Cross-border e-commerce loan monitoring method, system, medium and device

Abstract

The application relates to the technical field of financial management, and provides a cross-border e-commerce loan monitoring method and system, a computer readable medium and an electronic device. Information of associated stores of cross-border e-commerce is acquired, operation information of the cross-border e-commerce is acquired through a graph database, whether historical transactions of the associated stores under the cross-border e-commerce are legal and true is verified, and a verification result is generated; according to business registration information and historical operation data of the cross-border e-commerce, market information representing industry trends and overall market risk conditions is analyzed in combination with macroeconomic indicators. A graph database is constructed to comprehensively integrate operation information of the cross-border e-commerce and the associated stores thereof, the legality and authenticity of the historical transactions are ensured through strict verification, accurate credit grades and credit limits are generated for the cross-border e-commerce, the efficiency and accuracy of credit decision-making are improved, credit risks are effectively reduced, and strong data support is provided for the healthy development of the cross-border e-commerce.

Description

Technical Field

[0001] The present application relates to the field of financial management technology, and more specifically, to a cross-border e-commerce loan monitoring method, system, computer-readable medium, and electronic device. Background Art

[0002] The background technology behind cross-border e-commerce loan monitoring methods stems primarily from the rapid development of e-commerce and innovations in financial technology. With the continued expansion of the global e-commerce market, a growing number of cross-border e-commerce companies are facing capital shortages, and traditional financing channels often struggle to meet their needs for fast and flexible funding. Consequently, cross-border e-commerce loans have emerged as a crucial solution to addressing these financing challenges.

[0003] Existing data sources for cross-border e-commerce include e-commerce platform transaction data, logistics data, customs data, and financial data. These data come in a variety of formats and standards, significantly limiting and inefficient credit assessment and monitoring during the cross-border e-commerce loan process. Summary of the Invention

[0004] The embodiments of the present application provide a cross-border e-commerce loan monitoring method, system, computer-readable medium and electronic device, which can at least to some extent solve the problem of low credit assessment and monitoring efficiency in the cross-border e-commerce loan process.

[0005] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by practice of the present application.

[0006] According to one aspect of the present application, a cross-border e-commerce loan monitoring method is provided, including: obtaining information of associated stores of the cross-border e-commerce, constructing a graph database based on the information of the associated stores, and obtaining operation information of the cross-border e-commerce through the graph database; verifying whether historical transactions of the associated stores under the cross-border e-commerce are legal and true based on the operation information, and generating a verification result; analyzing market information representing industry trends and overall market risks based on the industrial and commercial registration information and historical operating data of the cross-border e-commerce, combined with macroeconomic indicators; generating a credit rating of the cross-border e-commerce based on the operation information, the verification result and the market information; and determining the credit limit of the cross-border e-commerce based on the credit rating of the cross-border e-commerce through a preset chain prediction model.

[0007] In the present application, based on the aforementioned scheme, the information of the associated stores of the cross-border e-commerce is obtained, and a graph database is constructed based on the information of the associated stores to obtain the operation information of the cross-border e-commerce through the graph database, including: obtaining the information of the associated stores of the cross-border e-commerce; based on the information of the associated stores, a relationship graph between the cross-border e-commerce and the associated stores is constructed through a graph database; according to the relationship graph, the first operation information of each of the associated stores is identified and extracted from the information of the associated stores; the first operation information of the associated stores is integrated to generate the second operation information of the cross-border e-commerce, and the second operation information includes transaction information, customs declaration information, logistics information, tax refund information and foreign exchange settlement information; the second operation information of the cross-border e-commerce is encrypting and storing it in the graph database.

[0008] In the present application, based on the aforementioned scheme, the historical transactions of the associated stores under the cross-border e-commerce are verified to be legal and true based on the operation information, and a verification result is generated, including: based on the operation information, verifying the integrity and authenticity of the operation information through a blockchain-based smart contract to generate a first verification result; performing big data analysis on the operation information to verify the legality of the historical transactions and generate a second verification result.

[0009] In the present application, based on the aforementioned scheme, the market information representing industry trends and the overall market risk situation is analyzed based on the industrial and commercial registration information and historical operating data of the cross-border e-commerce, combined with macroeconomic indicators, including: predicting industry trends based on the industrial and commercial registration information and historical operating data of the cross-border e-commerce, combined with historical macroeconomic indicators, using long-short-term memory networks and attention mechanisms; using topic models to analyze industry news and determine consumer dynamics; analyzing key market events through event detection algorithms to generate risk information; generating the market information based on the industry trends, the consumer dynamics and the risk information.

[0010] In the present application, based on the aforementioned scheme, the credit rating of the cross-border e-commerce is generated according to the operation information, the verification results and the market information, including: calculating the credit score through hierarchical analysis based on the operation information, the verification results and the market information; based on the credit score, dynamically adjusting the credit rating of the cross-border e-commerce according to the historical credit records and real-time operation information of the cross-border e-commerce.

[0011] In the present application, based on the aforementioned scheme, the credit limit of the cross-border e-commerce is determined according to the credit rating of the cross-border e-commerce through a preset chain prediction model, including: constructing a chain prediction model based on a deep learning framework, and optimizing the model parameters through a learning mechanism; inputting the credit rating of the cross-border e-commerce into the chain prediction model, and outputting the credit limit of the cross-border e-commerce.

[0012] In this application, based on the above-mentioned solution, it also includes: real-time monitoring of the operation information of the cross-border e-commerce; when abnormal transactions are detected through the operation information, risk control measures are automatically triggered.

[0013] According to one aspect of the present application, a cross-border e-commerce loan monitoring system is provided, comprising:

[0014] An acquisition unit, configured to acquire information of associated stores of a cross-border e-commerce company, and to construct a graph database based on the information of the associated stores, so as to acquire operational information of the cross-border e-commerce company through the graph database;

[0015] A verification unit, configured to verify, based on the operation information, whether historical transactions of the associated stores under the cross-border e-commerce platform are legal and authentic, and generate a verification result;

[0016] A market unit is used to analyze market information representing industry trends and overall market risks based on the business registration information and historical operating data of the cross-border e-commerce company in combination with macroeconomic indicators;

[0017] A credit unit, configured to generate a credit rating of the cross-border e-commerce company based on the operation information, the verification result, and the market information;

[0018] The credit limit unit is used to determine the credit limit of the cross-border e-commerce company based on the credit rating of the cross-border e-commerce company through a preset chain prediction model.

