A Method for Constructing and Visualizing and Analyzing a Geo-Environmental Database

Abstract

The present invention discloses a method for constructing and visually analyzing a geopolitical environment database, which collects data on the natural resources, social economy, and scientific and technological innovation of various countries, and constructs a geopolitical environment database through cleaning, fusion, and relationship association of multi-source heterogeneous data with a long time series; by means of visualization analysis tools such as semi-variogram maps, heat maps, chord diagrams, network diagrams, flow diagrams, and story maps, from four perspectives of statistics, space, association, and evolution, it explores the role status, interaction patterns, and geopolitical relationship evolution mechanism of each geopolitical entity in the regional geopolitical relationship network. For example, a geopolitical relationship network is constructed based on events and connectivity, and the behaviors of geopolitical entities such as constraints, interactions, and identity construction in various spaces are analyzed and illustrated, and the multi-level cascades between geopolitical entities, the power spaces of each geopolitical entity, the action paths, and the transmission mechanisms are revealed in the form of knowledge graphs, video maps, story maps, etc., providing decision-making support for national geopolitical risk identification and strategy response.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing and analysis. Specifically, it relates to a method for constructing and visually analyzing a geopolitical environment database. Background Art

[0002] Geopolitics is a science that assists national strategic decision-making and has the characteristics of comprehensiveness and intersectionality. The geopolitical environment involves all internal and external environments, conditions, and elements that affect a country's survival and development, including geographical environment, geopolitical relations, and geopolitical structure, etc. It is a multi-agent, multi-element, multi-scale, multi-level, and dynamically correlated complex evolution system. Geopolitical environment analysis requires cross-level and cross-scale comprehensive analysis of the statistical, spatial, correlated, and evolutionary characteristics and influencing factors of the geopolitical environment of each geopolitical entity, especially the mechanism of causal correlation and spatial diffusion.

[0003] In today's world, geopolitical risks are constantly rising. There is an urgent need to construct a set of geopolitical environment databases and conduct visual analysis of the geopolitical environment to provide data support and visualization technology for comprehensively grasping the multi-scale geopolitical environment status, multi-element system evolution, event causal mechanism, and geopolitical environment risk prevention; and also provide method references for multi-scale, multi-element, multi-dimensional, and multi-perspective modeling and visualization of geopolitical environment analysis. Summary of the Invention

[0004] The present invention aims to provide a method for constructing and visually analyzing a geopolitical environment database, which cleans multi-source heterogeneous big data, formulates unified rules for warehousing and storage, realizes on-demand calling and automatic updating of data, and conducts multi-scale, multi-level, and multi-dimensional correlation feature and evolution analysis of geopolitical data in terms of time, space, semantics, etc., analyzes and diagrams the constraints, interactions, identity construction, and complex interest conduction behaviors of geopolitical entities in various spaces, reveals the role status, interaction patterns, and evolution paths of geopolitical entities in single-layer and multi-layer networks, depicts the multi-level cascading between geopolitical entities, divides the power spaces, action paths, and transmission mechanisms of each geopolitical entity through methods such as knowledge graphs, video maps, and story maps, and provides decision-making support for geopolitical risk identification and strategy response.

[0005] The present invention is realized by the following technical solutions:

[0006] A method for constructing and visually analyzing a geopolitical environment database is proposed, including three steps: data collection, storage, and visual analysis:

[0007] The data collection step includes:

[0008] 1) Obtain geopolitical environment data and Gdelt global event data; among them, the geopolitical environment data includes attribute data and spatial data; the attribute data includes natural resource data, socio-economic data, and scientific and technological innovation data;

[0009] 2) Clean, fuse, and extract association relationships from the data to build a unified data interface; specifically including:

[0010] Based on events and connectivity respectively, conduct data fusion and extraction of temporal, spatial, and semantic association relationships between elements, and formulate underlying data standards; among them, the said connectivity includes trade cooperation, transportation, and technology transfer;

[0011] The said extraction of association relationships includes logical association of attribute data, mutual association of connectivity data, and association of event data;

[0012] Among them, the logical association of attribute data includes: identifying the economic and cultural attributes of geopolitical entities (geopolitical relationship actors) through the names or IDs of geopolitical entities, identifying temporal associations, and identifying the mutual associations existing between different attributes; for example, the ports can construct mathematical and topological associations between attribute fields through flow direction, throughput data composition, and physical classification;

[0013] The mutual association of connectivity data includes: spatial associations of geopolitical entities in aspects of trade cooperation, transportation, and technology transfer activities; for example, the physical space associations between different geopolitical entities, the connections between different geopolitical entities in technology transfer, patent cooperation, talent flow, etc., and define the connection weights and directions according to the actual application scenarios;

[0014] The association of event data includes: causal association, geopolitical entity association, and temporal association of events; among them, the causal association includes the analysis of internal or external causes of geopolitical entities in a specified field (cooperation topics, etc.); the geopolitical entity association includes the associations in time, space, and semantics between the geopolitical entities on both sides of the event (such as causality, similarity, word frequency co-occurrence, etc.), and the temporal association is reflected as coexistence or sequential evolution;

[0015] 3) Construction of the underlying data relationships; including formulating underlying data standards for the data related to geopolitical entities and the data with geographical mapping functions, and realizing the mapping of the data of each attribute field to geographical data;

[0016] Data storage steps:

[0017] Use a distributed database to store multi-time series natural resource data, socioeconomic data, and scientific and technological innovation data;

[0018] Visualization analysis steps:

[0019] 1) Conduct four types of analyses: statistical, spatial, associative, and evolutionary analyses on event-driven, spatio-temporal multi-scale associations, and relationship network evolution;

[0020] 2) Based on the analysis, use statistical charts, thematic maps, story maps, video maps, and / or flow maps to present the current situation and spatiotemporal evolution of the geopolitical environment in multiple dimensions;

[0021] Among them, event-driven includes:

[0022] (1) For hot events, emergencies, and high-frequency events in the Gdelt global event data, the spatial pattern and spatiotemporal evolution of the gravity values of various geopolitical entities before and after the events are compared, and the correlation strength and change trend between the geopolitical entities are analyzed; the geopolitical entity gravity value is a comprehensive geopolitical environmental influence indicator composed of economy, technology, and / or other components;

[0023] (2) Combining spatial proximity analysis with spatial correlation analysis, a spatial regression analysis model of the gravity values of various geopolitical entities is constructed to reveal the influencing factors of the gravity values of geopolitical entities;

[0024] Multi-scale temporal and spatial correlations include: analyzing events at different time intervals, such as days, weeks, months, and years, or analyzing the impact of events at multiple spatial scales, such as sub-national units, countries, regions, and the world;

[0025] The evolution of the relationship network includes: analyzing the evolution characteristics of the network structure characteristics by calculating the network size, density, hierarchy, characteristic path length, clustering coefficient, degree distribution power law coefficient, and / or structural fractal dimension.

[0026] For example, a geo-relationship network is constructed based on events and connections, and the constraints, interactions, identity construction and complex interest transmission behaviors of geo-subjects in various spaces are analyzed and illustrated. The multi-layer cascades between geo-subjects are revealed through knowledge graphs, video maps, story maps, etc., and the power space, action paths and transmission mechanisms of various geo-subjects are divided.

