Foreign trade information processing method and system

Abstract

The invention provides a foreign trade information processing method and system, and the method comprises the following steps: S1, extracting heterogeneous data from a customs system, a payment platform and a logistics tracking system in real time, and analyzing an unstructured text through a regular expression and a Bi LSTM model; s2, constructing a foreign trade entity knowledge graph taking the order ID as a core node, and mapping an association relationship among enterprises, commodities, logistics and payment entities; s3, based on a time sequence convolutional network (TCN), fusing an exchange rate fluctuation rate, a geographical political risk index and a buyer historical payment record to generate a dynamic risk score; s4, when the risk score exceeds a threshold value, automatically triggering a plan in a coping strategy library, such as credit clause modification and cargo port transfer; and S5, calculating an optimal purchase order time window by using a reinforcement learning model and taking the shipping price fluctuation curve and the inventory holding cost as constraints. According to the method, multi-source data intelligent extraction is carried out, and a foreign trade entity knowledge graph data model is constructed through butt joint of an AP I gateway and a customs / bank / logistics system.

Description

Technical Field

[0001] This invention relates to the fields of big data analysis and artificial intelligence technology, and in particular to a method and system for processing foreign trade information. Background Technology

[0002] The current foreign trade industry faces three core challenges in information processing: 1. Data silos and heterogeneity conflicts: Foreign trade data is scattered across independent systems such as customs, logistics, payment, and taxation (e.g., EDI customs declarations, SWIFT payment records, IoT logistics sensor data), with significant format differences (structured / unstructured), making it difficult for traditional ETL tools to achieve real-time correlation. For example, the inability to automatically map customs HS codes to logistics bills of lading numbers leads to order status tracking delays exceeding 48 hours. 2. Lagging dynamic risk identification: Existing risk control systems rely on static rule bases (e.g., blacklist matching), failing to respond in real-time to sudden risks (e.g., exchange rate fluctuations, geopolitical events). According to IMF statistics, in 2023, bad debts in foreign trade due to the failure to identify buyer credit risk in a timely manner reached $22 billion. 3. Lack of predictive supply chain decision-making: Traditional ERP systems only support historical data statistics and lack the ability to predict based on market dynamics. For example, the mismatch between shipping price fluctuation cycles and purchase order windows results in approximately 15% wasted inventory costs.

[0003] Chinese patent application number 202411121214.4 proposes a method for storing foreign trade data based on blockchain, but it does not solve the problem of semantic fusion of multi-source data;

[0004] The Chinese patent with application number 202510137388.8 uses machine learning to detect payment fraud, but the model update cycle is long (>72 hours) and it is difficult to cover emerging risk patterns; the commercial software SAP Global Trade Services (GTS) relies on manual configuration of rules, which has poor adaptability and high maintenance costs.

[0005] Therefore, there is an urgent need for an intelligent processing system that supports real-time heterogeneous data fusion, dynamic risk modeling, and supply chain collaborative optimization. Summary of the Invention

[0006] The purpose of this invention is to address the problems of relying on manual rule configuration, poor adaptability, and high maintenance costs by providing a foreign trade information processing method and system.

[0007] The above-mentioned technical objective of the present invention is achieved through the following technical solution:

[0008] The foreign trade information processing method and system of the present invention includes the following steps:

[0009] S1. Extract heterogeneous data in real time from customs systems, payment platforms, and logistics tracking systems, and parse unstructured text using regular expressions and BiLSTM models;

[0010] S2. Construct a foreign trade entity knowledge graph with order ID as the core node, mapping the relationships between enterprises, commodities, logistics, and payment entities;

[0011] S3. Based on the Temporal Convolutional Network (TCN), dynamic risk scores are generated by integrating exchange rate volatility, geopolitical risk index, and buyer's historical payment records.

[0012] S4. When the risk score exceeds the threshold, the contingency plan in the response strategy library will be automatically triggered, such as modification of letter of credit terms or diversion of goods to another port.

[0013] S5. Using a reinforcement learning model, with shipping price fluctuation curves and inventory holding costs as constraints, calculate the optimal purchase order time window.

[0014] Preferably, the heterogeneous data parsing includes: using regular expressions to extract structured fields such as bill of lading number, HS code, and SWIFTCODE; and using a BiLSTM+CRF model to identify semantic entities such as liability exemption statements and force majeure clauses in contract terms.

[0015] Preferably, the dynamic risk score generation process includes: the input layer receiving real-time risk indicators from an external API: Standard & Poor's country risk rating and Reuters exchange rate data; standardizing multi-dimensional data through a sliding window mechanism; and the TCN network outputting the risk probability and confidence interval for the next 7 days.

