Digital geographic environment modeling methods, equipment and media

Abstract

The present invention relates to a digital geographic environment modeling method, which comprises: S1. acquiring the elevation data of each small grid in the gridded geographic environment image; S2. calculating the slope data of each grid; S3. recording the slope data of each grid into a two-dimensional array of the slope, and performing a 0, 1 division operation on each slope data; S4. calculating the number of non-flat areas in 8 directions of the non-flat area grid, and assigning the number of non-flat areas to the non-flat areas, so as to establish the boundary when the flat areas are aggregated for the first time; S5. aggregating the flat areas; S6. screening the highest aggregation level; S7. grouping the highest aggregation level; S8. generating eight neighborhood nodes. Through the present invention, the machine can quickly understand the flat and non-flat geographic information in the geographic environment and perform corresponding image reconstruction.

Description

Technical Field

[0001] The present invention relates to the field of geographic information system technology, and in particular to a digital geographic environment modeling method, equipment and medium therein. Background Art

[0002] There is vector data in the geographic environment database of the existing geographic information system, which can express geographic information such as mountains and plains. However, this type of data is usually large in volume and complex in expression, making it impossible for machines to quickly understand the location and shape of flat and non-flat plots. As a result, some activities based on the flatness of the geographical environment cannot be carried out quickly, for example, when it is necessary to quickly determine flat and non-flat land and deploy emergency resources accordingly. Summary of the invention

[0003] In view of the above existing technical problems, the present invention proposes a digital geographic environment modeling method, which includes:

[0004] S1. Obtaining the elevation data of each small grid in the gridded geographic environment image;

[0005] S2. Calculate the slope data of each grid;

[0006] S3. The slope data of each grid is recorded into a two-dimensional array of slopes, and the following 0, 1 division operation is performed on each slope data: the attribute of the grid portion whose slope data is less than a threshold is regarded as a flat area, and the slope data is reset to 0; the attribute of the grid portion whose slope data is greater than or equal to the threshold is regarded as a non-flat area, and the slope data is reset to 1;

[0007] S4. Calculate the number of non-flat areas in 8 directions of the non-flat area grid, and assign the number of non-flat areas to the non-flat areas, so as to establish the boundary when the flat areas are first aggregated;

[0008] S5. Aggregating the flat land, which includes: using an eight-neighborhood algorithm, wherein, for the flat area, the aggregation level is continuously aggregated from the edge of the non-flat area to the central part of the flat area away from the non-flat area, wherein the maximum aggregation level appearing in a plot indicates the size and position of the flat area plot;

[0009] S6. Filtering the highest aggregation level, which includes: traversing the two-dimensional array of the aggregation results of step S5, retaining the values ​​of the elements in the two-dimensional array that are larger than the elements in other positions in its eight neighborhoods, and obtaining multiple groups of aggregated data for representing the flat area in the current data;

[0010] S7. Grouping the highest aggregation level, which includes: using a breadth-first algorithm, managing each filtered data in a group and using it to represent the group level, wherein the data under the same group are configured with the same group data; and also includes: recording in each group the corner marks of the east, west, south, and north boundaries created by each group in the two-dimensional array;

[0011] S8. Eight-neighborhood node generation, which includes: recording the group level and the east-west, south-north and north-south directions of the group in the group data into the eight-neighborhood nodes, and traversing the four corners of the eight-neighborhood nodes in two directions toward the center of the group until group level data appears, and recording the number of missing items in two directions of the four corners.

[0012] According to some embodiments of the present invention, the calculating the slope data of each grid includes:

[0013] Get the elevation data of a current grid and its adjacent grids in the four positive directions of east, west, south and north and the adjacent grids in the four oblique directions of northeast, northwest, southeast and southwest, calculate the height difference between the elevation data of the current grid and the elevation data of each adjacent grid, calculate the slope data in each direction by comparing the height difference with the distance on the plane, and compare the maximum slope data as the slope data of the current grid.

[0014] According to some embodiments of the present invention, the method also includes step S9: eight-neighborhood node merging, which includes: when the distance between two nodes with the same group level is less than a threshold, merging the nodes and creating a new eight-neighborhood node, recording the east, west, south, and north boundaries of the two merged eight-neighborhood nodes, and recording the missing information of the four corners of the new node based on the merged position.

