Cellular automaton stock land vectorization method and system

Abstract

The application belongs to the technical field of calculation, and particularly relates to a cellular automaton stock land vectorization method and system, wherein the cellular automaton stock land vectorization method comprises the following steps: a control module acquires map data corresponding to land needing planning, 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; and the control module acquires a land planning strategy by using a cellular automaton according to the processed map edge and the processed map data, so that the data at the edge of the map is accurately acquired, and the land planning strategy at the edge of the map can be accurately acquired after the cellular automaton is input subsequently.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of computing, and particularly relates to electric digital data processing, and especially relates to a cellular automaton stock land vectorization method and system. BACKGROUND

[0002] When the data of a map is vectorized, the edge area cannot form a complete cell, thereby affecting the analysis of the data by the cellular automaton. Due to the division of administrative regions, the data corresponding to the edge area cannot be accurately obtained, so that the edge area cannot be accurately analyzed during vectorization analysis.

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

[0004] It should be noted that the above information disclosed in the background section of the present application is only used to understand the background of the present application, and therefore, the above description is not considered to constitute prior art information. SUMMARY

[0005] The present application provides at least a cellular automaton stock land vectorization method and system.

[0006] In a first aspect, the present application provides a cellular automaton stock land vectorization method, comprising:

[0007] The control module obtains map data corresponding to the land to be planned, and processes the map data;

[0008] The control module marks the edge of the map on the processed map data;

[0009] The control module processes the edge of the map;

[0010] The control module obtains a land use planning strategy by using a cellular automaton according to the processed map edge and the processed map data.

[0011] In an optional implementation, the method of obtaining a land use planning strategy by using a cellular automaton according to the processed map edge and the processed map data includes:

[0012] The control module obtains a current actual state parameter corresponding to the processed map edge, and obtains a current actual state parameter corresponding to other positions in the map data except the map edge;

[0013] The control module uses a cellular automaton to obtain a land use planning strategy for the land to be planned according to the current actual state parameter corresponding to the processed map edge, the current actual state parameter corresponding to other positions in the map data except the map edge, and a constraint factor.

[0014] In an optional embodiment, the method for the control module to obtain the map data corresponding to the land to be planned and process the map data comprises:

[0015] After the control module obtains the map data corresponding to the land to be planned, the control module 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 parameter of other grid areas after removing the edge area in the map data.

[0016] In an optional embodiment, the method for the control module to process the edge of the map comprises:

[0017] The control module determines whether the edge of the map exists corresponding next-level map, and the control module selects a corresponding processing strategy according to the determination result to mark the corresponding current actual state parameter of the edge of the map.

[0018] In an optional embodiment, if the control module determines that the edge of the map exists corresponding next-level map, the control module obtains the next-level map, obtains the current actual state parameter 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 type of the cell type in the outlined edge area, and obtains the theoretical current state parameter of each divided area according to the historical state parameter of the area and the corresponding cell type.

[0019] The control module compares the theoretical current state parameter and the current actual state parameter, and obtains the most matched cell type according to the comparison result, that is, the closer the theoretical current state parameter and the current actual state parameter, the more matched the cell type.

[0020] 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 parameter of the edge area in the map data.

[0021] In an optional embodiment, if the control module determines that the edge of the map does not exist corresponding next-level map, the control module determines the proportion of each grid occupying the standard grid in the area corresponding to the edge of the map, and determines the filling strategy of each grid according to the proportion; that is,

[0022] 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 of the grid in the edge surrounding range;

[0023] If the ratio is higher than the preset maximum ratio and lower than 1, the current actual state parameter of the missing part of the grid is the latest historical state parameter of the missing part of the grid, and the current actual state parameter of the grid is a combination of the current actual state parameter of the part of the grid in the edge surrounding range and the latest historical state parameter of the missing part of the grid;

[0024] If the ratio is between the preset minimum ratio and the preset maximum ratio, the current actual state parameter of the part of the missing part of the grid with the same ratio as the part of the grid in the edge surrounding range is the latest historical state parameter, the current actual state parameter of the remaining part of the missing part of the grid is the current actual state parameter of an adjacent grid of the grid in the edge surrounding range, and the current actual state parameter of the grid is a combination of the current actual state parameter of the part of the grid in the edge surrounding range, 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 in the edge surrounding range.

