Geographical environment prediction method, device, equipment, storage medium and program product

Abstract

This application discloses a method, apparatus, device, storage medium, and program product for predicting the geopolitical environment. The method includes: acquiring multiple original factors corresponding to at least two target objects in the geopolitical environment, i.e., first original data, and publicly available behavioral information of multiple other objects; performing regression analysis on the original factors and the first original data to output a first degree of influence value of the original factors on the geopolitical environment; determining core factors based on the first degree of influence value; using a preset simulation model to simulate the changing trend of the geopolitical environment based on the first original data and publicly available behavioral information corresponding to the core factors, obtaining a second degree of influence value of the core factors on the geopolitical environment; determining key factors based on the second degree of influence value; and predicting changes in the geopolitical environment based on the key factors. According to the embodiments of this application, the changes in the geopolitical environment are assessed through multiple determined key factors to accurately predict the geopolitical environment.

Description

Technical Field

[0001] This application belongs to the technical field of geopolitical environment analysis, and particularly relates to a method, apparatus, equipment, storage medium and program product for predicting geopolitical environment. Background Technology

[0002] In an era where globalization and regionalization are intertwined, the geopolitical environment, as part of a complex system, exerts a profound influence on regional and global stability through its dynamic changes. The geopolitical environment refers to the dynamic structure and evolutionary trends formed by the comprehensive interaction of geographical, economic, military, technological, and cultural factors within the international system. It encompasses not only traditional physical factors such as geographical location, resource distribution, and distribution boundaries, but also the spatial patterns shaped by the interactions of various actors around strategic interests against the backdrop of globalization, technological change, power competition, and normative shaping. The complex interactions and dynamic feedback among these factors further increase the difficulty of assessing the geopolitical environment.

[0003] Geopolitical environment assessment research typically combines theories and methods from multiple disciplines. Among the relevant technologies, data-driven techniques, model building and quantitative analysis, and qualitative case studies are primarily used to study and predict geopolitical events. However, these technologies have limitations when facing multidimensional, dynamic, and complex situations, making it difficult to comprehensively and systematically assess the evolutionary trends and potential impacts of geopolitical events.

[0004] Currently, traditional databases face challenges in geopolitical analysis due to their lack of granularity and outdated updates. Because these databases typically rely on structured data storage, their long update cycles result in slow responses to dynamically changing geopolitical environments, making it difficult to promptly capture sudden events or rapidly evolving trends. Furthermore, data classification and labeling are often coarse, failing to reach the more granular interaction levels within complex geopolitical environments, thus limiting the accuracy and applicability of the analysis.

[0005] In contrast, emerging big data analytics methods utilize open-source data sources (such as news reports, social media, and website announcements) for real-time trend analysis, but they also face challenges. For example, improper data encoding can lead to the spread of misinformation, redundant information and duplicate records increase the complexity of data cleaning, and may even introduce noise, affecting the scientific validity and reliability of the analysis and preventing accurate predictions. Summary of the Invention

[0006] This application provides a method, apparatus, device, storage medium, and program product for predicting geopolitical environment, which can assess changes in geopolitical environment by identifying multiple key factors to obtain accurate prediction results.

[0007] On the one hand, embodiments of this application provide a method for predicting the geopolitical environment, the method comprising:

[0008] Acquire multiple raw factors of at least two target objects in a geopolitical environment, first raw data corresponding to the multiple raw factors, and public behavioral information of multiple other objects. The other objects include objects other than the target objects. The public behavioral information includes the behavioral information of other objects on geopolitical events in the geopolitical environment within a preset time scale. The public behavioral information includes behavioral information under each raw factor. The raw factors include at least two of the following: available resource factors, external environmental pressure factors, and internal stability factors.

[0009] A preset regression analysis model is used to perform regression analysis on the original factors and their corresponding first original data to output the first degree of influence of the original factors on the geopolitical environment.

[0010] Based on the first influence value, at least one core factor is determined from a variety of original factors;

[0011] The first raw data corresponding to the core factor and the public behavior information of multiple other objects are input into a preset simulation model. The preset simulation model simulates the change trend of the geopolitical environment within a preset time scale based on the first raw data corresponding to the core factor and the public behavior information, and obtains a second influence degree value of the influence degree of each core factor on the geopolitical environment.

[0012] Based on the second degree of influence value, at least one key factor is determined from at least one core factor;

[0013] Based on the aforementioned key factors, information on changes in the geopolitical environment related to the geopolitical events is predicted.

[0014] Optionally, for each original factor, semantic analysis is performed on the first original data to obtain analysis results, wherein the analysis results include at least two assignments corresponding to the first original data representing the same semantic meaning;

[0015] The first raw data is encoded according to the analysis results to obtain the encoded value corresponding to the analysis results;

[0016] The original factors are semantically associated with the encoded values ​​to obtain a regression analysis dataset.

[0017] The step of performing regression analysis on the original factors and their corresponding first original data using a preset regression analysis model to output the first degree of influence of the original factors on the geopolitical environment includes:

[0018] A pre-defined regression analysis model is used to perform regression analysis on the regression analysis dataset to output the first degree of influence of the original factors on the geopolitical environment.

[0019] Optionally, the preset regression analysis model performs benchmark regression analysis on the regression analysis dataset to obtain the regression coefficient and error of each original factor on the geopolitical environment;

[0020] Based on the regression coefficients and errors corresponding to the original factors, a first degree of influence of each original factor on the geopolitical environment is determined.

[0021] Optionally, for each target original factor, multiple component factors corresponding to each target original factor are obtained. The component factors are used to represent multiple dimensions of the impact of geopolitical events on the geopolitical environment. The target original factors include original factors whose first impact value is greater than a preset impact threshold.

[0022] Retrieve the second original data corresponding to the constituent factors from the preset database;

[0023] Based on the preset regression analysis model, stability regression analysis is performed on the second original data corresponding to multiple component factors to obtain stability test results.

[0024] If the stability test result is consistent with the first influence value, the original factor is identified as the core factor.

[0025] Optionally, based on the first original data corresponding to each of the core factors, a virtual geopolitical environment for the at least two target objects within a preset simulation model is constructed;

[0026] Based on the publicly available behavioral information and the virtual geopolitical environment, a causal network graph of the target object and the other objects is determined. The nodes of the causal network graph are used to represent the action information performed by the other objects on the target object in each core factor.

[0027] Based on the causal network graph and the preset simulation function, the change trend of the geopolitical environment within a preset time scale is simulated to obtain the influence score of the public behavior information of other objects on the geopolitical event.