[0027] Preferably: the statistical visualization analysis of natural resources and socio-economic data is based on the attribute values of geo-subjects or the aggregation function values of indicators, and the time axis of Echarts and D3.js is used to distinguish the time scale. Polar coordinate charts, scatter plots, bar charts, box plots, bubble charts, radar charts, etc. are used to present the data imbalance, heterogeneity, diversity and correlation characteristics, and visualize the rank-scale distribution and hierarchical characteristics of the geo-subject advantage and geo-potential.

[0028] Preferably: The spatial interaction analysis of geopolitical entities reveals the power space and distribution characteristics, spatial interaction mechanism, environmental constraints and spatial response of geopolitical entities through morphological analysis, pattern analysis, spatial autocorrelation analysis and geographically weighted regression analysis.

[0029] Preferably: The multi-layer network analysis of geopolitical entities takes each geopolitical entity as the basic modeling unit, combines the network associations in multiple aspects such as transportation, economy, and technology among different geopolitical entities, and constructs the multi-layer network of each geopolitical entity, including the interdependence network, the multiplex network, and the temporal network. Construct the tensor expression and the aggregated expression of the multi-layer network, and use the node participation coefficient and entropy to characterize the diversity and non-equilibrium of the connections of nodes in different network layers; calculate the overlap, correlation, and centrality of each network layer to reveal the compactness and robustness of the network connections. Adopt a local community division algorithm considering attribute similarity to identify the core interest groups, key links, and network energy level potential differences of multi-dimensional geopolitical relations.

[0030] Further, the method further includes constructing a database, including:

[0031] 1) Data layer separation: For the data structures and data types of the geopolitical environment data of each country, including natural resources, social economy, scientific and technological innovation, and Gdelt global event data, use the PostgreSQL hybrid mode for storage;

[0032] For example, social economy data includes attribute data and geographic data. Attribute data is usually stored in the form of a two-dimensional table (usually using a relational database), and geographic data includes map raster data and map vector data (non-relational database). In addition to social economy data, there are also relational topology data (such as trade network data and scientific and technological innovation cooperation data) and event data. Some of these data can be stored in the form of a two-dimensional table, while some must be stored using non-relational data. Using a single storage method cannot meet the application needs. This database uses the PostgreSQL hybrid mode for storage and adopts different storage methods for different data types.

[0033] 2) Logical layer association: Perform data association and query at the logical layer. On the basis of the data interfaces for adding, deleting, modifying, and querying, compile SQL encapsulation functions and encapsulate them again to implement the functions of reading, modifying, deleting, and adding business logical data for the visual analysis of the geopolitical environment.

[0034] Preferably: Customize the data interfaces for adding, deleting, modifying, and querying according to the actual application of the visual analysis of the geopolitical environment; for example, querying, modifying, adding, and deleting the geopolitical environment attributes of a certain country within a certain time period.

[0035] In addition, make associated fields according to the actual application of the visual analysis of the geopolitical environment to realize retrieving different data according to the unique associated field; for example, for the geopolitical visual analysis display of the relationship between a certain country and its surrounding countries in the geopolitical environment, it is necessary to query the separated stored data through the country ID (including social economy data, the geographic data of this country, the geopolitical relationship data between this country and its surrounding countries, trade association data, etc.).

[0036] Furthermore, clean, fuse, and extract association relationships from the data, and formulate underlying data standards, specifically including:

[0037] 1) After data cleaning, the structured data of the name, location, attributes, and time of the obtained geopolitical entities are entered into the database using a unified data standard and model;

[0038] 2) Clean and convert the formats of various types of data including spatial data, statistical data, image data, and text data, and perform data association and loading according to time, space, and semantics;

[0039] Preferably: The manifestation form of the data standard is that the field names and field types of each piece of data are consistent, and the data conversion model is to classify different data source forms for corresponding fields, and formulate unified field extraction and data collation (merging the data fields of the same geopolitical entity id).

[0040] 3) Perform georegistration, geometric correction, scale conversion, spatial aggregation, and / or schematic expression processing on the obtained vector maps and raster data;

[0041] 4) After spatial matching, semantic association, and / or temporal sequencing of the obtained trajectory data and relationship data, make a relationship dataset using a unified data standard and model;

[0042] 5) Formulate underlying data standards for all geopolitical entity-related data and data with geographical mapping functions, and realize the mapping of each attribute field data to geographical data.

[0043] Preferably: The event data is sourced from the Gdelt Global Event Database, mainly including the quantification indicators of event types, event influence, event tone, as well as the spatio-temporal information of the events. Among them, the country identifier uses the 2-digit FIPS10-4 country code and the 2-digit FIPS10-4 administrative division 1 (ADM1) code; among them, the event type is the behavior of participant 1 towards participant 2 described by the CAMEO code; the event influence score uses the Goldstein score, and each event is assigned a value between -10 and +10 to measure the potential impact of the event on the country in theory; the quantification indicator of the event tone uses the average value of the "tone" when all articles mention the event, and its value range is from -100 (extremely negative) to +100 (extremely positive).

[0044] Furthermore, perform statistics and visualization on event-driven, spatio-temporal multi-scale association, and relationship network evolution, specifically including:

[0045] 1) Statistical analysis of the natural resources and socioeconomic data of geopolitical entities: According to the statistical characteristics and presentation requirements of the data, after the operation of aggregation functions, the time axis of Echarts and D3.js is used to distinguish time scales, and polar coordinate graphs, scatter plots, bar graphs, box plots, bubble graphs, radar graphs, and / or rank clocks are used to present the non-equilibrium, diversity, correlation, and rank-size characteristics of the data, visually presenting the dominance and hierarchical characteristics of geopolitical entities;

[0046] 2) Statistical analysis of event data: Statistically analyze the quantity, type, frequency, and intensity of events in the Gdelt global event data by region, time period, and theme, and use probability graphs, chord graphs, and / or network graphs to reveal the association types, interaction modes, association directions, and / or connection intensities between geopolitical entities; Characterize and dynamically track the game characteristics between geopolitical entities; Analyze the influence of the Gdelt global event data, and statistically calculate the cumulative influence of each event on the geopolitical entities involved within a set time interval, where the value is the product of the influence of a single event and its tone quantification index; Use kernel density graphs, core-periphery graphs, cohesive subgroup division graphs, and / or multi-layer clustering graphs to reveal the individuals, core groups, and competition-cooperation relationships that play a dominant role in the geopolitical relationship network at a given spatial scale, realizing the quantitative display of the comprehensive influence of events and the excavation of the association methods and action paths between geopolitical entities.

[0047] Preferably: The statistical analysis and visualization method for trajectory and association type data is to count the total number of all trajectories between two regions, and use thick and thin degrees and color gamut range trajectory displays to replace the single trajectory display of the refined position under the region.