[0025] In an optional implementation, the control module obtains the land use planning strategy corresponding to the land to be planned by using a cellular automaton according to the current actual state parameters of other grid regions in the map data after the edge region is removed, and the current actual state parameters of the edge of the map.

[0026] In a second aspect, the embodiments of the present disclosure further provide a cellular automaton stock land vectorization system, comprising:

[0027] The acquisition module is configured to acquire map data corresponding to the land to be planned, and process the map data;

[0028] The marking module is configured to mark the edge of the map on the processed map data;

[0029] The processing module is configured to process the edge of the map;

[0030] The strategy module is configured to obtain the land use planning strategy by using a cellular automaton according to the processed edge of the map and the processed map data.

[0031] In a third aspect, the embodiments of the present disclosure further provide a computer readable storage medium having a computer program / instruction stored thereon, and the computer program / instruction is executed by a processor to implement the steps of the above cellular automaton stock land vectorization method.

[0032] In a fourth aspect, the present disclosure also provides a computer program product comprising computer programs / instructions for implementing the steps of the above-mentioned method for vectorizing land use of cellular automata stock.

[0033] The present application has the beneficial effect that the method for vectorizing land use of cellular automata stock comprises: a control module acquires map data corresponding to land that needs 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; and the control module acquires land use planning strategies by using cellular automata according to the processed map edges and the processed map data, thereby accurately acquiring data at the edges of the map, so that the land use planning strategies at the edges of the map can be accurately acquired after the subsequent input of cellular automata.

[0034] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application will be realized and attained by the structure particularly pointed out in the description and the drawings.

[0035] In order to make the above-mentioned objects, features and advantages of the present application more apparent, a preferred embodiment is described in detail below, and the accompanying drawings are described as follows. BRIEF DESCRIPTION OF DRAWINGS

[0036] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the drawings needed in the specific embodiments or the prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0037] Figure 1 A flowchart of a method for vectorizing land use of cellular automata stock provided by the present disclosure is shown in the figure.

[0038] Figure 2(a) is a schematic diagram of a Von Neumann mode provided by an embodiment of the present disclosure;

[0039] Figure 2(b) is a schematic diagram of a Moore mode provided by an embodiment of the present disclosure;

[0040] Figure 2(c) is a schematic diagram of a self-defined neighbor rule type mode provided by an embodiment of the present disclosure;

[0041] Figure 3 A schematic diagram of a filling strategy for each grid provided by the present disclosure is shown in the figure. DETAILED DESCRIPTION

[0042] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions of the present application will be described clearly and completely below with reference to the drawings. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0043] As used herein, the phrases "in an embodiment," "according to an embodiment," "in some embodiments," and the like generally mean the particular feature, structure, or characteristic following the phrase can be included in at least one embodiment of the present disclosure. Thus, features, structures, or characteristics can be included in more than one embodiment of the present disclosure, and the phrases "in an embodiment" and the like are not necessarily referring to the same embodiment. As used herein, the terms "for example," "e.g.," and the like indicate that the named item is one of a possible list of items. The term "example" is used to provide one or more examples of a possible implementation, aspect, or design. Any implementation, aspect or design described herein as "example" or "exemplary" is not necessarily to be construed as preferred or advantageous over other implementations, aspects, or designs. Rather, the use of the terms "example," "exemplary," and the like is intended to present concepts in a concrete manner.

[0044] When planning the inventory land, the cellular automaton is used, and the state parameters of each grid in the map data corresponding to the land to be planned are input into the responding cellular automaton to generate land planning strategies. However, the inventors find that, in the map data corresponding to the land to be planned, the grids in the edge area are not complete, because the grids in the edge area belong to at least two administrative regions due to factors such as administrative regions, and it is relatively cumbersome and difficult to ask for map data from another administrative region, so that the current actual state parameters of these incomplete grids are unclear and inaccurate. When the cellular automaton is input, there is no accurate current actual state parameter data in these incomplete grids, the cellular automaton divides the map into small cells, and then brings the cells into the model for related calculation. However, the boundary of the data of the map is irregular, that is, the grids in the edge position of the map are incomplete, the cells in the edge area have no adjacent grids, the boundary condition is imperfect, and the cells cannot be accurately predicted, so that the land planning strategies finally obtained in these regions are inaccurate.

