Intelligent international trade risk prevention and control method and system based on multi-modal data analysis

Abstract

The present invention relates to the technical field of international trade risk management, and particularly relates to an intelligent international trade risk prevention and control method and system based on multi-modal data analysis. The method comprises: collecting transaction records, logistics information, policies, laws and regulations, and dynamic market data; extracting abnormal behavior features and evaluating a transaction risk grade; in view of logistics and policy data, evaluating the degree of influence of a cross-border transportation risk, so as to obtain a comprehensive risk index; and by means of the dynamic market data, estimating an overall risk level for early warning. The method can improve the accuracy and real-time response capability of international trade risk prediction, thereby meeting intelligent risk management requirements in complex environments.

Description

International trade risk intelligent prevention and control method and system based on multi-modal data analysis TECHNICAL FIELD

[0001] The present application belongs to the technical field of international trade and data analysis, specifically an international trade risk intelligent prevention and control method and system based on multi-modal data analysis. BACKGROUND

[0002] With the deepening development of global economic integration, the scale and complexity of international trade continue to increase, and the uncertainty and risk it faces are increasingly prominent. In this context, using multi-modal data analysis for international trade risk intelligent prevention and control has become a research hotspot. Multi-modal data analysis methods can more comprehensively reflect complex real-world scenarios by integrating multiple types of data (such as structured data, unstructured data, time series data, etc.), thereby providing support for risk prediction and prevention. However, although existing multi-modal data analysis methods have achieved certain results in specific fields, their application in the international trade context still has significant shortcomings, especially in the areas of cross-domain data fusion, dynamic risk prediction, and real-time response capabilities.

[0003] After searching, it was found that a patent with publication number CN118398217B proposes a multi-modal fusion hemodialysis data analysis method. This method collects multi-modal data such as patient biological characteristics and clinical indicators, combines an attention mechanism and a deep neural network model (Attention-DNN) to predict the probability of complications after hemodialysis treatment, and realizes personalized prediction and management. However, this technical solution mainly focuses on a single application scenario in the medical field and lacks comprehensive analysis capabilities for multi-domain data. In addition, the model design does not fully consider the complex dynamic environment and real-time risk changes in international trade, making it difficult to be directly applied to the international trade risk prevention and control scenario.

[0004] Another patent with publication number CN117972359B proposes an intelligent data analysis method based on multi-modal data. This method obtains clustering results through a directed graph composed of word vectors and target regions, and combines context semantic similarity and public semantic base similarity to determine the double-modal semantic expandable overlap degree, thereby realizing efficient fusion of multi-modal data. However, this technical solution mainly focuses on the semantic consistency problem of text and image data, and has certain limitations when dealing with diverse data types involved in international trade (such as transaction records, logistics information, policies and regulations, etc.). At the same time, this solution does not fully consider the heterogeneous data characteristics between different countries and regions in international trade, which may lead to a decrease in the accuracy and applicability of the fusion results, making it difficult to meet actual application needs. TECHNICAL PROBLEM

[0005] The above problems show that the existing multi-modal data analysis methods still have significant deficiencies in the field of international trade risk intelligent prevention and control. First, the cross-domain data fusion capability is limited, making it difficult to effectively integrate multi-source heterogeneous data involved in international trade. Second, the dynamic risk prediction accuracy is not high, making it difficult to adapt to the complex and changing risk environment in international trade. Finally, the real-time response capability is insufficient, and it is difficult to respond to sudden risk events in a timely manner. Therefore, an international trade risk intelligent prevention and control method and system based on multi-modal data analysis is urgently needed to integrate multi-source heterogeneous data, improve dynamic risk prediction capability, and achieve efficient real-time response, thereby meeting the needs of intelligent risk management in international trade. Technical solution

[0006] In order to overcome the defects and deficiencies of the prior art, the present application provides an international trade risk intelligent prevention and control method and system based on multi-modal data analysis, which integrates multi-source heterogeneous data and combines dynamic scenario modeling to improve the accuracy of international trade risk prediction and real-time response capability, and meets the needs of intelligent risk management in complex international trade environment.

[0007] In a first aspect, the present application provides an international trade risk intelligent prevention and control method based on multi-modal data analysis, comprising the following steps: collecting transaction record data, logistics information data, policy and regulation data, and market dynamic data related to international trade; extracting abnormal behavior features from transaction record data and evaluating transaction risk level based on abnormal behavior features; evaluating the influence degree of cross-border transportation risk based on logistics information data and policy and regulation data, and obtaining a comprehensive risk index in combination with the transaction risk level; estimating the overall risk level of international trade by combining the comprehensive risk index and market dynamic data, and conducting risk warning according to the overall risk level of international trade.

