Regional country risk transmission analysis method and system

Abstract

The application relates to the technical field of regional country risk assessment, and specifically provides a regional country risk transmission analysis method and system, which has the following steps: S1, constructing a regional country risk knowledge graph; S2, a dynamic relationship strength calculation rule; S3, non-uniform key percolation modeling and Monte Carlo simulation; and S4, systematic risk analysis and key element identification. Compared with the prior art, the application can construct a risk knowledge graph containing dynamic relationship strength, combine a non-uniform key percolation model with Monte Carlo simulation, quantify the criticality of a risk transmission path and the critical condition of a systematic risk outbreak, and thus realize early risk warning and accurate prevention and control.

Description

Technical Field

[0001] This invention relates to the field of regional and country risk assessment technology, and specifically provides a method and system for analyzing the transmission of regional and country risks. Background Technology

[0002] Existing regional and country-specific risk assessment methods reveal several structural limitations when addressing the increasingly complex and highly interconnected global risk environment. First, their analytical frameworks are largely based on static correlation models. For example, while traditional knowledge graphs can describe the topological connections between risk entities, they struggle to dynamically capture the evolution of these connections over time, policy, market sentiment, and other factors, and cannot quantify the impact of such dynamic connections on the speed and scope of risk transmission across regions. Second, existing methods generally lack a systemic perspective, often focusing on single events or local transmission chains, neglecting the nonlinear process by which risks amplify and aggregate through multiple feedback loops and cascading effects in complex networks, ultimately triggering systemic collapse. This makes it difficult to identify the critical turning point from quantitative to qualitative change. Third, the risk transmission mechanism itself remains ambiguous: although academia generally acknowledges that risks can spread through multiple channels such as trade dependence, financial exposure, and geopolitical alliances, the specific transmission paths, interaction coupling methods, and probabilities of these channels have not yet been integrated into a unified, computable model. This results in delayed risk warnings, passive responses, and a lack of precise targeting in prevention and control strategies.

[0003] Percolation theory provides a powerful theoretical tool for overcoming the aforementioned bottlenecks. As a classic paradigm in statistical physics for studying connectivity phase transitions, percolation theory, by setting the activation probability of nodes or edges and calculating the critical threshold (i.e., the "percolation threshold") corresponding to the global connectivity mutation of the system, can effectively characterize the abrupt transition behavior of complex networks from a fragmented state to a large-scale connected state. This characteristic perfectly matches the inherent mechanism by which regional and national risks evolve from local disturbances to systemic crises—when the propagation intensity of risk in a multidimensional interconnected network exceeds a certain critical point, originally isolated risk events may rapidly spread and trigger global instability.

[0004] Current research has not fully integrated percolation theory and dynamic knowledge graph technology: on the one hand, dynamic knowledge graphs can integrate multi-source heterogeneous data (such as news sentiment, economic indicators, and policy changes) in real time and continuously update the semantics and weights of risk entities and their associations; on the other hand, percolation models can simulate the risk propagation process under different scenarios on this dynamic network and identify key vulnerable nodes and systemic critical thresholds.

[0005] Therefore, how to construct a risk knowledge graph that includes the strength of dynamic relationships, combine non-uniform bond percolation models and Monte Carlo simulations, quantify the criticality of risk transmission paths and the critical conditions for the outbreak of systemic risks, and thus achieve early warning and precise prevention and control of risks is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] This invention addresses the shortcomings of the existing technology by providing a highly practical method for analyzing the transmission of regional and national risks.

[0007] A further technical objective of this invention is to provide a reasonably designed, safe, and applicable regional and national risk transmission analysis device.

[0008] The technical solution adopted by this invention to solve its technical problem is: The regional and country-specific risk transmission analysis method includes the following steps: S1. Construct a regional and country-specific risk knowledge graph; S2, Rules for calculating the strength of dynamic relationships; S3, Non-uniform bond percolation modeling and Monte Carlo simulation; S4. Systemic risk analysis and key element identification.

