Method for constructing TIN_DDM buffer surface rapidly by key sampling point efficient determination

Abstract

The application provides a TIN_DDM buffer surface fast construction method for efficient determination of key sampling points, and belongs to the technical field of seabed terrain surface buffer analysis and application. The application can realize fast construction of a TIN_DDM buffer surface construction database through critical rolling ball radius trial iteration and spatial data block index modeling oriented to fast solution of key sampling point candidate points, and further achieves the purpose of single TIN_DDM buffer surface fast construction.

Description

Technical Field

[0001] This invention belongs to the field of seabed topographic surface buffer analysis and application technology, and relates to a method for rapidly constructing a TIN_DDM buffer surface for efficient determination of key sampling points. Background Technology

[0002] Digital Depth Model (DDM) is a commonly used numerical 3D model that reflects changes in water depth. DDM buffer surface analysis is an important function of GIS spatial analysis, used to determine the spatial proximity of seabed topography and to analyze seabed topographic features. In recent years, with the world's further exploration of the ocean, seabed buffer surface analysis has provided an important theoretical basis for seabed resource exploration and the safety of underwater vehicles.

[0003] Current methods for constructing 3D buffer volumes are mainly divided into vector methods and raster methods. Vector methods offer higher accuracy in buffer volume construction, but are limited by the complex calculations of topological relationships and 3D spatial geometric intersections, resulting in relatively high time complexity and difficulties in storing and representing the results. Compared to vector methods, raster methods offer advantages such as simple construction principles and high operational efficiency, and are widely used in the construction and feature analysis of 3D geographic features. To further accelerate the efficiency of these methods and meet the efficiency requirements of current commercial 3D GIS software, some researchers have improved the 3D Euclidean distance transformation method within the raster approach. This improvement primarily reduces the number of sampling points required for distance calculation, thereby lowering the time complexity of the 3D Euclidean distance transformation method to O(n^2). 3 In addition, some scholars, considering the geometric characteristics of single-valued surfaces in DDM, have proposed a raster generation method for GRID_DDM single-valued surface buffer surfaces based on a rolling sphere model, with a time complexity of O(nr). 2 However, to further improve the efficiency of generating TIN_DDM buffer surfaces using the rolling ball model, a rolling ball acceleration optimization method for rapid TIN_DDM buffer surface construction is proposed, based on quantitative control of the overall model accuracy. This method can achieve buffer surface generation with a time complexity of O(n). However, considering the time complexity of a single buffer surface construction using an associated data linked list, which is O(n...), further improvements are needed. 2 Therefore, the generation method based on the rolling sphere model can meet the efficiency requirements of DDM buffer surface analysis applications that focus on the same spatial range, such as submarine engineering construction planning, but it cannot meet the efficiency requirements of generating dynamic buffer surfaces that change in real time, such as terrain matching navigation for underwater vehicles. Summary of the Invention

[0004] To address the limitations of low efficiency in generating associated data linked lists, which cannot match the efficiency of DDM buffer surface modeling and thus fails to meet the application requirements for efficient analysis of dynamic DDM buffer surfaces in time-varying domains, this invention provides a rapid construction method for TIN_DDM buffer surfaces based on a rolling ball model, to meet the application needs of current commercial 3D GIS software.

[0005] The technical solution adopted by the present invention to achieve the above objectives is as follows:

[0006] A fast construction method for TIN_DDM buffer surface for efficient determination of key sampling points includes the following steps:

[0007] (1) Set the maximum buffer radius r of the TIN_DDM buffer surface. max With index space data block S fhg (f, h, g represent S respectively) fhg The row width (RowWidth), column width (ColumnWidth), and vertical height (VerticalHeight) of the row number, column number, and layer number of the location, where RowWidth = ColumnWidth;

[0008] (2) Using sampling point P in TIN_DDM i (i represents the number of selected sampling points, i = 1, 2, 3…n, where n is the total number of sampling points) and index space data block S fhg The mapping formula is used to calculate P. i S fhg line number f i Column number h i Layer number g i This establishes the correspondence between sampling points and index space data blocks;

[0009] (3) Calculate S for each grid cell fh The maximum value of the layer number where the internal sampling point is located Among them, grid S fh The index space data block S represents the data where row number f and column number h are the same. fhg gather;