[0008] Preferably, the transaction risk level is evaluated based on abnormal behavior features, including: obtaining transaction record data related to international trade and extracting abnormal behavior features from transaction record data, abnormal behavior features including transaction amount fluctuation, transaction frequency change, transaction time distribution, and transaction participant credit score.

[0009] The transaction risk level is calculated based on abnormal behavior features, and the transaction risk level is used to quantify the potential risk degree in the transaction process. The weight distribution relationship between different features and the influence intensity of abnormal behavior on the overall transaction are considered comprehensively during calculation.

[0010] Preferably, the evaluating the cross-border transportation risk impact degree based on the logistics information data and the policy and regulation data comprises: acquiring the logistics information data and the policy and regulation data, the logistics information data containing the cargo transportation path, the transportation time, and the transit node information, and the policy and regulation data containing the import and export policy and the tariff adjustment information of the target country or region; identifying the high-risk link in the cross-border transportation through the logistics information data and the policy and regulation data, the high-risk link containing the unstable area in the transportation path and the restrictive clause in the policy and regulation; and calculating the cross-border transportation risk impact index through the high-risk link analysis, the cross-border transportation risk impact index being used to quantify the probability of the delay, the detention, or other adverse events and the impact degree thereof that may occur in the cross-border transportation process.

[0011] Preferably, the obtaining the comprehensive risk index comprises: acquiring the transaction risk level and the cross-border transportation risk impact index; and calculating the comprehensive risk index through the transaction risk level and the cross-border transportation risk impact index, the comprehensive risk index being used to quantify the overall risk level in the international trade, and a dynamic adjustment factor being introduced in the calculation to adapt to the risk change characteristics in different trade scenarios.

[0012] Preferably, the estimating the overall risk level of the international trade comprises: acquiring the comprehensive risk index and the market dynamic data, the market dynamic data containing the exchange rate fluctuation, the commodity price change, and the supply and demand adjustment information; and calculating the overall risk level of the international trade through the comprehensive risk index and the market dynamic data, the overall risk level of the international trade being used to quantify the risk exposure degree of the current trade activity, and the influence difference of the market dynamic data on the short-term and long-term risks being comprehensively considered in the calculation.

[0013] Preferably, the risk warning according to the overall risk level of the international trade comprises: acquiring the overall risk level of the international trade, and issuing the international trade risk warning when the overall risk level of the international trade is higher than a preset risk threshold, and not issuing the international trade risk warning when the overall risk level of the international trade is lower than or equal to the preset risk threshold.

[0014] It is to be noted that the dynamic adjustment factor and the preset risk threshold are obtained in the following manner: 10,000 groups of international trade related transaction record data, logistics information data, policy and regulation data, and market dynamic data are collected, the international trade risk is distinguished into whether reaching the warning level, the above data is substituted into the international trade overall risk level calculation model for calculation, the calculation result and the distinguished result are simultaneously introduced into a fitting software, and the optimal dynamic adjustment factor and the preset risk threshold that meet the accuracy rate of the distinguished result are output.

[0015] In a second aspect, the present application provides an international trade risk intelligent prevention and control system based on multi-modal data analysis, comprising: a data acquisition module for acquiring transaction record data, logistics information data, policy and regulation data and market dynamic data related to international trade; a transaction risk assessment module for extracting abnormal behavior features in the transaction record data and assessing transaction risk levels based on the abnormal behavior features; a cross-border transportation risk assessment module for assessing the cross-border transportation risk impact degree based on the logistics information data and the policy and regulation data, and obtaining a comprehensive risk index in combination with the transaction risk levels; and a risk warning module for estimating the overall risk level of international trade by combining the comprehensive risk index and the market dynamic data, and performing risk warning according to the overall risk level of international trade.

[0016] In a third aspect, the present application provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes an international trade risk intelligent prevention and control method based on multi-modal data analysis by calling the computer program stored in the memory.