[0019] In the present application, based on the aforementioned scheme, the information of the associated stores of the cross-border e-commerce is obtained, and a graph database is constructed based on the information of the associated stores to obtain the operation information of the cross-border e-commerce through the graph database, including: obtaining the information of the associated stores of the cross-border e-commerce; based on the information of the associated stores, a relationship graph between the cross-border e-commerce and the associated stores is constructed through a graph database; according to the relationship graph, the first operation information of each of the associated stores is identified and extracted from the information of the associated stores; the first operation information of the associated stores is integrated to generate the second operation information of the cross-border e-commerce, and the second operation information includes transaction information, customs declaration information, logistics information, tax refund information and foreign exchange settlement information; the second operation information of the cross-border e-commerce is encrypting and storing it in the graph database.

[0020] In the present application, based on the aforementioned scheme, the historical transactions of the associated stores under the cross-border e-commerce are verified to be legal and true based on the operation information, and a verification result is generated, including: based on the operation information, verifying the integrity and authenticity of the operation information through a blockchain-based smart contract to generate a first verification result; performing big data analysis on the operation information to verify the legality of the historical transactions and generate a second verification result.

[0021] In the present application, based on the aforementioned scheme, the market information representing industry trends and the overall market risk situation is analyzed based on the industrial and commercial registration information and historical operating data of the cross-border e-commerce, combined with macroeconomic indicators, including: predicting industry trends based on the industrial and commercial registration information and historical operating data of the cross-border e-commerce, combined with historical macroeconomic indicators, using long-short-term memory networks and attention mechanisms; using topic models to analyze industry news and determine consumer dynamics; analyzing key market events through event detection algorithms to generate risk information; generating the market information based on the industry trends, the consumer dynamics and the risk information.

[0022] In the present application, based on the aforementioned scheme, the credit rating of the cross-border e-commerce is generated according to the operation information, the verification results and the market information, including: calculating the credit score through hierarchical analysis based on the operation information, the verification results and the market information; based on the credit score, dynamically adjusting the credit rating of the cross-border e-commerce according to the historical credit records and real-time operation information of the cross-border e-commerce.

[0023] In the present application, based on the aforementioned scheme, the credit limit of the cross-border e-commerce is determined according to the credit rating of the cross-border e-commerce through a preset chain prediction model, including: constructing a chain prediction model based on a deep learning framework, and optimizing the model parameters through a learning mechanism; inputting the credit rating of the cross-border e-commerce into the chain prediction model, and outputting the credit limit of the cross-border e-commerce.

[0024] In this application, based on the above-mentioned solution, it also includes: real-time monitoring of the operation information of the cross-border e-commerce; when abnormal transactions are detected through the operation information, risk control measures are automatically triggered.

[0025] According to one aspect of the present application, a computer-readable medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the cross-border e-commerce loan monitoring method as described in the above embodiment is implemented.

[0026] According to one aspect of the present application, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs, which, when executed by the one or more processors, enables the one or more processors to implement the cross-border e-commerce loan monitoring method as described in the above embodiments.

[0027] According to one aspect of the present application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the cross-border e-commerce loan monitoring method provided in the various optional implementations described above.

[0028] The technical solution of this application obtains information about the associated stores of a cross-border e-commerce company, builds a graph database based on the information about the associated stores, and obtains the operational information of the cross-border e-commerce company through the graph database; verifies whether the historical transactions of the associated stores under the cross-border e-commerce company are legal and true based on the operational information, and generates a verification result; analyzes market information representing the overall situation of industry trends and market risks based on the business registration information and historical operating data of the cross-border e-commerce company, combined with macroeconomic indicators; generates a credit rating for the cross-border e-commerce company based on the operational information, the verification result, and the market information; and determines the credit limit of the cross-border e-commerce company based on the credit rating of the cross-border e-commerce company through a preset chain prediction model. By building a graph database to comprehensively integrate the operational information of the cross-border e-commerce company and its associated stores, the legitimacy and authenticity of historical transactions are ensured through strict verification. At the same time, in-depth analysis of industry trends and market risks is conducted in combination with macroeconomic indicators to generate an accurate credit rating for the cross-border e-commerce company. Finally, based on the credit rating, the credit limit is scientifically determined through a chain prediction model. This process not only improves the efficiency and accuracy of credit decision-making, but also effectively reduces credit risk, providing strong data support for the healthy development of cross-border e-commerce.

[0029] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0031] Figure 1The following schematically illustrates a flow chart of a cross-border e-commerce loan monitoring method in one embodiment of the present application.

[0032] Figure 2 A flowchart for obtaining cross-border e-commerce operation information in one embodiment of the present application is schematically shown.

[0033] Figure 3 A schematic diagram of a cross-border e-commerce loan monitoring system in one embodiment of the present application is shown schematically.

[0034] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing an electronic device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0035] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0036] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner.In the following description, many specific details are provided so as to provide a full understanding of the embodiments of the present application. However, it will be appreciated by those skilled in the art that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps etc. can be adopted. In other cases, known methods, systems, implementations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0037] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0038] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, while others may be combined or partially combined. Therefore, the actual execution order may vary depending on the actual situation.

[0039] The implementation details of the technical solution of this application are described in detail below:

[0040] Figure 1 A flow chart of a cross-border e-commerce loan monitoring method according to an embodiment of the present application is shown. Figure 1 As shown, the cross-border e-commerce loan monitoring method includes at least steps S110 to S150, which are described in detail as follows:

[0041] In step S110, information of associated stores of the cross-border e-commerce is obtained, and a graph database is constructed based on the information of the associated stores, so as to obtain operation information of the cross-border e-commerce through the graph database.

[0042] In one embodiment of the present application, a cross-border e-commerce company may include multiple associated stores to carry out specific production, sales and management work. In order to fully understand and monitor the operational status of cross-border e-commerce companies, detailed information of its associated stores is first widely collected, including but not limited to transactions between stores, ownership relationships, and supply chain connections. Subsequently, a sophisticated graph database is constructed using this information, which graphically and intuitively displays the intricate network relationships between cross-border e-commerce companies and their associated stores. Through this graph database, the operational information of cross-border e-commerce companies can be tracked and analyzed, thereby achieving deep insight into and real-time monitoring of the operational status of cross-border e-commerce companies.