[0028] Based on the sum of each of the said influence scores, a second influence value is determined for the degree of influence of the other objects on the geopolitical environment under each core factor at any time within the preset time scale.

[0029] Optionally, semantic extraction can be performed on the publicly disclosed behavioral information to obtain semantic information of the other objects regarding the geopolitical event;

[0030] Based on the semantic information, the association between the other objects and the target object is determined;

[0031] Based on the aforementioned relationships, a causal network graph of the target object and the other objects is drawn.

[0032] Optionally, for each original factor, keyword extraction is performed on the semantic information to obtain at least one keyword information, the keyword information including keywords used to represent the attitude of the other objects towards the target object;

[0033] Search the preset keyword library for a preset keyword that corresponds to the at least one keyword information to obtain the search results;

[0034] If the search results include at least one preset keyword corresponding to the keyword, the influence score is determined based on the preset keyword and the correspondence between the keyword and the influence score.

[0035] Optionally, in the preset simulation model, the first original data is updated in response to the user's modification operation on the first original data corresponding to a core factor;

[0036] Based on the updated first original data, a simulation is performed to obtain an intensity curve at a preset time scale. The intensity curve is used to represent the changes of geopolitical events within the preset time scale.

[0037] Based on the intensity curve, determine the third degree of influence value at any moment within the preset time scale;

[0038] The deviation index value is obtained based on the third influence value and the second influence value at the same time.

[0039] If the deviation index value is within the preset error range, the core factor is identified as the key factor.

[0040] On the other hand, embodiments of this application provide a geopolitical environment prediction device, the device comprising:

[0041] The acquisition module is used to acquire multiple raw factors of at least two target objects in a geopolitical environment, first raw data corresponding to the multiple raw factors, and public behavioral information of multiple other objects. The other objects include objects other than the target objects. The public behavioral information includes behavioral information of other objects on geopolitical events in the geopolitical environment within a preset time scale. The behavioral information includes behavioral information under each raw factor. The raw factors include at least two of the following: available resource factors, external environmental pressure factors, and internal stability factors.

[0042] The regression analysis module is used to perform regression analysis on the original factors and their corresponding first original data using a preset regression analysis model, so as to output the first degree of influence of the original factors on the geopolitical environment.

[0043] The determination module is used to determine at least one core factor from a variety of original factors based on the first influence degree value;

[0044] The simulation module is used to input the first raw data corresponding to the core factor and the public behavior information of multiple other objects into a preset simulation model. The preset simulation model simulates the change trend of the geopolitical environment within a preset time scale based on the first raw data corresponding to the core factor and the public behavior information, and obtains a second influence degree value of the degree of influence of each core factor on the geopolitical environment.

[0045] The determining module is further configured to determine at least one key factor from at least one core factor based on the second degree of influence value;

[0046] The prediction module is used to predict changes in the geopolitical event within the geopolitical environment based on the key factors.

[0047] In another aspect, embodiments of this application provide an electronic device, the device including: a processor and a memory storing computer program instructions;

[0048] When the processor executes the computer program instructions, it implements the geopolitical environment prediction method as described in the first aspect.

[0049] In another aspect, embodiments of this application provide a computer storage medium storing computer program instructions, which, when executed by a processor, implement the geopolitical environment prediction method as described in the first aspect.

[0050] In another aspect, embodiments of this application provide a computer program product in which instructions, when executed by a processor of an electronic device, cause the electronic device to perform the geopolitical environment prediction method as described in the first aspect.

[0051] The geopolitical environment prediction method, apparatus, device, storage medium, and program product of this application embodiment can, when predicting the geopolitical environment, acquire the original factors of at least two multidimensional and complex target objects in the geopolitical environment, the first original data corresponding to multiple original factors, and the public behavioral information of multiple other objects. Then, through a preset regression analysis model, it analyzes from multiple dimensions corresponding to the original factors to comprehensively analyze the complexity of the target objects in the geopolitical environment, obtains the first influence degree value corresponding to the dimensions of multiple original factors, and filters out the core factors that affect changes in the geopolitical environment from multiple original factors. Then, it further analyzes the public behavioral information of other objects in a preset... In the simulation model, simulations are performed to observe the public behavior of other objects within a preset simulation model, thereby obtaining the trend of changes in the geopolitical environment over a preset time scale. From this trend, a second degree of influence value is obtained for the core factors' impact on the geopolitical environment. Then, key factors are identified from the core factors based on this second degree of influence value. This allows for regression analysis using multiple key factors, providing a more comprehensive reflection of the geopolitical environment's changing trends. Furthermore, the simulation model can simulate changes over a preset time scale, enabling more accurate predictions of geopolitical environment trends and ensuring the scientific validity and reliability of the analysis results, leading to precise predictions. Attached Figure Description

[0052] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 This is a flowchart illustrating a method for predicting the geopolitical environment provided in one embodiment of this application;

[0054] Figure 2 This is a flowchart illustrating a method for predicting the geopolitical environment provided in another embodiment of this application;

[0055] Figure 3 This is a flowchart illustrating a method for predicting the geopolitical environment provided in another embodiment of this application;

[0056] Figure 4 This is a flowchart illustrating a method for predicting the geopolitical environment provided in another embodiment of this application;

[0057] Figure 5 This is a flowchart illustrating a method for predicting the geopolitical environment provided in another embodiment of this application;

[0058] Figure 6 This is a flowchart illustrating a method for predicting the geopolitical environment provided in another embodiment of this application;

[0059] Figure 7 This is a flowchart illustrating a method for predicting the geopolitical environment provided in another embodiment of this application;

[0060] Figure 8 This is a flowchart illustrating a method for predicting the geopolitical environment provided in another embodiment of this application;

[0061] Figure 9 This is a schematic diagram of the structure of a geopolitical environment prediction device provided in another embodiment of this application;

[0062] Figure 10 This is a schematic diagram of the structure of an electronic device provided in another embodiment of this application. Detailed Implementation

[0063] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0064] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0065] Before introducing the technical solutions of the embodiments of this application, let's first introduce the background technology of the embodiments of this application:

[0066] In an era where globalization and regionalization are intertwined, the geopolitical environment, as part of a complex system, has a profound impact on regional and global stability due to its dynamic changes. The geopolitical environment refers to the geographical location of a country and the sum of its various natural conditions, human environment and geopolitical relations, including the spatial pattern and dynamic changes of geopolitical elements such as geographical location, territorial size, orientation and distance, land and sea boundaries, human conditions and natural conditions. There are complex interactions and dynamic feedbacks among these factors, making it extremely difficult to predict the geopolitical environment.