[0045] The defects of the above solutions are the results obtained by the inventors after practice and careful research, and therefore, the discovery process of the above problems and the solutions to the above problems proposed by the present disclosure in this paper should be the contributions made by the inventors to the present disclosure in the process of the present disclosure.

[0046] It should be noted that similar reference numerals and letters refer to similar items throughout the drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0047] Some embodiments of the present application will be described in detail with reference to the drawings, which are shown by way of illustration. The following embodiments and features of the embodiments can be combined with each other, without conflict.

[0048] As shown in Figure 1 At least one disclosed embodiment provides a cellular automaton land use vectorization method, including: a control module acquires map data corresponding to 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; and the control module acquires land use planning strategies using a cellular automaton based on the processed map edges and the processed map data, thereby accurately acquiring data at the edges of the map, so that the land use planning strategy at the edges of the map can be accurately acquired after inputting the cellular automaton.

[0049] In this embodiment, the current actual state parameters of the edge regions in the map data are acquired, and the land use planning strategies corresponding to these regions can be more accurately acquired, so that the land use planning is more accurate and reasonable.

[0050] In an optional implementation, the method of acquiring land use planning strategies using a cellular automaton based on the processed map edges and the processed map data includes: the control module acquires current actual state parameters corresponding to the processed map edges, and acquires corresponding current actual state parameters of other positions in the map data except the map edges; and the control module acquires land use planning strategies corresponding to the land to be planned using a cellular automaton based on the current actual state parameters corresponding to the processed map edges, the corresponding current actual state parameters of other positions in the map data except the map edges, and constraint factors.

[0051] In this embodiment, the state parameters corresponding to the grids in the map data can include land properties (such as commercial land, planting land, etc.), land types (such as mountains, plains, rivers, etc.), and the like, which facilitates accurate determination of the states of the grids in the map data, and facilitates accurate generation of land use planning strategies.

[0052] In this embodiment, the constraint factors can include traffic factors (such as required traffic conditions), urban development boundaries, development potential, economic factors, land type restrictions, and the like; the constraint factors can be set or adjusted according to actual needs; and the land use planning strategies can be more accurately acquired based on the constraint factors and the current actual state parameters of the grids.

[0053] 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 comprises: after the control module obtains the map data corresponding to the land to be planned, the control module rasterizes and divides the map data, then marks the edges of the map in the rasterized and divided map data, and marks the current actual state parameters of other grid areas in the map data after removing the edge areas.

[0054] In the present embodiment, the rasterization and division of the map data allows the corresponding current actual state parameters to be marked in each grid, which facilitates the input of the cellular automaton for processing.

[0055] In an alternative embodiment, the method for the control module to process the edges of the map comprises: the control module determines whether the edges of the map exist corresponding next-level map, and the control module selects a corresponding processing strategy according to the determination result to mark the corresponding current actual state parameters in the edges of the map.

[0056] In the present embodiment, the next-level map is a map with a larger scale than the present map data, for example, the map data corresponding to the land to be planned is a part of a district-level administrative area, and the next-level map is a town. The next-level map can display the content in more detail.

[0057] In an alternative embodiment, if the control module determines that the edges of the map exist corresponding 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, 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 the cell types in the outlined edge area, and obtains the theoretical present state parameters of each divided area according to the historical state parameters of the area and the corresponding cell types;

[0058] The control module compares the theoretical present state parameters and the current actual state parameters of each area, and obtains the most matched cell type according to the comparison result, that is, the closer the theoretical present state parameters and the current actual state parameters, the more matched the cell type.

[0059] The control module combines the current actual state parameters of the corresponding grid 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.

[0060] As shown in FIG. 2(a), FIG. 2(b) and FIG. 2(c), in the present embodiment, the cell types are von Neumann mode, Moore mode and custom neighbor rule type mode, wherein the custom neighbor rule type can exist in any of the 8 adjacent grids around the central cell.

[0061] In this embodiment, when there is a next-level map, the edge area of ​​the map corresponding to the land that needs to be planned will be outlined in the next-level map, and the current actual state parameters corresponding to each grid in the outlined area in the next-level map will be obtained and marked. The edge area of ​​the map corresponding to the land that needs to be planned can include more grids in the next-level map, so that the corresponding current actual state parameters are more accurate and detailed.

