A visual analysis decision-making method and system based on big data and GIS

Abstract

The application discloses a kind of based on big data and GIS visual analysis decision-making method and system, its method includes: obtaining the region basic data of user input, according to the region basic data in big database call region high-order data, obtain region characteristic distribution by default GIS based on the region high-order data, according to the region characteristic distribution to target region is carried out visual space simulation, obtain simulation result, the analysis result is obtained to the simulation result, according to the analysis result generates region decision upload to user terminal.Over in big database call the high-order data of target region can obtain more reference region parameters to be used as analysis and decision index so as to make the final analysis decision result more convincing and inclusiveness, objectivity and rationality, lay a solid foundation for subsequent user to carry out region planning, improve the experience and practicality of user.

Description

Technical Field

[0001] This invention relates to the field of big data analytics, and in particular to a visualization analysis and decision-making method and system based on big data and GIS. Background Technology

[0002] With the widespread application of mobile internet and IoT technologies, the volume of spatial multi-dimensional information data is rapidly increasing. This massive amount of data presents new challenges to traditional spatial multi-dimensional data indexing. The main purpose of establishing a spatial indexing mechanism is to facilitate the location of spatial targets and various retrieval operations. To this end, scientists have proposed using large databases to store spatial multi-dimensional information data. Geographic Information Systems (GIS) are used to collect, store, manage, display, and analyze data related to the Earth's surface and spatial and geographical distribution. They are used for inputting, storing, querying, analyzing, and displaying geographic data, and can accurately generate various thematic maps required for planning. This not only improves the visualization of planning but also provides strong technical support for scientific decision-making in regional agricultural planning. Existing GIS decision-making systems analyze and make decisions based on user-input regional parameters and upload the results to the user terminal. However, these systems are limited to the basic parameters input by the user and do not include all regional information, leading to errors in the final analysis and decision-making results, which in turn leads to irrationality in subsequent regional planning and reduces the user experience. Summary of the Invention

[0003] To address the problems mentioned above, this invention provides a visualization analysis and decision-making method and system based on big data and GIS to solve the problem mentioned in the background art that existing GIS decision-making systems are limited to basic parameters input by users and do not include all information about the region, resulting in errors in the final analysis and decision-making results, leading to irrationality in subsequent regional planning and reducing the user experience.

[0004] A visualization-based analysis and decision-making method using big data and GIS includes the following steps:

[0005] Obtain basic regional data input by the user, and retrieve high-level regional data from the large database based on the basic regional data;

[0006] The regional feature distribution is obtained by pre-setting a GIS based on the high-order data of the region;

[0007] Based on the regional feature distribution, a visual spatial simulation of the target region is performed to obtain the simulation results;

[0008] The simulation results are centrally analyzed to obtain analysis results, and regional decisions are generated based on the analysis results and uploaded to the user terminal.

[0009] Preferably, the step of obtaining the user-inputted regional basic data and retrieving the regional high-level data from the large database based on the regional basic data includes:

[0010] Key regional features are obtained based on the aforementioned regional basic data;

[0011] Generate region input parameters based on the key features of the region;

[0012] The region input parameters are input into the large database to obtain the region matching data source;

[0013] Retrieve high-level regional data of the target region from the region matching data source.

[0014] Preferably, the step of obtaining the regional feature distribution based on the high-order data of the region using a preset GIS includes:

[0015] The spatial information data of the target region is determined based on the high-order data of the region.

[0016] A spatial distribution map of the target area is constructed based on the spatial information data using a pre-set GIS;

[0017] The spatial distribution map is divided into grid regions, and the element distribution characteristics of each grid region are extracted based on the division results;

[0018] The feature distribution of the region is determined based on the element distribution characteristics of each grid region and the convergence of each element feature.