[0048] Furthermore, perform spatial analysis on the geopolitical environment data and Gdelt global event data, specifically including:

[0049] For the geopolitical environment data of geopolitical entities and the cumulative influence data of said geopolitical entities, through morphological analysis, pattern analysis, spatial autocorrelation analysis, and geographically weighted regression analysis, reveal the power space and distribution characteristics, spatial interaction mechanisms, environmental constraints, and spatial responses of events of each geopolitical entity; Among them, morphological analysis refers to the clustering analysis of spatial point patterns, the curvature of line elements, mesh density, geometric fractal dimension, and topological connectivity; The morphological analysis of planar elements is the morphological complexity of the boundary and filling, which is measured by geometric fractal dimension, shape index, Voronoi diagram area, and proximity relationship; Pattern analysis is used to reveal the spatial agglomeration, spatial differentiation, and scale effect of the attribute values of geopolitical entities; Spatial agglomeration is characterized by the Moran's I index, spatial differentiation is described by isolines and / or spatial interpolation, and the scale effect is described by the correspondence between geographical basic analysis units and geographical features;

[0050] Among them, for regional hotspot detection and its impact on bilateral relations, first calculate the cumulative weighted average of events of geopolitical entities in the neighborhood of the current geopolitical entity, and then calculate the ratio of the cumulative value of events of the current geopolitical entity to the cumulative weighted average of events of geopolitical entities in its neighborhood.

[0051] Use semi-variance function, kernel density map, statistical map, story map, and / or video map to present the spatial dependence, spatial heterogeneity, and spatial autocorrelation characteristics of geopolitical entities; reveal the scale effect of spatial interaction in the geopolitical environment through the analysis of the impact degree, duration, and / or impact mode of the same event in different scale spatial ranges.

[0052] Furthermore, for geopolitical environment data, construct single-layer and multi-layer networks for Gdelt global event data, specifically including:

[0053] Construct single-layer or multi-layer networks based on spatial proximity, attribute correlation, or semantic similarity, including:

[0054] Construct a geopolitical entity relationship network according to attribute relationships, including transportation network, economic and trade network, and scientific and technological innovation network;

[0055] Construct a geopolitical event network under a certain field or topic based on the topicality and semantic similarity of geopolitical events. By calculating the centrality index of nodes or connections in a single network, and their multiplicity, PageRank value, multi-feature vector centrality, multi-rank, multi-functionality, and multi-layer interaction ability value in two or more layers of networks, identify important nodes and key connections within each network layer and across network layers; use the random attack algorithm to calculate the robustness of the network and find weak points; use the multi-network community division algorithm to identify the core groups of multi-dimensional geopolitical relations; use the bus type, star type, ring type, and / or tree type layout forms, set their sizes and colors according to node attributes, and do the same for connections, and analyze the compactness, heterogeneity, hierarchical hierarchy, small world, and scale-free characteristics of the network;

[0056] Construct a semantic network based on the events in the Gdelt global event database, and present the location, actor, dissemination method, and attention heat of the events with statistical charts, kernel density maps, chord diagrams, flow diagrams, and / or knowledge graphs to depict the network conduction and feedback paths of major events; among them, in the semantic network, the size of the node represents the cumulative impact degree of a certain type of semantic event frequency, and the connection direction represents causality, action, and reaction;

[0057] Regarding the intensity of interaction between geopolitical entities, a modified gravity model is used to characterize bilateral relations: a modified gravity model for each geopolitical entity is constructed, and the gravity between each geopolitical entity is determined by the comprehensive mass and distance; among them, the comprehensive mass is the standardized total value constructed based on the natural resources, social economy, transportation, and scientific and technological innovation data of the geopolitical entity; the distance between geopolitical entities can be measured according to the Euclidean distance, traffic distance, topological distance, and the weighted comprehensive value of the three distances, and the weight of the distance is determined by expert scoring or the entropy weight method.

[0058] Preferably: taking each geopolitical entity as the basic modeling unit, combining the network associations in multiple aspects such as transportation, economy, and technology among each geopolitical entity, a multi-layer network for each geopolitical entity is constructed, including an interdependent network, a multiplex network, and a temporal network. The tensor expression and aggregation expression of the multi-layer network are constructed, and the diversity and non-equilibrium of the connections of nodes in different network layers are characterized by means of the node participation coefficient and entropy; the overlap, correlation, and centrality of each network layer are calculated to reveal the compactness and robustness of the network connection. A local community division algorithm considering attribute similarity is used to identify the core interest groups, key links, and network energy level potential differences of multi-dimensional geopolitical relations.

[0059] Preferably: a modified gravity model is used to characterize bilateral relations: a modified gravity model for each geopolitical entity is constructed, and the interaction force between each geopolitical entity is jointly determined by the comprehensive mass and distance. Among them, the comprehensive mass should consider the natural resources, social economy, transportation, and scientific and technological innovation of the geopolitical entity, and standardize the statistical indicators corresponding to these attributes, and the sum of them is the comprehensive mass of the geopolitical entity. For the measurement of distance, three types of distances, namely geographical distance, traffic distance, and topological distance, are comprehensively considered, or a weighted distance value constructed based on the three distances.

[0060] Furthermore, the method further includes:

[0061] Using open-source data and open-source frameworks to package a visualization framework, and at the same time, realizing statistical visual analysis of data, spatial pattern analysis, and visual analysis of the association and evolution between the geopolitical environment and geopolitical entities:

[0062] For geopolitical environment data, based on the data of natural resources, social economy, and scientific and technological innovation, generate annual statistical reports, change curves, heat maps, chord diagrams, and / or story maps, visually present the resource endowments, environmental profiles, and change characteristics of each geopolitical entity, and realize a single display of a single requirement for the results of the statistical analysis in claim 4.

[0063] Geo-environmental analysis requires comprehensive consideration of multiple factors, including temporal, spatial, state, and event-based geopolitical elements. Event data enriches the structured, quantitative information of the geo-environment, enabling the characterization of its uncertainty. The geo-event analysis network constructed by Power 6 can explore the evolutionary paths and mechanisms of single- and multi-layer geo-relationship networks. Event analysis combines the changes in the degree and mode of spatial interaction between geo-agents derived from Power 5 with the cross-layer connectivity and interaction paths of single- and multi-layer networks derived from Power 6. This allows for the detection of the internal drivers of bilateral relations between geo-agents, the evolution of the "cooperation-competition" model in bilateral relations, the detection of regional hotspots, and the calculation of their influence on bilateral relations.

[0064] Among them, the association of geo-subjects is achieved by detecting the internal causes of bilateral relations between geo-subjects, analyzing the evolution of the "cooperation-competition" model of bilateral relations, detecting regional hotspots, and calculating the influence on bilateral relations, so as to characterize the scope of influence and mode of action of different types of events; among them, the internal cause detection of bilateral relations between geo-subjects is obtained by calculating the cumulative influence scores of various types of events on both sides of geo-subjects, and exploring the correlation between the cumulative influence of events and the national economy, culture and resource endowment through path analysis; the evolution analysis of the "cooperation-competition" model of bilateral relations between geo-subjects includes the changes in the cooperation-competition relationship between geo-subjects before and after the event, the partners and competitors of each geo-subject, and the analysis of the changes in the direction, method, object, frequency and intensity of "cooperation-competition".

[0065] Through the above analysis, we can finally measure the strength, process, mechanism and effect of the connection between various geopolitical subjects, and reveal the identity, status, main stakeholders, discourse mutual construction and power space of various geopolitical subjects in the geopolitical relationship network at different times and scales.