[0067] Among the related technologies, the prediction of changes in the geopolitical environment is mainly achieved through data-driven techniques, model building and quantitative analysis, and qualitative case studies. However, these technologies have limitations when facing multidimensional, dynamic and complex situations, and it is difficult to comprehensively and systematically assess the evolution trend and potential impact of geopolitical events.

[0068] In related technologies, the process of using big data analysis methods to integrate open data sources to identify geopolitical events and trends involves a large number of erroneous codes and assignments, making it difficult to accurately capture changes in the geopolitical environment. As a result, it is difficult to accurately predict changes in the geopolitical environment.

[0069] To address the problems of the prior art, embodiments of this application provide a method, apparatus, device, storage medium, and program product for predicting geopolitical environment. In this embodiment, when predicting the geopolitical environment, the process involves acquiring the original factors of at least two complex target objects within the geopolitical environment, the first original data corresponding to multiple original factors, and the public behavioral information of multiple other objects. Then, a preset regression analysis model is used to analyze the multiple dimensions corresponding to the original factors, comprehensively analyzing the complexity of the target objects in the geopolitical environment. This yields the first influence value corresponding to the dimensions of multiple original factors. Core factors influencing changes in the geopolitical environment are then selected from the multiple original factors. Next, simulations are performed in a preset simulation model using the public behavioral information of other objects. By simulating the public behavior of other objects in the preset simulation model, the changing trend of the geopolitical environment within a preset time scale is obtained. From this changing trend, a second influence value of the core factors' impact on the geopolitical environment is obtained. Then, key factors are determined from the core factors using the second influence value. This allows for regression analysis using multiple key factors, more comprehensively reflecting the changing trend of the geopolitical environment. Simultaneously, the preset simulation model can simulate changes within a preset time scale, enabling more accurate prediction of geopolitical environment trends, ensuring the scientific validity and reliability of the analysis results, and obtaining accurate prediction results.

[0070] The following section first introduces the method for predicting the geopolitical environment provided in the embodiments of this application.

[0071] Figure 1 A flowchart illustrating a geopolitical environment prediction method according to an embodiment of this application is shown. Figure 1 As shown, the methods for predicting the geopolitical environment may include S101-S106:

[0072] S101, acquire multiple raw factors of at least two target objects in the geopolitical environment, the first raw data corresponding to the multiple raw factors, and the public behavior information of multiple other objects.

[0073] In some embodiments, in S101, other objects include objects other than the target object, and the public behavioral information includes the behavioral information of other objects on geopolitical events in the geopolitical environment within a preset time scale.

[0074] As an example, a geopolitical event can be an event in which Party A and Party B have a conflict, where Party A and Party B are the target objects, and other objects can be Party C or Party D. As an example, the target objects or other objects can be any enterprise or organization.

[0075] In this embodiment, the original factors include at least two of the following: available resource factors, external environmental pressure factors, and internal stability factors. Available resource factors may include various factors such as economic resource factors, material resource factors, mineral resource factors, and human resource factors. External environmental pressure factors may include various factors such as external public opinion factors and economic sanctions factors. Internal stability factors may include various factors such as internal conflict factors and internal public opinion factors.

[0076] In this implementation, multiple databases of original factors in corresponding dimensions can be selected as the data source for the original factors, and the variable can be recoded to obtain the dependent variable for regression analysis of geopolitical events.

[0077] In some embodiments, when obtaining the first raw data, the first raw data corresponding to each raw factor can be obtained by comprehensively integrating conflict event association database, management status index database, manager database, external environmental pressure application mode database and conflict event database of both parties.

[0078] As an example, public behavioral information can include behavioral information under each original factor. For example, in terms of economic resources, Party C can provide economic support to Party A. In terms of external public opinion, Party C can exert reverse public opinion on Party B, which can indicate that it will provide economic support to Party A.

[0079] S102, use a preset regression analysis model to perform regression analysis on the original factors and their corresponding first original data, so as to output the first degree of influence of the original factors on the geopolitical environment.

[0080] In some other embodiments, in S102, the preset regression analysis model can be a fixed effects model. The fixed effects model can control the influence of original factors that do not change over time and cannot be directly observed on the geopolitical environment. That is, the preset regression analysis model can reduce the interference of other factors and perform regression analysis on the original factors and the first original data more accurately, so as to make the obtained first influence value more accurate.

[0081] S103, based on the first degree of influence value, determine at least one core factor from multiple original factors;

[0082] In other embodiments, in S103, multiple original factors can be screened by setting a preset influence degree threshold to obtain at least one core factor. Alternatively, the original factors can be arranged in a certain order according to the first influence degree value to screen out at least one core factor from the sequence. This is not limited here.

[0083] S104, input the first original data corresponding to the core factor and the public behavior information of multiple other objects into the preset simulation model. The preset simulation model simulates the change trend of the geopolitical environment within a preset time scale based on the first original data and public behavior information corresponding to the core factor, and obtains the second influence degree value of the influence degree of each core factor on the geopolitical environment.

[0084] In some embodiments, in S104, the preset simulation model can be a system dynamics model with statistical analysis, which can use system dynamics methods to construct a dynamic simulation model of the geopolitical environment.

[0085] In this embodiment, the preset simulation model can use the public behavioral information of other objects as independent variables. After the public behavioral information of other objects is added to the simulation, the geopolitical environment changes at a preset time scale. While simulating the trend of geopolitical environment changes at the preset time scale, the preset simulation model can analyze the core factors and the corresponding first original data to obtain the second influence value of the first original data corresponding to each core factor on the geopolitical environment, so as to analyze the evolution process of the core factors in the geopolitical environment and provide support for the comprehensive analysis of the dynamic path of the geopolitical environment.

[0086] S105, based on the second degree of influence value, determine at least one key factor from at least one core factor.

[0087] In some embodiments, in S105, in order to further determine which core factors affect changes in the geopolitical environment and improve the accuracy of geopolitical environment change prediction, key factors can be screened from the core factors through a second degree of influence to improve the accuracy of geopolitical environment prediction.

[0088] S106, based on key factors, predicts the changes in geopolitical events within the geopolitical environment.

