Cellular automaton stock land vectorization method and system

Abstract

The invention belongs to the technical field of calculation, and particularly relates to electric digital data processing, in particular to a cellular automaton stock land vectorization method and system.The cellular automaton stock land vectorization method comprises the steps that a control module obtains map data corresponding to land needing to be planned and processes the map data; marking the edge of the map on the processed map data through the control module; processing the edge of the map through the control module; the land use planning strategy is acquired by the cellular automaton according to the processed map edge and the processed map data through the control module, so that the accurate acquisition of the data at the map edge is realized, and the land use planning strategy at the map edge can be accurately acquired after the data is subsequently input into the cellular automaton.

Description

Technical Field

[0001] The present invention belongs to the field of computing technology, specifically relates to electronic digital data processing, and particularly relates to a method and system for vectorizing the stock land using a cellular automaton. Background Art

[0002] When vectorizing the data of a map, the edge areas cannot form a complete cell, which affects the analysis of the data by the cellular automaton. Due to the division of administrative regions in the edge areas of the map, the corresponding data in the edge areas cannot be accurately obtained, resulting in inaccurate analysis of the edge areas during vectorization analysis.

[0003] Therefore, due to the technical problem that the data in the edge areas of the map cannot be accurately analyzed, a method and system for vectorizing the stock land using a cellular automaton need to be proposed.

[0004] It should be noted that the above information disclosed in this background art section is only used to understand the background art of the concept of this application. Therefore, the above description is not considered as information of the prior art. Summary of the Invention

[0005] The embodiments of the present disclosure at least provide a method and system for vectorizing the stock land using a cellular automaton.

[0006] In a first aspect, the embodiments of the present disclosure provide a method for vectorizing the stock land using a cellular automaton, including: The control module acquires the map data corresponding to the land to be planned and processes the map data; The control module marks the edge of the map on the processed map data; The control module processes the edge of the map; The control module uses the cellular automaton to obtain the land use planning strategy according to the processed map edge and the processed map data.

[0007] In an optional embodiment, the method for the control module to use the cellular automaton to obtain the land use planning strategy according to the processed map edge and the processed map data includes: The control module acquires the current actual state parameters corresponding to the processed map edge, and acquires the current actual state parameters corresponding to other positions in the map data except the map edge; The control module uses the cellular automaton to obtain the land use planning strategy corresponding to the land to be planned according to the current actual state parameters corresponding to the processed map edge, the current actual state parameters corresponding to other positions in the map data except the map edge, and the constraint factor.

[0008] In an alternative embodiment, the method for the control module to obtain the map data corresponding to the land to be planned and process the map data includes: After the control module obtains the map data corresponding to the land to be planned, it rasterizes the map data, then marks the edges of the map in the rasterized map data, and marks the current actual state parameters of other raster areas after removing the edge areas in the map data.

[0009] In an alternative embodiment, the method for the control module to process the edges of the map includes: The control module determines whether there is a corresponding lower-level map for the edge of the map, and the control module selects a corresponding processing strategy according to the judgment result to mark the corresponding current actual state parameters for the edge of the map.

[0010] In an alternative embodiment, if the control module determines that there is a corresponding lower-level map for the edge of the map, the control module obtains the lower-level map, obtains the current actual state parameters of each raster in the lower-level map, outlines the edge area of the map corresponding to the land to be planned in the lower-level map, divides the edge area according to the types of cell types in the outlined edge area, and obtains the theoretical current state parameters of each area according to the historical state parameters of the area and through the corresponding cell types; The control module compares the theoretical current state parameters of each area with the current actual state parameters, and obtains the most matching cell type according to the comparison result, that is, the closer the theoretical current state parameter is to the current actual state parameter, the more matching the cell type is; The control module combines the current actual state parameters of the corresponding rasters in the lower-level map for the edge area in the map data to obtain the current actual state parameters of the edge area in the map data.