[0019] Preferably, the step of performing a visual spatial simulation of the target region based on the regional feature distribution and obtaining the simulation results includes:

[0020] Determine spatial grid parameters based on the regional characteristic distribution;

[0021] Based on the spatial grid parameters, spatial modeling of the target area is performed to obtain a spatial grid model;

[0022] The time-varying parameters of the corresponding spatial grid parameters are determined based on the time-varying characteristics in the region feature distribution;

[0023] The spatial grid model is dynamically processed based on the time-varying parameters of the spatial grid parameters, and spatial simulation data of the target area is obtained through the processed spatial grid model.

[0024] Preferably, the step of centrally analyzing the simulation results, obtaining analysis results, generating regional decisions based on the analysis results, and uploading them to the user terminal includes:

[0025] Perform three-dimensional situational analysis on the simulation results to obtain the analysis results;

[0026] Based on the analysis results, determine the current status, planning positioning, functional zoning, and project estimation of the target area;

[0027] Based on the current state of the target area, a planning decision is generated for the planning and positioning; based on the functional zoning and project estimation of the target area, a project decision is generated.

[0028] The planning decisions and project decisions are integrated to generate the regional decision and then uploaded to the user terminal.

[0029] Preferably, obtaining key regional features based on the regional basic data includes:

[0030] Extract the region contour data based on the region's basic data;

[0031] Determine the periodic and discrete features in the region contour data;

[0032] Obtain the weights of the periodic and discrete features in the region contour data, and select the target feature from the two based on the weights to extract samples as key features;

[0033] The key regional features are selected from the target features based on the correlation of the feature sequences of each selected target feature.

[0034] Preferably, determining the spatial information data of the target region based on the high-order data of the region includes:

[0035] Determine the data type of the high-order data in the region, and select the target mapping method for the data based on the data type;

[0036] Extract the spatial attributes corresponding to the high-order data of the region;

[0037] Based on the spatial attributes, the spatial attributes are mapped using the target mapping method to construct an attribute mapping cluster;

[0038] The attribute mapping cluster is parsed to obtain spatial information data of the target area.

[0039] Preferably, determining the spatial grid parameters based on the regional feature distribution includes:

[0040] Retrieve the spatial grid with initial parameterization, and construct a local grid based on the initial parameterized spatial grid;

[0041] The regional feature distribution is imported into the local grid to determine the embedded feature data of each spatial grid;

[0042] The embedded parameter data of a spatial grid is determined based on the parameter variables corresponding to the embedded feature data of each spatial grid.

[0043] The grid parameters of each spatial grid are determined based on the relationship between the embedded parameter data of each spatial grid and the initialization parameters of that spatial grid.

[0044] Preferably, the step of performing three-dimensional situational analysis on the simulation results to obtain analysis results includes:

[0045] The analysis dimensions are selected based on the simulation results and the user's expected indicators for the target area.

[0046] The simulation results are subjected to three-dimensional situational analysis using the preset analysis method corresponding to the analysis dimension to obtain multi-dimensional situational data of the target area.

[0047] Internal data constraint analysis is performed on the multi-dimensional situation data, and interference data in the multi-dimensional situation data is removed according to the analysis content.

[0048] The multi-dimensional situational data after removing interference data is used as the analysis result.

[0049] A visualization analysis and decision-making system based on big data and GIS, the system comprising:

[0050] The retrieval module is used to obtain the basic regional data input by the user and retrieve the high-level regional data from the large database based on the basic regional data.

[0051] The acquisition module is used to acquire the regional feature distribution based on the high-order data of the region using a preset GIS;

[0052] The simulation module is used to perform a visual spatial simulation of the target area based on the distribution of regional characteristics and obtain simulation results;

[0053] The analysis module is used to perform centralized analysis on the simulation results, obtain analysis results, generate regional decisions based on the analysis results, and upload them to the user terminal.