[0066] Furthermore, the specific methods for analyzing the evolution of the "cooperation-competition" relationship between the two countries are as follows:

[0067] Multi-layer network data is based on shared nodes across different network layers. Geopolitical entities are interconnected across multiple networks, including spatial, social, economic, transportation, and event-based networks. Multi-layer network analysis focuses on cross-layer cascades and community structures, achieved through the analysis of node importance, multi-layer connection paths, and information transmission mechanisms.

[0068] Determining the importance of geopolitical nodes: The relationships between geopolitical entities are primarily characterized by the modified gravity model and the influence of events. Geopolitical entities can be divided into different types based on their level of development, cultural beliefs, and other factors. The importance of nodes is defined based on the frequency and intensity of their events and their overall geopolitical influence.

[0069] In the geopolitical relationship network of a region (such as the scope of the Maritime Silk Road), the size of the node represents the status of the geopolitical entity (in the fields of economic trade, transportation, technological innovation, etc., or in the comprehensive field). The specific calculation method of the node size is based on the indicators obtained through statistical analysis (selecting to construct a linear weight index model in a certain field or the comprehensive field).

[0070] For example: For economic and trade relations, analyze the trade data of two geopolitical entities in industries such as industry, agriculture, and the tertiary industry, standardize and sum up the data to obtain the economic and trade correlation values between geopolitical entities. Similarly, the correlation values of transportation (sea transportation, shipping, etc.) and technological innovation can be obtained. After standardizing the above values and summing them up, the comprehensive influence value of the two geopolitical entities is finally obtained.

[0071] Taking the country as the basic analysis unit, analyze the sea transportation network layer separately. Through degree centrality analysis, identify the key hub ports and shipping routes of the global sea transportation network, and identify the interest communities of countries at different scales through community structure analysis; identify the innovation entities in the global technological innovation cooperation network from the perspective of the industrial chain

[0072] Relationship prediction: The relationship between geopolitical entities is mainly characterized by event influence and gravity model. For example, in order to predict the competition or cooperation relationship between two geopolitical entities under the influence of the non-policy environment, statistically, historical economic trade, transportation, technological cooperation, etc. can be used as independent variables, and the bilateral relationship assignment between countries based on events can be used as the dependent variable. Through the combination of time series analysis and grey system, the bilateral relationship between countries can be predicted.

[0073] Furthermore, the encapsulated open-source data visualization framework includes: constructing a special data dashboard according to the special needs, and conducting comprehensive demonstration and integrated analysis of geopolitical environment data.

[0074] Compared with the prior art, the advantages and positive effects of the present invention are as follows: A method for constructing and visualizing and analyzing a geopolitical environment database proposed by the present invention is based on geopolitical environment attribute data and Gdelt global event data, with time, space, and semantics as the associations, performs data cleaning, model construction and fusion, and formulates underlying data standards. A unified data interface is constructed for large-scale spatio-temporal data, remote sensing image data, and vector map data. Through data layer separation and logical layer association, a multi-spatio-temporal scale geopolitical environment database is constructed. From the four perspectives of statistics, space, network, and evolution, the numerical distribution, spatial pattern, network association, and temporal evolution characteristics of each geopolitical entity are mined. Among them, the modified gravity model takes into account the comprehensive quality and various types of distances, which is conducive to revealing the competition-cooperation relationship between geopolitical entities under different distance definitions; this patent constructs a multi-layer network analysis method for geopolitical entities, and through nested analysis, cross-layer analysis, community division, etc. of single-layer and multi-layer networks, provides a new perspective for identifying core interest groups, key links, and transmission paths of multi-dimensional geopolitical relationships. In addition, the present invention adopts a variety of visual analysis means, integrates exploratory data analysis and big data analysis, and solves the urgent problems for the current analysis and application of geopolitical environment big data.

[0075] The method for expressing the present invention in the form of triples based on geopolitical environment knowledge constructs an association network diagram based on events or connections by selecting, filtering, abstracting / concretely expressing, reconfiguring, and encoding geopolitical environment elements, generates a vector-based semantic network of geopolitical knowledge, and uses a variety of analysis and visualization technologies to mine the statistical characteristics, spatial characteristics, network association characteristics, and spatio-temporal evolution characteristics of the geopolitical environment, so as to clarify the spatial power, spatial interaction, spatial response, spatial dependence, and multi-layer association of each geopolitical entity, and provide technical support for understanding the geopolitical environment potential of each country, the association characteristics of geopolitical entities, and geopolitical risk prevention, etc.

[0076] After reading the detailed description of the embodiments of the present invention in conjunction with the accompanying drawings, other features and advantages of the present invention will become clearer. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 It is a specific implementation scheme of visual analysis for a method for constructing and visualizing and analyzing a geopolitical environment database proposed by the present invention;

[0078] Figure 2 It is one of the system example diagrams of a method for constructing and visualizing and analyzing a geopolitical environment database proposed by the present invention;

[0079] Figure 3 It is the second of the system example diagrams of a method for constructing and visualizing and analyzing a geopolitical environment database proposed by the present invention;

[0080] Figure 4This is the third system example diagram of a method for constructing and visualizing an analysis of a geopolitical environment database proposed by the present invention;

[0081] Figure 5 This is the fourth system example diagram of a method for constructing and visualizing an analysis of a geopolitical environment database proposed by the present invention;

[0082] Figure 6 This is the fifth system example diagram of a method for constructing and visualizing an analysis of a geopolitical environment database proposed by the present invention;

[0083] Figure 7 This is the sixth system example diagram of a method for constructing and visualizing an analysis of a geopolitical environment database proposed by the present invention. Detailed implementation manners

[0084] The following further details the detailed implementation manners of the present invention with reference to the accompanying drawings.

[0085] A method for constructing and visualizing an analysis of a geopolitical environment database proposed by the present invention, as Figure 1 shown, includes a data collection step, a data storage step, and a visualization analysis step.

[0086] In the data collection step, it mainly involves obtaining multi-source heterogeneous data, performing data cleaning, data fusion, and formulating underlying data standards on the data; constructing a unified data interface for the multi-source heterogeneous data to achieve data association, and is used to implement functions such as massive data collection, multi-source heterogeneous data sorting, massive geographic data indexing, massive data storage, and multi-source heterogeneous data sharing.

[0087] This system takes the geopolitical environment as the research object and obtains data through the following methods: 1. Data crawling: Through web crawler technology, quickly obtain spatio-temporal big data that is multi-source, heterogeneous, multi-scale, multi-dimensional, and multi-correlated; 2. Obtaining open-source data: Through the data interfaces provided by relevant open websites, or automatically obtaining relevant data through hidden interfaces, making the data acquisition method more convenient, and then performing data crawling; 3. Some data is obtained by manual collection.

[0088] The formats and forms of data sources are diverse, including file forms, and files include csv, excel, txt text data, geographic data (administrative vector data, natural and human vector data of each country), and image data (tile raster data, satellite image data). The types of data are different, including social economy and attribute data, network association data, and event data (Gdelt). Among them, network association data belongs to the large category of relational data (network association is a relationship, but a relationship is not necessarily a network association).