[0011] In an alternative embodiment, if the control module determines that there is no corresponding lower-level map for the edge of the map, it determines the proportion of each raster in the area corresponding to the edge of the map occupying the standard raster, and determines the filling strategy for each raster according to the proportion; that is If the proportion is lower than the preset minimum proportion, the current actual state parameter of the raster is the current actual state parameter of an adjacent raster in the edge surrounding range of the raster; If the proportion is higher than the preset maximum proportion and less than 1, the current actual state parameter of the missing part of the raster is the nearest historical state parameter of the missing part of the raster, and the current actual state parameter of the raster is the combination of the current actual state parameter of the part of the raster located within the area enclosed by the map edge and the nearest historical state parameter of the missing part of the raster; If the ratio is between the preset minimum ratio and the preset maximum ratio, in the missing part of the grid, the current actual state parameter corresponding to the part with the same proportion as the part of the grid within the area surrounded by the map edge is the most recent historical state parameter. The current actual state parameter of the remaining part in the missing part of the grid is the current actual state parameter of an adjacent grid within the edge surrounding range of the grid. 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 as the part of the grid within the area surrounded by the map edge, the current actual state parameter of the remaining part in the missing part of the grid, and the current actual state parameter corresponding to the part of the grid within the area surrounded by the map edge.

[0012] In an alternative embodiment, the control module obtains the land use planning strategy for the land to be planned through a cellular automaton based on the current actual state parameters of other grid areas after removing the edge area in the map data and the current actual state parameters corresponding to the edge of the map.

[0013] In a second aspect, an embodiment of the present disclosure further provides a cellular automaton stock land vectorization system, including: An acquisition module, configured to acquire map data corresponding to the land to be planned and process the map data; A marking module, configured to mark the edge of the map on the processed map data; A processing module, configured to process the edge of the map; A strategy module, configured to obtain the land use planning strategy by using a cellular automaton based on the processed map edge and the processed map data.

[0014] In a third aspect, an embodiment of the present disclosure further provides a computer-readable storage medium, on which a computer program / instructions are stored. When the computer program / instructions are executed by a processor, the steps of the above-mentioned cellular automaton stock land vectorization method are implemented.

[0015] In a fourth aspect, an embodiment of the present disclosure further provides a computer program product, including computer program / instructions. When the computer program / instructions are executed by a processor, the steps of the above-mentioned cellular automaton stock land vectorization method are implemented.

[0016] The beneficial effects of the present invention are as follows. The method for vectorizing the inventory land of the cellular automaton includes: the control module acquires the map data corresponding to the land to be planned and processes the map data; the control module marks the edge of the map on the processed map data; the control module processes the edge of the map; the control module adopts the cellular automaton according to the processed map edge and the processed map data to obtain the land use planning strategy, thereby realizing the accurate acquisition of the data at the map edge, so that the land use planning strategy at the map edge can be accurately obtained after being input into the cellular automaton.

[0017] Other features and advantages of the present invention will be described in the following specification, and, in part, will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention are achieved and obtained by the structures specifically pointed out in the specification and the drawings.

[0018] To make the above objectives, features, and advantages of the present invention more obvious and understandable, specific preferred embodiments are hereby given, and in conjunction with the accompanying drawings, the detailed description is as follows. Description of the Drawings

[0019] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0020] Figure 1 It is a flowchart of a method for vectorizing the inventory land of a cellular automaton provided by an embodiment of the present disclosure; Figure 2(a) is a schematic diagram of a von Neumann pattern provided by an embodiment of the present disclosure; Figure 2(b) is a schematic diagram of a Moore pattern provided by an embodiment of the present disclosure; Figure 2(c) is a schematic diagram of a custom neighbor rule type pattern provided by an embodiment of the present disclosure; Figure 3 It is a schematic diagram of the filling strategy for each grid provided by an embodiment of the present disclosure. Detailed Embodiments

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the drawings. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0022] As used herein, phrases such as "in one embodiment", "according to one embodiment", "in some embodiments", etc. generally refer to the fact that a particular feature, structure, or characteristic after such phrase may be included in at least one embodiment of the present disclosure. Thus, a particular feature, structure, or characteristic may be included in more than one embodiment of the present disclosure, such that these phrases do not necessarily refer to the same embodiment. As used herein, terms such as "example", "exemplary", etc. are used "as an example, instance, or illustration. Any embodiment, aspect, or design described herein as "example" or "exemplary" is not necessarily to be construed as preferred or superior to other embodiments, aspects, or designs. Instead, the use of terms such as "example", "exemplary", etc. is intended to present concepts in a concrete manner.

