh in the SoilProfiles;

(8) converting the irregular triangulation network TIN data set into a triangular surface to generate a three-dimensional model of a soil occurrence layer gh;

2. The method for three-dimensional modeling of a generation layer of arborescent distribution soil according to claim 1, wherein: the step (1) specifically comprises the following steps:

(1-3) loading the actually measured soil profile data to generate an actually measured soil profile r_i(X_i,Y_i,sl_i) And storing the measured soil profile data set RP ═ r_i1,2, …, m, where r is_iRepresents the ith measured soil profile, X_iAnd Y_iCoordinate information of the ith measured profile is shown, m is the number of measured soil profiles, sl is_iInformation of soil formation layer, 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,jRepresents the ith soil section, the jth soil occurrence layer, n_iNumber of soil-growing layers of i-th soil section, gc_jThe generation layer coding symbols of the jth generation layer are represented, and the coding rules of the coding symbols are shown in the following table from top to bottom according to the generation depth; zUp_jDenotes the top depth of the jth generation layer, zDown_jDenotes the bottom depth, h, of the jth generation layer_jRepresents the thickness of the jth generation layer;

coding rule of surface soil occurrence layer

3. The method for three-dimensional modeling of a generation layer of arborescent distribution soil according to claim 1, wherein: the step (2) specifically comprises the following steps:

(2-2) cutting each linear water system in the linear water system set WT by utilizing the SoilPolygon on the dendritic pattern spot surface;

(2-3) extracting a linear water system positioned on the SoilPolygon on the dendritic pattern spot surface after cutting, taking the linear water system as a skeleton line of the SoilPolygon, and storing the skeleton line set SKL (SKL) ═ in a skeleton line set_x1,2, …, lx }, wherein skl_xThe x-th skeleton line is shown, and lx represents the number of skeleton lines.

4. The method for three-dimensional modeling of a generation layer of arborescent distribution soil according to claim 1, wherein: the step (3) specifically comprises the following steps:

(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 the tempRaster of grid data according to the attribute field Value by using a natural neighborhood interpolation method;

5. The method for three-dimensional modeling of a generative layer of arborescent distribution soil as recited in claim 1, wherein: the step (4) specifically comprises the following steps:

6. The method for three-dimensional modeling of a generative layer of arborescent distribution soil as recited in claim 1, wherein: the step (5) specifically comprises the following steps:

(5-1) reading any sampling line gl from the set SL _ A of sampling lines_v；

(5-2) extraction of sampling line gl_vAll the break points are taken as sampling points and stored into a sampling point set SampPointA ═ sp_v,w1,2, …, W, where sp is_v,wRepresenting the W sampling point of the v sampling line, wherein W represents the number of sampling points on the v sampling line;

7. The method for three-dimensional modeling of a generation layer of arborescent distribution soil according to claim 6, wherein: the step (5-4) specifically comprises the following steps:

(5-4-1) each actually measured soil profile corresponds to a nearest sampling line, and all nearest sampling lines are searched for gl from actually measured soil profile data RP_vAnd a temporary profile set TP ═ r is stored in the measured soil profile of (a)_v,z1,2, Z, wherein r_v,zIndicating a distance sampling line gl_vThe nearest Z-th actually measured soil profile, wherein Z represents the number of the profiles; if Z is 1, performing step (5-4-2); if Z is>1, executing the step (5-4-3);

(5-4-3) for each profile in the temporary set of profiles TP, find the sampling line gl_vThe sampling point closest to the section is arranged upwards, the thickness of each generation layer of the sampling point is assigned to be the corresponding generation layer thickness of the section, and a temporary sampling point set TSP { sp ═ is stored_z|z＝1,2,...,Z}；

h_u＝Min(h_z1,h_z2)+u*(|h_z1-h_z2|)/N

8. The method for three-dimensional modeling of a generation layer of arborescent distribution soil according to claim 1, wherein: the step (6) specifically comprises the following steps:

(6-1) reading any sampling line gl from the other sampling line set SL _ B_v；

(6-7) measuring the soil profile r based on the measured soil profile_cThe generation layers of all the sampling points in the sampling point set SampPointC are given to r_cAnd generating a virtual soil profile by adopting the sampling point set SampPointC according to 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 a generation layer of dendrite distribution soil of claim 8, wherein: the step (6-3) specifically comprises the following steps:

(6-3-2) extraction of sampling line gl_fForming a sampling point set of SampPoint_fAnd calculating a sampling point set SampPoint_fEvaluating the average value of the thickness of each generation layer of all the sampling points in the sampling point set SampPointB to the corresponding generation layer of all the sampling points;

10. The method for three-dimensional modeling of a generation layer of arborescent distribution soil according to claim 1, wherein: the step (7) specifically comprises:

11. A device for three-dimensional modeling of a generation layer of a dendrite distribution 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 1-10 when executing the program.