[0016] Preferably, the method for constructing the response strategy library is as follows: a decision tree classifier is trained based on a historical dispute case library to match the current risk scenario with the optimal response action; the strategies include: increasing the prepayment ratio, activating the China Export & Credit Insurance Corporation (SINOSURE) policy, and initiating an alternative port contingency plan.

[0017] A preferred configuration includes: a multi-source data access module with an API gateway and SSL encrypted channel, supporting integration with the General Administration of Customs Single Window, PayPal, and Maersk Shipping systems; a knowledge graph construction module using Neo4j database to store entity relationships and Apache Flink to implement streaming relationship updates; a dynamic risk engine module deploying a TCN model on a GPU cluster to update the global order risk matrix every 5 minutes; and a supply chain optimization module based on PyTorch's RL agent to output procurement decision reports to the ERP system.

[0018] Preferably, the knowledge graph construction module includes an entity disambiguation unit: resolving differences in the same company name across different systems, such as "Co., Ltd" and "Inc."; and a relation reasoning unit: predicting unspecified logistics transit dependency paths using the TransE algorithm.

[0019] Preferably, the dynamic risk engine module integrates: a visual early warning dashboard: displaying the distribution of orders in high-risk areas around the world using a heat map; and an audit trail unit: recording the risk decision-making logic chain for compliance review.

[0020] Preferably, the supply chain optimization module includes: a cost simulator: simulating combinations of taxes, freight, and warehousing costs under different purchase volumes; and a strategy interpreter: converting RL actions into natural language suggestions, such as "It is recommended to place an order before March 21 to lock in freight rates".

[0021] This invention offers the following advantages: Intelligent extraction of multi-source data: By connecting to customs / bank / logistics systems through an API gateway, it utilizes regular expressions and deep learning to parse unstructured documents (such as bills of lading and letters of credit); Construction of a unified data model, the "Foreign Trade Entity Knowledge Graph": Using order IDs as the root node, it links enterprises / commodities / logistics / payment entities. Input layer: Exchange rate volatility + political risk index + buyer credit history; Algorithm layer: Temporal Convolutional Network (TCN) predicts short-term risk probabilities; Output layer: Generates red / yellow / green risk labels and a response strategy library. Supply chain adaptive optimization: Based on reinforcement learning (RL), it simulates the game between shipping prices and inventory costs, outputting the optimal procurement time window. Attached Figure Description

[0022] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0023] Figure 1 This is the core flowchart of the present invention. Detailed Implementation

[0024] The present invention will be further described in detail below with reference to the accompanying drawings. Identical components are indicated by the same reference numerals.

[0025] A method and system for processing foreign trade information includes the following steps:

[0026] S1. Extract heterogeneous data in real time from customs systems, payment platforms, and logistics tracking systems, and parse unstructured text using regular expressions and Bi LSTM models;

[0027] S2. Construct a foreign trade entity knowledge graph with order ID as the core node, mapping the relationships between enterprises, commodities, logistics, and payment entities;

[0028] S3. Based on the Temporal Convolutional Network (TCN), dynamic risk scores are generated by integrating exchange rate volatility, geopolitical risk index, and buyer's historical payment records.

[0029] S4. When the risk score exceeds the threshold, the contingency plan in the response strategy library will be automatically triggered, such as modification of letter of credit terms or diversion of goods to another port.

[0030] S5. Using a reinforcement learning model, with shipping price fluctuation curves and inventory holding costs as constraints, calculate the optimal purchase order time window.

[0031] Preferably, the heterogeneous data parsing includes: using regular expressions to extract structured fields such as bill of lading number, HS code, and SWIFTCODE; and using a BiLSTM+CRF model to identify semantic entities such as liability exemption statements and force majeure clauses in contract terms.

[0032] Preferably, the dynamic risk score generation process includes: the input layer receiving real-time risk indicators from an external API: Standard & Poor's country risk rating and Reuters exchange rate data; standardizing multi-dimensional data through a sliding window mechanism; and the TCN network outputting the risk probability and confidence interval for the next 7 days.

[0033] Preferably, the method for constructing the response strategy library is as follows: a decision tree classifier is trained based on a historical dispute case library to match the current risk scenario with the optimal response action; the strategies include: increasing the prepayment ratio, activating the China Export & Credit Insurance Corporation (SINOSURE) policy, and initiating an alternative port contingency plan.

[0034] A preferred configuration includes: a multi-source data access module with an API gateway and SSL encrypted channel, supporting integration with the General Administration of Customs Single Window, PayPal, and Maersk Shipping systems; a knowledge graph construction module using Neo4j database to store entity relationships and Apache Flink to implement streaming relationship updates; a dynamic risk engine module deploying a TCN model on a GPU cluster to update the global order risk matrix every 5 minutes; and a supply chain optimization module based on PyTorch's RL agent to output procurement decision reports to the ERP system.