[0023] When planning the stock land, cellular automata are adopted. After inputting the state parameters of each grid in the map data corresponding to the land to be planned into the response cellular automata, a land use planning strategy can be generated. However, the inventor found that in the map data corresponding to the land to be planned, the grids in the edge area are not complete. Because of factors such as administrative regions, the grids corresponding to the edge area belong to at least two administrative regions. It is rather cumbersome and difficult to request map data from another administrative region, resulting in unclear and inaccurate current actual state parameters of these incomplete grids. When inputting into the cellular automata, there is no accurate current actual state parameter data in these incomplete grids. The cellular automata will divide the map into small cells and then bring them into the model for relevant calculations. However, the boundary of the map data is irregular, that is, the grids at the edge position of the map are incomplete. The cells in the edge area have no adjacent cells, and the boundary conditions are imperfect. The cells cannot predict accurately, resulting in inaccurate land use planning strategies finally obtained for these areas.

[0024] Regarding the defects existing in the above solutions, they are all the results obtained by the inventor after practice and careful research. Therefore, the process of discovering the above problems and the solutions proposed in the present disclosure by the present disclosure for the above problems should both be the contributions made by the inventor to the present disclosure during the process of the present disclosure.

[0025] It should be noted that: Similar reference numerals and letters indicate similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0026] The following will, with reference to the drawings, elaborate on some embodiments of the present invention. Without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0027] As Figure 1As shown, at least one disclosed embodiment provides a method for vectorizing the stock land of a cellular automaton, including: the control module acquires the map data corresponding to the land to be planned and processes the map data; the control module marks the edge of the map on the processed map data; the control module processes the edge of the map; the control module uses the cellular automaton to obtain the land use planning strategy according to the processed map edge and the processed map data, thereby achieving accurate acquisition of the data at the map edge, so that the land use planning strategy at the map edge can be accurately obtained after inputting into the cellular automaton.

[0028] In this embodiment, after obtaining the current actual state parameters of the edge regions in the map data, the land use planning strategies corresponding to these regions can be obtained more accurately, making the land use planning more accurate and reasonable.

[0029] In an alternative embodiment, the method for the control module to obtain the land use planning strategy by using the cellular automaton according to the processed map edge and the processed map data includes: the control module acquires the current actual state parameters corresponding to the processed map edge, and acquires the current actual state parameters corresponding to other positions in the map data except the map edge; the control module uses the cellular automaton to obtain the land use planning strategy corresponding to the land to be planned according to the current actual state parameters corresponding to the processed map edge, the current actual state parameters corresponding to other positions in the map data except the map edge, and the constraint factor.

[0030] In this embodiment, the state parameters corresponding to the grids in the map data may include: land nature (such as commercial land, planting land, etc.), land type (such as mountain, plain, river, etc.), etc., which is convenient for accurately determining the states of each grid in the map data and facilitating the subsequent accurate generation of the corresponding land use planning strategy.

[0031] In this embodiment, the constraint factor may include: traffic factor (such as the required traffic conditions), urban development boundary, development potential, economic factor, land use type restriction, etc.; the constraint factor can be set or adjusted according to actual needs; the land use planning strategy can be obtained more accurately through the constraint factor and the current actual state parameters of each grid.

[0032] In an alternative embodiment, the method for the control module to acquire the map data corresponding to the land to be planned and process the map data includes: after the control module acquires the map data corresponding to the land to be planned, it performs rasterization division on the map data, then marks the edge of the map in the rasterized map data, and marks the current actual state parameters of other grid regions except the edge region in the map data.

[0033] In this embodiment, by rasterizing the map data, the corresponding current actual state parameters can be marked in each grid, which is convenient for input into the cellular automaton for processing.

[0034] In an alternative embodiment, the method for the control module to process the edge of the map includes: the control module determines whether there is a corresponding next-level map at the edge of the map, and the control module selects a corresponding processing strategy according to the judgment result to mark the corresponding current actual state parameters at the edge of the map.

[0035] In this embodiment, the next-level map is a map with a larger scale than the current map data. For example, if the map data corresponding to the land to be planned is part of a district-level administrative region, the next-level map is a town, and the next-level map can display the content in more detail.

[0036] In an alternative embodiment, if the control module determines that there is a corresponding next-level map at the edge of the map, the control module obtains the next-level map, obtains the current actual state parameters of each grid in the next-level map, outlines the edge area of the map corresponding to the land to be planned in the next-level map, divides the edge area according to the types of cell types in the outlined edge area, and obtains the theoretical current state parameters of each area according to the historical state parameters of the area and through the corresponding cell types; The control module compares the theoretical current state parameters of each area with the current actual state parameters, and obtains the most matching cell type according to the comparison result, that is, the closer the theoretical current state parameter is to the current actual state parameter, the more matching the cell type is; The control module combines the current actual state parameters of the corresponding grids in the edge area of the map data in the next-level map to obtain the current actual state parameters of the edge area of the map data.