[0089] In this embodiment, when predicting the geopolitical environment, the process involves acquiring the original factors of at least two complex target objects within the geopolitical environment, the first original data corresponding to multiple original factors, and the public behavioral information of multiple other objects. Then, a preset regression analysis model is used to analyze the multiple dimensions corresponding to the original factors, comprehensively analyzing the complexity of the target objects in the geopolitical environment. This yields the first influence value corresponding to the dimensions of multiple original factors. Core factors influencing changes in the geopolitical environment are then selected from the multiple original factors. Next, simulations are performed in a preset simulation model using the public behavioral information of other objects. By simulating the public behavior of other objects in the preset simulation model, the changing trend of the geopolitical environment within a preset time scale is obtained. From this changing trend, a second influence value of the core factors' impact on the geopolitical environment is obtained. Then, key factors are determined from the core factors using the second influence value. This allows for regression analysis using multiple key factors, more comprehensively reflecting the changing trend of the geopolitical environment. Simultaneously, the preset simulation model can simulate changes within a preset time scale, enabling more accurate prediction of geopolitical environment trends, ensuring the scientific validity and reliability of the analysis results, and obtaining accurate prediction results.

[0090] Reference Figure 2 In some embodiments, taking into account both the degree of data loss and the complexity of the geographical environment, the method may further include, after S101:

[0091] S201, For each original factor, perform semantic analysis on the first original data to obtain the analysis results;

[0092] S202, Encode the first raw data according to the analysis results to obtain the coded value corresponding to the analysis results;

[0093] S203, semantically associate the original factors with the coded values ​​to obtain the regression analysis dataset;

[0094] S102 may include:

[0095] S204 uses a preset regression analysis model to perform regression analysis on the regression analysis dataset to output the first degree of influence of the original factors on the geopolitical environment.

[0096] In some embodiments, in S201, since data redundancy may occur when acquiring the first original data, it is necessary to perform semantic analysis on the first original data and merge at least two types of first original data representing the same semantics to reduce redundancy.

[0097] Specifically, taking a conflict event as a geopolitical event as an example, the first set of raw data can include: "0" conflict in progress, "1" victory for Party A, "2" victory for Party B, "3" defeat for Party A, "4" defeat for Party B, "5" stalemate, "6" compromise, "7" conflict resolved, "8" outcome unclear, and "9" missing data. Among these, 0-9 represent the assigned values ​​for the first set of raw data. Through semantic analysis, it can be seen that both victory for Party A and defeat for Party B express the same outcome. Therefore, the corresponding first set of raw data can be merged to obtain multiple analysis results.

[0098] In some embodiments, in S202, after semantic analysis, the assignments corresponding to at least two first original data can be encoded to obtain the encoded value corresponding to each semantic.

[0099] Specifically, taking the first original data mentioned above as an example, the data assigned a value of 9 can be deleted, and the first original data assigned a value of 1 "A wins" or 4 "B loses" can be encoded as 1, the first original data assigned a value of 2 "B wins" or 3 "A loses" can be encoded as -1, and the above-mentioned 5 "stalemate", 6 "compromise", 7 "resolve conflict" and 8 "outcome unknown" can all be encoded as 0.

[0100] In some embodiments, as another example, taking material resource factors as the original factor, the difference between the comprehensive capability index of Party A and the comprehensive capability index of Party B can be used to obtain the encoded value of the first original data corresponding to the material resource factor; taking loss resource factors as the original factor, they can be encoded according to the proportion of the target object's lost resources to the total resources, that is, the proportion is used as the encoded value; taking external environmental pressure factors as the original factor, the difference between the number of adverse development behaviors received by Party A and the number of adverse development behaviors received by Party B can be used as the encoded value of the first original data corresponding to the external environmental pressure factor; taking internal conflict factors as the original factor, the internal conflict factor is set as a dichotomous variable, that is, the encoded value of the first original data corresponding to the internal conflict event is encoded as 1, and the encoded value of the first original data corresponding to the internal conflict event is encoded as 0. Other original factors can be encoded according to any of the above encoding methods, which will not be elaborated further here.

[0101] In some other embodiments, in S203, after encoding the acquired first raw data, in order to facilitate subsequent data analysis of the first raw data, the encoded values ​​are semantically associated with the original factors to obtain a regression analysis dataset, so as to quickly perform semantic analysis.

[0102] In other embodiments, in S204, the first original technical data corresponding to all original factors is encoded according to a unified method to reduce data redundancy and provide a structured basis for subsequent analysis and simulation.

[0103] Reference Figure 3 In some other embodiments, in order to more accurately determine the first degree of influence of the original factor on the geopolitical environment, S102 may include:

[0104] S1021, The preset regression analysis model performs benchmark regression analysis on the regression analysis dataset to obtain the regression coefficient and error of each original factor on the geopolitical environment;

[0105] S1022, based on the regression coefficients and errors corresponding to the original factors, determine the first degree of influence of each original factor on the geopolitical environment.

[0106] In this embodiment, when inputting the original factors and their corresponding first original data into the preset regression analysis model, the preset regression analysis model can first be deployed on an electronic device using Stata software. Then, the user can use the software to input the original factors and their corresponding first original data into the preset regression analysis model. The preset regression analysis model can perform benchmark regression analysis on the original factors and their corresponding first original data. At this time, the Stata software can output regression coefficients and errors based on the input original factors and their corresponding first original data. The regression coefficients can represent the direction and magnitude of the influence of the original factors on the geopolitical environment. Positive coefficients indicate positive correlation, and negative coefficients indicate negative correlation. The error can be the standard error, which represents the estimation error of the regression coefficients. The smaller the standard error, the more accurate the regression coefficients.

[0107] In some other embodiments, the Stata software can also output a t-value, which represents the ratio of the regression coefficient to its standard error; the larger the t-value, the more significant the regression coefficient. A p-value represents the probability of the regression coefficient being significant; if the p-value is less than 0.05, the regression coefficient is more significant. A confidence interval represents the confidence range of the regression coefficient.

[0108] In this embodiment, in S1022, the first degree of influence value can be calculated based on the regression coefficient and the error. Specifically, to simplify the calculation process, the first degree of influence value can be determined using the linear equation y = ax + b, where y is the first degree of influence value, a is the regression coefficient, x is the encoding value corresponding to the first original data, and b is the error.

[0109] The first degree of influence value determined by the above method can control the first original data of the original factors that do not change over time and cannot be directly observed, so as to accurately predict the impact of the original factors on changes in the geopolitical environment.