[0009] Furthermore, step S1 includes: Node definition: includes risk entities, each node is associated with basic attributes and real-time risk status; Edge definition: Represents the relationship between risk entities, covering multiple dimensions such as trade, finance, politics, society, and geography.

[0010] Furthermore, step S2 includes: Relationship strength of each edge (i→j) From static correlation strength With dynamic state factor The decision is made jointly, and the formula is as follows: ; in: Static correlation strength The basic data for each dimension are normalized, and weights are assigned according to research needs. Calculated by weighted summation: ; Dynamic state factor The real-time driving and accepting capacity of risk transmission is reflected and calculated as follows: ; In the formula, Let be the current risk index of node i. Let α be the vulnerability index of node j, and α be the adjustment parameter.

[0011] Furthermore, step S3 includes: Treating each edge in a knowledge graph as a "key", the probability of it being activated... The relationship strength is directly equal to the edge strength. Due to different sides The networks vary, forming a non-uniform bond percolation network. Simulation process: Set initial conditions: Select one or more nodes as risk sources and mark them as "active"; Randomly activate edges: Generate edges for all edges in the network. A random number for the interval, if the random number is less than or equal to the edge number. If the edge is active, then the edge is activated. Transmission Result Statistics: Based on graph theory algorithms, identify all nodes reachable from the risk source through activated edges, i.e., the set of nodes affected by the risk. Repeated simulation: Perform N independent simulations and record the number of nodes affected and their specific paths in each simulation.

[0012] Furthermore, step S4 includes: Systemic risk probability: The percentage of simulations in which the number of affected nodes exceeds a threshold in N simulations is used as the probability of systemic risk outbreak in the current scenario; Key transmission paths: The edges with the highest activation frequency in all simulations are identified as key risk transmission paths; Key nodes: Analyze the nodes most frequently affected and identify them as "vulnerable points" or "super-spreaders" of systemic risk; Simulation module: Dynamically adjusts parameters and reruns the simulation to assess the impact of interventions on the probability of systemic risk.

[0013] The regional and national risk transmission analysis system first constructs a regional and national risk knowledge graph, then calculates dynamic relationship strength rules, performs non-uniform bond percolation modeling and Monte Carlo simulation, and finally conducts systemic risk analysis and key element identification.

[0014] Furthermore, when constructing a regional country risk knowledge graph, the following are included: Node definition: includes risk entities, each node is associated with basic attributes and real-time risk status; Edge definition: Represents the relationship between risk entities, covering multiple dimensions such as trade, finance, politics, society, and geography.

[0015] Furthermore, the rules for calculating the strength of dynamic relationships include: Relationship strength of each edge (i→j) From static correlation strength With dynamic state factor The decision is made jointly, and the formula is as follows: ; in: Static correlation strength The basic data for each dimension are normalized, and weights are assigned according to research needs. Calculated by weighted summation: ; Dynamic state factor The real-time driving and accepting capacity of risk transmission is reflected and calculated as follows: ; In the formula, Let be the current risk index of node i. Let α be the vulnerability index of node j, and α be the adjustment parameter.

[0016] Furthermore, in non-uniform bond percolation modeling and Monte Carlo simulation, include: Treating each edge in a knowledge graph as a "key", the probability of it being activated... The relationship strength is directly equal to the edge strength. Due to different sides The networks vary, forming a non-uniform bond percolation network. Simulation process: Set initial conditions: Select one or more nodes as risk sources and mark them as "active"; Randomly activate edges: Generate edges for all edges in the network. A random number for the interval, if the random number is less than or equal to the edge number. If the edge is active, then the edge is activated. Transmission Result Statistics: Based on graph theory algorithms, identify all nodes reachable from the risk source through activated edges, i.e., the set of nodes affected by the risk. Repeated simulation: Perform N independent simulations and record the number of nodes affected and their specific paths in each simulation.

[0017] Furthermore, in systemic risk analysis and key element identification, this includes: Systemic risk probability: The percentage of simulations in which the number of affected nodes exceeds a threshold in N simulations is used as the probability of systemic risk outbreak in the current scenario; Key transmission paths: The edges with the highest activation frequency in all simulations are identified as key risk transmission paths; Key nodes: Analyze the nodes most frequently affected and identify them as "vulnerable points" or "super-spreaders" of systemic risk; Simulation module: Dynamically adjusts parameters and reruns the simulation to assess the impact of interventions on the probability of systemic risk.