[0054] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0055] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0056] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0057] Figure 1 A flowchart illustrating the workflow of a visualization analysis and decision-making method based on big data and GIS provided by this invention;

[0058] Figure 2 Another workflow diagram for a visualization analysis and decision-making method based on big data and GIS provided by this invention;

[0059] Figure 3 This is another flowchart of a visualization analysis and decision-making method based on big data and GIS provided by the present invention;

[0060] Figure 4 This is a schematic diagram of the structure of a visualization analysis and decision-making system based on big data and GIS provided by the present invention. Detailed Implementation

[0061] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0062] With the widespread application of mobile internet and IoT technologies, the volume of spatial multi-dimensional information data is rapidly increasing. This massive amount of data presents new challenges to traditional spatial multi-dimensional data indexing. The main purpose of establishing a spatial indexing mechanism is to facilitate the location of spatial targets and various retrieval operations. To this end, scientists have proposed using large databases to store spatial multi-dimensional information data. Geographic Information Systems (GIS) are used to collect, store, manage, display, and analyze data related to the Earth's surface and spatial and geographical distribution. They are used for inputting, storing, querying, analyzing, and displaying geographic data, and can accurately generate various thematic maps required for planning. This not only improves the visualization of planning but also provides strong technical support for scientific decision-making in regional agricultural planning. Existing GIS decision-making systems analyze and make decisions based on user-input regional parameters and upload the results to the user terminal. However, these systems are limited to the basic parameters input by the user and do not include all regional information, leading to errors in the final analysis and decision-making results, which in turn leads to irrationality in subsequent regional planning and reduces the user experience. To solve the above problems, this embodiment discloses a visualization analysis and decision-making method based on big data and GIS.

[0063] A visualization-based analysis and decision-making method based on big data and GIS, such as Figure 1 As shown, it includes the following steps:

[0064] Step S101: Obtain the basic regional data input by the user, and retrieve the high-level regional data from the large database based on the basic regional data;

[0065] Step S102: Obtain the regional feature distribution based on the high-order data of the region using a preset GIS;

[0066] Step S103: Perform a visual spatial simulation of the target area based on the regional feature distribution, and obtain the simulation results;

[0067] Step S104: Perform centralized analysis on the simulation results, obtain analysis results, generate regional decisions based on the analysis results, and upload them to the user terminal.

[0068] The working principle of the above technical solution is as follows: acquire basic regional data input by the user, retrieve high-level regional data from a large database based on the basic regional data, obtain regional feature distribution based on the high-level regional data through a preset GIS, perform visual spatial simulation of the target region based on the regional feature distribution, obtain simulation results, perform centralized analysis on the simulation results, obtain analysis results, generate regional decisions based on the analysis results, and upload them to the user terminal.

[0069] The beneficial effects of the above technical solution are as follows: by retrieving high-level data of the target area from a large database, more referential regional parameters can be obtained as analysis and decision indicators, thereby making the final analysis and decision results more persuasive, inclusive, objective, and reasonable. This lays a solid foundation for subsequent regional planning by users, improves user experience and practicality, indirectly saves planning costs, and solves the problem that existing GIS decision-making systems are limited to basic parameters input by users and do not include all regional information, resulting in errors in the final analysis and decision results, leading to irrationality in subsequent regional planning and reduced user experience.

[0070] In one embodiment, obtaining the user-inputted regional basic data and retrieving the regional high-level data from a large database based on the regional basic data includes:

[0071] Key regional features are obtained based on the aforementioned regional basic data;

[0072] Generate region input parameters based on the key features of the region;

[0073] The region input parameters are input into the large database to obtain the region matching data source;

[0074] Retrieve high-level regional data of the target region from the region matching data source.

[0075] The beneficial effects of the above technical solution are as follows: by generating regional input parameters, the parameter matching features of the target region can be accurately generated, and then matching can be performed quickly to obtain regional matching data sources and thus obtain high-order regional data of the target region, thereby improving stability, practicality, data extraction efficiency and accuracy.