[0110] Reference Figure 4 In other embodiments, in order to more accurately identify core factors and thus more accurately predict changes in the geopolitical environment, S103 may include:

[0111] S1031, For each original factor of the target, obtain multiple component factors corresponding to each original factor of the target;

[0112] S1032, retrieve the second original data corresponding to the constituent factors from the preset database;

[0113] S1033, based on the preset regression analysis model, perform stability regression analysis on the second original data corresponding to multiple component factors to obtain stability test results;

[0114] S1034, if the stability test results are consistent with the first degree of influence value, the original factor is determined as the core factor.

[0115] In some embodiments, as an example, the constituent factors are used to represent multiple dimensions of the impact of geopolitical events on the geopolitical environment. Accordingly, the constituent factors can be factors of each dimension included in each of the above-mentioned original factors. For example, available resource factors can include economic resource factors, material resource factors, mineral resource factors, human resource factors, and other factors; external environmental pressure factors can include external public opinion factors, economic sanctions factors, and other factors; and internal stability factors can include internal conflict factors, internal public opinion factors, and other factors.

[0116] In some other embodiments, in order to reduce the amount of computation, the target original factors can be first screened out by the first influence value, that is, the original factors whose first influence value is greater than the preset influence threshold are determined as the target original factors.

[0117] In some embodiments, in S1032, the preset database can be a corresponding database for storing the first original data of each original factor. For example, for the available resource factor, it can be obtained through the available resource database of the target object. It is worth noting that the first original data can be obtained and published through media such as the internet and newspapers.

[0118] In some other embodiments, in S1033, in order to more accurately determine the core factors, the second original data can also be subjected to stability regression analysis by different constituent factors of the original factors. That is, the original factors can be used to determine whether they can affect changes in the geopolitical environment through different dimensions.

[0119] Specifically, for example, in the original factor of available resources, we can first determine the first degree of influence value through economic reserves, and then use mineral resources, such as gold reserves or oil reserves, to conduct a stability regression analysis on the available resource factor to obtain the stability test results. That is, the stability test results include the third degree of influence value. When the third degree of influence value is consistent with the first degree of influence value, the original factor can be identified as the core factor.

[0120] In this embodiment, by calculating and verifying the degree of influence of different constituent factors of the original factors, the core factors can be determined more accurately, and the accuracy of predicting the geopolitical environment through the core factors can be improved.

[0121] Reference Figure 5 In some other embodiments, in order to comprehensively analyze the different impacts of the original factors on changes in the geopolitical environment, S104 may include:

[0122] S1041, Based on the first original data corresponding to each core factor, construct a virtual geopolitical environment for at least two target objects within a preset simulation model;

[0123] S1042, Based on publicly available behavioral information and virtual geopolitical environment, determine the causal network graph between the target object and other objects;

[0124] S1043, based on the causal network graph and the preset simulation function, simulates the changing trend of the geopolitical environment within a preset time scale, and obtains the impact score of the public behavior information of other objects on geopolitical events;

[0125] S1044, based on the sum of each influence score, determine the second influence value of the degree of influence of other objects on the geopolitical environment under each core factor at any time within a preset time scale.

[0126] In some embodiments, by using the key variables identified above and employing system dynamics methods, a dynamic simulation model of the geopolitical environment, i.e., a preset simulation model, is constructed. A virtual geopolitical environment consistent with the actual geopolitical environment can be built in the preset simulation model. Then, by using public behavioral information and the virtual geopolitical environment, a causal network graph of the target object and other objects is determined, so that the preset simulation model can simulate the changes in the geopolitical environment after the core factors are affected by the public behavioral information of other objects. The changes in the geopolitical environment are continuously evolved to determine the degree of influence of the core factors on the changes in the geopolitical environment.

[0127] In this embodiment, the preset simulation model may include table functions to guide the simulation of the geopolitical environment.

[0128] In other implementations, the preset simulation model may also include a preset time function to define the time range of geopolitical events.

[0129] In other embodiments, in S1041, in a preset simulation model, a virtual geopolitical environment concerning the target object involved in a geopolitical event can be constructed on an electronic device using core factors and their corresponding first original data, so as to facilitate the simulation of changes in the geopolitical environment.

[0130] In some other embodiments, as an example, the nodes of the causal network graph are used to represent the action information performed by other objects on the target object in each core factor, that is, the causal network graph can characterize the attitudes and influences of other objects on the target object.

[0131] Reference Figure 6 Specifically, in order to more accurately determine the causal network graph between other objects and the target object, S1042 may include:

[0132] S10421, Semantic extraction is performed on publicly available behavioral information to obtain semantic information about geopolitical events from other objects;

[0133] S10422, Determine the association relationship between other objects and the target object based on semantic information;

[0134] S10423, Draw a causal network diagram of the target object and other objects based on the correlation.

[0135] In this embodiment, when determining the causal network graph, it can be determined by the public behavioral information of other objects. As an example, other objects and the target object can be used to construct causal network graphs in different dimensions. Alternatively, a causal network graph can be constructed based on the dimensions of all core factors. This is not limited here. The following explanation uses the dimension of a core factor as an example to construct a causal network graph.

[0136] For example, taking external environmental pressure factors as an example, video data from public meetings or publicly available print media data from other entities can be obtained through the internet to acquire information on their public behavior. Specifically, semantics can be extracted from the video or print media data to obtain semantic information about other entities' attitudes toward geopolitical events. This semantic information can represent the attitudes of other entities toward geopolitical events. Then, by analyzing the semantic information, the relationship between other entities and the target entity can be determined. For example, if Party C supports Party A, then Party C can be associated with Party A. Correspondingly, semantics can be extracted from all publicly available video or print media data of other entities related to geopolitical events to construct a more comprehensive causal network graph.

[0137] In this embodiment, a causal network map of all target objects and other objects in the geopolitical environment is constructed by using publicly available video data or print media data from other objects, so as to provide correlations for subsequent simulation and make it closer to the actual geopolitical environment.

[0138] Reference Figure 7 In other embodiments, in S1043, to better express the impact of publicly disclosed information on geopolitical changes, it is also necessary to precisely determine the impact score of publicly disclosed information on geopolitical events. Therefore, S1043 may include:

[0139] S10431, For each original factor, extract keywords from the semantic information to obtain at least one keyword information;

[0140] S10432, Search for a preset keyword in the preset keyword library that corresponds to at least one keyword information to obtain the search results;

[0141] S10433, if the search results include at least one preset keyword corresponding to the keyword, determine the influence score based on the preset keyword and the correspondence between the keyword and the influence score.

