CN120144685A - Cellular automaton stock land vectorization method and system - Google Patents
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Abstract
The invention belongs to the technical field of computation, in particular to an electric digital data processing, and particularly relates to a cellular automaton stock land vectorization method and a cellular automaton stock land vectorization system, wherein the cellular automaton stock land vectorization method comprises the steps that a control module acquires map data corresponding to a planned land and processes the map data; the map edge planning method comprises the steps of marking the edge of a map on processed map data through a control module, processing the edge of the map through the control module, and acquiring a land planning strategy by using a cellular automaton through the control module according to the processed map edge and the processed map data, so that the data at the map edge can be accurately acquired, and the land planning strategy at the map edge can be accurately acquired after the cellular automaton is input subsequently.
Description
Technical Field
The invention belongs to the technical field of computation, in particular to an electric digital data processing method and system, and particularly relates to a cellular automaton stock land vectorization method and system.
Background
When vectorization is carried out on the data of the map, the edge region cannot form a complete cell, so that analysis of the data by the cellular automaton is affected, the data corresponding to the edge region cannot be accurately acquired due to division of administrative regions in the edge region of the map, and the edge region cannot be accurately analyzed during vectorization analysis.
Therefore, due to the technical problem that the data of the map edge area cannot be accurately analyzed, a vectorization method and a vectorization system for the stock of the cellular automaton are required.
It should be noted that the above information disclosed in this background section is only for understanding the background of the inventive concept and therefore the above description is not to be construed as constituting prior art information.
Disclosure of Invention
The embodiment of the disclosure at least provides a cellular automaton stock land vectorization method and a cellular automaton stock land vectorization system.
In a first aspect, an embodiment of the present disclosure provides a cellular automaton stock land vectorization method, including:
the control module acquires map data corresponding to the land to be planned and processes the map data;
marking the edges of the map on the processed map data by a control module;
processing the edge of the map through a control module;
and acquiring a land planning strategy by using a cellular automaton according to the processed map edge and the processed map data through a control module.
In an alternative embodiment, the method for obtaining the land planning strategy by using the cellular automaton according to the processed map edge and the processed map data by the control module includes:
acquiring current actual state parameters corresponding to the processed map edges through a control module, and acquiring corresponding current actual state parameters of other positions in the map data except the map edges;
And acquiring a land planning strategy corresponding to the land to be planned by the control module according to the current actual state parameters corresponding to the processed map edges, the corresponding current actual state parameters and constraint factors of other positions in the map data after the map edges are removed by adopting a cellular automaton.
In an alternative embodiment, the method for obtaining map data corresponding to a land to be planned by the control module and processing the map data includes:
After the control module obtains the map data corresponding to the land needing planning, the map data is rasterized, then the edges of the map are marked in the rasterized map data, and the current actual state parameters of other grid areas after the edges are removed are marked in the map data.
In an alternative embodiment, the method for processing the edge of the map by the control module includes:
Judging whether the edge of the map corresponds to the next-stage map or not through the control module, and selecting a corresponding processing strategy by the control module according to a judging result so as to mark the corresponding current actual state parameter on the edge of the map.
In an alternative implementation manner, if the control module judges that the edge of the map has a corresponding next-level map, the control module acquires the next-level map, acquires current actual state parameters of each grid in the next-level map, draws out an edge area of the map corresponding to the planned land in the next-level map, divides the edge area according to the types of cell types in the drawn-out edge area, and acquires theoretical current state parameters of each area according to historical state parameters of the area and through the corresponding cell types in each divided area;
The control module compares the theoretical present state parameter of each area with the current actual state parameter, and obtains the most matched cell type according to the comparison result, namely, the closer the theoretical present state parameter is to the current actual state parameter, the more matched the cell type is indicated;
the control module combines the current actual state parameters of the corresponding grids of the edge area in the map data in the next level map to obtain the current actual state parameters of the edge area in the map data.