[0018] Compared with existing technologies, the regional and national risk transmission analysis method and system of the present invention have the following outstanding advantages: (1) This invention is the first to apply the idea of ​​“critical phase transition” of percolation theory to regional and national risk analysis, and accurately identify the threshold condition of “local risk → global crisis” through the non-uniform bond percolation model.

[0019] (2) The relationship strength calculation integrates static structure and dynamic state factors, which can reflect the changes in the output pressure of risk sources and the vulnerability of receivers in real time, and adapt to the complex and ever-changing international environment.

[0020] (3) Identify key transmission paths and vulnerable nodes through Monte Carlo simulation to provide a basis for targeted intervention in risk prevention and control.

[0021] (4) The simulation module supports hypothesis-verification analysis to help formulate accurate risk mitigation strategies. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating a method for analyzing the transmission of regional and national risks. Figure 2 This is a schematic diagram of the Monte Carlo simulation process in a regional and national risk transmission analysis method. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present invention, the present invention will be further described in detail below with reference to specific embodiments. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] The following is a preferred embodiment: like Figure 1-2 As shown, a regional and national risk transmission analysis method in this embodiment includes the following steps: S1. Construct a regional and country-specific risk knowledge graph; include: Node definition: including but not limited to risk entities such as countries / regions, institutions, key industries, international organizations, and social groups. Each node is associated with its basic attributes (such as GDP, population, and system) and real-time risk status (such as stability index and debt default probability).

[0026] Edge definition: Represents the relationship between risk entities, covering multiple dimensions such as trade (import and export volume / GDP, dependence on key commodities), finance (cross-border investment ratio, bond holdings), politics (alliance treaty level, frequency of diplomatic visits), society (intensity of personnel exchanges, social media interaction), and geography (whether they are adjacent, distance decay function).

[0027] S2, Rules for calculating the strength of dynamic relationships; include: Relationship strength of each edge (i→j) From static correlation strength With dynamic state factor The decision is made jointly, and the formula is as follows: ; in: Static correlation strength Normalize the basic data across various dimensions such as trade, finance, and politics (e.g., Min-Max standardization to...). ), and allocate weights according to research needs. (e.g., trade weight 0.3, finance weight 0.4, politics weight 0.3), calculated by weighted summation: ; Dynamic state factor The real-time driving and accepting capacity of risk transmission is reflected and calculated as follows: ; In the formula, This represents the current risk index of node i (such as volatility score or debt crisis probability). Let α be the vulnerability index of node j (such as economic external dependence or social stability score), and α be the adjustment parameter (usually taken as 1 to 2).

[0028] S3, Non-uniform bond percolation modeling and Monte Carlo simulation; Each edge in the knowledge graph is considered a "key," and its activation (i.e., successful risk transmission) is determined by the probability of that edge being activated. Directly equal to the relation strength of that edge Due to different sides The diverse structures form a non-uniform bond percolation network. Simulation process: Set initial conditions: Select one or more nodes as risk sources (such as countries experiencing debt crises) and mark them as "active"; Randomly activate edges: Generate edges for all edges in the network. A random number within an interval, if the random number is less than or equal to the value of the edge. If so, the edge is activated (allowing the risk to pass). Transmission Result Statistics: Based on graph theory algorithms (such as BFS / DFS), identify all nodes reachable from the risk source through activated edges (i.e. the set of nodes affected by the risk). Repeated simulation: Perform N independent simulations (e.g., 1000-10000), and record the number of nodes affected and their specific paths in each simulation.

[0029] S4. Systemic risk analysis and key element identification; include: Systemic risk probability: The percentage of simulations in which the number of affected nodes exceeds a threshold (e.g., 50%) out of N simulations is used as the probability of systemic risk outbreak in the current scenario.