[0043] like Figure 2 As shown, in one embodiment of the present application, information of associated stores of a cross-border e-commerce company is obtained, and a graph database is constructed based on the information of the associated stores to obtain operational information of the cross-border e-commerce company through the graph database, including:

[0044] S210, obtaining information of associated stores of the cross-border e-commerce company;

[0045] S220: Building a graph database based on the information of the associated stores, and building a relationship graph between the cross-border e-commerce company and the associated stores through the graph database;

[0046] S230, identifying and extracting first operation information of each of the associated stores from the information of the associated stores according to the relationship graph;

[0047] S240, integrating the first operating information of the associated stores to generate second operating information of the cross-border e-commerce company, where the second operating information includes transaction information, customs declaration information, logistics information, tax refund information, and foreign exchange settlement information;

[0048] S250: Encrypt the second operation information of the cross-border e-commerce and store it in the graph database.

[0049] In this embodiment, information of all stores that have direct or indirect relationships with cross-border e-commerce is collected. This information can be obtained from the internal database of the cross-border e-commerce platform, or from other e-commerce platforms or partners through a data transmission interface. The method of obtaining this information is not specifically limited here.

[0050] Optionally, in addition to the e-commerce platform's internal database and data transmission interface, social media, industry reports, government public data, etc. can also be considered as supplementary data sources.

[0051] Select an appropriate graph database based on the data scale and query performance requirements. Use a batch import tool to import the associated store information into the graph database and index key attributes to improve query efficiency. Methods for building a graph database can be selected from existing technologies and will not be detailed here.

[0052] Based on the relationship graph in the graph database, text recognition is used to perform sentiment analysis and topic identification on the product descriptions and user reviews of related stores, obtaining the first-line operational information of the related stores. Text recognition is the process of extracting text from images or documents through technical means and converting it into editable and processable text.

[0053] A relationship graph in a graph database is a visual representation of nodes and the connections between them. Nodes represent entities, such as people, places, events, and other objects. Relationships represent the connections between these nodes, such as "belongs to," "associated with," "friends," and "colleagues." A relationship graph clearly displays the complex web of connections between different entities.

[0054] For example, based on the transaction records in the first operational information of each associated store, indicators such as total sales, transaction volume, and average order value of cross-border e-commerce are calculated as the second operational information. Customs data and logistics interfaces are combined to extract information such as customs declaration status, logistics trajectory, and estimated arrival time. Through tax and banking system interfaces, data such as tax refund application status, refund amount, and foreign exchange settlement records are obtained. This data is aggregated and used as the second operational information of cross-border e-commerce.

[0055] In one embodiment of the present application, encrypting the second operation information of the cross-border e-commerce and storing it in the graph database includes:

[0056] Performing hash processing on various types of data in the second operation information to obtain a hash value set consisting of multiple hash values;

[0057] Performing a linear operation on multiple hash values ​​in the hash value set to generate a backup key;

[0058] The backup key is subjected to nonlinear transformation and obfuscation processing to generate an encryption key, and the second operation information of the cross-border e-commerce is encrypted by the encryption key and stored in the graph database.

[0059] Specifically, in this embodiment, the SHA-256 hash function is selected to perform hash processing on various types of data in the second operation information, and the output is a 256-bit hash value, ensuring the high security and uniqueness of the hash value.

[0060] For each type of data M in the second operation information, calculate its hash value , the generated hash value set is ,in i is the data identifier, n is the number of data categories.

[0061] Perform linear operations on multiple hash values ​​in the hash value set to generate an intermediate value S, which is used as a backup key:

[0062]

[0063] in, is the linear coefficient, Remaining operation, p is a large prime number.

[0064] Backup key S Perform nonlinear transformation to generate the first parameter , the first parameter for:

[0065]

[0066] in, is a nonlinear function, such as polynomial transformation.

[0067] After that, the first parameter is iterated multiple times, and each iteration applies nonlinear transformation and confusion operation to generate the second parameter for:

[0068]

[0069] in, g and h is a nonlinear function, Represents the exclusive OR operation, j is the number of iterations.

[0070] For the first parameter t After iterations, the intermediate key is obtained . For the intermediate key Perform the last nonlinear transformation to generate the encryption key for:

[0071]

[0072] in,F The encryption process is nonlinear, such as permutation or diffusion operations. By introducing complex mathematical formulas and algorithms, the technical complexity and logical consistency of the encryption process can be ensured. These complex mathematical methods and algorithms provide higher security and reliability for the encryption process.

[0073] This embodiment obtains information about cross-border e-commerce companies' associated stores and constructs a graph database based on this information, clearly demonstrating the complex relationships between these businesses. The graph database structure enables efficient information retrieval and integration, facilitating the rapid generation of secondary operational information for cross-border e-commerce companies. This secondary operational information encompasses transactions, customs declarations, logistics, tax refunds, and foreign exchange settlement. By encrypting and storing this secondary operational information, the confidentiality and integrity of the information is ensured, preventing the risk of information leakage and tampering.

[0074] In step S120, based on the operation information, it is verified whether the historical transactions of the associated stores under the cross-border e-commerce are legal and true, and a verification result is generated.

[0075] In one embodiment of the present application, after obtaining the operational information of a cross-border e-commerce company and its associated stores, a series of rigorous verification procedures are used to verify the legitimacy and authenticity of the historical transactions of these associated stores. This includes, but is not limited to, a detailed comparison of transaction records, a review of the qualifications of both parties to the transaction, and a comprehensive verification of logistics, payment, and tax information involved in the transaction process. Through these measures, a detailed verification result can be generated that clearly shows whether the historical transactions of the associated stores comply with relevant laws and regulations and are authentic, truly and accurately reflecting the actual operation of the cross-border e-commerce company.

[0076] In one embodiment of the present application, based on the operation information, verifying whether the historical transactions of the associated stores under the cross-border e-commerce are legal and true, and generating a verification result includes:

[0077] Verifying the integrity and authenticity of the operation information through a blockchain-based smart contract based on the operation information, and generating a first verification result;

[0078] Performing big data analysis on the operational information, verifying the legitimacy of the historical transactions, and generating a second verification result.