[0142] In this embodiment, in S10431, keywords can be extracted from semantic information using a natural language model. Natural language models can not only recognize audio data in video data but also text data in print media data, so keywords can be extracted from semantic information using a natural language model.

[0143] In this embodiment, it is worth noting that the natural language model can be a conventional language recognition model in related technologies, which can recognize speech and text, and will not be described in detail here.

[0144] As an example, keyword information includes keywords used to express the attitudes of other objects towards the target object. For example, the keywords could include strong support, support, vague stance, evasive stance, opposition, and strong opposition.

[0145] In some other embodiments, in S10432, a preset keyword library can be pre-set in the electronic device. After extracting keywords from the video data or print media data, the keyword information can be compared with the keywords in the preset keyword library. If there is a preset keyword in the preset keyword library that corresponds to the keyword information, it means that the other object has a clear attitude towards the geopolitical event. At this time, the influence score can be determined according to the preset keyword and the correspondence between the keyword and the influence score.

[0146] As an example, the impact score for strongly supporting or strongly opposing a statement could be 4 points, the impact score for supporting or opposing a statement could be 3 points, a vague statement could be 2 points, and an evasive statement could be 1 point.

[0147] As another example, we can take the interactive behavior dimension, economic strength dimension, and defense exercise behavior dimension as examples. The following is the correspondence between the keywords and the impact scores of the above three dimensions.

[0148] Table 1. Comparison of Interaction Behavior Dimensions of Various Other Objects

[0149]

[0150]

[0151] Table 2 Comparison of Economic Strength Dimensions of Various Other Objects

[0152]

[0153] Table 3. Comparison of defensive exercise behavior dimensions for various other team targets.

[0154]

[0155] It is worth noting that the correspondence between keywords and influence scores can be set manually or determined based on the scoring model in the relevant technology, which will not be elaborated on here.

[0156] In some other embodiments, in S1044, a second influence value of the influence of the public behavior information of other objects on the geopolitical environment can be determined based on the sum of the influence scores of each other object under each core factor, so as to further determine the influence of the core factor on geopolitical environment changes.

[0157] It is worth noting that since geopolitical events are constantly changing within a preset timescale, the determined second degree of influence value can be the degree of influence at any moment within the preset timescale.

[0158] Reference Figure 8 In some other embodiments, after obtaining the second degree of influence value through the above method, it is necessary to further determine whether the degree of influence of the core factor on the geopolitical environment is accurate. Therefore, S105 may include:

[0159] S1051, In the preset simulation model, in response to the user's modification operation on the first original data corresponding to a core factor, update the first original data;

[0160] S1052, Based on the updated first original data, a simulation is performed to obtain the intensity curve under the preset time scale;

[0161] S1053, Based on the intensity curve, determine the third degree of influence value at any moment within the preset time scale;

[0162] S1054, based on the third degree of influence value and the second degree of influence value at the same time, the deviation index value is obtained;

[0163] S1055: If the deviation index value is within the preset error range, the core factor is identified as the key factor.

[0164] In this embodiment, to accurately determine the degree of influence of core factors on the geopolitical environment, the user can adjust the first original data corresponding to the core factor through an electronic device. The adjusted first original data is then input into a preset simulation model for further simulation, resulting in an intensity curve at a preset time scale. As an example, the intensity curve represents the changes in geopolitical events within the preset time scale. Specifically, the user changes the first original data corresponding to a single core factor to cause different changes in the geopolitical environment across different dimensions. The intensity curve output by the preset simulation model characterizes the severity of these changes. Based on the intensity curve, a third degree of influence value is determined at any given time. The deviation index between the third degree of influence value and the second degree of influence value quantifies the sensitivity and influence of the core factor on the geopolitical event. The deviation index is compared with a preset error range. If the deviation index is within the preset error range, it indicates that the core factor has a severe impact on the geopolitical environment, and the core factor is identified as a key factor; otherwise, it is not considered a key factor.

[0165] It is worth noting that when users adjust the first raw data, the adjustment range is within a preset range in order to more accurately determine the key factors. As an example, the preset range can be ±10%.

[0166] In this embodiment, when simulating the geopolitical environment, the simulation results of the preset simulation model can be used to show the changes in the geopolitical environment within a preset time scale, and the changes can be used to reflect the influence of key factors in the geopolitical environment, so as to make accurate predictions of the actual geopolitical environment.

[0167] In other embodiments, after obtaining the key factors, the changes in the geopolitical environment of geopolitical events can be predicted based on the key factors and their corresponding first raw data, as well as the publicly available behavioral information of other objects, so as to obtain a more accurate final result of the geopolitical events.

[0168] Based on the geopolitical environment prediction method provided in the above embodiments, this application also provides specific implementations of a geopolitical environment prediction device. Please refer to the following embodiments.

[0169] See Figure 9 This application embodiment also provides a geopolitical environment prediction device 900, which may include:

[0170] The acquisition module 901 is used to acquire multiple original factors of at least two target objects in the geopolitical environment, first original data corresponding to the multiple original factors, and public behavioral information of multiple other objects. Other objects include objects other than the target objects. Public behavioral information includes behavioral information of other objects on geopolitical events in the geopolitical environment within a preset time scale. Behavioral information includes behavioral information under each original factor. Original factors include at least two of the following: available resource factors, external environmental pressure factors, and internal stability factors.

[0171] The regression analysis module 902 is used to perform regression analysis on the original factors and their corresponding first original data using a preset regression analysis model, so as to output the first degree of influence of the original factors on the geopolitical environment.

[0172] The determination module 903 is used to determine at least one core factor from a variety of original factors based on a first degree of influence value;

[0173] The simulation module 904 is used to input the first original data corresponding to the core factors and the public behavior information of multiple other objects into the preset simulation model. The preset simulation model simulates the change trend of the geopolitical environment within a preset time scale based on the first original data and public behavior information corresponding to the core factors, and obtains the second influence degree value of the influence of each core factor on the geopolitical environment.

[0174] The determination module 903 is also used to determine at least one key factor from at least one core factor based on the second degree of influence value;

[0175] Prediction module 905 is used to predict changes in geopolitical events in the geopolitical environment based on key factors.

[0176] As an optional implementation, the acquisition module 901 can also be used for:

[0177] For each original factor, semantic analysis is performed on the first original data to obtain the analysis results. The analysis results include at least two assignments corresponding to the first original data that represent the same semantic meaning.