In an alternative embodiment, if the control module determines that the map edge does not have a map corresponding to the next level, the control module determines the proportion of each grid occupying the standard grid in the region corresponding to the map edge, and determines the filling strategy of each grid according to the proportion, that is
If the ratio is lower than the preset minimum ratio, the current actual state parameter of the grid is the current actual state parameter of the adjacent grid in the edge surrounding range of the grid;
If the ratio is higher than the preset maximum ratio and is smaller than 1, the current actual state parameter of the grid missing part is the latest historical state parameter of the grid missing part, and the current actual state parameter of the grid is the combination of the current actual state parameter corresponding to the part of the grid located in the area surrounded by the edge of the map and the latest historical state parameter of the grid missing part;
If the proportion is between the preset minimum proportion and the preset maximum proportion, the current actual state parameter corresponding to the part with the same proportion of the part of the grid in the map edge enclosing area in the grid missing part is the latest historical state parameter, the current actual state parameter of the rest part in the grid missing part is the current actual state parameter of the adjacent grid in the edge enclosing range, and the current actual state parameter of the grid is the combination of the current actual state parameter corresponding to the part with the same proportion of the part of the grid in the map edge enclosing area, the current actual state parameter of the rest part in the grid missing part and the current actual state parameter corresponding to the part of the grid in the map edge enclosing area.
In an alternative embodiment, the control module obtains the land planning strategy corresponding to the land to be planned through the cellular automaton according to the current actual state parameters of other grid areas after the edge areas are removed from the map data and the current actual state parameters corresponding to the edges of the map.
In a second aspect, embodiments of the present disclosure further provide a cellular automaton stock land vectorization system, including:
the acquisition module is configured to acquire map data corresponding to the land to be planned and process the map data;
A marking module configured to mark an edge of a map on the processed map data;
A processing module configured to process edges of the map;
And a policy module configured to employ cellular automaton to obtain a geodesic policy based on the processed map edges and the processed map data.
In a third aspect, the disclosed embodiments also provide a computer readable storage medium having stored thereon a computer program/instruction which, when executed by a processor, implements the steps of the cellular automaton stock location vectorization method described above.
In a fourth aspect, the presently disclosed embodiments also provide a computer program product comprising computer programs/instructions which, when executed by a processor, implement the steps of the cellular automaton stock land vectorization method described above.
The land used vectorization method for the cellular automaton has the advantages that the control module obtains map data corresponding to the land to be planned and processes the map data, the control module marks the edges of the map on the processed map data, the control module processes the edges of the map, the control module adopts the land used planning strategy according to the processed map edges and the processed map data, and further accurate data acquisition at the edges of the map is achieved, so that the land used planning strategy at the edges of the map can be accurately obtained after the cellular automaton is input subsequently.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a cellular automaton stock land vectorization method provided by an embodiment of the disclosure;
Fig. 2 (a) is a schematic diagram of a von neumann mode provided by an embodiment of the present disclosure;
FIG. 2 (b) is a schematic diagram of Moore mode according to an embodiment of the present disclosure;
FIG. 2 (c) is a schematic diagram of a custom neighbor rule type pattern provided by an embodiment of the present disclosure;
Fig. 3 is a schematic diagram of a filling policy of each grid according to an embodiment of the disclosure.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As used herein, the phrases "in one embodiment," "according to one embodiment," "in some embodiments," and the like generally refer to the fact that a particular feature, structure, or characteristic following the phrase may be included in at least one embodiment of the present disclosure. Thus, a particular feature, structure, or characteristic may be included within more than one embodiment of the disclosure, such that the phrases are not necessarily referring to the same embodiment. As used herein, the terms "exemplary," "exemplary," and the like are used for purposes of illustration, example, or description. Any embodiment, aspect, or design described herein as "example" or "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments, aspects, or designs. Rather, use of the terms "example," "exemplary," and the like are intended to present concepts in a concrete fashion.
When planning the storage land, a cellular automaton is adopted, state parameters of grids in map data corresponding to the land to be planned are input into the response cellular automaton, and then a land planning strategy can be generated, but the inventor finds that the grids in the border area are incomplete in the map data corresponding to the land to be planned, because the grids corresponding to the border area belong to at least two administrative areas due to factors such as administrative areas and the like, when the map data are required to be required by another administrative area, the current actual state parameters of the incomplete grids are complicated and difficult, the current actual state parameters of the incomplete grids are unclear and inaccurate, when the cellular automaton is input, the incomplete grids are not provided with accurate current actual state parameter data, the cellular automaton can divide the map into small cells, then carry the relevant calculation into a model, but the boundary of the data of the map is irregular, namely the grids in the border position of the map are incomplete, the cells in the border area are not adjacent, the boundary conditions are imperfect, the cells are not accurately predicted, and the finally acquired land strategy of the areas is inaccurate.