[0079] In one embodiment of this application, operational information includes first and second operational information. A smart contract is designed based on the characteristics of the operational information and verification requirements. This smart contract includes verification rules, data format requirements, and conditions that trigger verification. The smart contract ensures that data cannot be tampered with once it is uploaded to the blockchain, leveraging its decentralized and transparent nature to safeguard information integrity.

[0080] Before operational information is recorded, its hash value is calculated. This hash value uniquely identifies the operational information and is stored on the blockchain. Hash values ​​should be calculated using a secure hash function to ensure uniqueness and irreversibility. When verifying the integrity and authenticity of operational information, the hash value of the information is first calculated. This newly calculated hash value is then compared with the hash value stored on the blockchain. If the two match, the smart contract generates a first verification result, indicating that the operational information has not been tampered with since being uploaded to the blockchain, maintaining its integrity and authenticity.

[0081] Historical transaction data related to operational information is extracted from the graph database. This historical transaction data is cleansed, formatted, and standardized to ensure data accuracy and consistency. Leveraging machine learning or deep learning techniques, a big data analytics model is constructed. This model can identify transaction patterns, abnormal behavior, and potential risks. During model training, a large amount of historical transaction data is used as training samples to improve the model's accuracy and generalization capabilities. The historical transaction data from the operational information to be verified is input into the big data analytics model. The model performs big data analysis on these transactions and outputs an assessment result indicating whether these transactions meet the expected legality and compliance standards. Based on the model's assessment results, a second verification result is generated. If the transaction is determined to be legal, the second verification result is positive; otherwise, it is negative.

[0082] The first and second verification results are integrated into a comprehensive verification report. This verification report will be used to guide subsequent decision-making, risk management, and compliance checks. If the verification results indicate any issues or risks with operational information, the corresponding early warning mechanism will be immediately triggered, and necessary corrective measures will be taken. By combining blockchain smart contracts and big data analytics technologies, we can build an efficient, secure, and reliable operational information verification system. This operational information verification system not only ensures the integrity and authenticity of information, but also identifies potential risks and abnormal behavior, providing strong support for enterprises' operational decision-making.

[0083] The above process verifies the integrity and authenticity of operational information through blockchain-based smart contracts, generating a first verification result. The immutability of blockchain provides a strong guarantee for the authenticity of this information. Big data analysis of operational information verifies the legitimacy of historical transactions, generating a second verification result. Big data analysis can uncover potential risks and abnormal patterns in transactions, thereby ensuring their legitimacy.

[0084] In step S130, based on the business registration information and historical operating data of the cross-border e-commerce, combined with macroeconomic indicators, market information representing industry trends and overall market risks is analyzed.

[0085] In one embodiment of the present application, an in-depth analysis and comprehensive evaluation is conducted based on the business registration information and accumulated historical operating data of cross-border e-commerce companies, while integrating macroeconomic indicators. This process aims to comprehensively analyze the industry trends in which cross-border e-commerce companies are located, including market growth potential, changes in the competitive landscape, and the development trends of emerging technologies. At the same time, it also closely monitors various factors that may trigger market risks, such as policy adjustments, exchange rate fluctuations, and changes in the global economic situation, so as to comprehensively extract market information that can accurately reflect the future direction of the industry and the overall risk status of the market.

[0086] In this embodiment, macroeconomic indicators are quantitative indicators that reflect the operating conditions and development trends of the macroeconomy. They primarily include gross domestic product, gross national product, producer price index, inflation rate, unemployment rate, trade balance, investment indicators, consumption indicators, financial indicators, and fiscal indicators. Macroeconomic indicators play an important analytical and reference role in macroeconomic forecasting.

[0087] In one embodiment of the present application, based on the business registration information and historical operating data of the cross-border e-commerce company, combined with macroeconomic indicators, market information representing industry trends and overall market risks is analyzed, including:

[0088] Based on the business registration information and historical operating data of the cross-border e-commerce companies, combined with historical macroeconomic indicators, long-short-term memory networks and attention mechanisms are used to predict industry trends;

[0089] Use topic models to analyze industry news and identify consumer trends;

[0090] Analyze key market events through event detection algorithms to generate risk information;

[0091] The market information is generated according to the industry trends, the consumer dynamics, and the risk information.

[0092] In one embodiment of the present application, based on the business registration information and historical operating data of cross-border e-commerce, combined with historical macroeconomic indicators, features related to industry trends are extracted, such as the number of company registrations, sales, growth rate, and inflation rate. These features will be used to construct the input of the long short-term memory network. On the basis of the long short-term memory network model, an attention mechanism is integrated to highlight data points that have an important impact on the prediction results. The attention mechanism enables the model to pay more attention to important historical information by calculating the weight of each time step. The long short-term memory network model is trained using historical data, and the model parameters are adjusted to minimize the prediction error. After training is completed, the model is used to predict industry trends in the future.

[0093] In one embodiment of this application, a long-short-term memory network and an attention mechanism are used to predict industry trends, combining cross-border e-commerce business registration information, historical operating data, and historical macroeconomic indicators. This approach can capture long-term dependencies and important features in the data, improving prediction accuracy.

[0094] Collect text data related to cross-border e-commerce from social media platforms and industry news websites. Use word embedding to convert this text data into vector representations. Use topic models to model these vector representations. Topic models can identify underlying themes and topics within text data, revealing consumer interests and trends. Analyze the results of the topic model to extract key topics and keywords.

[0095] Topic modeling is a technique used to discover underlying themes from text data. The main principle of topic modeling is to represent text as a combination of topics. By analyzing and counting large amounts of text, the co-occurring word patterns are identified to determine the themes.

[0096] Using topic models to analyze industry news and identify consumer trends, and event detection algorithms to analyze key market events and generate risk information, this helps cross-border e-commerce companies stay informed of market trends, changes in consumer demand, and potential market risks.