[0037] As shown in FIGS. 2(a), 2(b) and 2(c), in this embodiment, the cell types are the von Neumann mode, the Moore mode and the custom neighbor rule type mode, where the custom neighbor rule type can exist arbitrarily in the 8 grids adjacent to the central cell.

[0038] In this embodiment, when there is a next-level map, the edge area of the map corresponding to the land to be planned is outlined in the next-level map, the current actual state parameters corresponding to each grid in the outlined area in the next-level map are obtained and marked. The edge area of the map corresponding to the land to be planned in the next-level map can include more grids, so that the corresponding current actual state parameters are more accurate and detailed.

[0039] In this embodiment, when performing cell types in the next-level map, the current actual state parameters corresponding to the grids with missing edges in the divided areas of the next-level map are deleted and not considered.

[0040] In this embodiment, the theoretical current state parameters of each area are obtained through the corresponding cell types according to the historical state parameters and compared with the current actual state parameters, so as to judge which cell type is more accurate, and the obtained cell type is assigned to the cellular automaton, so that the same type of cell is also adopted in the cellular automaton.

[0041] In this embodiment, for the grids with missing states corresponding to the edge areas in the map corresponding to the planned land as required, the range corresponding to each grid with a missing state is determined in the next-level map, and the current actual state parameters of each grid in this range are combined to assign the current actual state parameters to the grids with missing states corresponding to the edge areas in the map corresponding to the planned land.

[0042] As Figure 3 shown, in an alternative embodiment, if the control module determines that there is no corresponding next-level map 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 judges the filling strategy of each grid according to the proportion; that is if the proportion is lower than the preset minimum proportion, the current actual state parameter of this grid is the current actual state parameter of an adjacent grid in the edge surrounding range of this grid; if the proportion is higher than the preset maximum proportion and less than 1, the current actual state parameter of the missing part of this grid is the nearest historical state parameter of the missing part of the grid, and the current actual state parameter of this grid is the combination of the current actual state parameter of the part of the grid located within the area surrounded by the map edge and the nearest historical state parameter of the missing part of the grid; if the proportion is between the preset minimum proportion and the preset maximum proportion, for the missing part of this grid, the current actual state parameter corresponding to the part with the same proportion as the part of the grid located within the area surrounded by the map edge is the nearest historical state parameter, the current actual state parameter of the remaining part of the missing part of this grid is the current actual state parameter of an adjacent grid in the edge surrounding range of this grid, and the current actual state parameter of this grid is the combination of the current actual state parameter corresponding to the part with the same proportion as the part of the grid located within the area surrounded by the map edge, the current actual state parameter of the remaining part of the missing part of the grid, and the current actual state parameter of the part of the grid located within the area surrounded by the map edge.

[0043] In this embodiment, the preset minimum ratio can be 30%, and the preset maximum ratio can be 50%. If the ratio of a grid at the edge to the standard grid is 60% now, that is, 60% of the grid is within the map data range of the land to be planned, then the current actual state parameters of the remaining 40% area of the grid are directly obtained from the historical data, the most recent historical state parameters. Then the current actual state parameters of the grid can be the current actual state parameters corresponding to 60% of the grid and the 40% most recent historical state parameters.

[0044] In this embodiment, if the ratio of a grid at the edge to the standard grid is 25% now, then the current actual state parameters of the grid are the current actual state parameters of an adjacent grid within the edge surrounding range. If the adjacent grid is not a complete grid either, then according to the above method, after obtaining the current actual state parameters of the adjacent grid, the current actual state parameters of the grid with a ratio of 25% to the standard grid currently occupied are obtained.

[0045] In this embodiment, if the ratio of a grid at the edge to the standard grid is 40% now, then the current actual state parameters of the grid are 40% of the current actual state parameters, 40% of the most recent historical state parameters, and 20% of the current actual state parameters of an adjacent grid.

[0046] In this embodiment, the most recent historical state parameters are obtained from the remote sensing data at the most recent time. For example, if the remote sensing data is updated every 10 days, then the most recent historical state parameters are the state parameters obtained at the remote sensing data update time closest to the current time.