[0015] According to some embodiments of the present invention, the method also includes step S10: eight-neighborhood node overlap processing, which includes: performing relationship judgment on all eight-neighborhood nodes that overlap with each other, and the relationship judgment includes: for each node, judging the area size relationship between it and the other eight-neighborhood nodes that overlap with it, and taking out the node with the largest area in comparison with the adjacent nodes as the node after the overlap processing.

[0016] According to some embodiments of the present invention, the method further comprises step S11: changing the type of the node representation with the largest area from 0 to 2, to indicate that the current flat area has been processed.

[0017] According to some embodiments of the present invention, the method further comprises step S12: eight-neighborhood node step iteration, which comprises sequentially repeating steps S4 to S11 for the two-dimensional array outside the flat area that has been processed.

[0018] According to some embodiments of the present invention, the method also includes step S13: eight-neighborhood node blocking, which includes: judging any two adjacent eight-neighborhood nodes, and having the eight-neighborhood node with a larger area record all adjacent eight-neighborhood nodes with a smaller area; sorting the created eight-neighborhood node set according to the area size of the eight-neighborhood nodes, taking out the node data of the larger area first, continuously recursively taking out the adjacent nodes, and recording the nodes that have been taken out to ensure that they are not taken out repeatedly when taking values ​​next time.

[0019] According to some embodiments of the present invention, the method further includes filtering out eight-neighborhood nodes that have no practical significance during the iteration process of the eight-neighborhood node step; and / or, between steps S3 and S4, aggregating the non-flat area based on the reset slope data, resetting the value of the grid corresponding to the non-flat area whose aggregation level is lower than a threshold after aggregation to 0, and adjusting the attribute of the grid corresponding to the non-flat area from a non-flat area to a flat area.

[0020] The present application also proposes a device for digital geographic environment modeling, which includes a processor and a memory; the processor executes the aforementioned method based on a computer program stored in the memory.

[0021] The present application also proposes a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the aforementioned method is executed.

[0022] The present invention can enable the machine to quickly understand the flat and non-flat geographical information in the geographical environment and perform corresponding image reconstruction. By quickly identifying and displaying flat and non-flat land, the corresponding plot can be selected according to the requirements of some emergency matters on whether the ground is flat or not. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings herein are incorporated into and constitute a part of the specification, and the accompanying drawings illustrate embodiments consistent with the present invention and are used together with the specification to explain the present invention. Elements with the same reference numerals in the accompanying drawings represent similar elements, and unless otherwise stated, the drawings in the accompanying drawings do not constitute a scale limitation.

[0024] Figure 1 A digital geographic environment modeling method according to some embodiments of the present invention is shown;

[0025] Figure 2 An example of the result after dividing the grid into 0 and 1 is shown;

[0026] Figure 3 The number of non-flat areas in the 9-square grid where the calculated non-flat areas are located is shown;

[0027] Figure 4 The aggregation results are shown;

[0028] Figure 5 The results after filtering at the highest aggregation level are shown;

[0029] Figure 6 The grouping process is shown;

[0030] Figure 7 The grouping results are shown;

[0031] Figure 8 The traversal direction of step S8 is shown;

[0032] Fig. 9 A reconstructed image containing a plurality of flat regions is shown;

[0033] Fig.10 A schematic structural block diagram of a digital geographic environment modeling device 5000 according to some embodiments of the present invention is shown;

[0034] Fig.11 A schematic structural diagram of a device 500 for digital geographic environment modeling according to some embodiments of the present invention is shown. DETAILED DESCRIPTION

[0035] The present invention will be exemplarily described below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments in this application can be combined with each other without conflict. In addition, the described embodiments are only some embodiments of the present invention, not all embodiments.

[0036] Figure 1 A digital geographic environment modeling method according to some embodiments of the present invention is shown, and the method includes the following steps S1-S9:

[0037] S1. Obtain elevation data of each small grid in the gridded geographic environment image.

[0038] Since the gridding process and the acquisition of data (including elevation data) of each small grid based on vector data of the geographic environment database are well known to those skilled in the art, they will not be described in detail here.