[0178] The first raw data is encoded based on the analysis results to obtain the encoded values ​​corresponding to the analysis results;

[0179] Semantically associate the original factors with the coded values ​​to obtain the regression analysis dataset;

[0180] A pre-defined regression analysis model is used to perform regression analysis on the original factors and their corresponding primary data to output the degree of influence of the original factors on the geopolitical environment, including:

[0181] A pre-defined regression analysis model is used to perform regression analysis on the regression analysis dataset to output the first degree of influence of the original factors on the geopolitical environment.

[0182] As an optional implementation, the regression analysis module 902 can also be used for:

[0183] A pre-defined regression analysis model is used to perform baseline regression analysis on the regression analysis dataset to obtain the regression coefficients and errors of each original factor on the geopolitical environment.

[0184] Based on the regression coefficients and errors corresponding to the original factors, the first degree of influence of each original factor on the geopolitical environment is determined.

[0185] As an optional implementation, the determining module 903 can also be used for:

[0186] For each target original factor, multiple component factors corresponding to each target original factor are obtained. The component factors are used to represent multiple dimensions of the impact of geopolitical events on the geopolitical environment. The target original factors include original factors whose first impact value is greater than the preset impact threshold.

[0187] Retrieve the second original data corresponding to the constituent factors from the preset database;

[0188] Based on the preset regression analysis model, stability regression analysis was performed on the second original data corresponding to multiple component factors to obtain stability test results.

[0189] If the stability test results are consistent with the first degree of influence value, the original factor is identified as the core factor.

[0190] As an optional implementation, the simulation module 904 can also be used for:

[0191] Based on the first raw data corresponding to each core factor, construct a virtual geopolitical environment for at least two target objects within a preset simulation model;

[0192] Based on publicly available behavioral information and a virtual geopolitical environment, a causal network graph of the target object and other objects is determined. The nodes of the causal network graph are used to represent the action information performed by other objects on the target object in each core factor.

[0193] Based on the causal network graph and the preset simulation function, the change trend of the geopolitical environment within a preset time scale is simulated to obtain the influence score of the public behavior information of other objects on geopolitical events.

[0194] Based on the sum of each influence score, determine the second influence value of the degree of influence of other objects on the geopolitical environment at any time within a preset time scale for each core factor.

[0195] As an optional implementation, the simulation module 904 can also be used for:

[0196] Semantic extraction is performed on publicly available behavioral information to obtain semantic information about geopolitical events from other objects;

[0197] Based on semantic information, determine the association between other objects and the target object;

[0198] Based on the relationships, draw a causal network diagram of the target object and other objects.

[0199] As an optional implementation, the simulation module 904 can also be used for:

[0200] For each original factor, semantic information is extracted by keyword extraction to obtain at least one keyword information, which includes keywords used to express the attitude of other objects towards the target object;

[0201] Search the preset keyword library for preset keywords that correspond to at least one keyword information to obtain the search results;

[0202] If the search results include at least one preset keyword that corresponds to the keyword, the influence score is determined based on the preset keyword and the correspondence between the keyword and the influence score.

[0203] As an optional implementation, the determining module 903 can also be used for:

[0204] In the preset simulation model, the first original data is updated in response to the user's modification operation on the first original data corresponding to a core factor.

[0205] Based on the updated first original data, a simulation is performed to obtain an intensity curve at a preset time scale. The intensity curve is used to represent the changes of geopolitical events within the preset time scale.

[0206] Based on the intensity curve, determine the third degree of influence value at any time within the preset time scale;

[0207] The deviation index value is obtained based on the third degree of influence value and the second degree of influence value at the same time.

[0208] If the deviation index value is within the preset error range, the core factor is identified as the key factor.

[0209] Figure 10 A schematic diagram of the hardware structure of the electronic device provided in an embodiment of this application is shown.

[0210] An electronic device may include a processor 1001 and a memory 1002 storing computer program instructions.

[0211] Specifically, the processor 1001 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0212] Memory 1002 may include mass storage for data or instructions. For example, and not limitingly, memory 1002 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 1002 may include removable or non-removable (or fixed) media. Where appropriate, memory 1002 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 1002 is non-volatile solid-state memory.

[0213] Memory 1002 may include read-only memory (ROM), random access memory (RAM), disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical / tangible memory storage devices. Therefore, typically, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform operations described with reference to a geopolitical environment prediction method according to one aspect of this disclosure.

[0214] The processor 1001 reads and executes computer program instructions stored in the memory 1002 to implement any of the geopolitical environment prediction methods in the above embodiments.

[0215] In one example, the electronic device may also include a communication interface 1003 and a bus 1004. For example, Figure 10 As shown, the processor 1001, memory 1002, and communication interface 1003 are connected through bus 1004 and complete communication with each other.

[0216] The communication interface 1003 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0217] Bus 1004 includes hardware, software, or both, that couples components of an online data traffic metering device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 1004 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.

[0218] This electronic device can execute the geopolitical environment prediction method in this application embodiment based on currently blocked spam text messages and text messages reported by users, thereby achieving a combination of... Figures 1-9 The method for predicting the geopolitical environment.

[0219] Furthermore, in conjunction with the geopolitical environment prediction methods in the above embodiments, this application embodiment can provide a computer storage medium for implementation. This computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the geopolitical environment prediction methods in the above embodiments.

[0220] This application also provides a computer program product, including a computer program that, when executed, implements any of the geopolitical environment prediction methods described in the above embodiments.

[0221] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0222] The functional blocks shown in the above-described structural diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0223] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0224] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0225] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A method, apparatus, device, storage medium, and program product for predicting a geospatial environment, characterized by, include: Acquire multiple raw factors of at least two target objects in a geopolitical environment, first raw data corresponding to the multiple raw factors, and public behavioral information of multiple other objects. The other objects include objects other than the target objects. The public behavioral information includes the behavioral information of other objects on geopolitical events in the geopolitical environment within a preset time scale. The public behavioral information includes behavioral information under each raw factor. The raw factors include at least two of the following: available resource factors, external environmental pressure factors, and internal stability factors. A preset regression analysis model is used to perform regression analysis on the original factors and their corresponding first original data to output the first degree of influence of the original factors on the geopolitical environment. Based on the first influence value, at least one core factor is determined from a variety of original factors; The first raw data corresponding to the core factor and the public behavior information of multiple other objects are input into a preset simulation model. The preset simulation model simulates the change trend of the geopolitical environment within a preset time scale based on the first raw data corresponding to the core factor and the public behavior information, and obtains a second influence degree value of the influence degree of each core factor on the geopolitical environment. Based on the second degree of influence value, at least one key factor is determined from at least one core factor; Based on the aforementioned key factors, information on changes in the geopolitical environment related to the geopolitical events is predicted.