[0017] In a fourth aspect, the present application provides a computer readable storage medium storing instructions, which, when executed on a computer, cause the computer to execute an international trade risk intelligent prevention and control method based on multi-modal data analysis. Advantages

[0018] The present application first extracts abnormal behavior features in transaction record data to assess transaction risk levels, then quantifies the cross-border transportation risk impact degree based on logistics information data and policy and regulation data and assesses a comprehensive risk index in combination with transaction risk levels, thereby improving the scientificity and accuracy of comprehensive risk assessment, and finally estimates the overall risk level of international trade by combining the comprehensive risk index and market dynamic data, thereby improving the timeliness and reliability of international trade risk prevention and control. BRIEF DESCRIPTION OF DRAWINGS

[0019] FIG. 1 is a schematic diagram of the overall flow of an international trade risk intelligent prevention and control method based on multi-modal data analysis according to an embodiment of the present application;

[0020] FIG. 2 is a schematic diagram of the flow of abnormal behavior feature extraction and transaction risk level assessment according to an embodiment of the present application;

[0021] FIG. 3 is a schematic diagram of the flow of cross-border transportation high-risk link identification and risk impact index calculation according to an embodiment of the present application;

[0022] FIG. 4 is a schematic diagram of the flow of comprehensive risk index calculation and dynamic adjustment factor introduction according to an embodiment of the present application;

[0023] FIG. 5 is a flowchart of an international trade overall risk level estimation and risk early warning mechanism according to an embodiment of the present application;

[0024] FIG. 6 is a structural diagram of an international trade risk intelligent prevention and control system based on multi-modal data analysis according to an embodiment of the present application;

[0025] FIG. 7 is a structural diagram of an electronic device according to an embodiment of the present application;

[0026] FIG. 8 is a structural diagram of a computer-readable storage medium according to an embodiment of the present application. Best Mode for Carrying Out the Invention

[0027] The present application provides an international trade risk intelligent prevention and control method and system based on multi-modal data analysis. The specific embodiments of the present application will be described in detail below with reference to FIGS. 1-8. First, starting from the overall flowchart of FIG. 1, which is the overall flowchart of the international trade risk intelligent prevention and control method based on multi-modal data analysis according to an embodiment of the present application, the key steps from data collection to risk early warning and their logical relationships are shown. In practical applications, the present method realizes accurate prediction and real-time response to international trade risks by integrating multiple types of data sources and combining dynamic scenario modeling technology.

[0028] In the first step, the data collection module is responsible for obtaining transaction record data, logistics information data, policy and regulation data, and market dynamic data related to international trade. These data sources collectively form the basis for subsequent analysis. The transaction record data includes the basic information of the buyer and seller, transaction amount, transaction frequency, transaction time distribution, etc.; the logistics information data covers the cargo transportation path, transportation time, transit node information, etc.; the policy and regulation data involves national or regional level regulation information such as import and export policy adjustment and tariff changes; the market dynamic data mainly includes economic indicators such as exchange rate fluctuations, commodity price changes, and supply and demand relationship adjustments. Through the system structure shown in FIG. 6, the data collection module (labeled 1) transmits the above data to the subsequent processing module for further analysis.

[0029] Next, the abnormal behavior feature extraction and transaction risk level assessment phase is entered, as shown in FIG. 2. In this step, the transaction record data is input into the transaction risk assessment module (labeled 2) to extract the abnormal behavior features therein. For example, transaction amount fluctuation can be quantified by calculating the standard deviation of transaction amounts within a certain time period, transaction frequency change is determined by calculating the change rate of the number of transactions per unit time, transaction time distribution focuses on whether there are abnormal transaction time period operations, and transaction participant credit score is comprehensively evaluated by third-party credit evaluation agency scoring data or other historical records. In order to calculate the transaction risk level, the application introduces a weighted scoring mechanism, the formula is as follows: transaction risk level = w₁ × transaction amount fluctuation + w₂ × transaction frequency change + w₃ × transaction time distribution + w₄ × transaction participant credit score, wherein w₁, w₂, w₃, w₄ are weight coefficients of each feature, and satisfy w₁ + w₂ + w₃ + w₄ = 1. The setting of the weight coefficient needs to be adjusted according to the actual application scene, so as to ensure that the influence of different features on the overall risk is reasonably reflected.

[0030] Subsequently, the cross-border transportation risk impact degree is evaluated based on logistics information data and policy and regulation data, as shown in FIG. 3. This step is completed by the cross-border transportation risk assessment module (labeled 3), and the core is to identify high-risk links in cross-border transportation. For example, unstable regions in the transportation path may include countries or regions with unstable political situations, and restrictive provisions in the policy and regulations may involve prohibitions on certain goods or tariff barriers. In order to quantify the cross-border transportation risk impact index, the application proposes a comprehensive evaluation model, the formula is as follows: cross-border transportation risk impact index = α × unstable region weight + β × restrictive provision weight + γ × transit node complexity, wherein α, β, γ are influence coefficients of each factor, and α + β + γ = 1. Through this formula, the probability and impact degree of delay, detention or other adverse events that may occur in the cross-border transportation process can be effectively evaluated.