[0010] (4) Select sampling points P in the TIN_DDM model in sequence. i , will P i Set as the initial key sampling point P ij (j=1), the corresponding initial critical radius r ij (j=1) is 0, and is therefore taken as point P. i The next key sampling point P ij+1 and its corresponding critical radius r ij+1 The baseline point for the solution; where j is point P in the TIN_DDM buffer surface fast construction database.i For the j-th key sampling point, take values ​​1, 2, 3...j i j i For P i The total number of key sampling points;

[0011] (5) Initial setting of the preset critical radius r ij+1 'for r max Key sampling point candidate point P C For P i ;

[0012] (6) Based on sampling point P i Grid Location, combined with a grid arrangement set based on ring layer order. Select grid quick sort set in sequence Grid in, Point P i Grid S fh ;l represents a grid quick permutation set The number of grid positions selected within the range, l = 1, 2, 3..., f l h l This represents the spatial index number of the l-th grid.

[0013] (7) Calculate the S of each grid cell based on the fast determination formula for index space data blocks distinguished by extreme values. fh Minimum value of the layer number where the internal sampling point is located Then combine the grid S calculated in step (3) fh The maximum value of the layer number where the internal sampling point is located Determine the selected grid It contains candidate point P C The collection of index space data blocks

[0014] (8) Calculate the selected index space data block set according to the critical radius solution formula. Internal sampling point P n With key sampling point P ij The corresponding critical rolling ball radius r n,ij ;

[0015] (9) Select the smallest critical rolling ball radius r n,ij , denoted as r min and its corresponding sampling point P n , denoted as P min ;

[0016] (10) If there exists a minimum critical rolling ball radius r min and the corresponding sampling point P minAnd r min Less than the preset critical radius r ij+1 ', then the preset critical radius r ij+1 'Reassigned to r min And the key sampling point candidate point P C Reset to point P min ;

[0017] (11) Select the next grid cell in sequence Repeat steps (7)-(10) until the required ring layer has been traversed, i.e., the selected grid. Number of ring layers in the ring layer Greater than the maximum number of ring layers c determined by the ring layer cover formula max Then the preset critical radius r ij+1 'and key sampling point candidate point P C The data is recorded in the data chain record of the corresponding sampling point in the TIN_DDM buffer surface fast construction database, i.e., r ij+1 =r ij+1 ', P ij+1 =P C ;

[0018] (12) Sample point P ij+1 P is calculated as a key sampling point in the next stage. ij+2 Based on the reference point, repeat steps (5)-(11) above to determine the next critical sampling point P. ij+2 Its corresponding critical radius r ij+2 Until that stage r ij+2 Less than the previous stage r ij+1 Or r does not exist ij+2 and the corresponding P ij+2 Then, repeat steps (4)-(11) above to calculate the next sampling point P. i+1 The associated data chain is established until all sampling points within TIN_DDM are selected. Then, the TIN_DDM buffer surface can be quickly constructed to build the key sampling points P corresponding to all sampling points in the database. ij With critical radius r ij ;

[0019] (13) Based on the calculated TIN_DDM buffer surface, quickly construct the database and the buffer radius R of the buffer surface, combined with the sampling point P. i The formula for calculating the water depth at the buffer surface with a buffer radius R is used to sequentially calculate the water depth z at all sampling points within the TIN_DDM. i This enables the TIN_DDM buffer surface analysis application to meet efficiency requirements within the same spatial range under any buffer radius R.

[0020] In step (2), it is known that sampling point P in TIN_DDMi coordinates (x) i ,y i ,z i Given the row width, column width, and vertical height of the index space data block, the sampling point P within TIN_DDM is... i (x i ,y i ,z i ) and index space data block S fhg The mapping formula is:

[0021]

[0022] In the formula, x min y min z min These represent the minimum coordinates of the space S containing TIN_DDM in the horizontal, vertical, and longitudinal directions, respectively. f represents rounding up the numerical value; i h i g i Indicates sampling point P i Data block S in the index space fhg Spatial index number.

[0023] Set the coordinates P of all sampling points within TIN_DDM i (x i ,y i ,z i ) and x min y min z min Substituting into the above formula, we can calculate the sampling point P. i Data block S in the index space fhg Spatial index number f i h i g i This means establishing the correspondence between sampling points and index space data blocks.