[0047] In an alternative embodiment, the control module obtains the land use planning strategy for the land to be planned through a cellular automaton based on the current actual state parameters of other grid areas after removing the edge area in the map data and the current actual state parameters corresponding to the edge of the map.

[0048] At least one other disclosed embodiment also provides a cellular automaton inventory land vectorization system, including: an acquisition module configured to acquire the map data corresponding to the land to be planned and process the map data; a marking module configured to mark the edge of the map on the processed map data; a processing module configured to process the edge of the map; and a strategy module configured to obtain the land use planning strategy using a cellular automaton based on the processed map edge and the processed map data.

[0049] In this embodiment, each module is a virtual module, and its functions can be integrated into the control module.

[0050] At least one other disclosed embodiment also provides a computer-readable storage medium having stored thereon computer programs / instructions, which when executed by a processor implement the steps of the above-mentioned cellular automata stock land vectorization method.

[0051] At least one other disclosed embodiment also provides a computer program product, including computer programs / instructions, which when executed by a processor implement the steps of the above-mentioned cellular automata stock land vectorization method.

[0052] In summary, the above-mentioned cellular automata stock land vectorization method includes: a control module obtains map data corresponding to the land to be planned and processes the map data; the control module marks the edge of the map on the processed map data; the control module processes the edge of the map; the control module adopts a cellular automata to obtain a land use planning strategy according to the processed map edge and the processed map data, thereby realizing accurate acquisition of data at the map edge, so that the land use planning strategy at the map edge can be accurately obtained after being input into the cellular automata.

[0053] The disclosures and other solutions, examples, embodiments, modules, and functional operations described in this document can be implemented in digital electronic circuits, or in computer software, firmware, or hardware, including the structures disclosed in this document and their structural equivalents, or a combination of one or more of them. The disclosed content and other embodiments can be implemented as one or more computer program products, that is, one or more modules of computer program instructions encoded on a tangible and non-volatile computer-readable medium for a data processing device to execute or control the operation of the data processing device. The computer-readable medium can be a machine-readable storage device, a machine-readable storage substrate, a storage device, a substance composition affecting a machine-readable propagation signal, or a combination of one or more of them. In addition to the hardware, the device may also include code for creating an execution environment for the computer program, for example, code constituting 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, for example, a machine-generated electrical, optical, or electromagnetic signal, which is generated to encode information for transmission to a suitable receiver device.

[0054] A computer program (also known as a program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, and 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. A computer program does not necessarily correspond to a file in a file system. The program can 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, subroutines, or portions of code). A computer program can be deployed on one or more computers to execute, which are located at one site or distributed across multiple sites and interconnected by a communication network.

[0055] The processes and logical flows described in this document can be executed by one or more programmable processors that execute one or more computer programs to perform functions by operating on input data and generating output. The processes and logical flows can also be executed by special-purpose logic circuitry, and the apparatus can also be implemented as special-purpose logic circuitry, e.g., an FPGA (Field Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit).

[0056] For example, processors suitable for executing computer programs include general and special-purpose microprocessors, and any one or more of any type of digital computer. Generally, a processor will receive instructions and data from a read-only memory or a random access memory or both. The basic components of a computer are a processor that executes instructions and one or more storage devices that store instructions and data. Generally, a computer will also include one or more mass storage devices for storing data, e.g., magnetic disks, magneto-optical disks, or optical disks, or operatively coupled to receive data from or transfer data to a mass storage device, or both. However, a computer does not necessarily have such devices. Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and memory devices, including, for 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 disc read-only memory (CD ROM) and digital versatile disc read-only memory (DVD-ROM) discs. The processor and the memory can be supplemented by, or incorporated in, special-purpose logic circuitry.

[0057] Although several embodiments are provided in the present 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 current examples are considered illustrative rather than restrictive and are not limited to the details given. For example, various elements or components may be combined or integrated into another system, or certain features may be omitted or not implemented.

[0058] In several embodiments provided herein, it should be understood that the disclosed apparatus and methods may also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the drawings show the possible architectures, functions, and operations of apparatus, methods, and computer program products according to multiple embodiments of the present invention. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system for performing the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

[0059] Based on the above inspiration from the ideal embodiments of the present invention, through the above description, relevant staff can completely make various changes and modifications without departing from the technical idea of this invention. The technical scope of this invention is not limited to the content in the specification and must be determined according to the scope of the claims.