[0031] After obtaining the transaction risk level and the cross-border transportation risk impact index, the next step is to calculate the comprehensive risk index, as shown in FIG. 4. The calculation formula of the comprehensive risk index is: comprehensive risk index = λ × transaction risk level + μ × cross-border transportation risk impact index + δ × dynamic adjustment factor, where λ and μ are the weight coefficients of the transaction risk level and the cross-border transportation risk impact index respectively, δ is the weight coefficient of the dynamic adjustment factor, and λ + μ + δ = 1. The introduction of the dynamic adjustment factor aims to adapt to the risk change characteristics in different trade scenarios. Its value is detailed in the summary of the invention, that is, the optimal value that meets the differentiated results is output by the fitting software. On this basis, the comprehensive risk index can comprehensively reflect the overall risk level in international trade.

[0032] Further, the overall risk level of international trade is estimated by combining the comprehensive risk index and market dynamic data, as shown in FIG. 5. Market dynamic data includes exchange rate fluctuations, changes in commodity prices, and adjustments in supply and demand relationships, etc. These data are integrated and analyzed by the risk warning module (labeled as 4). The calculation formula of the overall risk level of international trade is: overall risk level of international trade = θ × comprehensive risk index + ρ × short-term market dynamic impact + σ × long-term market dynamic impact, where θ, ρ, σ are the weight coefficients of each factor, and θ + ρ + σ = 1. The short-term market dynamic impact mainly focuses on the immediate changes of exchange rate fluctuations and commodity prices, while the long-term market dynamic impact focuses on the sustained impact of supply and demand relationship adjustments on trade activities. Through this formula, the risk exposure degree of current trade activities can be accurately quantified.

[0033] Finally, risk warning is carried out according to the overall risk level of international trade. When the overall risk level of international trade is higher than the preset risk threshold, the risk warning module sends a warning signal; otherwise, it does not send a warning signal. The value of the preset risk threshold is also output by the fitting software, ensuring that it can accurately distinguish whether the international trade risk has reached the warning level. In practical application, the risk warning mechanism can be connected with the enterprise internal management system or the monitoring platform of government regulatory departments, so as to realize the real-time transmission and rapid response of risk information.

[0034] In addition, the present application also provides a specific implementation of an international trade risk intelligent prevention and control system based on multi-modal data analysis, as shown in FIG. 6. The system includes a data acquisition module (labeled 1), a transaction risk assessment module (labeled 2), a cross-border transportation risk assessment module (labeled 3), and a risk warning module (labeled 4). Each module efficiently cooperates through data flow to ensure the smoothness and reliability of the entire system. FIG. 7 shows a structural diagram of an electronic device, which includes a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes the above method by calling the program. FIG. 8 shows a structural diagram of a computer-readable storage medium, which stores instructions that make the computer execute the international trade risk intelligent prevention and control method based on multi-modal data analysis when the instructions are run on the computer.

[0035] In summary, the present application extracts abnormal behavior features from transaction record data to evaluate transaction risk levels, then quantifies the impact of cross-border transportation risk through logistics information data and policy and regulation data, and combines transaction risk level evaluation to assess comprehensive risk index, improving the scientificity and accuracy of comprehensive risk assessment. Finally, the overall risk level of international trade is evaluated by combining the comprehensive risk index and market dynamic data, thereby improving the timeliness and reliability of international trade risk prevention and control. The technical solution of the present application is not only suitable for trade risk management at the enterprise level, but also can provide strong support for government departments to formulate international trade policies.

Claims

1. An international trade risk intelligent prevention and control method based on multi-modal data analysis, characterized in that, The method comprises the following steps: Collecting transaction record data, logistics information data, policy and regulation data, and market dynamic data related to international trade; extracting abnormal behavior features from the transaction record data and evaluating the transaction risk level based on the abnormal behavior features; Evaluating the cross-border transportation risk impact degree based on the logistics information data and the policy and regulation data, and obtaining a comprehensive risk index in combination with the transaction risk level; Estimating the overall risk level of international trade based on the comprehensive risk index and market dynamic data, and conducting risk warning according to the overall risk level of international trade.