[0076] In this embodiment, key regional features are obtained based on the regional basic data, specifically as follows:

[0077] Topographic features are extracted based on the aforementioned regional basic data;

[0078] The terrain features are input into a preset terrain factor evaluation model to obtain the corresponding relevant terrain factors;

[0079] Generate a terrain factor feature set based on the feature parameters of the relevant terrain factors;

[0080] Quantitative calculations are performed using each topographic factor feature in the topographic factor feature set to obtain the calculation results;

[0081] The spatial autocorrelation range of each terrain factor is determined based on the calculation results.

[0082] The multiple terrain factors are clustered according to the spatial autocorrelation range of each terrain factor to obtain the clustering results;

[0083] The clustering coefficients of the terrain factors are determined based on the clustering results;

[0084] Key terrain factors are selected based on the clustering coefficients and the range of multimodal feature variation parameters for each terrain factor;

[0085] Construct a spatial matrix for each key terrain factor, determine the association weight value between each matrix factor in the spatial matrix and the regional basic data, and select the key matrix factors.

[0086] Integrate the key matrix factors of each key terrain factor to obtain the key terrain sequence;

[0087] The sequence features in the key terrain sequence are obtained as the key features of the region.

[0088] The beneficial effects of the above technical solution are as follows: by acquiring terrain factors to construct terrain sequences, key terrain features can be selected based on the distribution of terrain features in regional basic data, thereby obtaining key regional features and improving the objectivity and accuracy of the acquisition results.

[0089] In one embodiment, such as Figure 2As shown, the step of obtaining the regional feature distribution based on the high-order data of the region using a preset GIS includes:

[0090] Step S201: Determine the spatial information data of the target area based on the high-order data of the region;

[0091] Step S202: Construct a spatial distribution map of the target area based on the spatial information data using a preset GIS;

[0092] Step S203: Divide the spatial distribution map into grid regions, and extract the element distribution characteristics of each grid region based on the division results;

[0093] Step S204: Determine the feature distribution of the region based on the element distribution characteristics of each grid region and the convergence of each element feature.

[0094] The beneficial effects of the above technical solution are: by constructing a spatial distribution map of the target area, regional features can be extracted intuitively based on the spatial distribution map, which improves work efficiency and practicality.

[0095] In one embodiment, such as Figure 3 As shown, the step of performing a visual spatial simulation of the target region based on the regional feature distribution and obtaining simulation results includes:

[0096] Step S301: Determine the spatial grid parameters based on the regional feature distribution;

[0097] Step S302: Perform spatial modeling on the target area according to the spatial grid parameters to obtain a spatial grid model;

[0098] Step S303: Determine the time-varying parameters of the corresponding spatial grid parameters based on the time-varying characteristics in the region feature distribution;

[0099] Step S304: Dynamically process the spatial grid model according to the time-varying parameters of the spatial grid parameters, and obtain spatial simulation data of the target area through the processed spatial grid model.

[0100] The beneficial effects of the above technical solution are as follows: by dynamically processing the spatial grid model through time-varying parameters, the dynamic feature changes within the target area can be accurately determined, thereby precisely determining the spatial changes within the target area, making the final spatial simulation data more dynamic and clear, and providing a foundation for subsequent analysis and decision-making.

[0101] In one embodiment, the step of centrally analyzing the simulation results, obtaining analysis results, generating regional decisions based on the analysis results, and uploading them to the user terminal includes:

[0102] Perform three-dimensional situational analysis on the simulation results to obtain the analysis results;

[0103] Based on the analysis results, determine the current status, planning positioning, functional zoning, and project estimation of the target area;

[0104] Based on the current state of the target area, a planning decision is generated for the planning and positioning; based on the functional zoning and project estimation of the target area, a project decision is generated.

[0105] The planning decisions and project decisions are integrated to generate the regional decision and then uploaded to the user terminal.

[0106] The beneficial effects of the above technical solution are as follows: by performing three-dimensional situational analysis on the simulation results, the analysis work can be fully realized, improving the analysis efficiency. Furthermore, by generating project decisions and planning decisions and uploading them to the user terminal, reasonable project planning decisions can be intelligently generated based on the actual situation of the target area, further improving practicality and user experience.