[0024] In step (4), the TIN_DDM buffer surface quickly constructs a database to store sampling points P. i The corresponding critical radii r of each stage ij and its corresponding key sampling point P ij The database is represented as follows:

[0025]

[0026]

[0027] Wherein, the initial reference point of any sampling point is itself, i.e., Pi1 =P i The initial critical radius is 0, i.e., r i1 =0.

[0028] In step (6), it is known that sampling point P in TIN_DDM i The grid in question is The formula for the quick permutation set of the grid according to the ring layer order is: In the formula: The grid S represents the order of ring layers. fh The set of grids constructed; c represents the grid S fh The number of ring layers in the ring layer; These represent the lower and upper boundaries of the number of ring layers determined by the ring layer cover formula, respectively; Representing grid Index number f i h i With the c-th ring layer grid S fh The function mapping relationship between index numbers f and h is as follows:

[0029]

[0030] In the formula, Rows and Columns represent the total number of rows and columns in the index space data block modeling, respectively; [h i -c:h i +c] indicates h i -c to h i Integers between +c, where the lower bound of the ring layer number is... upper boundary The formula for determining the value of is:

[0031]

[0032] In the formula, a(P) i ,P ij-1 ) indicates the selected sampling point P i With key sampling point P ij-1 Horizontal distance, This indicates rounding up. The sampling point P... i Grid index number f i h i Substituting into the formula for the quick permutation set of the grid, we construct the quick permutation set of the grid arranged in the order of the ring layers.

[0033] In step (7), the preset critical radius is known to be r. ij+1 ', sampling point P i The grid in question is The set of index space data blocks distinguished by extreme values The quick determination formula is:

[0034]

[0035] In the formula, and These represent the sets of data blocks in the index space. The lower and upper boundaries of the layer number g; Ω represents the candidate point set constructed according to a preset critical radius. ij (r ij ') Minimum water depth within the spatial range; Represents the key sampling point P ij-1 water depth value; a(P) ij-1 ,P i ) represents the key sampling point P ij-1 With the selected sampling point P i Horizontal distance; Representing grid Inner candidate point set Ω ij (r ij ') Spatial range of the lowest water depth at the spatial point With the selected sampling point P i The horizontal distance. Among them, the grid. Relative to the selected sampling point P i Different grid locations result in different spatial points. The location of the selected position varies, depending on the grid. Spatial points with different index numbers The formula for selecting the position is as follows:

[0036]

[0037] Set the preset critical radius r ij+1 '、Selected grid Spatial index number (f l h l ), the spatial index number of the grid where the selected sampling point is located (f i h i ), n x im , n y im , n z im P ij-1 With P i Substituting the coordinate values ​​into equation (5), we can obtain the set of index space data blocks.

[0038] In step (8), the known set of index space data blocks Any sampling point P within nKey sampling point P ij Then the critical rolling ball radius r n,ij for:

[0039]

[0040] In the formula: For the collection of index space data blocks Any sampling point P within n The water depth value; For key sampling point P ij water depth value; a(P) n ,P i ) represents sampling point P n to sampling point P i Horizontal distance; a(P) ij ,P i ) represents the key sampling point P ij to sampling point P i Horizontal distance.

[0041] In step (12), the TIN_DDM associated data list and the buffer radius R of the desired buffer surface are known, and the sampling point P is... i The formula for calculating the water depth at the buffer surface with a buffer radius R is:

[0042] when R∈[r ij ,r ij+1 then

[0043] In the formula: z i For sampling point P i The water depth at the upper buffer surface with a buffer radius of R.

[0044] The beneficial effects of the present invention are as follows: The TIN_DDM buffer surface rapid construction method for rapidly establishing key sampling points described in the present invention can achieve rapid construction of the TIN_DDM buffer surface construction database through the iterative exploration of the critical rolling sphere radius and the spatial data block index modeling for rapid solution of candidate key sampling points, thereby achieving the goal of rapid construction of TIN_DDM buffer surfaces in a single operation. Attached Figure Description

[0045] Figure 1 This is a flowchart illustrating the method for rapidly constructing the TIN_DDM buffer surface to quickly establish key sampling points.

[0046] Figure 2 This is a schematic diagram of TIN_DDM spatial data block index modeling.