[0039] S2. Calculate the slope data of each grid.

[0040] According to some embodiments of the present invention, the calculating the slope data of each grid includes:

[0041] Get the elevation data of a current grid and its adjacent grids in the four positive directions of east, west, south and north and the adjacent grids in the four oblique directions of northeast, northwest, southeast and southwest, calculate the height difference between the elevation data of the current grid and the elevation data of each adjacent grid, calculate the slope data in each direction by comparing the height difference with the distance on the plane, and compare the maximum slope data as the slope data of the current grid.

[0042] S3. The slope data of each grid is recorded into a two-dimensional array of slope, and the following 0, 1 division operation is performed on each slope data: the attribute of the grid portion whose slope data is less than a threshold is regarded as a flat area, and the slope data is reset to 0; the attribute of the grid portion whose slope data is greater than or equal to the threshold is regarded as a non-flat area, and the slope data is reset to 1.

[0043] For example, after obtaining the slope data, the part with slope data less than 11.3 (i.e., within 20 meters of height difference per 100 meters) can be regarded as a flat area, and the grid value of the flat area can be set to 0, and the rest can be regarded as a non-flat area and set to 1, such as Figure 2 As shown, an example of the result after dividing the grid into 0 and 1 is shown.

[0044] According to some embodiments of the present invention, based on the reset slope data, the non-flat area is aggregated, the value of the grid corresponding to the non-flat area whose aggregation level is lower than a threshold after aggregation is reset to 0, and the attribute of the grid corresponding to the non-flat area is adjusted from a non-flat area to a flat area. It is also possible to calculate the area ratio of the grid whose grid data is 1 relative to the area where the surrounding grids are 0, and when the area ratio is less than a threshold, the data of the grid whose grid data is 1 is reset to 0. Through the above content of the present invention, the following problem can be well solved: there are some non-flat areas with very small footprints in the actual data. Because the footprint is very small, when performing some activities, the small non-flat land has no great influence on the activities, that is, it does not affect the flat attribute of the large flat land, but if it is not processed, it will have a great influence on the actual presentation effect. The present invention realizes the filtering of the above noise data. The threshold standard can be slightly adjusted according to the actual map sheet size, so that the number of filters is within a suitable range.

[0045] S4. Calculate the number of non-flat areas in the eight directions of the non-flat area grid, and assign the number of non-flat areas to the non-flat areas for establishing boundaries when the flat areas are aggregated for the first time.

[0046] like Figure 3As shown, the number of non-flat areas in the 9-square grid where the calculated non-flat area is located is shown. The red frame line in the figure can be used as the boundary of the central flat area when performing the first aggregation.

[0047] S5. Aggregate the flat land, which includes: using an eight-neighborhood algorithm, wherein, for the flat area, the aggregation level is continuously aggregated from the edge close to the non-flat area to the central part of the flat area away from the non-flat area, wherein the maximum aggregation level appearing in a plot represents the size and position of the flat area plot.

[0048] like Figure 4 As shown, the aggregation result is shown. After this step, data representing different aggregation levels created in different aggregation steps are formed in the array position representing the flat area. In the present invention, in order to achieve equivalent representation of the flat area, the aggregation process and aggregation level are introduced, especially the maximum level appearing in a plot is used to represent the size and position of the flat area plot, and the problem is correspondingly converted into the processing of the grid position data of the maximum level formed, as will be described later, thereby realizing a method of efficiently expressing the geographical environment with a concise structure, so that both humans and machines can quickly understand the location and shape of the flat area plot.

[0049] S6. Filter the highest aggregation level, which includes: traversing the two-dimensional array of the aggregation results of step S5, retaining the values ​​of the elements in the two-dimensional array that are larger than the elements in other positions in its eight-neighborhood, and obtaining multiple groups of aggregated data for representing the flat areas in the current data.