[0089] Original data is usually incomplete, noisy, and inconsistent in format. This system first performs data cleaning on the original data, including handling missing data, noise, and inconsistent data. The technical means used include, but are not limited to: ignoring data, manually filling in missing data, filling in missing data with default values, filling in missing data with the mean value, filling in missing data with the mean value of the same category, and noise processing.

[0090] Among them, for csv, excel, txt table formats, and structured data, the Python language is used to clean the table. The process of sorting and cleaning different types of data is as follows:

[0091] For structured data in attribute data such as natural resources and social economy, duplicate data and irrelevant data in the table need to be removed, missing data needs to be supplemented, entire columns need to be deleted or reorganized, and only country, attribute, and time data are retained.

[0092] For network-related data, it is necessary to construct node associations, and also define node types and sizes, connection sizes and weights. For shipping and maritime transport networks, nodes and connections can be directly defined according to location and traffic volume; for cleaning social network data such as trade and technological innovation networks, it includes processing duplicate data in the data source, removing irrelevant data, filling in missing data, converting the data source format, and unifying data standards.

[0093] For trajectory data (the map representation of network-related data, which is the connection of geopolitical entities in the 2D or 3D map space), in the data source obtained through web crawlers, each trajectory needs to satisfy the integrity of the starting point coordinates and the ending point coordinates, and there are multiple data at the same time; for network-related data, it is necessary to have basic information such as node names and sizes, as well as the relationships between nodes; therefore, the data cleaning of the above two types of data includes removing duplicate data and irrelevant data, filling in missing data, converting the data source format, and unifying data standards.

[0094] For event source data, the main source of the data is the Gdelt Global Event Database. Among them, the country identifier uses the 2-digit FIPS10-4 country code and the 2-digit FIPS10-4 administrative division 1 (ADM1) code. Among them, the event type is the behavior of participant 1 towards participant 2 described by the CAMEO code; the event influence score uses the Goldstein score, and each event is assigned a value between -10 and +10 to measure the potential impact of the event on the country in theory; the quantitative index of the event tone uses the average value of the "tone" when all articles mention the event, and its value range is from -100 (extremely negative) to +100 (extremely positive).

[0095] After data cleaning, data model construction and data fusion are carried out, including:

[0096] 1. Enter the obtained geopolitical entities, attributes, and time data into the database using a unified data standard model. Extract data from different sources and types, clean, transform, and integrate it, and then load it into the database.

[0097] For the analysis of natural resources and socioeconomic indicators, it is mainly to count the frequency and spatial distribution of a certain indicator in a certain region, time, or type. According to the type of characteristics to be presented, such as non-equilibrium, concentration, difference, etc., operate and associate (aggregate) the attribute dimension; for example, to present the spatial autocorrelation of data, the attribute needs to be associated with the location; for example, to present the evolution and fluctuation of data, operations and associations (aggregations) need to be carried out in the time dimension.

[0098] 2. Use a unified data standard and model to create a relationship dataset for the obtained trajectory data and topological relationship data, and perform statistics on the basis of the created network association dataset to obtain multi-weight flow data after statistics of geopolitical entities, types, and time.

[0099] The statistical method used is to perform fusion display on the trajectory data: taking the display of trajectory data as an example, if all trajectory information is simply displayed, when the number of trajectories is large, problems such as trajectory mixing and difficulty in identification will occur, and it is also not easy to extract the trajectory distribution pattern. Therefore, statistical methods are used to perform fusion display on the trajectory data, such as counting the total number of all trajectories between two regions and using the display of trajectory thickness instead of the simultaneous display of all single trajectories.

[0100] The same method also applies to the construction of the topological relationship of network data.

[0101] 3. For the obtained geopolitical environment event data, mainly select the association method according to the analysis method to be demonstrated, event (geopolitical entity) influence analysis, and modified gravity model. Event influence analysis includes accumulating the influence of geopolitical entity events of the event and analyzing the association strength, potential, and influence between geopolitical entities.

[0102] 4. Perform georegistration, geometric correction, and remote sensing image processing on the obtained geographic vectors and remote sensing raster data.

[0103] Among them, remote sensing image processing includes, but is not limited to, comprehensive processing operations such as stretching, extraction, and fusion.

[0104] 5. After completing data cleaning, data model construction, and data fusion, formulate underlying data standards for all country-related data and data with geographical mapping functions, and realize the mapping of name field data to geographical data.

[0105] Establishing the underlying data standard includes: establishing the standard between names and coordinates; and establishing the standard between names.

[0106] The standard between name and coordinates. For example, in a large-scale geographic space, Beijing is a point and can be displayed using point coordinates containing longitude and latitude. However, China is a surface element. How to determine China's point coordinates will affect subsequent distance calculations and network flow displays. In the example of the present invention, China's location is defined as the longitude and latitude coordinates corresponding to the capital Beijing.

[0107] Standards between names. For example, different data sources may have different names for the same country. Therefore, it is necessary to build a unified naming benchmark, including English and Chinese standards. When processing data from different data sources, these names will be converted to achieve complete unification.

[0108] 6. After data cleaning, data model construction, data fusion, and formulation of underlying standards for the acquired multi-source heterogeneous data, a unified data interface is constructed for the established data set to achieve data association.

[0109] Specifically, for multi-source heterogeneous data that has been stored in the database: based on the existing data interface for addition, deletion, modification and query, SQL encapsulation functions are compiled and re-encapsulated as addition, deletion, modification and query functions to realize the reading, modification, deletion and addition functions for business data. The outermost function that connects to the front end is the data interface.

[0110] Among them, the time of a single table is sorted, and the attribute values of different countries at the same time are stored in an array; the country of a single table is sorted, and the attribute values of different times in the same country are stored in an array; for trajectory data and correlation data, the basic form of the return data structure is constructed based on the existing statistical functions, and the correlation situation is defined.

[0111] Furthermore, for the data interfaces created above that allow for the addition, deletion, modification, and query of a specific data set, it is sometimes necessary to access multiple data sets simultaneously. Therefore, when building the data interfaces, it is necessary to consider the actual application of the specific business module and create associated fields. When multiple data sets need to be accessed simultaneously, different data sets can be retrieved based on unique associated fields. For example, when searching a table for a specific attribute of a country, the output may be the trajectory data associated with that country and other attributes.

[0112] The data storage step uses a distributed database to store multi-source heterogeneous data, specifically including:

[0113] 1) For relational data storage.

[0114] 2) Store vector data, remote sensing image data, geographic object data, and JSON data formats.

[0115] 3) Implement data separation calls for different source data through data isolation; among them, data isolation includes: for relational data, construct different attribute tables for different attributes, and each table stores the values of the same attribute of each country at different times; for vector data and remote sensing image data, use PostGIS for data storage, and then publish the data through GeoServer to construct a pyramid model; for geographic object data and JSON format data, use Postgresql to store JSON objects.