[0047] Figure 3 This is a schematic diagram of the sampling point database records in the TIN_DDM buffer surface construction model database.

[0048] Figure 4 This is a schematic diagram of grid quicksort based on ring layer order.

[0049] Figure 5 This is a schematic diagram of the selection profile of the index space data block.

[0050] Figure 6 This is a schematic diagram showing the horizontal distribution of the minimum water depth values ​​at spatial points in different grid areas. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of the present invention clearer, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0052] The rapid TIN_DDM buffer surface construction method for quickly establishing key sampling points described in this invention takes a rolling ball accelerated optimization model with controllable accuracy thresholds as the analysis object. Through iterative exploration of the critical rolling ball radius and spatial data block index modeling for rapid solution of candidate key sampling points, it achieves rapid establishment of key sampling points, thereby effectively improving the construction efficiency of the buffer surface while ensuring its construction accuracy. Taking the construction of a buffer surface on TIN_DDM as an example (the process is as follows), this method for efficiently determining key sampling points in rapid TIN_DDM buffer surface construction is discussed below. Figure 1 (The construction of the associated data linked list of the buffer surface under TIN_DDM is similar.)

[0053] Following step (1), set the maximum buffer radius r of the TIN_DDM buffer surface construction. max To determine sampling point P i The maximum range of values ​​for the critical radius in a database record, i.e., r ij ∈[0,r max ], set index space data block S fhg (f, h, g represent S respectively) fhg The row width, column width, and vertical height of the row, column, and floor number of the location.

[0054] Following step (2), use the index space data block S fhg With sampling point P in TIN_DDM i The mapping formula (where i represents the number of selected sampling points, i = 1, 2, 3…n, and n is the total number of sampling points) is used to calculate P. i Data block S in the index space fhg line number f i Column number h i Layer number g iEstablish the sampling points and index space data blocks S within TIN_DDM. fhg The containment relationship is stored; such as Figure 2 The diagram shows a schematic of TIN_DDM spatial data block index modeling. The black dashed lines represent the intersections of the TIN_DDM index spatial data block boundaries with the horizontal plane, and each dashed square represents a grid cell S. fh Sampling point P i Located in the grid Inside.

[0055] Following step (3), traverse each grid S. fh Calculate S from all sampling points within the range. fh The maximum value of the layer number g where the internal sampling point is located

[0056] Following step (4), select sampling points P in the TIN_DDM model sequentially. i (i = 1, 2…n), P i Set as the initial key sampling point P ij (j=1), i.e., P i1 =P i The corresponding initial critical radius r ij (j=1) is 0, and point P is calculated accordingly. i The next key sampling point P ij+1 and its corresponding critical radius r ij+1 This is to complete the construction of the TIN_DDM buffer surface fast construction database; such as Figure 3 The diagram shown illustrates the rapid database construction using the TIN_DDM buffer plane, where P1 to P... n For each sampling point within TIN_DDM, j1 to j n This represents the number of reference sampling points corresponding to each sampling point. For sampling point P i A series of benchmark sampling points, For sampling point P i Critical radii corresponding to a series of benchmark sampling points.

[0057] Following step (5), set the preset critical radius r. ij+1 'for r max Key sampling point candidate point P C For P i .

[0058] Following step (6), sample point P i Grid Substitute the positions into the formula for the quick permutation set of the grid according to the ring layer order to construct the quick permutation set of the grid. Select in sequence The l-th grid like Figure 4 As shown, dark gray striped grids and light gray striped grids are distributed in a ring around the grid. The ring layer number is 0, and it is related to the grid. The adjacent dark gray striped grid rings have 1 layer, and the adjacent light gray striped grid rings have 2 layers. The grids S are arranged in ascending order of ring layer number. fh This allows for the rapid arrangement of a grid in a ring-layer sequence.