[0050] The present invention adopts a new type to define and represent the data of the flat area, wherein the result after aggregation is first processed, and by traversing, the value of the element in the two-dimensional array that is larger than the other position elements in its eight neighborhoods is retained, and multiple groups of aggregated data that can represent the flat area in the current data are obtained, such as Figure 4 As shown in the figure, the 1 in the middle of the green box is not the largest value in its eight-neighborhood, so the element 1 at this position will be filtered out. Figure 4 In the case of the red box, the 4 in the middle is the largest value in its eight-neighborhood, so the element 4 at this position will be retained. Figure 5 The results after filtering are shown.

[0051] S7. Grouping the highest aggregation level includes: using a breadth-first algorithm, managing each filtered data in a group and using it to represent the group level, wherein the data under the same group are configured with the same group data; and also includes: recording in each group the subscripts of the east, west, south, and north boundaries created by each group in the two-dimensional array.

[0052] refer to Figure 6, which shows the grouping process, in which a part of the data is selected as an example, and the data in each red box will be managed as an independent group. Figure 7 , which shows the grouping results. The value in each group is Figure 6 The data in each box in, minRow, maxRow, minCol, maxCol are the four corner positions of each box, where, Figure 6 The starting coordinates of the upper left corner are (0,0).

[0053] S8. Eight-neighborhood node generation, which includes: recording the group level and the east-west, south-north and north-south directions of the group in the group data into the eight-neighborhood nodes, and traversing the four corners of the eight-neighborhood nodes in two directions toward the center of the group until group level data appears, and recording the number of missing items in two directions of the four corners.

[0054] refer to Figure 8 , which shows the traversal direction of step S8. As shown in the figure, traversing the four corners of the eight-neighborhood node in two directions toward the center of the group until group-level data appears means: traversing along the two arrow directions in the figure (towards the center of the group where the red box is located) until 5 of them appear. Recording the number of missing in two directions of the four corners means: recording the number of data where the arrow passes through is 0. The upper left corner of the red box in the figure is one of the corners, and the number of missing in two directions of this corner is 2 and 3 respectively. In this way, the number of missing in two directions of the other three corners of the red box can be taken.

[0055] Through the above-mentioned eight-neighborhood nodes and associated data structure of the present invention, a machine can simply and quickly identify multiple areas formed by a map.

[0056] Due to the characteristics of the map shape, two nodes of the same level may be divided into two groups. We need to limit the distance and level conditions so that the same nodes of high level can be merged within a certain distance range. Therefore, according to some embodiments of the present invention, the method may further include step S9: merging eight-neighborhood nodes, which includes: when the distance between two nodes of the same group level is less than a threshold, merging the nodes and creating a new eight-neighborhood node, recording the east, west, south, and north boundaries of the two merged eight-neighborhood nodes, and recording the missing information of the four corners of the new node according to the merged position.

[0057] 6. The eight-neighborhood nodes created above can basically represent all the flat land data, but there is a problem, that is, the ranges represented by two adjacent eight-neighborhood nodes may overlap. Therefore, according to some embodiments of the present invention, the method further includes step S10: eight-neighborhood node overlap processing, which includes: judging the relationship of all eight-neighborhood nodes overlapping with each other, and the relationship judgment includes: for each node, judging the area size relationship between it and the other eight-neighborhood nodes that overlap with it, and taking out the node with the largest area in the comparison with the adjacent nodes as the node after the overlap processing. According to some embodiments of the present invention, it also includes step S11: changing the type represented by the node with the largest area from 0 to 2, which is used to indicate that the current flat land area has been processed.

[0058] After taking out the node with the largest area in the first round, the flat land type now has three types of data: 0, 1, and 2. For the array data with data 0, the above steps are performed again, and the newly created eight-neighborhood node data is taken out in multiple rounds to ensure that all eight-neighborhood nodes do not overlap. That is, the method also includes step S12: eight-neighborhood node step iteration, which includes repeating steps S4 to S11 for the two-dimensional array outside the flat land area that has been processed. At the same time, the eight-neighborhood nodes that are very small and no longer have practical significance are filtered.