2. The method of claim 1, wherein, After acquiring multiple raw factors of at least two target objects in the geopolitical environment, first raw data corresponding to the multiple raw factors, and public behavioral information of multiple other objects, the method further includes: For each original factor, semantic analysis is performed on the first original data to obtain analysis results. The analysis results include at least two assignments corresponding to the first original data that represent the same semantic meaning. The first raw data is encoded according to the analysis results to obtain the encoded value corresponding to the analysis results; The original factors are semantically associated with the encoded values ​​to obtain a regression analysis dataset. The step of performing regression analysis on the original factors and their corresponding first original data using a preset regression analysis model to output the first degree of influence of the original factors on the geopolitical environment includes: A pre-defined regression analysis model is used to perform regression analysis on the regression analysis dataset to output the first degree of influence of the original factors on the geopolitical environment.

3. The method of claim 1, wherein, The step of performing regression analysis on the original factors and their corresponding first original data using a preset regression analysis model to output the first degree of influence of the original factors on the geopolitical environment includes: The preset regression analysis model performs benchmark regression analysis on the regression analysis dataset to obtain the regression coefficient and error of each original factor on the geopolitical environment; Based on the regression coefficients and errors corresponding to the original factors, the first degree of influence of each of the original factors on the geopolitical environment is determined.

4. The method according to any one of claims 1 to 3, characterized in that, The determination of at least one core factor from multiple original factors based on the first influence value includes: For each target original factor, multiple component factors corresponding to each target original factor are obtained. The component factors are used to represent multiple dimensions of the impact of geopolitical events on the geopolitical environment. The target original factors include original factors whose first impact value is greater than a preset impact threshold. Retrieve the second original data corresponding to the constituent factors from the preset database; Based on the preset regression analysis model, stability regression analysis is performed on the second original data corresponding to multiple component factors to obtain stability test results. If the stability test result is consistent with the first influence value, the original factor is identified as the core factor.

5. The method of claim 1, wherein, The first original data corresponding to the core factor and the publicly disclosed behavioral information of multiple other objects are input into a preset simulation model. The preset simulation model, based on the first original data corresponding to the core factor and the publicly disclosed behavioral information, simulates the changing trend of the geopolitical environment within a preset time scale, obtaining a second impact value for the degree of influence of each core factor on the geopolitical environment, including: Based on the first original data corresponding to each of the core factors, construct a virtual geopolitical environment for the at least two target objects within a preset simulation model; Based on the publicly available behavioral information and the virtual geopolitical environment, a causal network graph of the target object and the other objects is determined. The nodes of the causal network graph are used to represent the action information performed by the other objects on the target object in each core factor. Based on the causal network graph and the preset simulation function, the change trend of the geopolitical environment within a preset time scale is simulated to obtain the influence score of the public behavior information of other objects on the geopolitical event. Based on the sum of each of the said influence scores, a second influence value is determined for the degree of influence of the other objects on the geopolitical environment under each core factor at any time within the preset time scale.

6. The method of claim 5, wherein, The step of determining the causal network graph between the target object and the other objects based on the publicly available behavioral information and the virtual geographical environment includes: Semantic extraction is performed on the publicly disclosed behavioral information to obtain the semantic information of the other objects regarding the geopolitical event; Based on the semantic information, the association relationship between the other objects and the target object is determined; Based on the aforementioned relationships, a causal network graph of the target object and the other objects is drawn.

7. The method of claim 6, wherein, The step of determining the influence score of the publicly disclosed behavioral information of other objects on the geopolitical event based on the causal network graph includes: For each original factor, keywords are extracted from the semantic information to obtain at least one keyword information, which includes keywords used to represent the attitudes of the other objects towards the target object; Search the preset keyword library for a preset keyword that corresponds to the at least one keyword information to obtain the search results; If the search results include at least one preset keyword corresponding to the keyword, the influence score is determined based on the preset keyword and the correspondence between the keyword and the influence score.

8. The method according to any one of claims 1-3 or 5-7, characterized in that, The determination of at least one key factor from at least one core factor based on the second degree of influence value includes: In the preset simulation model, the first original data is updated in response to the user's modification operation on the first original data corresponding to a core factor; Based on the updated first original data, a simulation is performed to obtain an intensity curve at a preset time scale. The intensity curve is used to represent the changes of geopolitical events within the preset time scale. Based on the intensity curve, determine the third degree of influence value at any moment within the preset time scale; The deviation index value is obtained based on the third influence value and the second influence value at the same time. If the deviation index value is within the preset error range, the core factor is identified as the key factor.

9. A device for predicting a geoenvironment, characterized by, The device includes: The acquisition module is used to acquire multiple raw factors of at least two target objects in a geopolitical environment, first raw data corresponding to the multiple raw factors, and public behavioral information of multiple other objects. The other objects include objects other than the target objects. The public behavioral information includes behavioral information of other objects on geopolitical events in the geopolitical environment within a preset time scale. The behavioral information includes behavioral information under each raw factor. The raw factors include at least two of the following: available resource factors, external environmental pressure factors, and internal stability factors. The regression analysis module is used to perform regression analysis on the original factors and their corresponding first original data using a preset regression analysis model, so as to output the first degree of influence of the original factors on the geopolitical environment. The determination module is used to determine at least one core factor from a variety of original factors based on the first influence degree value; The simulation module is used to input the first raw data corresponding to the core factor and the public behavior information of multiple other objects into a preset simulation model. The preset simulation model simulates the change trend of the geopolitical environment within a preset time scale based on the first raw data corresponding to the core factor and the public behavior information, and obtains a second influence degree value of the degree of influence of each core factor on the geopolitical environment. The determining module is further configured to determine at least one key factor from at least one core factor based on the second degree of influence value; The prediction module is used to predict changes in the geopolitical event within the geopolitical environment based on the key factors.

10. An electronic device, comprising: The device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the geopolitical environment prediction method as described in any one of claims 1-8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions that, when executed by a processor, implement the geopolitical environment prediction method as described in any one of claims 1-8.

12. A computer program product, characterized in that, When the instructions in the computer program product are executed by the processor of the electronic device, the electronic device causes the electronic device to perform the geopolitical environment prediction method as described in any one of claims 1-8.

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