[0059] Following step (7), select the grid. Substituting the index number into the formula for quickly determining the index space data block for extreme value differentiation, the selected grid is determined. It contains candidate points P for key sampling points C The collection of index space data blocks like Figure 5 As shown, the black dashed lines represent the profile lines of the index space data blocks, and the union of each vertical dashed square represents a grid S. fh ; Region represents candidate points P for key sampling points C A cross-section of the spatial area; the black, curved dashed line represents point P. C The profile lines of the spatial boundary; the black curved solid lines represent the profile lines of the TIN_DDM surface, where the profile lines are located at the boundary of the TIN_DDM surface. Thicken the lines within the area; Region representation The cross-section; The region represents the area containing point P. C The index space data block profile; The bordered area represents the index space data block (containing the index space data block of the water depth maximum sampling point) located within the black area and below the water depth maximum sampling point of each grid. (Index space data block set) upper boundary can be Maximum water depth at internal sampling points Confirmed, lower boundary By grid Point P within the range C Minimum water depth within the spatial range Confirmed. The differences are... Inside The selection location is different, such as Figure 6 As shown, the solid black line represents P. i Location The boundaries of the plane are such that the extended lines of each boundary divide the plane into eight neighborhoods (neighborhoods X1, X2, X3, X4 are defined by...). This indicates that the neighboring regions Y1, Y2, Y3, and Y4 adopt... express); Border representation The internal spatial data block is projected horizontally; black dots represent different neighborhoods and P. i The horizontal distance to the nearest point (equivalent to) (Horizontal projection point).

[0060] Following step (8), to determine the selected index space data block Candidate points for key sampling points within the index space are determined using the critical radius calculation formula, and the selected index space data block is obtained. Internal sampling point P n With key sampling point P ij The corresponding critical rolling ball radius r n,ij .

[0061] Following step (9), select the minimum critical rolling ball radius r. n,ij , denoted as r min and its corresponding sampling point P n , denoted as P min .

[0062] According to step (10), if there exists a minimum critical rolling ball radius r min and the corresponding sampling point P min And the minimum critical rolling ball radius is less than the preset critical radius r ij+1 ', then the preset critical radius r ij+1 'Reassigned to the minimum critical rolling ball radius r min And the key sampling point candidate point P C Reset to the selected corresponding sampling point P min .

[0063] Following step (11), select the next grid cell in sequence. Repeat steps (7)-(10) until the required ring layer has been traversed, i.e., the selected grid. Number of ring layers in the ring layer Greater than the maximum number of ring layers c determined by the ring layer cover formula max Then the preset critical radius r ij+1 'and key sampling point candidate point P C The data is recorded in the data chain record of the corresponding sampling point in the TIN_DDM buffer surface fast construction database, i.e., r ij+1 =r ij+1 ', P ij+1 =P C .

[0064] Following step (12), the key sampling point P ij+1As a reference point for the next stage, repeat steps (5)-(11) above to determine the next key sampling point P. ij+2 With the corresponding critical radius r ij+2 Until that stage r ij+2 Less than the previous stage r ij+1 Or r does not exist ij+2 and the corresponding sampling point P ij+2 Repeat steps (4) to (11) above to calculate the associated data chain for the next sampling point until all sampling points in TIN_DDM have been selected. Then, the key sampling point P for all sampling points in the TIN_DDM model can be established. ij With critical radius r ij The database.

[0065] Following step (13), based on the solved TIN_DDM associated data list and the buffer radius R of the desired buffer surface, combined with the sampling point P... i The formula for calculating the water depth at the buffer surface with a buffer radius R is used to sequentially calculate the water depth z at all sampling points within the TIN_DDM. i This enables the TIN_DDM buffer surface analysis application to meet efficiency requirements within the same spatial range under any buffer radius R.

[0066] The method for rapidly constructing TIN_DDM buffer surfaces, which utilizes the aforementioned key sampling points for rapid establishment, establishes a TIN_DDM buffer surface rapid construction model database through preprocessing of the Discrete Digital In-Depth Model (TIN_DDM). In later applications, a simple query is all that's needed to construct buffer surfaces of arbitrary buffer distances from the Discrete Digital In-Depth Model (TIN_DDM), reducing the time complexity to O(n), thus meeting the application requirements of current commercial 3D GIS software.

[0067] This invention has been described through embodiments. Those skilled in the art will understand that various changes or equivalent substitutions can be made to these features and embodiments without departing from the spirit and scope of the invention. Furthermore, under the teachings of this invention, these features and embodiments can be modified to adapt to specific situations and materials without departing from the spirit and scope of the invention. Therefore, this invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the protection scope of this invention.