[0059] After taking out all the eight-neighborhood nodes, the machine can understand the shape and position of the flat area, but a piece of flat land is still represented by multiple eight-neighborhood nodes, which is not conducive to display. Therefore, the method of the present invention can also include step S13: eight-neighborhood node block, which includes: judging any two adjacent eight-neighborhood nodes, and recording all adjacent eight-neighborhood nodes with small areas by the eight-neighborhood nodes with large areas; sorting the created eight-neighborhood node set according to the area size of the eight-neighborhood nodes, taking out the node data of the large area first, and continuously recursively taking out the adjacent nodes, and recording the nodes that have been taken out to ensure that they are not taken out repeatedly when the value is taken next time. In this way, the created flat land is divided into blocks.

[0060] According to some embodiments of the present invention, the method may further include step S14: image reconstruction, which includes, based on the east-west, south-north boundaries of the flat area represented by the eight-neighborhood node, traversing the represented area, assigning values ​​at designated positions in the two-dimensional array, and outputting an image according to the assigned values. The output image may also include displaying the image superimposed on the base map. Fig. 9 As shown, a reconstructed image including a plurality of flat areas is shown, wherein blocks 1, 2, 3, 4, 5, and 6 are flat areas.

[0061] Through the modeling method of the present invention, the machine can quickly understand the flat and non-flat geographical information in the geographical environment and perform corresponding image reconstruction. Through the rapid identification and display of flat and non-flat land, the corresponding plots can be selected according to the requirements of some emergency matters on whether the ground is flat or not. Among them, flat land is convenient for mobility and firepower advantages, but has poor concealment and protection, while non-flat land provides better concealment and tactical flexibility, but may limit mobility and firepower. Therefore, in practical applications, trade-offs and selections can be made according to specific circumstances, and even a mixed deployment strategy may be adopted to fully utilize the advantages of different terrains.

[0062] Fig.10 A schematic structural block diagram of a digital geographic environment modeling device 5000 according to some embodiments of the present invention is shown. As shown in the figure, the device 5000 may include: an acquisition unit 5001, a calculation unit 5002, an aggregation unit 5003, a screening unit 5004, a grouping unit 5005, and a node generation unit 5006.

[0063] The acquisition unit 5001 is used to acquire the elevation data of each small grid in the gridded geographic environment image.

[0064] The calculation unit 5002 is used to calculate the slope data of each grid; record the slope data of each grid into a two-dimensional array of slopes, and perform the following 0, 1 division operation on each slope data: regard the attribute of the grid part with slope data less than a threshold as a flat area, and reset the slope data to 0; regard the attribute of the grid part with slope data greater than or equal to the threshold as a non-flat area, and reset the slope data to 1; calculate the number of non-flat areas in 8 directions of the non-flat area grid, and assign the number of non-flat areas to the non-flat area, so as to establish the boundary when the flat area is first aggregated.

[0065] The aggregation unit 5003 is used to aggregate the flat land, which includes: using an eight-neighborhood algorithm, wherein, for the flat area, the aggregation level is continuously aggregated from the edge close to the non-flat area to the central part of the flat area away from the non-flat area, wherein the maximum aggregation level appearing in a plot represents the size and position of the flat area plot.

[0066] The screening unit 5004 is used to screen the highest aggregation level, and the screening of the highest aggregation level includes: traversing the two-dimensional array of the aggregation results of step S5, retaining the values ​​of the elements in the two-dimensional array that are larger than the elements at other positions in its eight-neighborhood, and obtaining multiple groups of aggregated data for representing the flat area in the current data.

[0067] The grouping unit 5005 is used to group the highest aggregation level, and the grouping of the highest aggregation level includes: through a breadth-first algorithm, each filtered data is uniformly managed using a group and used to represent the group level, wherein the data under the same group is configured with the same group data; and also includes: recording in each group the subscripts of the east, west, south, and north boundaries created by each group in the two-dimensional array.

[0068] The node generation unit 5006 is used for eight-neighborhood node generation, which includes: recording the group level and the east-west, south-north and north-south directions of the group in the group data into the eight-neighborhood node, and traversing the four corners of the eight-neighborhood node in two directions toward the center of the group until group level data appears, and recording the number of missing points in two directions of the four corners.

[0069] In addition, the device may further include more units for respectively implementing the various steps in the aforementioned digital geographic environment modeling method.

[0070] In the present application, the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present invention.