[0097] There are many types of event detection algorithms, including rule-based algorithms, which rely on pre-set clear rules to capture specific events; there are also statistical pattern recognition algorithms, which discover event patterns through statistical analysis of data, such as clustering algorithms; machine learning algorithms also play an important role, such as support vector machines and decision trees, which can learn from data and detect events; deep learning algorithms are even more prominent, such as recurrent neural networks and their variants, which are good at processing sequence data to effectively detect events; in addition, dynamic time warping algorithms can compare time series similarities to assist in event detection, and hidden Markov models are suitable for handling event detection tasks with hidden states.

[0098] Optionally, use visualization tools to present the results of the thematic analysis to facilitate understanding of consumer dynamics.

[0099] Leveraging natural language processing and machine learning technologies, we build an event detection algorithm capable of identifying key events within text, such as policy changes, natural disasters, or market fluctuations. We then conduct further analysis of these detected key events to assess their impact on the cross-border e-commerce market. Based on these analysis results, we generate risk information, including risk type, impact scope, and potential losses.

[0100] Integrate industry trend forecasts, consumer dynamics analysis, and key market event analysis results. Using data analysis tools, automatically collect, clean, analyze, and interpret this diverse data, identifying correlations and trends between the data, extracting key information and patterns, and then synthesizing this information into intuitive, easy-to-understand reports or charts to reveal the overall market situation and future development trends. Based on the results of this comprehensive analysis, a cross-border e-commerce market information report is generated.

[0101] In step S140, the credit rating of the cross-border e-commerce company is generated based on the operation information, the verification result and the market information.

[0102] In this example, after comprehensively considering the cross-border e-commerce company's operational information, verification results of historical transactions of associated stores, and market information derived from analysis of macroeconomic indicators, this complex and multi-dimensional data is thoroughly analyzed and carefully weighed. This process not only examines the cross-border e-commerce company's actual operational performance, compliance, and market presence, but also fully considers its competitive position in the industry and its ability to withstand market risks. Based on these comprehensive considerations, an objective and fair credit rating can be accurately generated for the cross-border e-commerce company, which intuitively reflects its overall credit status and reliability.

[0103] In one embodiment of the present application, generating the credit rating of the cross-border e-commerce company based on the operation information, the verification result, and the market information includes:

[0104] Calculating a credit score through hierarchical analysis based on the operation information, the verification results, and the market information;

[0105] Based on the credit score, the credit rating of the cross-border e-commerce company is dynamically adjusted according to the historical credit record and real-time operation information of the cross-border e-commerce company.

[0106] In one embodiment of the present application, a hierarchical model is constructed based on the key factors of cross-border e-commerce credit. Generally, the hierarchical model includes a target layer (credit score), a criterion layer (such as operational performance, verification results, market adaptability, etc.), and an indicator layer (specific operational indicators, verification indicators, and market indicators). The indicators at each level are compared pairwise to construct a judgment matrix. The elements of the judgment matrix represent the relative importance of the indicators. For example, the elements of the judgment matrix A are: Representation Guidelines Relative to the standards The degree of importance is usually expressed using a 1-9 scale.

[0107] A hierarchical structure model is a model that organizes and represents complex systems or data in a hierarchical relationship. In a hierarchical structure model, elements are arranged in layers, with upper-level elements having a general and overarching role over lower-level elements, and lower-level elements providing specific details and support to the upper-level elements. This model has a clear structure and explicit hierarchical relationship, which helps better understand the overall architecture of the system and the relationship between the parts. It is often applied in the fields of organization management, classification system, software architecture design, etc., and can make complex things more organized and easy to analyze and process.

[0108] The transpose matrix of the judgment matrix A is The matrix parameters are calculated according to the judgment matrix and its transpose as follows:

[0109]

[0110] The eigenvalues and eigenvectors of the matrix parameters are calculated, and the eigenvector corresponding to the maximum eigenvalue is selected as the weight vector. The elements of the judgment matrix A are normalized to obtain the weights of each index in the index layer corresponding to each index. From the bottom layer (index layer) to the top layer (target layer), the weights are synthesized layer by layer to obtain the weight of each index relative to the total target. According to the standardized data and index weight, the standardized value of each index is multiplied by the corresponding weight, and then summed to obtain the credit score of cross-border e-commerce.

[0111] The historical credit records and credit rating change history of cross-border e-commerce are obtained from the database. Real-time monitoring of the operation information of cross-border e-commerce, including but not limited to real-time changes in key indicators such as sales, user satisfaction and return rate. According to the real-time operation information, historical credit records and current credit score, combined with the credit rating adjustment rules, dynamically adjust the credit rating of cross-border e-commerce. If the credit score rises or falls significantly, the credit rating may need to be adjusted upwards, and if negative events appear in the historical credit records, the credit rating may need to be adjusted downwards.

[0112] In this embodiment, the credit score is calculated based on the operation information, verification results and market information through hierarchical analysis; and the credit rating of cross-border e-commerce is dynamically adjusted based on the credit score, historical credit records and real-time operation information. This evaluation method takes into account both historical data and real-time information, ensuring the comprehensiveness and dynamics of the credit rating.

[0113] In step S150, according to the credit rating of the cross-border e-commerce, the credit limit of the cross-border e-commerce is determined through a preset chain prediction model.

[0114] In this embodiment, after determining the credit rating of a cross-border e-commerce company, a pre-built and optimized chain prediction model is used to calculate and predict the credit rating, taking the credit rating as the key input variable and combining multiple factors such as historical credit data, industry credit policies, and current financial market conditions. This process aims to scientifically and rationally assess the repayment capacity and credit risk of the cross-border e-commerce company, thereby tailoring a credit line that both meets its actual needs and effectively controls risk, ensuring the optimal allocation of credit resources and the effectiveness of risk control.

[0115] In one embodiment of the present application, before determining the credit limit of the cross-border e-commerce company based on the credit rating of the cross-border e-commerce company through a preset chain prediction model, the method further includes:

[0116] A chain prediction model is constructed based on the deep learning framework, and the model parameters are optimized through the learning mechanism.

[0117] In one embodiment of the present application, a chain prediction model is constructed based on a deep learning framework. The chain model is generally composed of multiple layers, including an input layer, a hidden layer, and an output layer. The input layer receives the credit rating of the cross-border e-commerce as a feature. The hidden layer may include one or more fully connected layers, recurrent layers, or attention mechanism layers to capture the potential relationship and long-term dependency between credit ratings. The output layer outputs the predicted credit limit. A suitable loss function, such as mean square error or mean absolute error, is selected to measure the difference between the model prediction value and the true value. The model parameters are updated through the optimizer backpropagation algorithm to minimize the loss function.