Claims

1. A method for rapidly constructing a TIN_DDM buffer surface for efficient determination of key sampling points, characterized in that, The method includes the following steps: (1) Set the maximum buffer radius for the TIN_DDM buffer surface construction. With index space data blocks line width Column width With floor height ,in ; , , They represent The row number, column number, and layer number of the location; (2) Using sampling points within TIN_DDM With index space data blocks The mapping formula is used to calculate... Location line number , column number Floor number This establishes the correspondence between sampling points and index space data blocks; among which... This indicates the number of selected sampling points. , This represents the total number of sampling points; (3) Calculate each grid cell The maximum value of the layer number where the internal sampling point is located Among them, grid Indicates line number With column number Consistent index space data blocks gather; (4) Select sampling points in the TIN_DDM model in sequence. ,Will Set as the initial key sampling point The corresponding initial critical radius =0, ; take this as a point The next key sampling point and its corresponding critical radius The reference point for the solution; where, Quickly construct database points for the TIN_DDM buffer surface No. Take 1 key sampling point, , for The total number of key sampling points; (5) Initial setting of preset critical radius for Key sampling point candidate points for ; (6) Based on sampling points Grid Location, combined with a grid arrangement set based on ring layer order. Select the grid quick sort set in sequence Grid ;in, Point Grid ; Represents a grid quick arrangement set The number of positions of the selected grid within, , , Indicates the first The spatial index number of each grid cell; (7) Calculate each grid cell based on the fast determination formula for index space data blocks distinguished by extreme values. Minimum value of the layer number where the internal sampling point is located Then, combined with the grid calculated in step (3) The maximum value of the layer number where the internal sampling point is located Determine the selected grid It contains candidate points The collection of index space data blocks ; (8) Calculate the set of selected index space data blocks according to the critical radius solution formula. Internal sampling points With key sampling points The corresponding critical rolling ball radius ; (9) Select the minimum critical rolling ball radius , recorded as and its corresponding sampling points , recorded as ; (10) If there exists a minimum critical rolling ball radius and corresponding sampling points and Less than the preset critical radius Then the preset critical radius Reassigned Candidate points for key sampling points Reset to point ; (11) Select the next grid in sequence Repeat steps (7)-(10) until the required ring layer has been traversed, i.e., the selected grid. Number of ring layers in the ring layer Greater than the maximum number of ring layers determined by the ring layer cover formula Then the preset critical radius will be set. and key sampling point candidate points The data is recorded in the data chain record of the corresponding sampling point in the TIN_DDM buffer surface fast construction database, i.e. , ; (12) Sampling points Calculation of key sampling points for the next stage Based on the reference point, repeat steps (5)-(11) above to determine the next key sampling point. Its corresponding critical radius Until that stage Smaller than the previous stage Or does not exist and corresponding Repeat steps (4)-(11) above to calculate the next sampling point. The associated data chain is established until all sampling points within TIN_DDM are selected. Then, the TIN_DDM buffer surface can be quickly constructed to build the key sampling points corresponding to all sampling points in the database. With critical radius ; (13) Quickly construct the database and the buffer radius of the required buffer surface based on the calculated TIN_DDM buffer surface. Combined with sampling points buffer radius The formula for calculating the buffer surface water depth is used to sequentially calculate the buffer surface water depth at all sampling points within the TIN_DDM. Thus, arbitrary buffer radius can be achieved. The efficiency requirements of the TIN_DDM buffer surface within the same spatial range are analyzed for application domains.

2. The method for rapidly constructing a TIN_DDM buffer surface for efficient determination of key sampling points according to claim 1, characterized in that, In step (2), the sampling points in TIN_DDM are known. coordinate Row width of index space data blocks Column width With floor height Then the sampling points within TIN_DDM With index space data blocks The mapping formula is: （1） In the formula, These represent the spaces where TIN_DDM resides. Minimum values ​​in the horizontal, vertical, and longitudinal directions; This indicates that the value is rounded up. , , Indicates sampling point Data block in the index space Spatial index number; Set the coordinates of all sampling points within TIN_DDM and Substitute into equation (1) to calculate the sampling points. Data block in the index space Spatial index number , , This means establishing the correspondence between sampling points and index space data blocks.