[0116] In the visualization analysis step, encapsulate an open-source data visualization framework to realize the online display of data, specifically including:

[0117] 1) For attribute data of different countries at different times, use the time axis of Echarts and D3.js to distinguish time scales, call OpenStreetMap to implement two-dimensional map display, and use the Echarts framework to present scatter points;

[0118] 2) For the thematic maps produced after remote sensing image processing, store them in PostGIS, publish them on GeoServer to provide a wms interface, and use Cenium to implement the display function of the thematic maps in a three-dimensional scene;

[0119] 3) For trajectory data and network-related data, use the 3D engine in Echarts to draw the three-dimensional trajectory of the trajectory data on the basis of making textures, and use echarts to display the two-dimensional trajectory data on the basis of two-dimensional display;

[0120] 4) For the display of related data, use the network display modules in Echarts and D3.js to realize visualizations related to the display of knowledge graph networks, circular networks, etc. and related data.

[0121] In the specific application implementation, for trajectory data, after statistical expression, map it to two-dimensional and three-dimensional maps geographically; for related data, after statistical expression, map it to two-dimensional and three-dimensional maps using a set layout method; for remote sensing image data, after stretching, extraction, and fusion, project and image it on two-dimensional and three-dimensional maps.

[0122] Among them, the statistical expression includes the expression of node weights, and the expression forms are ground color, size, shape, filling, etc., and data statistical methods including but not limited to counting, summation, mean, piecewise functions, etc. are used.

[0123] The setting layout here needs to correspond to the statistical means used in data model construction and data fusion. For example, the number of trajectories between all countries, count the number of trajectories between each pair of countries, and stipulate the thickness standard of each single trajectory data after counting. With different thickness levels in different ranges, after determining the coordinates representing each country, display the trajectories of all countries on a 2D / 3D map in terms of thickness, which is a setting layout method.

[0124] In some embodiments of the present invention, it is necessary to construct system open data processing standard interfaces, analysis model standard interfaces, storage standard interfaces, and visualization standard interfaces, laying a foundation for realizing collaborative analysis, collaborative work, and collaborative decision-making of expert knowledge.

[0125] The following gives an explanation of a method for constructing and visualizing an analysis of a geopolitical environment database. This system adopts a distributed server deployment. Tomcat realizes the function of the front-end page server, Node.js realizes the function of the basic business function server, and GeoServer realizes the function of the distributed server for geographical vector and remote sensing image data interfaces.

[0126] 1. Visual analysis of geopolitical relations and impacts mainly realizes the explicit or implicit impacts on relevant interest subjects generated in ways such as potential or explicit economic blockade, technological competition, and cultural infiltration. The basis for visual division is the type of data, including structured socio-economic data, map data, trajectory data, topological relationship data, event data, etc.

[0127] Visual analysis means include performing statistical analysis on the obtained data in the intermediate link between storage and output, using map visual analysis for the obtained data with location information, using network analysis means for the obtained data that can be interconnected and associated, and for the obtained event data, comprehensively judging bilateral and even multilateral relationships based on the spatio-temporal and associated attributes of the event occurrence. Through the combined analysis of the above four methods, a comprehensive display of the geopolitical environment and geopolitical relations is realized in a multi-modal visual way.

[0128] For the multi-network analysis module, it is mainly divided into three major parts: dataset preparation, node classification, and relationship reasoning. The specific steps are as follows: divide the data according to the node type and relationship reasoning, annotate the historical data, then input it into the Neo4j database, and then, combine the node classification and relationship prediction methods in claim 9 to finally realize the classification of geopolitical subject nodes and the prediction of the relationships between nodes.

[0129] 2. Realize the online map display of data, which can visualize natural resources, socio-economy, transportation, scientific and technological innovation, and event data in the geopolitical environment, specifically including:

[0130] For natural resources and socio-economic data of different countries at different times, the time axis of Echarts and D3.js is used to distinguish time scales, OpenStreetmap is called to implement two-dimensional map display, and scatter plots are made with the help of the Echarts framework;

[0131] For the thematic maps made after remote sensing image processing, they are stored in PostGIS, a wms interface is provided, and they are published on GeoServer. Cenium is used to implement the display function of thematic maps in a three-dimensional scene, enriching the three-dimensional visualization of the geopolitical environment, such as the three-dimensional spherical expression of night light data;

[0132] For trajectory data and associated data, the 3D engine in Echarts is used to draw three-dimensional trajectories on the basis of making textures, and echarts is used to display two-dimensional trajectory data;

[0133] For event data, a map loading engine is used. For example, online maps such as Baidu Map, Amap, Mapbox, and OpenStreetMap are used as online 2D base maps. The Echarts or D3.JS data loading engine is used, and in a time-axis sequence manner, the geographical location information of the event occurrence is loaded and marked on the underlying map.

[0134] 3. A network analysis method in the construction and visualization analysis of a geopolitical environment database, and the specific implementation method is as follows:

[0135] For the display of associated data, including the science and technology innovation network and the regional trade network, the network display modules in Echarts and D3.js are used to achieve network visualizations such as bus type, star type, ring type, and tree type. Among them, the size of the node is the cumulative statistical value of the relevant attributes of a single geopolitical entity within a certain time range, the length of the connection line represents the geographical projection distance between geopolitical entities, and the thickness of the connection line represents the cumulative statistical value of bilateral trade and innovation cooperation within a certain time range.

[0136] For the network expression of event data, the network display modules in Echarts and D3.js are used to achieve network visualizations such as bus type, star type, ring type, and tree type. The size of the node in the event relationship network represents the cumulative influence of a single geopolitical entity within a certain time range, the length of the connection line represents the geographical projection distance between geopolitical entities, and the thickness of the connection line represents the interaction intensity between geopolitical entities

[0137] For the network expression of event data, the network display modules in Echarts and D3.js are used to achieve network visualization of bus type, star type, ring type, tree type, etc. Among them, the hotspot detection of the network includes the evolution analysis of the bilateral relationship "cooperation - competition", regional hotspot detection and the influence detection of bilateral relationships. The specific implementation method of the evolution analysis of the bilateral relationship "cooperation - competition" is to retrieve the cumulative influence of various types of events of geopolitical entities. Among them, for the regional hotspot detection and the analysis of its influence on bilateral relationships, the specific implementation method is to first calculate the cumulative weighted average value of the events of geopolitical entities in the neighborhood of the current geopolitical entity, and then calculate the ratio of the cumulative value of the events of the current geopolitical entity to the cumulative weighted average value of the events of the geopolitical entities in its neighborhood.

[0138] As Figure 2 shown in the system operation interface, the user logs in to the system, enters the account number, and enters the main interface. The upper side is the navigation bar, and selection controls such as "display by country", "route data", "vector database", "remote sensing database", etc. can be designed. Click the control such as "display by country", as Figure 3 shown, it is possible to display or hide country information on the left side. Click the country name, and a line chart of the annual indicators of the corresponding country will pop up in the middle of the page. In the upper right corner of the page, there are indicators such as natural resources, social economy, and scientific and technological innovation. When clicking the "social economy" control, the secondary indicators under this indicator classification can be popped up on the right side of the page. Click the indicator name to achieve indicator switching. Correspondingly, click the controls such as "natural resources", "social economy", "scientific and technological innovation", etc. to switch to the corresponding indicators.

[0139] In the lower left corner of the main interface, click "detailed time bar chart", and a small window will appear. In this window, there is a bar chart with the indicators arranged from small to large. Click "-" to zoom back to the original position. Click "visual sorting chart", and a new floating window will appear on the page. The overall layout of this window is in a circular distribution and consists of many small circles. Each circle represents a country, and the larger the circle, the higher the value of the indicator. Click "-" to zoom back to the original position; at the bottom of the page, there is a time axis. Click the white dot on the time axis, and the indicators of the corresponding year will be displayed on the main page; click the play button on the leftmost side of the time axis, and the changes of this indicator at different times can be automatically displayed.