Claims

1. A cellular automaton stock land vectorization method, characterized in that: include: The control module obtains the 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 edge of the map on the processed map data by the control module; Process the edge of the map through the control module; The control module uses cellular automation to obtain the land use planning strategy according to the processed map edges and the processed map data.

2. The method for vectorizing stock land using cellular automata according to claim 1, characterized in that: The method of obtaining the land use planning strategy by using a cellular automation according to the processed map edge and the processed map data through a control module includes: Acquire the current actual state parameters corresponding to the processed map edge through the control module, and acquire the current actual state parameters corresponding to other positions in the map data except the map edge; The control module adopts cellular automation to obtain the land use planning strategy corresponding to the land that needs to be planned according to the current actual state parameters corresponding to the processed map edge, the corresponding current actual state parameters and constraint factors of other positions in the map data after removing the map edge.

3. The method for vectorizing stock land using cellular automata according to claim 1, characterized in that: The control module obtains map data corresponding to the land to be planned, and the method for processing the map data includes: After the control module obtains the map data corresponding to the land to be planned, it rasterizes the map data, then marks the edges of the map in the rasterized map data, and marks the current actual state parameters of other grid areas after removing the edge areas in the map data.

4. The method for vectorizing stock land using cellular automata according to claim 3, characterized in that: The method for processing the edge of the map by the control module includes: The control module determines whether there is a corresponding next-level map at the edge of the map, and the control module selects a corresponding processing strategy according to the determination result to mark the corresponding current actual state parameters on the edge of the map.

5. The method for vectorizing stock land using cellular automata according to claim 4, characterized in that: If the control module determines that the edge of the map corresponds to a next-level map, the control module obtains the next-level map, obtains the current actual state parameters of each grid in the next-level map, delineates the edge area of ​​the map corresponding to the land to be planned in the next-level map, divides the edge area according to the type of cell type in the delineated edge area, and obtains the theoretical current state parameters of each area in each divided area according to the historical state parameters of the area and the corresponding cell type; The control module compares the theoretical current state parameters of each region with the current actual state parameters, and obtains the most matching cell type according to the comparison results, that is, the closer the theoretical current state parameters are to the current actual state parameters, the more matching the cell type is; The control module combines the current actual state parameters of the edge area in the map data with the corresponding grids in the next level map to obtain the current actual state parameters of the edge area in the map data.

6. The method for vectorizing stock land using cellular automata according to claim 4, characterized in that: If the control module determines that the edge of the map does not correspond to the next level map, it determines the proportion of each grid in the area corresponding to the edge of the map that occupies the standard grid, 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 an adjacent grid in the edge enclosing range of the grid; If the ratio is higher than the preset maximum ratio and less than 1, the current actual state parameters of the missing part of the grid are the most recent historical state parameters of the missing part of the grid, and the current actual state parameters of the grid are the current actual state parameters corresponding to the part of the grid within the area enclosed by the edge of the map combined with the most recent historical state parameters of the missing part of the grid; If the ratio is between the preset minimum ratio and the preset maximum ratio, the current actual state parameters corresponding to the part of the missing part of the grid that has the same proportion as the part of the grid located in the area enclosed by the edge of the map are the most recent historical state parameters, the current actual state parameters of the remaining part of the missing part of the grid are the current actual state parameters of an adjacent grid in the edge enclosed range of the grid, and the current actual state parameters of the grid are the current actual state parameters corresponding to the part of the grid that has the same proportion as the part of the grid located in the area enclosed by the edge of the map, the current actual state parameters of the remaining part of the missing part of the grid, and the current actual state parameters corresponding to the part of the grid located in the area enclosed by the edge of the map combined.

7. The method for vectorizing stock land using cellular automata according to claim 5 or claim 6, characterized in that: The control module obtains the land use planning strategy corresponding to the land to be planned through cellular automation according to the current actual state parameters of other grid areas after removing the edge areas marked in the map data and the current actual state parameters corresponding to the edges of the map.

8. A cellular automaton stock land vectorization system, characterized in that: include: An acquisition module, which is configured to acquire map data corresponding to the land to be planned and process the map data; a marking module configured to mark the edge of the map on the processed map data; A processing module configured to process the edge of the map; A strategy module is configured to obtain a land use planning strategy using a cellular automation according to the processed map edge and the processed map data.

9. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the cellular automaton stock land vectorization method described in any one of claims 1-7 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the cellular automaton stock land vectorization method described in any one of claims 1-7 are implemented.

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