3. The method for rapidly constructing a TIN_DDM buffer surface for efficient determination of key sampling points according to claim 1 or 2, characterized in that, In step (4), the TIN_DDM buffer surface quickly constructs a database to store sampling points. The corresponding critical radii for each stage and its corresponding key sampling points The database is represented as follows: The initial reference point for any sampling point is itself, i.e. The initial critical radius is 0, i.e. .

4. The method for rapidly constructing a TIN_DDM buffer surface for efficient determination of key sampling points according to claim 1 or 2, characterized in that, In step (6), the sampling points in TIN_DDM are known. The grid in question is The formula for the quick arrangement set of the grid according to the ring layer order is: In the formula: A grid representing a sequence of ring layers. The constructed grid set; Representing grid The ring number of the ring layer; , These represent the lower and upper boundaries of the number of ring layers determined by the ring layer cover formula, respectively; Representing grid Index number , With the Ring-layered grid Index number , The function mapping relationship, that is: （2） In the formula, , These represent the total number of rows and columns in the index space data block modeling, respectively. express to Integers between the specified values; where the lower boundary of the ring layer number is... Upper boundary The formula for determining the value of is: （3） In the formula, Indicates the selected sampling points With key sampling points Horizontal distance, Indicates rounding up; sampling points Grid index number , Substituting into the formula for the quick permutation set of the grid, we construct the quick permutation set of the grid arranged in the order of the ring layers. .

5. The method for rapidly constructing a TIN_DDM buffer surface for efficient determination of key sampling points according to claim 3, characterized in that, In step (6), the sampling points in TIN_DDM are known. The grid in question is The formula for the quick arrangement set of the grid according to the ring layer order is: In the formula: A grid representing a sequence of ring layers. The constructed grid set; Representing grid The ring number of the ring layer; , These represent the lower and upper boundaries of the number of ring layers determined by the ring layer cover formula, respectively; Representing grid Index number , With the Ring-layered grid Index number , The function mapping relationship, that is: （2） In the formula, , These represent the total number of rows and columns in the index space data block modeling, respectively. express to Integers between the specified values; where the lower boundary of the ring layer number is... Upper boundary The formula for determining the value of is: （3） In the formula, Indicates the selected sampling points With key sampling points Horizontal distance, Indicates rounding up; sampling points Grid index number , Substituting into the formula for the quick permutation set of the grid, we construct the quick permutation set of the grid arranged in the order of the ring layers. .

6. A method for rapidly constructing a TIN_DDM buffer surface for efficient determination of key sampling points according to claim 1, 2, or 5, characterized in that, In step (7), the preset critical radius is known to be Sampling points The grid in question is The set of index space data blocks distinguished by extreme values The quick determination formula is: （4） In the formula, and These represent the sets of data blocks in the index space. number of floors The lower and upper boundaries; This represents the candidate point set constructed according to a preset critical radius. Minimum water depth within the spatial range; Indicates key sampling points The water depth value; Indicates key sampling points With the selected sampling points Horizontal distance; Representing grid Internal candidate point set The spatial point with the lowest water depth within the spatial range With the selected sampling points The horizontal distance; where the grid is. Relative to the selected sampling point Different grid locations result in different spatial points. The location of the selected position varies, depending on the grid. Spatial points with different index numbers The formula for selecting the position is as follows: （5） Preset critical radius Selected grid Spatial index number ( , ), the spatial index number of the grid where the selected sampling point is located ( , ), , and Substituting the coordinate values ​​into equation (5), we obtain the set of index space data blocks. .

7. A method for rapidly constructing a TIN_DDM buffer surface for efficient determination of key sampling points according to claim 1, 2, or 5, characterized in that, In step (8), the known set of index space data blocks Any sampling point within Key sampling points Then the critical rolling ball radius for: （6） In the formula: For the collection of index space data blocks Any sampling point within The water depth value; Key sampling points The water depth value; Sampling points to sampling point Horizontal distance; Key sampling points to sampling point Horizontal distance.

8. A method for rapidly constructing a TIN_DDM buffer surface for efficient determination of key sampling points according to claim 1, 2, or 5, characterized in that, In step (12), the TIN_DDM associated data linked list and the buffer radius of the desired buffer surface are known. Sampling points buffer radius The formula for calculating the water depth of the buffer surface is: In the formula: Sampling points The buffer radius is The water depth value of the upper buffer surface.

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