[0030] Key transmission paths: The edges that are activated most frequently in all simulations (i.e. the relationships that are most often involved in risk transmission) are identified as key risk transmission paths.

[0031] Key nodes: Analyze the nodes that are most frequently affected (i.e., the entities most vulnerable to risk) and identify them as "vulnerabilities" or "super-spreaders" of systemic risk.

[0032] Simulation module: Supports dynamic adjustment of parameters (such as cutting off the risk transmission path corresponding to a critical edge, reducing the risk index or vulnerability of a node), and rerunning the simulation to assess the impact of intervention measures on the probability of systemic risk.

[0033] Based on the above methods, the regional and national risk transmission analysis system in this embodiment first constructs a regional and national risk knowledge graph, then calculates dynamic relationship strength rules, performs non-uniform bond percolation modeling and Monte Carlo simulation, and finally conducts systemic risk analysis and key element identification.

[0034] When constructing a regional country risk knowledge graph, the following are included: Node definition: includes risk entities, each node is associated with basic attributes and real-time risk status; Edge definition: Represents the relationship between risk entities, covering multiple dimensions such as trade, finance, politics, society, and geography.

[0035] The rules for calculating the strength of dynamic relationships include: Relationship strength of each edge (i→j) From static correlation strength With dynamic state factor The decision is made jointly, and the formula is as follows: ; in: Static correlation strength The basic data for each dimension are normalized, and weights are assigned according to research needs. Calculated by weighted summation: ; Dynamic state factor The real-time driving and accepting capacity of risk transmission is reflected and calculated as follows: ; In the formula, Let be the current risk index of node i. Let α be the vulnerability index of node j, and α be the adjustment parameter.

[0036] In non-uniform bond percolation modeling and Monte Carlo simulation include: Treating each edge in a knowledge graph as a "key", the probability of it being activated... The relationship strength is directly equal to the edge strength. Due to different sides The networks vary, forming a non-uniform bond percolation network. Simulation process: Set initial conditions: Select one or more nodes as risk sources and mark them as "active"; Randomly activate edges: Generate edges for all edges in the network. A random number for the interval, if the random number is less than or equal to the edge number. If the edge is active, then the edge is activated. Transmission Result Statistics: Based on graph theory algorithms, identify all nodes reachable from the risk source through activated edges, i.e., the set of nodes affected by the risk. Repeated simulation: Perform N independent simulations and record the number of nodes affected and their specific paths in each simulation.

[0037] Systemic risk analysis and key element identification include: Systemic risk probability: The percentage of simulations in which the number of affected nodes exceeds a threshold in N simulations is used as the probability of systemic risk outbreak in the current scenario; Key transmission paths: The edges with the highest activation frequency in all simulations are identified as key risk transmission paths; Key nodes: Analyze the nodes most frequently affected and identify them as "vulnerable points" or "super-spreaders" of systemic risk; Simulation module: Dynamically adjusts parameters and reruns the simulation to assess the impact of interventions on the probability of systemic risk.

[0038] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for analyzing the transmission of regional and national risks, characterized in that... It has the following steps: S1. Construct a regional and country-specific risk knowledge graph; S2, Rules for calculating the strength of dynamic relationships; S3, Non-uniform bond percolation modeling and Monte Carlo simulation; S4. Systemic risk analysis and key element identification.

2. The regional and national risk transmission analysis method according to claim 1, characterized in that, Step S1 includes: Node definition: includes risk entities, each node is associated with basic attributes and real-time risk status; Edge definition: Represents the relationship between risk entities, covering multiple dimensions such as trade, finance, politics, society, and geography.

3. The regional and national risk transmission analysis method according to claim 2, characterized in that, Step S2 includes: Relationship strength of each edge (i→j) From static correlation strength With dynamic state factor The decision is made jointly, and the formula is as follows: ； in: Static correlation strength The basic data for each dimension are normalized, and weights are assigned according to research needs. Calculated by weighted summation: ； Dynamic state factor The real-time driving and accepting capacity of risk transmission is reflected and calculated as follows: ； In the formula, Let be the current risk index of node i. Let α be the vulnerability index of node j, and α be the adjustment parameter.