[0107] In one embodiment, obtaining key regional features based on the regional basic data includes:

[0108] Extract the region contour data based on the region's basic data;

[0109] Determine the periodic and discrete features in the region contour data;

[0110] Obtain the weights of the periodic and discrete features in the region contour data, and select the target feature from the two based on the weights to extract samples as key features;

[0111] The key regional features are selected from the target features based on the correlation of the feature sequences of each selected target feature.

[0112] The beneficial effects of the above technical solution are as follows: by selecting target features as key feature extraction samples, the number of samples to be screened can be greatly reduced, the influence of useless features on the extraction results can be avoided, and the efficiency and accuracy of feature extraction can be improved. Furthermore, by screening key regional features based on feature sequences, the core features can be determined based on the root association of feature sequences, thus ensuring the accuracy of the screening results.

[0113] In one embodiment, determining the spatial information data of the target region based on the high-order regional data includes:

[0114] Determine the data type of the high-order data in the region, and select the target mapping method for the data based on the data type;

[0115] Extract the spatial attributes corresponding to the high-order data of the region;

[0116] Based on the spatial attributes, the spatial attributes are mapped using the target mapping method to construct an attribute mapping cluster;

[0117] The attribute mapping cluster is parsed to obtain spatial information data of the target area.

[0118] The beneficial effects of the above technical solution are: it can achieve information acquisition with minimized error and maximized accuracy based on attribute mapping clusters, thereby improving the accuracy and completeness of the acquired information.

[0119] In one embodiment, determining the spatial grid parameters based on the regional feature distribution includes:

[0120] Retrieve the spatial grid with initial parameterization, and construct a local grid based on the initial parameterized spatial grid;

[0121] The regional feature distribution is imported into the local grid to determine the embedded feature data of each spatial grid;

[0122] The embedded parameter data of a spatial grid is determined based on the parameter variables corresponding to the embedded feature data of each spatial grid.

[0123] The grid parameters of each spatial grid are determined based on the relationship between the embedded parameter data of each spatial grid and the initialization parameters of that spatial grid.

[0124] The beneficial effects of the above technical solution are: it can obtain the grid parameters of each spatial grid in detail, avoid omissions and errors, and improve stability and practicality.

[0125] In one embodiment, performing three-dimensional situational analysis on the simulation results to obtain the analysis results includes:

[0126] The analysis dimensions are selected based on the simulation results and the user's expected indicators for the target area.

[0127] The simulation results are subjected to three-dimensional situational analysis using the preset analysis method corresponding to the analysis dimension to obtain multi-dimensional situational data of the target area.

[0128] Internal data constraint analysis is performed on the multi-dimensional situation data, and interference data in the multi-dimensional situation data is removed according to the analysis content.

[0129] The multi-dimensional situational data after removing interference data is used as the analysis result.

[0130] The beneficial effects of the above technical solution are as follows: by selecting the analysis dimension, the analysis can be carried out accurately according to the user's needs, which further improves the practicality and user experience. Furthermore, by removing the interference data in the multi-dimensional situation data, the final multi-dimensional situation data can be made more detailed, which further improves the practicality.

[0131] This embodiment also discloses a visualization analysis and decision-making system based on big data and GIS, such as Figure 4 As shown, the system includes:

[0132] The retrieval module 401 is used to obtain the basic regional data input by the user and retrieve the high-level regional data from the large database based on the basic regional data.

[0133] The acquisition module 402 is used to acquire the regional feature distribution based on the high-order data of the region through a preset GIS;

[0134] Simulation module 403 is used to perform a visual spatial simulation of the target area based on the distribution of regional features and obtain simulation results;

[0135] The analysis module 404 is used to perform centralized analysis on the simulation results, obtain analysis results, generate regional decisions based on the analysis results, and upload them to the user terminal.