2. The method of claim 1, wherein, The evaluation of the transaction risk level based on the abnormal behavior features comprises: obtaining the transaction record data related to international trade and extracting abnormal behavior features from the transaction record data, wherein the abnormal behavior features include transaction amount fluctuation, transaction frequency change, transaction time distribution, and transaction participant credit score; and calculating the transaction risk level through the abnormal behavior features, wherein the calculation formula of the transaction risk level is: transaction risk level = w₁ × transaction amount fluctuation + w₂ × transaction frequency change + w₃ × transaction time distribution + w₄ × transaction participant credit score, wherein w₁, w₂, w₃, and w₄ are weight coefficients of the respective features, and satisfy w₁ + w₂ + w₃ + w₄ = 1.

3. The method of claim 1, wherein, The evaluation of the cross-border transportation risk impact degree based on the logistics information data and the policy and regulation data comprises: obtaining the logistics information data and the policy and regulation data, wherein the logistics information data includes cargo transportation path, transportation time, and transit node information, and the policy and regulation data includes import and export policies and tariff adjustment information of the target country or region; identifying high-risk links in cross-border transportation through the logistics information data and the policy and regulation data, wherein the high-risk links include unstable regions in the transportation path and restrictive clauses in the policy and regulation; and calculating the cross-border transportation risk impact index through the high-risk link analysis, wherein the calculation formula of the cross-border transportation risk impact index is: cross-border transportation risk impact index = α × unstable region weight + β × restrictive clause weight + γ × transit node complexity, wherein α, β, and γ are influence coefficients of the respective factors, and α + β + γ = 1.

4. The method of claim 1, wherein, The obtaining of the comprehensive risk index comprises: obtaining the transaction risk level and the cross-border transportation risk impact index; and calculating the comprehensive risk index through the transaction risk level and the cross-border transportation risk impact index, wherein the calculation formula of the comprehensive risk index is: comprehensive risk index = λ × transaction risk level + μ × cross-border transportation risk impact index + δ × dynamic adjustment factor, wherein λ, μ, and δ are weight coefficients of the respective factors, and λ + μ + δ = 1.

5. The method of claim 1, wherein, The estimated overall risk level of international trade includes: obtaining a comprehensive risk index and market dynamic data, the market dynamic data contains exchange rate fluctuations, commodity price changes and supply and demand relationship adjustment information; the overall risk level of international trade is calculated by the comprehensive risk index and market dynamic data, the overall risk level of international trade is calculated by the formula: overall risk level of international trade = θ × comprehensive risk index + ρ × short-term market dynamic influence + σ × long-term market dynamic influence, wherein θ, ρ, σ are weight coefficients of each factor, and θ + ρ + σ = 1.

6. The method of claim 1, wherein, The risk warning according to the overall risk level of international trade includes: obtaining the overall risk level of international trade, when the overall risk level of international trade is higher than the preset risk threshold, the international trade risk warning is issued, and when the overall risk level of international trade is lower than or equal to the preset risk threshold, the international trade risk warning is not issued.

7. An international trade risk intelligent prevention and control system based on multi-modal data analysis, characterized in that, Including: The data acquisition module (1) is used for collecting transaction record data, logistics information data, policy and regulation data and market dynamic data related to international trade; The transaction risk assessment module (2) is used for extracting abnormal behavior characteristics in the transaction record data and assessing the transaction risk level based on the abnormal behavior characteristics; the cross-border transportation risk assessment module (3) is used for assessing the cross-border transportation risk influence degree based on the logistics information data and the policy and regulation data, and obtaining the comprehensive risk index combined with the transaction risk level; The risk warning module (4) is used for estimating the overall risk level of international trade by combining the comprehensive risk index and the market dynamic data, and warning the risk according to the overall risk level of international trade.

8. An electronic device comprising a processor and a memory, characterized in that The memory stores a computer program that can be called by the processor, and the processor executes the method of any one of claims 1 to 6 by calling the computer program stored in the memory.

9. A computer-readable storage medium, characterized in that, The instructions are stored in the computer, and when the instructions are run on the computer, the computer executes the method of any one of claims 1 to 6.

10. The method of claim 1, wherein, The dynamic adjustment factor and the preset risk threshold are obtained by: collecting 10,000 groups of transaction record data, logistics information data, policy and regulation data and market dynamic data related to international trade, distinguishing whether the international trade risk reaches the warning level, substituting the above data into the overall risk level calculation model of international trade to calculate, and importing the calculation results and the distinguishing results into the fitting software to output the optimal dynamic adjustment factor and the preset risk threshold with the highest distinguishing accuracy.

Images