[0140] Click "route data" in the upper navigation bar to jump to the Figure 4 route data display page as shown; in the middle is a rotating globe, and the golden glowing curve on the surface of the globe is the route data; click the "ring association chart" in the lower left corner, and a floating window will pop up. The overall layout of the floating window is in a circular ring shape, as Figure 5As shown, each circular node represents a country. There are multiple connections between countries. When the mouse moves over a circular node, the country name is displayed, and the connections between this country and other countries are highlighted. Clicking on "-" can zoom back to the original position; clicking on "Association Diagram" pops up a floating window with circular nodes of different sizes inside, representing different countries. When the mouse moves over a circular node, the country name is displayed, and the other countries connected to this country are displayed in a color-linked manner; clicking on the "Return to Main Page" button in the upper left corner can return to the main page for other operations.

[0141] Click on "Vector Database" in the upper navigation bar. As Figure 6 shown, it jumps to the vector database display page; on the left is the data selection box, and on the right is the data display interface; clicking on the box to the left of the index name can perform the operation of showing or hiding the layer; according to the selection needs, different data can be displayed in an overlay combination.

[0142] Click on "Remote Sensing Database" in the upper navigation bar. As Figure 7 shown, it jumps to the remote sensing database display page; in the middle of the page is the Earth, and its surface is the visualization projection interface of remote sensing data.

[0143] It should be noted that the above description is not a limitation of the present invention, and the present invention is not limited to the above examples either. Changes, modifications, additions, or substitutions made by those of ordinary skill in the art within the scope of the essence of the present invention should also fall within the protection scope of the present invention.

Claims

1. A method for constructing and visually analyzing a geopolitical environment database, characterized in that Including: It includes three steps: data collection, storage, and visual analysis: The data collection step includes: 1) Obtain geopolitical environment data and Gdelt global event data; among them, the geopolitical environment data includes attribute data and spatial data; the attribute data includes natural resource data, socioeconomic data, and scientific and technological innovation data; 2) Clean, fuse, and extract correlation relationships from the data to construct a unified data interface; specifically including: Based on events and connections respectively, perform data fusion and extract temporal, spatial, and semantic correlation relationships between elements, and formulate underlying data standards; among them, the connections include trade cooperation, transportation, and technology transfer; the extraction of correlation relationships includes the logical correlation of attribute data, the mutual correlation of connection data, and the correlation of event data; among them, the logical correlation of attribute data includes: identifying the economic and cultural attributes of geopolitical entities through the names or IDs of geopolitical entities, identifying temporal correlations, and identifying the mutual correlations existing between different attributes; the mutual correlation of connection data includes: the spatial correlation of geopolitical entities in trade cooperation, transportation, and technology transfer activities; the correlation of event data includes: the causal correlation, geopolitical entity correlation, and temporal correlation of events; among them, the causal correlation includes the internal or external cause analysis of geopolitical entities in a specified field; the geopolitical entity correlation includes the temporal, spatial, and semantic correlation between the geopolitical entities on both sides of the event occurrence, and the temporal correlation is reflected as coexistence or sequential evolution; 3) Construct the underlying data relationships; including formulating underlying data standards for data related to geopolitical entities and data with geographical mapping functions, and realizing the mapping of each attribute field data to geographical data; The data storage step: Use a distributed database to store multi-time series natural resource data, socioeconomic data, and scientific and technological innovation data; The visual analysis step: 1) Perform four types of analysis on the geopolitical environment data: statistical, spatial, network, and evolutionary analysis, specifically including: event-driven, spatio-temporal multi-scale correlation, and relationship network evolution; 2) Based on the analysis, use statistical charts, thematic maps, story maps, video maps, and / or flow map mapping methods to multi-dimensionally present the current situation pattern and spatio-temporal evolution of the geopolitical environment; Among them, event-driven includes: (1) For hot events, emergency events, and high-frequency events in the Gdelt global event data, compare the spatial pattern and spatio-temporal evolution of the gravitational values of each geopolitical entity before and after the event, and analyze the correlation intensity and change trend between each geopolitical entity; the gravitational value of the geopolitical entity is a comprehensive geopolitical environment influence index composed of economy, technology, and / or others; (2) Combine spatial proximity analysis and spatial correlation analysis to construct a spatial regression analysis model of the gravitational values of each geopolitical entity, and reveal the influencing factors of the gravitational values of geopolitical entities; Spatio-temporal multi-scale correlation includes: for the analysis of events, analyze the intensity of the event's effect at different time intervals of day, week, month, year, or from multiple spatial scales such as sub-national units, countries, regions, and the world; The evolution of the relationship network includes: analyzing the evolution characteristics of the network structure characteristics by calculating the scale, density, hierarchical level, characteristic path length, clustering coefficient, degree distribution power-law coefficient, and / or structural fractal dimension of the network.

2. The method for constructing and visually analyzing a geopolitical environment database according to claim 1, characterized in that The method further includes: Constructing a database; including: 1) Data layer separation: For the data structures and data types of the geopolitical environment data of each country, including natural resources, social economy, scientific and technological innovation, and Gdelt global event data, use the PostgreSQL hybrid mode for storage; 2) Logical layer association: Perform data association and query in the logical layer. Based on the data interfaces for adding, deleting, modifying, and querying data, compile SQL encapsulation functions and encapsulate them again to implement the functions of reading, modifying, deleting, and adding business logical data for geopolitical environment visualization analysis.

3. The method for constructing and visually analyzing a geopolitical environment database according to claim 1, wherein Clean, fuse, and extract association relationships from the data, and formulate underlying data standards, specifically including: 1) After data cleaning, the structured data of the name, location, attributes, and time of the obtained geopolitical entities are entered into the database using a unified data standard and model; 2) Clean and convert the formats of various types of data, including spatial data, statistical data, picture data, and text data, and perform data association and loading according to time, space, and semantics; 3) Perform georegistration, geometric correction, scale conversion, spatial aggregation, and / or schematic expression processing on the obtained vector maps and raster data; 4) After spatial matching, semantic association, and / or temporal arrangement of the obtained trajectory data and relationship data, make relationship data sets using a unified data standard and model; 5) Formulate underlying data standards for all data related to geopolitical entities and data with geographic mapping functions, and implement the mapping of each attribute field data to geographic data.