[0136] The working principle and beneficial effects of the above technical solution have been explained in the method claims, and will not be repeated here.

[0137] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0138] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A visualization analysis and decision-making method based on big data and GIS, characterized in that, Includes the following steps: Obtain basic regional data input by the user, and retrieve high-level regional data from the large database based on the basic regional data; The regional feature distribution is obtained by pre-setting a GIS based on the high-order data of the region; Based on the regional feature distribution, a visual spatial simulation of the target region is performed to obtain the simulation results; The simulation results are centrally analyzed to obtain analysis results, and regional decisions are generated and uploaded to the user terminal based on the analysis results. The process of obtaining basic regional data input by the user and retrieving higher-level regional data from a large database based on this basic regional data includes: Key regional features are obtained based on the aforementioned regional basic data; Generate region input parameters based on the key features of the region; The region input parameters are input into the large database to obtain the region matching data source; Retrieve high-level regional data of the target region from the region matching data source; Based on the aforementioned regional basic data, key regional features are obtained, specifically: Topographic features are extracted based on the aforementioned regional basic data; The terrain features are input into a preset terrain factor evaluation model to obtain the corresponding relevant terrain factors; Generate a terrain factor feature set based on the feature parameters of the relevant terrain factors; Quantitative calculations are performed using each topographic factor feature in the topographic factor feature set to obtain the calculation results; The spatial autocorrelation range of each terrain factor is determined based on the calculation results. Based on the spatial autocorrelation range of each terrain factor, the terrain factors are clustered to obtain the clustering results. The clustering coefficients of the terrain factors are determined based on the clustering results; Key terrain factors are selected based on the clustering coefficients and the range of multimodal feature variation parameters for each terrain factor; Construct a spatial matrix for each key terrain factor, determine the association weight value between each matrix factor in the spatial matrix and the regional basic data, and select the key matrix factors. The key matrix factors of each key terrain factor are integrated to obtain the key terrain sequence; The sequence features in the key terrain sequence are obtained as the key features of the region.

2. The visualization analysis and decision-making method based on big data and GIS according to claim 1, characterized in that, The step of obtaining the regional feature distribution based on the regional high-order data using a preset GIS includes: The spatial information data of the target region is determined based on the high-order data of the region. A spatial distribution map of the target area is constructed based on the spatial information data using a pre-set GIS; The spatial distribution map is divided into grid regions, and the element distribution characteristics of each grid region are extracted based on the division results; The feature distribution of the region is determined based on the element distribution characteristics of each grid region and the convergence of each element feature.

3. The visualization analysis and decision-making method based on big data and GIS according to claim 1, characterized in that, The step of performing a visual spatial simulation of the target region based on the regional feature distribution and obtaining the simulation results includes: Determine spatial grid parameters based on the regional characteristic distribution; Based on the spatial grid parameters, spatial modeling of the target area is performed to obtain a spatial grid model; The time-varying parameters of the corresponding spatial grid parameters are determined based on the time-varying characteristics in the region feature distribution; The spatial grid model is dynamically processed based on the time-varying parameters of the spatial grid parameters, and spatial simulation data of the target area is obtained through the processed spatial grid model.

4. The visualization analysis and decision-making method based on big data and GIS according to claim 1, characterized in that, The step of centrally analyzing the simulation results, obtaining analysis results, generating regional decisions based on the analysis results, and uploading them to the user terminal includes: Perform three-dimensional situational analysis on the simulation results to obtain the analysis results; Based on the analysis results, determine the current status, planning positioning, functional zoning, and project estimation of the target area; Based on the current state of the target area, a planning decision is generated for the planning and positioning; based on the functional zoning and project estimation of the target area, a project decision is generated. The planning decisions and project decisions are integrated to generate the regional decision and then uploaded to the user terminal.