The defects of the scheme are all results obtained by the inventor after practice and careful study, and therefore, the discovery process of the above problems, and the solutions proposed herein by the present disclosure for the above problems, should be all the contribution of the inventors to the present disclosure in the process of the present disclosure.
It should be noted that like reference numerals and letters refer to like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
Some embodiments of the invention are described in detail below with reference to the accompanying drawings. The following embodiments and features of the embodiments may be combined with each other without conflict.
As shown in FIG. 1, at least one disclosed embodiment provides a cellular automaton stock land vectorization method, which comprises the steps of acquiring map data corresponding to a land to be planned by a control module, processing the map data, marking edges of a map on the processed map data by the control module, processing the edges of the map by the control module, adopting a cellular automaton land planning strategy according to the processed map edges and the processed map data by the control module, and further realizing accurate acquisition of data at the edges of the map, so that the land planning strategy at the edges of the map can be accurately acquired after the cellular automaton is input subsequently.
In this embodiment, after the current actual state parameters of the edge areas in the map data are acquired, the land planning strategies corresponding to the areas can be acquired more accurately, so that the land planning is more accurate and reasonable.
In an optional implementation manner, the method for acquiring the land planning strategy by using the cellular automaton according to the processed map edge and the processed map data comprises the steps of acquiring current actual state parameters corresponding to the processed map edge and corresponding current actual state parameters of other positions of the map data except the map edge by using the control module, and acquiring the land planning strategy corresponding to the land to be planned by using the cellular automaton according to the current actual state parameters corresponding to the processed map edge, the corresponding current actual state parameters of other positions of the map data except the map edge and constraint factors by using the control module.
In this embodiment, the state parameters corresponding to the grids in the map data may include land properties (such as commercial land, planting land, etc.), land types (such as mountain land, plain, river, etc.), etc., so as to facilitate accurate determination of the state of each grid in the map data and facilitate accurate subsequent generation of application land planning strategies.
In the embodiment, the constraint factors can comprise traffic factors (such as needed traffic conditions), city development boundaries, development potential, economic factors, land type restrictions and the like, the constraint factors can be set or adjusted according to actual demands, and land planning strategies can be acquired more accurately through the constraint factors and current actual state parameters of grids.
In an alternative implementation mode, the control module acquires map data corresponding to the land needing planning and processes the map data, and the control module acquires the map data corresponding to the land needing planning, performs rasterization division on the map data, marks edges of a map in the rasterized map data and marks current actual state parameters of other grid areas after the edges are removed in the map data.
In this embodiment, by performing rasterization division on the map data, a corresponding current actual state parameter may be marked in each grid, so that the cellular automaton may be conveniently input for processing.
In an alternative implementation mode, the method for processing the edges of the map through the control module comprises the steps of judging whether the edges of the map correspond to the next-level map through the control module, and selecting a corresponding processing strategy according to a judging result by the control module so as to mark corresponding current actual state parameters on the edges of the map.
In this embodiment, the map data corresponding to the map data of the next level, that is, the map with a larger scale than the current map data, for example, the map data corresponding to the planned land is part of the district-level administrative district, the next level map is town, and the next level map can display the content in more detail.
In an alternative implementation manner, if the control module judges that the edge of the map has a corresponding next-level map, the control module acquires the next-level map, acquires current actual state parameters of each grid in the next-level map, draws out an edge area of the map corresponding to the planned land in the next-level map, divides the edge area according to the types of cell types in the drawn-out edge area, and acquires theoretical current state parameters of each area according to historical state parameters of the area and through the corresponding cell types in each divided area;
The control module compares the theoretical present state parameter of each area with the current actual state parameter, and obtains the most matched cell type according to the comparison result, namely, the closer the theoretical present state parameter is to the current actual state parameter, the more matched the cell type is indicated;
the control module combines the current actual state parameters of the corresponding grids of the edge area in the map data in the next level map to obtain the current actual state parameters of the edge area in the map data.
As shown in fig. 2 (a), 2 (b) and 2 (c), in the present embodiment, the cell types are von neumann mode, moore mode and custom neighbor rule type mode, where the custom neighbor rule type is that 8 lattices adjacent around the central cell may exist arbitrarily. .