[0118] Collect historical data, including the credit ratings and corresponding credit limits of cross-border e-commerce companies. Divide the data into training, validation, and test sets. Encode the credit ratings and standardize or normalize the credit limits to improve the model's convergence speed and performance. Train the chain prediction model using the training set data. During training, calculate gradients using the backpropagation algorithm and use the optimizer to update the model parameters. Regularly evaluate model performance on the validation set, such as calculating loss and accuracy, to monitor for overfitting. Adjust the model architecture, loss function, and optimizer parameters based on the performance evaluation results on the validation set.

[0119] Receive credit rating input from cross-border e-commerce companies in real time. Encode and preprocess the input credit rating to ensure consistency with the data format used during training. Input the preprocessed credit rating into the chain prediction model for real-time prediction. Obtain the predicted credit limit from the model and output it to the relevant decision-making system or user interface.

[0120] Through the above computer execution process, a chain prediction model based on a deep learning framework can be constructed for predicting credit limits based on the credit rating of cross-border e-commerce. After training and optimization, the model can be deployed in a production environment for real-time prediction to provide credit decision support for cross-border e-commerce platforms.

[0121] The embodiment is based on a deep learning framework to construct a chain prediction model and optimize model parameters through a learning mechanism. The credit rating of cross-border e-commerce is input into the chain prediction model, and the credit limit is output. The deep learning model can capture the complex relationship between the credit rating and the credit limit, thereby outputting accurate credit limit recommendations.

[0122] In an embodiment of the present application, it further includes: monitoring the operation information of the cross-border e-commerce in real time; when detecting abnormal transactions through the operation information, automatically triggering risk control measures.

[0123] In an embodiment of the present application, a system is designed and built for monitoring the operation information of cross-border e-commerce. The system should be able to collect, process and analyze key operation information such as transaction data, user behavior data and logistics data of cross-border e-commerce platforms in real time. According to business needs and risk management strategies, a series of risk control rules are set. These rules involve multiple dimensions such as transaction amount, transaction frequency, user behavior patterns, and geographic location.

[0124] The output results of the anomaly detection algorithm are input into the real-time monitoring system. When the system detects abnormal transactions, the alarm mechanism is triggered immediately to notify relevant personnel or system components. According to the risk control rules, configure the corresponding risk control measures such as transaction interception, secondary verification, etc.

[0125] After executing the risk control measures, the system should be able to record the execution of the measures and provide a feedback mechanism to allow relevant personnel to evaluate and adjust the effectiveness of the measures. The above process monitors the operation information of cross-border e-commerce in real time and automatically triggers risk control measures when abnormal transactions are detected. This helps cross-border e-commerce to discover and respond to potential risks in a timely manner, ensuring the stable operation of the business.

[0126] The technical solution of this application obtains information about the associated stores of a cross-border e-commerce company, builds a graph database based on the information about the associated stores, and obtains the operational information of the cross-border e-commerce company through the graph database; verifies whether the historical transactions of the associated stores under the cross-border e-commerce company are legal and true based on the operational information, and generates a verification result; analyzes market information representing the overall situation of industry trends and market risks based on the business registration information and historical operating data of the cross-border e-commerce company, combined with macroeconomic indicators; generates a credit rating for the cross-border e-commerce company based on the operational information, the verification result, and the market information; and determines the credit limit of the cross-border e-commerce company based on the credit rating of the cross-border e-commerce company through a preset chain prediction model. By building a graph database to comprehensively integrate the operational information of the cross-border e-commerce company and its associated stores, the legitimacy and authenticity of historical transactions are ensured through strict verification. At the same time, in-depth analysis of industry trends and market risks is conducted in combination with macroeconomic indicators to generate an accurate credit rating for the cross-border e-commerce company. Finally, based on the credit rating, the credit limit is scientifically determined through a chain prediction model. This process not only improves the efficiency and accuracy of credit decision-making, but also effectively reduces credit risk, providing strong data support for the healthy development of cross-border e-commerce.

[0127] The following describes an apparatus embodiment of the present application, which can be used to implement the cross-border e-commerce loan monitoring method described in the aforementioned embodiments of the present application. It is understood that the apparatus can be a computer program (including program code) running on a computer device, such as application software; the apparatus can be used to perform the corresponding steps of the method provided in the embodiments of the present application. For details not disclosed in the apparatus embodiments of the present application, please refer to the aforementioned embodiments of the cross-border e-commerce loan monitoring method of the present application.

[0128] Figure 3 A block diagram of a cross-border e-commerce loan monitoring system according to an embodiment of the present application is shown.

[0129] Reference Figure 3 As shown, a cross-border e-commerce loan monitoring system according to one embodiment of the present application includes:

[0130] An acquisition unit 310 is configured to acquire information about associated stores of a cross-border e-commerce company, and to construct a graph database based on the information about the associated stores, so as to acquire operational information about the cross-border e-commerce company through the graph database.

[0131] A verification unit 320 is configured to verify, based on the operation information, whether the historical transactions of the associated stores under the cross-border e-commerce are legal and authentic, and generate a verification result;

[0132] The market unit 330 is configured to analyze market information representing industry trends and overall market risks based on the cross-border e-commerce business registration information and historical operating data, combined with macroeconomic indicators;

[0133] A credit unit 340 is configured to generate a credit rating for the cross-border e-commerce company based on the operation information, the verification result, and the market information;

[0134] The credit limit unit 350 is used to determine the credit limit of the cross-border e-commerce company based on the credit rating of the cross-border e-commerce company through a preset chain prediction model.

[0135] In the present application, based on the aforementioned scheme, the information of the associated stores of the cross-border e-commerce is obtained, and a graph database is constructed based on the information of the associated stores to obtain the operation information of the cross-border e-commerce through the graph database, including: obtaining the information of the associated stores of the cross-border e-commerce; based on the information of the associated stores, a relationship graph between the cross-border e-commerce and the associated stores is constructed through a graph database; according to the relationship graph, the first operation information of each of the associated stores is identified and extracted from the information of the associated stores; the first operation information of the associated stores is integrated to generate the second operation information of the cross-border e-commerce, and the second operation information includes transaction information, customs declaration information, logistics information, tax refund information and foreign exchange settlement information; the second operation information of the cross-border e-commerce is encrypting and storing it in the graph database.