4. The regional and national risk transmission analysis method according to claim 3, characterized in that, Step S3 includes: Treating each edge in a knowledge graph as a "key", the probability of it being activated... The relationship strength is directly equal to the edge strength. Due to different sides The networks vary, forming a non-uniform bond percolation network. Simulation process: Set initial conditions: Select one or more nodes as risk sources and mark them as "active"; Randomly activate edges: Generate edges for all edges in the network. A random number for the interval, if the random number is less than or equal to the edge number. If the edge is active, then the edge is activated. Transmission Result Statistics: Based on graph theory algorithms, identify all nodes reachable from the risk source through activated edges, i.e., the set of nodes affected by the risk. Repeated simulation: Perform N independent simulations and record the number of nodes affected and their specific paths in each simulation.

5. The regional and national risk transmission analysis method according to claim 4, characterized in that, Step S4 includes: Systemic risk probability: The percentage of simulations in which the number of affected nodes exceeds a threshold in N simulations is used as the probability of systemic risk outbreak in the current scenario; Key transmission paths: The edges with the highest activation frequency in all simulations are identified as key risk transmission paths; Key nodes: Analyze the nodes most frequently affected and identify them as "vulnerabilities" or "super-spreaders" of systemic risk; Simulation module: Dynamically adjusts parameters and reruns the simulation to assess the impact of interventions on the probability of systemic risk.

6. A regional and national risk transmission analysis system, characterized in that, First, a regional and national risk knowledge graph is constructed. Then, dynamic relationship strength calculation rules are developed, non-uniform bond percolation modeling and Monte Carlo simulation are performed, and finally, systemic risk analysis and key element identification are conducted.

7. The regional and national risk transmission analysis system according to claim 6, characterized in that, When constructing a regional country risk knowledge graph, the following are included: Node definition: includes risk entities, each node is associated with basic attributes and real-time risk status; Edge definition: Represents the relationship between risk entities, covering multiple dimensions such as trade, finance, politics, society, and geography.

8. The regional and national risk transmission analysis system according to claim 7, characterized in that, The rules for calculating the strength of dynamic relationships include: Relationship strength of each edge (i→j) From static correlation strength With dynamic state factor The decision is made jointly, and the formula is as follows: ； in: Static correlation strength The basic data for each dimension are normalized, and weights are assigned according to research needs. Calculated by weighted summation: ； Dynamic state factor The real-time driving and accepting capacity of risk transmission is reflected and calculated as follows: ； In the formula, Let be the current risk index of node i. Let be the vulnerability index of node j, and α be the adjustment parameter.

9. The regional and national risk transmission analysis system according to claim 8, characterized in that, In non-uniform bond percolation modeling and Monte Carlo simulation include: Treating each edge in a knowledge graph as a "key", the probability of it being activated... The relationship strength is directly equal to the edge strength. Due to different sides The networks vary, forming a non-uniform bond percolation network. Simulation process: Set initial conditions: Select one or more nodes as risk sources and mark them as "active"; Randomly activate edges: Generate edges for all edges in the network. A random number for the interval, if the random number is less than or equal to the edge number. If the edge is active, then the edge is activated. Transmission Result Statistics: Based on graph theory algorithms, identify all nodes reachable from the risk source through activated edges, i.e., the set of nodes affected by the risk. Repeated simulation: Perform N independent simulations and record the number of nodes affected and their specific paths in each simulation.

10. The regional and national risk transmission analysis system according to claim 9, characterized in that, Systemic risk analysis and key element identification include: Systemic risk probability: The percentage of simulations in which the number of affected nodes exceeds a threshold in N simulations is used as the probability of systemic risk outbreak in the current scenario; Key transmission paths: The edges with the highest activation frequency in all simulations are identified as key risk transmission paths; Key nodes: Analyze the nodes most frequently affected and identify them as "vulnerabilities" or "super-spreaders" of systemic risk; Simulation module: Dynamically adjusts parameters and reruns the simulation to assess the impact of interventions on the probability of systemic risk.

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