In this embodiment, when there is a next-level map, an edge area of the map corresponding to the planned land is drawn in the next-level map, current actual state parameters corresponding to each grid in the drawn area are acquired and marked in the next-level map, and the edge area of the map corresponding to the planned land may include more grids in the next-level map, so that the corresponding current actual state parameters are more accurate and detailed.
In this embodiment, when the cell type is performed in the next-level map, the current actual state parameter corresponding to the grid whose edge is in the missing state in the divided area in the next-level map is deleted without consideration.
In this embodiment, the current state parameter of each region theory is obtained by corresponding to the cell type according to the historical state parameter, and compared with the current actual state parameter, so that which cell type is more accurate can be determined, and the obtained cell type is given to the cellular automaton, so that the cell type of the type is also adopted in the cellular automaton.
In this embodiment, grids of missing states corresponding to edge areas in a map corresponding to planned land are performed according to needs, a range corresponding to each missing state grid is determined in a next-stage map, and current actual state parameters of grids in the range are combined to give current actual state parameters of grids of missing states corresponding to edge areas in the map corresponding to planned land.
As shown in FIG. 3, in an alternative embodiment, if the control module determines that there is no map corresponding to the next level at the edge of the map, it determines the proportion of each grid occupying the standard grid in the area corresponding to the edge of the map, and determines the filling policy of each grid according to the proportion, that is
If the ratio is lower than the preset minimum ratio, the current actual state parameter of the grid is the current actual state parameter of the adjacent grid in the edge surrounding range of the grid;
If the ratio is higher than the preset maximum ratio and is smaller than 1, the current actual state parameter of the grid missing part is the latest historical state parameter of the grid missing part, and the current actual state parameter of the grid is the combination of the current actual state parameter corresponding to the part of the grid located in the area surrounded by the edge of the map and the latest historical state parameter of the grid missing part;
If the proportion is between the preset minimum proportion and the preset maximum proportion, the current actual state parameter corresponding to the part with the same proportion of the part of the grid in the map edge enclosing area in the grid missing part is the latest historical state parameter, the current actual state parameter of the rest part in the grid missing part is the current actual state parameter of the adjacent grid in the edge enclosing range, and the current actual state parameter of the grid is the combination of the current actual state parameter corresponding to the part with the same proportion of the part of the grid in the map edge enclosing area, the current actual state parameter of the rest part in the grid missing part and the current actual state parameter corresponding to the part of the grid in the map edge enclosing area.
In this embodiment, the preset minimum proportion may be 30%, the preset maximum proportion may be 50%, if the proportion of one grid at the current edge to occupy the standard grid is 60%, that is, 60% of the grid is in the map data range corresponding to the planned land, at this time, the current actual state parameter of the remaining 40% area of the grid is directly obtained from the historical data, and the current actual state parameter of the grid may be the current actual state parameter corresponding to the 60% grid and the 40% recent historical state parameter.
In this embodiment, if the proportion of one grid at the current edge occupying the standard grid is 25%, the current actual state parameter of the grid is the current actual state parameter of an adjacent grid in the edge surrounding range, and if the adjacent grid is not a complete grid, the current actual state parameter of the grid occupying the standard grid at the current is 25% after the current actual state parameter of the adjacent grid is acquired according to the above method.
In this embodiment, if the proportion of one grid at the present edge to occupy the standard grid is 40%, the current actual state parameter of that grid is 40% of the current actual state parameter, 40% of the most recent historical state parameter, 20% of the current actual state parameter of the adjacent one grid.
In this embodiment, the latest historical state parameter is acquired from the remote sensing data in the latest time, for example, the remote sensing data is updated every 10 days, and then the latest historical state parameter is the state parameter acquired in the latest remote sensing data updating time with the current time.
In an alternative embodiment, the control module obtains the land planning strategy corresponding to the land to be planned through the cellular automaton according to the current actual state parameters of other grid areas after the edge areas are removed from the map data and the current actual state parameters corresponding to the edges of the map.