[0136] In the present application, based on the aforementioned scheme, the historical transactions of the associated stores under the cross-border e-commerce are verified to be legal and true based on the operation information, and a verification result is generated, including: based on the operation information, verifying the integrity and authenticity of the operation information through a blockchain-based smart contract to generate a first verification result; performing big data analysis on the operation information to verify the legality of the historical transactions and generate a second verification result.

[0137] In the present application, based on the aforementioned scheme, the market information representing industry trends and the overall market risk situation is analyzed based on the industrial and commercial registration information and historical operating data of the cross-border e-commerce, combined with macroeconomic indicators, including: predicting industry trends based on the industrial and commercial registration information and historical operating data of the cross-border e-commerce, combined with historical macroeconomic indicators, using long-short-term memory networks and attention mechanisms; using topic models to analyze industry news and determine consumer dynamics; analyzing key market events through event detection algorithms to generate risk information; generating the market information based on the industry trends, the consumer dynamics and the risk information.

[0138] In the present application, based on the aforementioned scheme, the credit rating of the cross-border e-commerce is generated according to the operation information, the verification results and the market information, including: calculating the credit score through hierarchical analysis based on the operation information, the verification results and the market information; based on the credit score, dynamically adjusting the credit rating of the cross-border e-commerce according to the historical credit records and real-time operation information of the cross-border e-commerce.

[0139] In the present application, based on the aforementioned scheme, the credit limit of the cross-border e-commerce is determined according to the credit rating of the cross-border e-commerce through a preset chain prediction model, including: constructing a chain prediction model based on a deep learning framework, and optimizing the model parameters through a learning mechanism; inputting the credit rating of the cross-border e-commerce into the chain prediction model, and outputting the credit limit of the cross-border e-commerce.

[0140] In this application, based on the above-mentioned solution, it also includes: real-time monitoring of the operation information of the cross-border e-commerce; when abnormal transactions are detected through the operation information, risk control measures are automatically triggered.

[0141] The technical solution of this application obtains information about the associated stores of a cross-border e-commerce company, builds a graph database based on the information about the associated stores, and obtains the operational information of the cross-border e-commerce company through the graph database; verifies whether the historical transactions of the associated stores under the cross-border e-commerce company are legal and true based on the operational information, and generates a verification result; analyzes market information representing the overall situation of industry trends and market risks based on the business registration information and historical operating data of the cross-border e-commerce company, combined with macroeconomic indicators; generates a credit rating for the cross-border e-commerce company based on the operational information, the verification result, and the market information; and determines the credit limit of the cross-border e-commerce company based on the credit rating of the cross-border e-commerce company through a preset chain prediction model. By building a graph database to comprehensively integrate the operational information of the cross-border e-commerce company and its associated stores, the legitimacy and authenticity of historical transactions are ensured through strict verification. At the same time, in-depth analysis of industry trends and market risks is conducted in combination with macroeconomic indicators to generate an accurate credit rating for the cross-border e-commerce company. Finally, based on the credit rating, the credit limit is scientifically determined through a chain prediction model. This process not only improves the efficiency and accuracy of credit decision-making, but also effectively reduces credit risk, providing strong data support for the healthy development of cross-border e-commerce.

[0142] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing an electronic device according to an embodiment of the present application is shown.

[0143] It should be noted that the computer system of the electronic device in this embodiment is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0144] In this embodiment, the computer system includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 402 or programs loaded from a storage unit 408 into a random access memory (RAM) 403, such as executing the cross-border e-commerce loan monitoring method described in the above embodiment. The RAM 403 also stores various programs and data required for system operation. The CPU 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output interface 405 is also connected to the bus 404.

[0145] The following components are connected to the input / output interface 405: an input section 406 including a keyboard, mouse, and the like; an output section 407 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and speakers; a storage section 408 including devices such as a hard disk; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card or a modem. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the input / output interface 405 as needed. Removable media 411, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 410 as needed, so that computer programs read from the removable media can be installed in the storage section 408 as needed.

[0146] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product that includes a computer program carried on a computer-readable medium, the computer program including a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 409 and / or installed from a removable medium 411. When the computer program is executed by the central processing unit 401, the various functions defined in the system of the present application are performed.

[0147] It should be noted that the computer-readable medium described in the embodiments of the present application may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present application, a computer-readable signal medium may include a data signal transmitted in baseband or as part of a carrier wave, which carries a computer-readable computer program. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. A computer program embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0148] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. Among them, each box in the flowchart or block diagram can represent a module, program segment, or part of the code, and the above-mentioned module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0149] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. In some cases, the names of these units do not constitute limitations on the units themselves.

[0150] According to one aspect of the present application, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various optional implementations described above.

[0151] As another aspect, the present application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments, or may exist independently without being incorporated into the electronic device. The computer-readable medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device implements the cross-border e-commerce loan monitoring method described in the above embodiments.

[0152] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiment of the application, the features and functions of two or more modules or units described above can be concretized in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0153] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes a number of instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present application.

[0154] Those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of this application and include common knowledge or customary techniques in the art that are not disclosed herein.