4. The method for constructing and visually analyzing a geopolitical environment database according to claim 1, wherein Perform statistics and visualization on event-driven, spatio-temporal multi-scale association, and relationship network evolution, specifically including: 1) Statistical analysis of the natural resources and social economy data of geopolitical entities: According to the statistical characteristics and presentation requirements of the data, after aggregation function operations, use the time axes of Echarts and D3.js to distinguish time scales, and use polar coordinate graphs, scatter plots, bar graphs, box plots, bubble graphs, radar graphs, and / or rank clocks to present the non-uniformity, diversity, correlation, and rank-size characteristics of the data, and visually present the dominance and hierarchical level characteristics of geopolitical entities; 2) Statistical analysis of event data: Statistically analyze the quantity, type, frequency, and intensity of events in Gdelt global event data by region, time period, and theme, and use probability plots, chord diagrams, and / or network diagrams to reveal the types of associations, modes of interaction, directions of association, and / or strengths of connection between geopolitical entities; characterize and dynamically track the characteristics of geopolitical games between geopolitical entities; analyze the influence of Gdelt global event data, and statistically calculate the cumulative influence of each event on the geopolitical entities involved within a set time interval, where the value is the product of the influence of a single event and the tone quantification index; use kernel density plots, core-periphery plots, cohesive subgroup division result plots, and / or multi-level clustering plots to reveal the individuals, core groups, and relationship competition and cooperation that play a dominant role in the geopolitical relationship network at a given spatial scale, and achieve a quantitative display of the comprehensive influence of events, as well as the association methods and action paths between geopolitical entities.

5. The method for constructing and visually analyzing a geopolitical environment database according to claim 1, wherein Conduct spatial analysis on geopolitical environment data and Gdelt global event data, specifically including: For the geopolitical environment data of geopolitical entities and the cumulative influence data of said geopolitical entities, through morphological analysis, pattern analysis, spatial autocorrelation analysis, and geographically weighted regression analysis, reveal the power space and distribution characteristics, spatial interaction mechanisms, environmental constraints, and spatial responses of events of each geopolitical entity; among them, morphological analysis refers to the clustering analysis of spatial point patterns, the curvature of line elements, mesh density, geometric fractal dimension, and topological connectivity; the morphological analysis of areal elements is the morphological complexity of boundaries and fills, and is measured by geometric fractal dimension, shape index, Voronoi diagram area, and proximity relationship; pattern analysis is used to reveal the spatial agglomeration, spatial differentiation, and scale effects of the attribute values of geopolitical entities; spatial agglomeration is characterized by Moran's I index, spatial differentiation is described by isolines and / or spatial interpolation, and scale effects are described by the correspondence between geographical basic analysis units and geographical features; Use semi-variograms, kernel density plots, statistical maps, story maps, and / or video maps to present the spatial dependence, spatial heterogeneity, and spatial autocorrelation characteristics of geopolitical entities; through the analysis of the degree of influence, duration, and / or mode of influence of the same event in different scale spatial ranges, reveal the scale effects of spatial interactions in the geopolitical environment.

6. The method for constructing and visually analyzing a geopolitical environment database according to claim 1, wherein, Construct single-layer and multi-layer networks for geopolitical environment data and Gdelt global event data, specifically including: Construct single-layer or multi-layer networks based on spatial proximity, attribute correlation, or semantic similarity, including: Construct a geopolitical entity relationship network according to attribute relationships, including transportation networks, economic and trade networks, and scientific and technological innovation networks; Construct a geopolitical event network in a certain field or topic based on the topicality and semantic similarity of geopolitical events. By calculating the centrality indicators of nodes or connections in a single network, as well as their multiplicity, PageRank value, multi-feature vector centrality, multi-rank, multi-functionality, and multi-layer interaction ability values in two or more layers of networks, identify important nodes and key connections within each network layer and across network layers; use the random attack algorithm to calculate the robustness of the network and find weak points and important hubs; use the multi-network community division algorithm to identify the core groups of multi-dimensional geopolitical relationships; adopt a bus-type, star-type, ring-type, and / or tree-type layout form, set its size and color according to the node attributes, and do the same for the connections, and analyze the compactness, heterogeneity, hierarchical hierarchy, small-world, and scale-free characteristics of the network; Construct a semantic network based on the events in the Gdelt global event database, and present the location, actor, propagation mode, and attention heat of the events with statistical charts, kernel density maps, chord diagrams, flow diagrams, and / or knowledge graphs to depict the network conduction and feedback paths of major events; among them, in the semantic network, the size of the node represents the cumulative influence degree of a certain type of semantic event frequency, and the connection direction represents causality, action, and reaction; Regarding the interaction intensity between geopolitical entities, use a modified gravity model to depict the bilateral relationship: construct a modified gravity model for each geopolitical entity, and the gravity between each geopolitical entity is determined by the comprehensive mass and distance; among them, the comprehensive mass is the standardized total value constructed based on the natural resources, social economy, and scientific and technological innovation data of the geopolitical entity; the distance between geopolitical entities can be measured according to the Euclidean distance, traffic distance, topological distance, and the weighted comprehensive value of the three distances, and the weight of the distance is determined by expert scoring or the entropy weight method.

7. The method for constructing and visually analyzing a geopolitical environment database according to claim 1, wherein The method specifically includes: Use open-source data and open-source frameworks to encapsulate the visualization framework. At the same time, realize the statistical visual analysis of data, spatial pattern analysis, and the visualization analysis of the association and evolution of the geopolitical environment and geopolitical entities: For geopolitical environment data, generate year statistical reports, change curves, heat maps, chord diagrams, and / or story maps based on natural resources, social economy, and scientific and technological innovation data, and visually present the resource endowments, environmental profiles, and change characteristics of each geopolitical entity; Among them, the association of geopolitical entities is depicted by detecting the internal causes of the bilateral relationship between geopolitical entities, analyzing the evolution of the "cooperation-competition" mode of the bilateral relationship, detecting regional hotspots, and calculating the influence of the bilateral relationship, and depicting the influence scope and action mode of different types of events; among them, the detection of the internal causes of the bilateral relationship between geopolitical entities is obtained by calculating the cumulative influence scores of various types of events of both geopolitical entities, and the correlation between the cumulative influence of events and the national economy, culture, and resource endowments is mined through path analysis; the evolution analysis of the "cooperation-competition" mode of the bilateral relationship between geopolitical entities includes the changes in the cooperation-competition relationship between geopolitical entities before and after the event, the cooperation parties and competition parties of each geopolitical entity, and analyzes the changes in the direction, mode, object, frequency, and intensity of "cooperation-competition".

8. The method for constructing and visually analyzing a geopolitical environment database according to claim 7, wherein The encapsulated open-source data visualization framework includes: constructing a special topic data dashboard according to the special topic requirements, and conducting comprehensive demonstration and integrated analysis of geopolitical environment data.

9. The method for constructing and visually analyzing a geopolitical environment database according to claim 7, characterized in that Analysis of the "cooperation-competition" evolution of bilateral relations, including: Judgment of the importance of geopolitical main nodes: Through the modified gravity model and event influence characterization, geopolitical entities are divided into different types according to development level, cultural belief, system, etc., and the importance of nodes is defined according to event frequency and intensity, and geopolitical comprehensive influence; In the geopolitical relationship network of the region, the size of the node represents the status of the geopolitical entity, among which the size of the node is calculated according to the indicators obtained from statistical analysis; Taking the country as the basic analysis unit, analyzing the seaborne network layer separately, identifying the hub ports and key shipping lanes of the global seaborne network through degree centrality analysis, and identifying the national interest communities at different scales through community structure analysis; analyzing the scientific and technological innovation cooperation network, and identifying global innovation entities from the perspective of the industrial chain; Relationship prediction: Characterize the relationship between geopolitical entities through event influence and the gravity model.

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