[0035] Preferably, the knowledge graph construction module includes an entity disambiguation unit: resolving differences in the same company name across different systems, such as "Co., Ltd" and "Inc."; and a relation reasoning unit: predicting unspecified logistics transit dependency paths using the TransE algorithm.

[0036] Preferably, the dynamic risk engine module integrates: a visual early warning dashboard: displaying the distribution of orders in high-risk areas around the world using a heat map; and an audit trail unit: recording the risk decision-making logic chain for compliance review.

[0037] Preferably, the supply chain optimization module includes: a cost simulator: simulating combinations of taxes, freight, and warehousing costs under different purchase volumes; and a strategy interpreter: converting RL actions into natural language suggestions, such as "It is recommended to place an order before March 21 to lock in freight rates".

[0038] This invention offers the following advantages: Intelligent extraction of multi-source data: It connects to customs / bank / logistics systems via an API gateway, utilizing regular expressions and deep learning to parse unstructured documents (such as bills of lading and letters of credit); It constructs a unified data model, a "foreign trade entity knowledge graph," using order IDs as the root node to link enterprises / commodities / logistics / payment entities. Input layer: exchange rate volatility + political risk index + buyer credit history; Algorithm layer: Temporal Convolutional Network (TCN) predicts short-term risk probabilities; Output layer: Generates red / yellow / green risk labels and a response strategy library. Supply chain adaptive optimization: Based on reinforcement learning (RL), it simulates the game between shipping prices and inventory costs, outputting the optimal procurement time window.

[0039] This specific embodiment is merely an explanation of the present invention and is not intended to limit the invention.

Claims

1. A method for processing foreign trade information, characterized in that, Includes the following steps: S1. Extract heterogeneous data in real time from customs systems, payment platforms, and logistics tracking systems, and parse unstructured text using regular expressions and BiLSTM models; S2. Construct a foreign trade entity knowledge graph with order ID as the core node, mapping the relationships between enterprises, commodities, logistics, and payment entities; S3. Based on the Temporal Convolutional Network (TCN), exchange rate volatility, geopolitical risk index, and buyer's historical payment records are integrated to generate a dynamic risk score; S4. When the risk score exceeds the threshold, the contingency plan in the response strategy library will be automatically triggered, such as modification of letter of credit terms or diversion of goods to another port. S5. Using a reinforcement learning model, with shipping price fluctuation curves and inventory holding costs as constraints, calculate the optimal purchase order time window.

2. The foreign trade information processing method according to claim 1, characterized in that: The heterogeneous data parsing includes: using regular expressions to extract structured fields such as bill of lading number, HS code, and SWIFT code; and using the BiLSTM+CRF model to identify semantic entities such as liability exemption statements and force majeure clauses in contract terms.

3. The foreign trade information processing method according to claim 1, characterized in that: The dynamic risk score generation process includes: the input layer receiving real-time risk indicators from external APIs: Standard & Poor's country risk rating and Reuters exchange rate data; standardizing multi-dimensional data through a sliding window mechanism; and the TCN network outputting the risk probability and confidence interval for the next 7 days.

4. The foreign trade information processing method according to claim 1, characterized in that: The method for constructing the response strategy library is as follows: a decision tree classifier is trained based on a historical dispute case library to match the current risk scenario with the optimal response action; The strategies include: increasing the prepayment ratio, activating China Export & Credit Insurance Corporation (SINOSURE) policies, and initiating alternative port contingency plans.

5. A foreign trade information processing system, characterized in that... include: Multi-source data access module: Configured with API gateway and SSL encrypted channel, supporting integration with General Administration of Customs Single Window, PayPal, and Maersk Shipping system; Knowledge graph construction module: uses Neo4j database to store entity relationships and Apache Flink to implement streaming relationship updates; Dynamic risk engine module: deploys TCN model on GPU cluster and updates global order risk matrix every 5 minutes; Supply chain optimization module: based on PyTorch RL agent, outputs procurement decision reports to ERP system.

6. The foreign trade information processing system according to claim 5, characterized in that: The knowledge graph construction module includes an entity disambiguation unit: resolving differences in the same company name across different systems, such as "Co.,Ltd" and "Inc."; and a relation reasoning unit: predicting unspecified logistics transit dependency paths using the TransE algorithm.

7. The foreign trade information processing system according to claim 5, characterized in that: The dynamic risk engine module integrates: a visual early warning dashboard that displays the distribution of orders in high-risk areas around the world using a heat map; and an audit trail unit that records the risk decision-making logic chain for compliance review.

8. The foreign trade information processing system according to claim 5, characterized in that: The supply chain optimization module includes: a cost simulator that simulates combinations of taxes, freight, and warehousing costs under different purchase volumes; and a strategy interpreter that converts RL actions into natural language suggestions, such as "It is recommended to place an order before March 21 to lock in freight rates".

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