At least one other disclosed embodiment also provides a cellular automaton stock land vectorization system comprising an acquisition module configured to acquire map data corresponding to a land to be planned and process the map data, a marking module configured to mark edges of a map on the processed map data, a processing module configured to process the edges of the map, and a policy module configured to employ a cellular automaton acquisition land planning policy based on the processed map edges and the processed map data.
In this embodiment, each module is a virtual module, and its functions may be integrated in the control module.
At least one other disclosed embodiment also provides a computer-readable storage medium having stored thereon a computer program/instruction which, when executed by a processor, implements the steps of the cellular automaton stock location vectorization method described above.
At least one other disclosed embodiment also provides a computer program product comprising computer programs/instructions which, when executed by a processor, implement the steps of the cellular automaton stock land vectorization method described above.
In summary, the cellular automaton stock land vectorization method comprises the steps of acquiring map data corresponding to a land to be planned by a control module, processing the map data, marking edges of a map on the processed map data by the control module, processing the edges of the map by the control module, and acquiring land planning strategies by the control module according to the processed map edges and the processed map data by the cellular automaton, so that accurate acquisition of data at the edges of the map is realized, and the land planning strategies at the edges of the map can be accurately acquired after the cellular automaton is input subsequently.
The disclosure and other solutions, examples, embodiments, modules and functional operations described in this document may be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this document and their structural equivalents, or in combinations of one or more of them. The disclosure and other embodiments may be implemented as one or more computer program products, i.e., one or more modules of computer program instructions encoded on a tangible and non-volatile computer readable medium for execution by, or to control the operation of, data processing apparatus. The computer readable medium can be a machine-readable storage device, a machine-readable storage substrate, a memory device, a composition of matter effecting a machine-readable propagated signal, or a combination of one or more of them. The apparatus may include, in addition to hardware, code that creates an execution environment for a computer program, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them. The propagated signal is an artificially generated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal, that is generated to encode information for transmission to suitable receiver apparatus.
A computer program (also known as a program, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. The computer program does not necessarily correspond to a file in a file system. A program may be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program, or in multiple coordinated files (e.g., files that store one or more modules, sub-programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers at one site or distributed across multiple sites and interconnected by a communication network.
The processes and logic flows described in this document can be performed by one or more programmable processors executing one or more computer programs to perform functions by operating on input data and generating output. The processing and logic flows may also be performed by, and apparatus may be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit).
Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more of any kind of digital computer. Generally, a processor will receive instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer are a processor for executing instructions and one or more memory devices for storing instructions and data. Typically, a computer will also include one or more mass storage devices, such as magnetic, magneto-optical disks, or optical disks, for storing data, or be operatively coupled to receive data from or transfer data to mass storage devices, or both. However, a computer does not necessarily have such a device. Computer readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices including by way of example semiconductor memory devices such as erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM) and flash memory devices, magnetic disks such as internal hard disks or removable disks, magneto-optical disks, and compact disk read-only memory (CD ROM) and digital versatile disk read-only memory (DVD-ROM) disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.
Although several embodiments are provided in this disclosure, it should be understood that the disclosed systems and methods may be embodied in many other specific forms without departing from the spirit or scope of the present disclosure. The present examples are to be considered as illustrative and not restrictive, and the intention is not to be limited to the details given herein. For example, various elements or components may be combined or integrated in another system, or certain features may be omitted or not implemented.
In the several embodiments provided herein, it should be understood that the disclosed apparatus and methods may be implemented in other ways as well. The apparatus embodiments described above are merely illustrative, for example, of the flowcharts and block diagrams in the figures that illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
With the above-described preferred embodiments according to the present invention as an illustration, the above-described descriptions can be used by persons skilled in the relevant art to make various changes and modifications without departing from the scope of the technical idea of the present invention. The technical scope of the present invention is not limited to the description, but must be determined according to the scope of claims.
Claims (10)
1.A cellular automaton stock land vectorization method, comprising:
the control module acquires map data corresponding to the land to be planned, and processes the map data to mark the map edge and the current actual state parameters of each grid in the map data;
marking the edges of the map on the processed map data by a control module;
processing the edge of the map through a control module;
and acquiring a land planning strategy by using a cellular automaton according to the processed map edge and the processed map data through a control module.