[0155] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A cross-border e-commerce loan monitoring method, characterized in that: include: Obtain information about associated stores of a cross-border e-commerce company, and build a graph database based on the information about the associated stores, so as to obtain operational information about the cross-border e-commerce company through the graph database; Verify, based on the operational information, whether historical transactions of the associated stores under the cross-border e-commerce platform are legal and authentic, and generate a verification result; Analyze market information that represents industry trends and overall market risks based on the cross-border e-commerce company's business registration information and historical operating data, combined with macroeconomic indicators; Generating a credit rating of the cross-border e-commerce company based on the operation information, the verification results, and the market information; Determine the credit limit of the cross-border e-commerce company based on the credit rating of the cross-border e-commerce company through a preset chain prediction model; The step of obtaining information about associated stores of a cross-border e-commerce company and constructing a graph database based on the information about the associated stores includes: Obtain information about the associated stores of the cross-border e-commerce company; Based on the information of the associated stores, a graph database is constructed, and a relationship graph between the cross-border e-commerce company and the associated stores is constructed through the graph database; Identifying and extracting first operation information of each of the associated stores from the information of the associated stores according to the relationship graph; Integrating the first operating information of the associated stores to generate second operating information of the cross-border e-commerce company, wherein the second operating information includes transaction information, customs declaration information, logistics information, tax refund information, and foreign exchange settlement information; Encrypting the second operation information of the cross-border e-commerce and storing it in the graph database; The second operation information of the cross-border e-commerce is encrypted and stored in the graph database, including: Perform hash processing on various data in the second operation information and calculate their hash values , get a hash value set consisting of multiple hash values ,in i is the data identifier, n is the number of data categories; Perform linear operations on multiple hash values ​​in the hash value set to generate a backup key: in, is the linear coefficient, Remaining operation, p is a large prime number; Perform nonlinear transformation on the backup key to generate the first parameter for: in, is a nonlinear function; The first parameter is iterated multiple times, and each iteration applies nonlinear transformation and confusion operation to generate the second parameter for: in, g and h is a nonlinear function, Represents the exclusive OR operation, j is the number of iterations; For the first parameter t After iterations, the intermediate key is obtained , for the intermediate key Perform the last nonlinear transformation to generate the encryption key for: in, F is a nonlinear function; The second operation information of the cross-border e-commerce is encrypted by the encryption key and stored in the graph database.

2. The cross-border e-commerce loan monitoring method according to claim 1, characterized in that: The verification result includes a first verification result and a second verification result. The verification result is generated by verifying whether the historical transactions of the associated store under the cross-border e-commerce are legal and true based on the operation information, including: Verifying the integrity and authenticity of the operation information through a blockchain-based smart contract based on the operation information to generate a first verification result; Perform big data analysis on the operation information, verify the legitimacy of the historical transactions, and generate the second verification result.

3. The cross-border e-commerce loan monitoring method according to claim 1, characterized in that: The above-mentioned cross-border e-commerce business registration information and historical operating data, combined with macroeconomic indicators, analyzes market information that represents industry trends and overall market risks, including: Based on the business registration information and historical operating data of the cross-border e-commerce companies, combined with historical macroeconomic indicators, long-short-term memory networks and attention mechanisms are used to predict industry trends; Use topic models to analyze industry news and identify consumer trends; Analyze key market events through event detection algorithms to generate risk information, and the consumer dynamics and risk information constitute the overall market risk situation; The market information is generated according to the industry trends, the consumer dynamics, and the risk information.

4. The cross-border e-commerce loan monitoring method according to claim 1, characterized in that: Generating the credit rating of the cross-border e-commerce company based on the operation information, the verification result, and the market information includes: Calculating a credit score through hierarchical analysis based on the operation information, the verification results, and the market information; Based on the credit score, the credit rating of the cross-border e-commerce company is dynamically adjusted according to the historical credit record and real-time operation information of the cross-border e-commerce company.

5. The cross-border e-commerce loan monitoring method according to claim 1, characterized in that: Before determining the credit limit of the cross-border e-commerce company based on the credit rating of the cross-border e-commerce company through a preset chain prediction model, the method further includes: A chain prediction model is constructed based on a deep learning framework, and model parameters of the chain prediction model are optimized through a learning mechanism.

6. The cross-border e-commerce loan monitoring method according to claim 1, characterized in that: The method further comprises: Real-time monitoring of the cross-border e-commerce operation information; When abnormal transactions are detected through the operational information, risk control measures are automatically triggered.

7. A cross-border e-commerce loan monitoring system, characterized in that: include: An acquisition unit, configured to acquire information of associated stores of a cross-border e-commerce company, and to construct a graph database based on the information of the associated stores, so as to acquire operational information of the cross-border e-commerce company through the graph database; A verification unit, configured to verify, based on the operation information, whether historical transactions of the associated stores under the cross-border e-commerce platform are legal and authentic, and generate a verification result; A market unit is used to analyze market information representing industry trends and overall market risks based on the business registration information and historical operating data of the cross-border e-commerce company in combination with macroeconomic indicators; A credit unit, configured to generate a credit rating of the cross-border e-commerce company based on the operation information, the verification result, and the market information; A credit limit unit, configured to determine the credit limit of the cross-border e-commerce company based on the credit rating of the cross-border e-commerce company through a preset chain prediction model; The step of obtaining information about associated stores of a cross-border e-commerce company and constructing a graph database based on the information about the associated stores includes: Obtain information about the associated stores of the cross-border e-commerce company; Based on the information of the associated stores, a graph database is constructed, and a relationship graph between the cross-border e-commerce company and the associated stores is constructed through the graph database; Identifying and extracting first operation information of each of the associated stores from the information of the associated stores according to the relationship graph; Integrating the first operating information of the associated stores to generate second operating information of the cross-border e-commerce company, wherein the second operating information includes transaction information, customs declaration information, logistics information, tax refund information, and foreign exchange settlement information; Encrypting the second operation information of the cross-border e-commerce and storing it in the graph database; The second operation information of the cross-border e-commerce is encrypted and stored in the graph database, including: Perform hash processing on various data in the second operation information and calculate their hash values , get a hash value set consisting of multiple hash values ,in i is the data identifier, n is the number of data categories; Perform linear operations on multiple hash values ​​in the hash value set to generate a backup key: in, is the linear coefficient, Remaining operation, p is a large prime number; Perform nonlinear transformation on the backup key to generate the first parameter for: in, is a nonlinear function; The first parameter is iterated multiple times, and each iteration applies nonlinear transformation and confusion operation to generate the second parameter for: in, g and h is a nonlinear function, Represents the exclusive OR operation, j is the number of iterations; For the first parameter t After iterations, the intermediate key is obtained , for the intermediate key Perform the last nonlinear transformation to generate the encryption key for: in, F is a nonlinear function; The second operation information of the cross-border e-commerce is encrypted by the encryption key and stored in the graph database.

8. A computer-readable medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the cross-border e-commerce loan monitoring method according to any one of claims 1 to 6 is implemented.

9. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, enables the one or more processors to implement the cross-border e-commerce loan monitoring method as described in any one of claims 1 to 6.

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