[0062] In this embodiment, when performing cell type in the next level map, the current actual state parameters corresponding to the grids with missing edges in the divided area of ​​the next level map are deleted and not considered.

[0063] In this embodiment, the theoretical current state parameters of each region are obtained through the corresponding cell type according to the historical state parameters and compared with the current actual state parameters to determine which cell type is more accurate. The obtained cell type is assigned to the cellular automaton so that the cell type of this type is also used in the cellular automaton.

[0064] In this embodiment, according to the grids in the missing state corresponding to the edge area of ​​the map corresponding to the land that needs to be planned, the range corresponding to each grid in the missing state is determined in the next level map, and the current actual state parameters of each grid in the range are combined to assign the current actual state parameters to the grids in the missing state corresponding to the edge area of ​​the map corresponding to the land that needs to be planned.

[0065] like Figure 3 As shown, in an optional embodiment, 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 based on the proportion; that is,

[0066] 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 enclosing range of the grid;

[0067] 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. 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.

[0068] If the proportion is between the preset minimum proportion and the preset maximum proportion, the current actual state parameter corresponding to the same proportion of the grid located in the area surrounded by the edge of the map in the missing part of the grid is the nearest 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 in the edge-enclosed range, and the current actual state parameter of the grid is combined from the current actual state parameter corresponding to the same proportion of the grid located in the area surrounded by the edge of the map, 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 same proportion of the grid located in the area surrounded by the edge of the map.

[0069] In the embodiment, the preset minimum proportion can be 30%, the preset maximum proportion can be 50%, if the proportion of a grid at the edge occupying a standard grid is 60% at present, that is, 60% of the grid is in the range of the map data corresponding to the land to be planned, at this time, the current actual state parameter of the remaining 40% of the grid is directly obtained from the historical data, which is the nearest historical state parameter, and the current actual state parameter of the grid can be the current actual state parameter corresponding to 60% of the grid and 40% of the nearest historical state parameter.

[0070] In the embodiment, if the proportion of a grid at the edge occupying a standard grid is 25% at present, the current actual state parameter of the grid is the current actual state parameter of an adjacent grid in the edge-enclosed range, if the adjacent grid is also not a complete grid, the current actual state parameter of the grid occupying 25% of the standard grid is obtained according to the above method after the current actual state parameter of the adjacent grid is obtained.

[0071] In the embodiment, if the proportion of a grid at the edge occupying a standard grid is 40% at present, the current actual state parameter of the grid is 40% of the current actual state parameter, 40% of the nearest historical state parameter and 20% of the current actual state parameter of the adjacent grid.

[0072] In the embodiment, the nearest historical state parameter is obtained from the remote sensing data at the nearest time, for example, the remote sensing data is updated every 10 days, and the nearest historical state parameter is the state parameter obtained at the remote sensing data update time closest to the current time.

[0073] In an optional implementation, the control module obtains the land use planning strategy corresponding to the land to be planned by a cellular automaton according to the current actual state parameters of other grid regions in the map data except the edge region and the current actual state parameter corresponding to the edge of the map.

[0074] 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 land that needs to be planned and process the map data; a marking module configured to mark the edges of the map on the processed map data; a processing module configured to process the edges of the map; and a strategy module configured to obtain a land use planning strategy using a cellular automaton based on the processed map edges and the processed map data.

[0075] In this embodiment, each module is a virtual module, and its functions can be integrated in the control module.

[0076] At least one other disclosed embodiment also provides a computer-readable storage medium having stored thereon a computer program / instruction that, when executed by a processor, implements the steps of the above-described cellular automaton stock land vectorization method.

[0077] At least one other disclosed embodiment also provides a computer program product comprising a computer program / instruction that, when executed by a processor, implements the steps of the above-described cellular automaton stock land vectorization method.

[0078] In summary, the present cellular automaton stock land vectorization method comprises: a control module acquiring map data corresponding to land that needs to be planned and processing the map data; the control module marking the edges of the map on the processed map data; the control module processing the edges of the map; and the control module obtaining a land use planning strategy using a cellular automaton based on the processed map edges and the processed map data, thereby accurately obtaining data at the edges of the map, and enabling accurate acquisition of the land use planning strategy at the edges of the map after inputting the cellular automaton.