2. The cellular automaton inventory land vectorization method of claim 1, wherein:
The method for acquiring the floor planning strategy by using the cellular automaton according to the processed map edge and the processed map data through the control module comprises the following steps:
acquiring current actual state parameters corresponding to the processed map edges through a control module, and acquiring corresponding current actual state parameters of other positions in the map data except the map edges;
And acquiring a land planning strategy corresponding to the land to be planned by the control module according to the current actual state parameters corresponding to the processed map edges, the corresponding current actual state parameters and constraint factors of other positions in the map data after the map edges are removed by adopting a cellular automaton.
3. The cellular automaton inventory land vectorization method of claim 1, wherein:
The control module acquires map data corresponding to the land to be planned, and the method for processing the map data comprises the following steps:
After the control module obtains the map data corresponding to the land needing planning, the map data is rasterized, then the edges of the map are marked in the rasterized map data, and the current actual state parameters of other grid areas after the edges are removed are marked in the map data.
4. The cellular automaton inventory land vectorization method of claim 3, wherein:
the method for processing the edges of the map through the control module comprises the following steps:
Judging whether the edge of the map corresponds to the next-stage map or not through the control module, and selecting a corresponding processing strategy by the control module according to a judging result so as to mark the corresponding current actual state parameter on the edge of the map.
5. The cellular automaton inventory land vectorization method of claim 4, wherein:
If the control module judges that the edge of the map corresponds to the next-level map, the control module acquires the next-level map, acquires current actual state parameters of each grid in the next-level map, draws out an edge area of the map corresponding to the planned land in the next-level map, divides the edge area according to the types of cell types in the drawn edge area, and acquires theoretical current state parameters of each area in each divided area according to historical state parameters of the area and through the corresponding cell types;
The control module compares the theoretical present state parameter of each area with the current actual state parameter, and obtains the most matched cell type according to the comparison result, namely, the closer the theoretical present state parameter is to the current actual state parameter, the more matched the cell type is indicated;
the control module combines the current actual state parameters of the corresponding grids of the edge area in the map data in the next level map to obtain the current actual state parameters of the edge area in the map data.
6. The cellular automaton inventory land vectorization method of claim 4, wherein:
If the control module judges that the edge of the map does not have the map corresponding to the next level, judging the proportion of each grid occupying the standard grid in the area corresponding to the edge of the map, and judging the filling strategy of each grid according to the proportion, namely
If the ratio is lower than the preset minimum ratio, the current actual state parameter of the grid is the current actual state parameter of the adjacent grid in the edge surrounding range of the grid;
If the ratio is higher than the preset maximum ratio and is smaller than 1, the current actual state parameter of the grid missing part is the latest historical state parameter of the grid missing part, and the current actual state parameter of the grid is the combination of the current actual state parameter corresponding to the part of the grid located in the area surrounded by the edge of the map and the latest historical state parameter of the grid missing part;
If the proportion is between the preset minimum proportion and the preset maximum proportion, the current actual state parameter corresponding to the part with the same proportion of the part of the grid in the map edge enclosing area in the grid missing part is the latest historical state parameter, the current actual state parameter of the rest part in the grid missing part is the current actual state parameter of the adjacent grid in the edge enclosing range, and the current actual state parameter of the grid is the combination of the current actual state parameter corresponding to the part with the same proportion of the part of the grid in the map edge enclosing area, the current actual state parameter of the rest part in the grid missing part and the current actual state parameter corresponding to the part of the grid in the map edge enclosing area.
7. The cellular automaton stock land vectorization method of claim 5 or claim 6, wherein:
And the control module acquires an application land planning strategy corresponding to the land to be planned through the cellular automaton according to the current actual state parameters of other grid areas after the edge areas are removed from the map data and the current actual state parameters corresponding to the edges of the map.
8. A cellular automaton stock land vectorization system, comprising:
the acquisition module is configured to acquire map data corresponding to the land to be planned and process the map data;
A marking module configured to mark an edge of a map on the processed map data;
A processing module configured to process edges of the map;
And a policy module configured to employ cellular automaton to obtain a geodesic policy based on the processed map edges and the processed map data.
9. A computer readable storage medium having stored thereon a computer program/instruction, which when executed by a processor, implements the steps of the cellular automaton stock location vectorization method of any of claims 1-7.
10. A computer program product comprising computer programs/instructions which, when executed by a processor, implement the steps of the cellular automaton stock location vectorization method of any of claims 1-7.
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