5. The visualization analysis and decision-making method based on big data and GIS according to claim 1, characterized in that, The process of obtaining key regional features based on the regional basic data includes: Extract the region contour data based on the region's basic data; Determine the periodic and discrete features in the region contour data; Obtain the weights of the periodic and discrete features in the region contour data, and select the target feature from the two based on the weights to extract samples as key features; The key regional features are selected from the target features based on the correlation of the feature sequences of each selected target feature.

6. The visualization analysis and decision-making method based on big data and GIS according to claim 2, characterized in that, The step of determining the spatial information data of the target region based on the high-order data of the region includes: Determine the data type of the high-order data in the region, and select the target mapping method for the data based on the data type; Extract the spatial attributes corresponding to the high-order data of the region; Based on the spatial attributes, the spatial attributes are mapped using the target mapping method to construct an attribute mapping cluster; The attribute mapping cluster is parsed to obtain spatial information data of the target area.

7. The visualization analysis and decision-making method based on big data and GIS according to claim 3, characterized in that, The step of determining spatial grid parameters based on the regional feature distribution includes: Retrieve the spatial grid with initial parameterization, and construct a local grid based on the initial parameterized spatial grid; The regional feature distribution is imported into the local grid to determine the embedded feature data of each spatial grid; The embedded parameter data of a spatial grid is determined based on the parameter variables corresponding to the embedded feature data of each spatial grid. The grid parameters of each spatial grid are determined based on the relationship between the embedded parameter data of each spatial grid and the initialization parameters of that spatial grid.

8. The visualization analysis and decision-making method based on big data and GIS according to claim 4, characterized in that, The step of performing a three-dimensional situational analysis on the simulation results to obtain the analysis results includes: The analysis dimensions are selected based on the simulation results and the user's expected indicators for the target area. The simulation results are subjected to three-dimensional situational analysis using the preset analysis method corresponding to the analysis dimension to obtain multi-dimensional situational data of the target area. Internal data constraint analysis is performed on the multi-dimensional situation data, and interference data in the multi-dimensional situation data is removed according to the analysis content. The multi-dimensional situational data after removing interference data is used as the analysis result.

9. A visualization analysis and decision-making system based on big data and GIS, characterized in that, The system includes: The retrieval module is used to obtain the basic regional data input by the user and retrieve the high-level regional data from the large database based on the basic regional data. The acquisition module is used to acquire the regional feature distribution based on the high-order data of the region using a preset GIS; The simulation module is used to perform a visual spatial simulation of the target area based on the distribution of regional characteristics and obtain simulation results; The analysis module is used to perform centralized analysis on the simulation results, obtain analysis results, generate regional decisions based on the analysis results, and upload them to the user terminal. The process of obtaining basic regional data input by the user and retrieving higher-level regional data from a large database based on this basic regional data includes: Key regional features are obtained based on the aforementioned regional basic data; Generate region input parameters based on the key features of the region; The region input parameters are input into the large database to obtain the region matching data source; Retrieve high-level regional data of the target region from the region matching data source; Based on the aforementioned regional basic data, key regional features are obtained, specifically: Topographic features are extracted based on the aforementioned regional basic data; The terrain features are input into a preset terrain factor evaluation model to obtain the corresponding relevant terrain factors; Generate a terrain factor feature set based on the feature parameters of the relevant terrain factors; Quantitative calculations are performed using each topographic factor feature in the topographic factor feature set to obtain the calculation results; The spatial autocorrelation range of each terrain factor is determined based on the calculation results. Based on the spatial autocorrelation range of each terrain factor, the terrain factors are clustered to obtain the clustering results. The clustering coefficients of the terrain factors are determined based on the clustering results; Key terrain factors are selected based on the clustering coefficients and the range of multimodal feature variation parameters for each terrain factor; Construct a spatial matrix for each key terrain factor, determine the association weight value between each matrix factor in the spatial matrix and the regional basic data, and select the key matrix factors. The key matrix factors of each key terrain factor are integrated to obtain the key terrain sequence; The sequence features in the key terrain sequence are obtained as the key features of the region.

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