[0071] Previous reference Fig.10 The corresponding detailed descriptions of the previous drawings are incorporated herein by reference and will not be repeated here.

[0072] Fig.11 FIG. 5 is a schematic structural diagram of a device 500 for digital geographic environment modeling according to some embodiments of the present invention. Fig.11 As shown, the device includes a processor 51 , a memory 52 and a bus 53 .

[0073] In some examples, the device may also include an input device 501, an input port 502, an output port 503, and an output device 504. The input port 502, the processor 51, the memory 52, and the output port 503 are interconnected through a bus 53, and the input device 501 and the output device 504 are connected to the bus 53 through the input port 502 and the output port 503, respectively, and then connected to other components of the device. It should be noted that the output interface and the input interface here can also be represented by an I / O interface. Specifically, the input device 501 receives input information from the outside, such as elevation data, and transmits the input information to the processor 51 through the input port 502; the processor 51 processes the input information based on the computer executable instructions stored in the memory 52 to generate output information, temporarily or permanently stores the output information in the memory 52, and then transmits the output information to the output device 504 through the output port 503; the output device 504 outputs the output information to the outside of the device.

[0074] The above-mentioned memory 52 includes a large capacity memory for data or instructions. For example, but not limitation, the memory 52 may include a HDD, a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory 52 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 52 may be inside or outside the device. In a specific embodiment, the memory 52 is a non-volatile solid-state memory. In a specific embodiment, the memory 52 includes a read-only memory (ROM). In appropriate cases, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM) or a flash memory or a combination of two or more of these.

[0075] The bus 53 includes hardware, software or both, and multiple components are coupled to each other. For example, but not limitation, the bus 53 may include an accelerated graphics port (AGP) or other graphics bus, an enhanced industrial standard architecture (EISA) bus, a front-side bus (FSB), a hypertransport (HT) interconnect, an industrial standard architecture (ISA) bus, an infinite bandwidth interconnect, a low pin count (LPC) bus, a memory bus, a microchannel architecture (MCA) bus, a peripheral component interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a serial advanced technology attachment (SATA) bus, a video electronics standard association local (VLB) bus or other suitable bus or a combination of two or more of these. Although the embodiments of the present invention describe and illustrate a specific bus, the present invention considers any suitable bus or interconnect.

[0076] The processor 51 executes a digital geographic environment modeling method based on a computer program stored in the memory 52 .

[0077] According to some further embodiments of the present invention, the computer program may be divided into one or more units in various ways and stored in the memory, and executed by the processor to complete the present invention. The one or more units may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the device. The computer program may be divided into multiple units according to the functions of the various units in the various embodiments described above with reference to the various figures, or include the various units in the various embodiments described above with reference to the various figures. For simplicity, it will not be repeated here.

[0078] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc. The processor is the control center of the device, and various interfaces and lines are used to connect the various parts of the entire device. The device may be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server, or a part thereof. The device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the schematic diagram is merely an example of a device and does not constitute a limitation on the device.

[0079] The corresponding detailed descriptions made above with reference to the various figures are incorporated herein by reference and will not be repeated here.

[0080] The present application also proposes a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and wherein the computer program implements the method of digital geographic environment modeling when executed by a processor.

[0081] The corresponding detailed descriptions made above with reference to the various figures are incorporated herein by reference and will not be repeated here.

[0082] The computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0083] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various changes and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention. It should be noted that although the structure of the device of the present invention and the method of its operation are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in this specific order, or that all the operations shown must be performed to achieve the desired results. On the contrary, the steps depicted in the flowchart can change the order of execution. Additionally or alternatively, certain steps can be omitted, multiple steps can be combined into one step for execution, and / or one step can be decomposed into multiple steps for execution.