[0079] The disclosures and other solutions, examples, embodiments, modules and functional operations described in this document can 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 disclosed and other embodiments can 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-transitory 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 can also include code that creates an execution environment for computer programs, 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.

[0080] 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 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. A computer program does not necessarily correspond to a file in a file system. A 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 in question, 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 that are located at one site or distributed across multiple sites and are interconnected by a communication network.

[0081] 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 processes and logic flows can also be performed by special purpose logic circuitry, and that 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).

[0082] 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 processors 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. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks. However, a computer need not 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 by way of example semiconductor memory devices, e.g., erasable programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto-optical disks; and compact disc read only memories (CD ROMs) and digital versatile disc read only memories (DVD ROMs). The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.

[0083] While several embodiments have been provided in the present disclosure, it should be understood that the disclosed system and method might be embodied in many other specific forms without departing from the spirit or scope of the present disclosure. The present examples are therefore to be considered as illustrative and not restrictive, and the intention is not to limit the concepts to the details presented, which can be modified in a variety of ways. For example, the various elements or components can be combined or integrated in another system or certain features can be omitted, or not implemented.

[0084] In several embodiments provided herein, it should be understood that the disclosed apparatus and methods might be implemented in other ways. The apparatus embodiments described above are merely illustrative, for example, the flowcharts and block diagrams in the accompanying drawings show possible implementation architectures, functions and operations of the apparatus, method and computer program product according to the embodiments of the present application. In this regard, each block in the flowcharts or block diagrams can represent a module, a program segment or a part of code, which contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur in different orders than those noted in the accompanying drawings. For example, two consecutive blocks can actually be executed in parallel, or they can be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and the combination of blocks in the block diagrams and / or flowcharts, can be implemented by a dedicated hardware-based system for performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0085] The above-described embodiments according to the present application are intended to be illustrative only. Changes can be made by those skilled in the art, without departing from the scope of the present application, which is defined by the following claims. The technical scope of the present application is not limited to the above-described embodiments. The technical scope of the present application must be determined based on the scope of the claims.

Claims

1. A cellular automaton method for vectorizing stock land, 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 edges 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 land use planning strategies based on the processed map edges and processed map data; The method for processing the edge of the map by the control module includes: The control module determines whether the edge of the map corresponds to the next level 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; 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 based on 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 based on 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 grid in the next level map to obtain the current actual state parameters of the edge area in the map data; 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 based on 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 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. 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 with 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, and the current actual state parameters of the remaining part of the missing part of the grid are the current actual state parameters of the adjacent grid in the edge enclosed range. The current actual state parameters of the grid are the current actual state parameters corresponding to the part of the grid located in the area enclosed by the edge of the map with the same proportion, 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.

2. The cellular automaton method for vectorizing land inventory according to claim 1, characterized in that: The method of obtaining the land use planning strategy by using a cellular automation based on the processed map edge and the processed map data through the control module includes: Obtaining, through the control module, current actual state parameters corresponding to the processed map edge, and current actual state parameters corresponding to other positions in the map data excluding 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 based on the current actual state parameters corresponding to the processed map edge, the current actual state parameters corresponding to other positions in the map data after removing the map edge, and the constraint factors.

3. The cellular automaton method for vectorizing land inventory according to claim 1, wherein: The control module obtains map data corresponding to the land to be planned and processes the map data in a method including: 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 cellular automaton method for vectorizing land inventory according to claim 1, wherein: The control module obtains the land use planning strategy corresponding to the land to be planned through cellular automation based on 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.

5. A cellular automaton stock land vectorization system for implementing the cellular automaton stock land vectorization method according to any one of claims 1 to 4, characterized in that: include: 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 an edge of the map; The strategy module is configured to obtain a land use planning strategy by using a cellular automation according to the processed map edge and the processed map data.

6. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instruction is executed by a processor, the steps of the cellular automaton stock land vectorization method described in any one of claims 1 to 4 are implemented.

7. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the steps of the cellular automaton stock land vectorization method described in any one of claims 1 to 4 are implemented.

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