Claims

1. A digital geographic environment modeling method, comprising: S1. Obtaining the elevation data of each small grid in the gridded geographic environment image; S2. Calculate the slope data of each grid; S3. The slope data of each grid is recorded into a two-dimensional array of slopes, and the following 0, 1 division operation is performed on each slope data: the attribute of the grid portion whose slope data is less than a threshold is regarded as a flat area, and the slope data is reset to 0; the attribute of the grid portion whose slope data is greater than or equal to the threshold is regarded as a non-flat area, and the slope data is reset to 1; S4. Calculate the number of non-flat areas in 8 directions of the non-flat area grid, and assign the number of non-flat areas to the non-flat areas, so as to establish the boundary when the flat areas are first aggregated; S5. Aggregating the flat land, which includes: using an eight-neighborhood algorithm, wherein, for the flat area, the aggregation level is continuously aggregated from the edge of the non-flat area to the central part of the flat area away from the non-flat area, wherein the maximum aggregation level appearing in a plot indicates the size and position of the flat area plot; S6. Filtering the highest aggregation level, which includes: traversing the two-dimensional array of the aggregation results of step S5, retaining the values ​​of the elements in the two-dimensional array that are larger than the elements in other positions in its eight neighborhoods, and obtaining multiple groups of aggregated data for representing the flat area in the current data; S7. Grouping the highest aggregation level, which includes: using a breadth-first algorithm, managing each filtered data in a group and using it to represent the group level, wherein the data under the same group are configured with the same group data; and also includes: recording in each group the corner marks of the east, west, south, and north boundaries created by each group in the two-dimensional array; S8. Eight-neighborhood node generation, which includes: recording the group level and the east-west, south-north and north-south directions of the group in the group data into the eight-neighborhood nodes, and traversing the four corners of the eight-neighborhood nodes in two directions toward the center of the group until group level data appears, and recording the number of missing items in two directions of the four corners.

2. The method according to claim 1, wherein: The calculation of the slope data of each grid includes: obtaining the elevation data of a current grid and its adjacent grids in the four positive directions of east, west, south and north and the adjacent grids in the four oblique directions of northeast, northwest, southeast and southwest, respectively calculating the height difference between the elevation data of the current grid and the elevation data of each adjacent grid, calculating the slope data in each direction by comparing the height difference with the distance on the plane, and comparing the maximum slope data as the slope data of the current grid.

3. The method according to claim 2 further comprises step S9: merging eight neighborhood nodes, which comprises: When the distance between two nodes with the same group level is less than a threshold, the nodes are merged and a new eight-neighborhood node is created. The east, west, south, and north boundaries of the two merged eight-neighborhood nodes are recorded, and the missing information of the four corners of the new node is recorded according to the merged position.

4. The method according to claim 3 further comprises step S10: eight-neighborhood node overlap processing, which comprises: The relationship judgment of the overlap between all eight-neighborhood nodes is performed, and the relationship judgment includes: for each node, judging the area size relationship between it and the other eight-neighborhood nodes that overlap with it, and taking the node with the largest area in the comparison with the adjacent nodes as the node after the overlap processing.

5. The method according to claim 4 further comprises step S11: changing the type of the node represented by the largest area from 0 to 2, to indicate that the current flat area has been processed.

6. The method according to claim 5 further comprises step S12: eight-neighborhood node step iteration, which comprises sequentially repeating steps S4 to S11 for the two-dimensional array outside the flat area that has been processed.

7. The method according to claim 6 further comprises step S13: dividing the eight neighborhood nodes into blocks, which comprises: For any two adjacent eight-neighborhood nodes, the eight-neighborhood node with the larger area records all the adjacent eight-neighborhood nodes with the smaller area. The created eight-neighborhood node set is sorted according to the area size of the eight-neighborhood nodes. The node data of the node with the largest area is taken out first, and the adjacent nodes are continuously recursively taken out, and the nodes that have been taken out are recorded to ensure that they are not taken out repeatedly when the value is taken next time.

8. The method according to claim 7 further includes filtering out eight neighborhood nodes that have no practical significance during the iteration process of the eight neighborhood node step; and / or, between steps S3 and S4, aggregating the non-flat area based on the reset slope data, resetting the value of the grid corresponding to the non-flat area whose aggregation level is lower than a threshold after aggregation to 0, and adjusting the attribute of the grid corresponding to the non-flat area from a non-flat area to a flat area.

9. A device for digital geographic environment modeling, comprising a processor and a memory; the processor executes the method according to any one of claims 1 to 8 based on a computer program stored in the memory.

10. A computer-readable storage medium storing a computer program, wherein: When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is performed.

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