Cross-border e-commerce compliance intelligent auditing platform and multi-language contract analysis method

Abstract

The invention discloses a cross-border e-commerce compliance intelligent auditing platform and a multi-language contract analysis method, and relates to the field of cross-border contract compliance auditing. In the multi-modal data access step, customs codes, laws and regulations and other multi-source data are collected, and 18 kinds of language contract texts are analyzed; in the cross-language semantic alignment step, a knowledge graph is constructed, and multi-language legal concept mapping is achieved; in the compliance risk reasoning step, a rule engine and an agent cooperatively check a contract, and the compliance conclusion confidence is calculated; the dynamic risk assessment step adopts an LSTM network to analyze historical data and predict a risk trend; in the multi-language report generation step, a multi-format bilingual or multilingual report is generated based on a template engine, encrypted and archived. According to the invention, cross-border contracts are audited efficiently and intelligently, dynamic adaptation laws and regulations are analyzed in multiple languages, compliance risks are identified accurately, and a multi-language report is generated quickly; therefore, the checking efficiency is improved, the manual workload is reduced, the compliance risk is reduced, and the enterprise cross-border business competitiveness and the risk response capability are enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cross-border contract compliance audit, and in particular to a cross-border e-commerce compliance intelligent audit platform and a multi-language contract analysis method. BACKGROUND

[0002] With the rapid development of cross-border e-commerce industry, the global trade volume continues to expand, and the compliance challenges faced by enterprises are becoming increasingly severe. Cross-border transactions involve multiple national laws, customs policies, tax systems and international treaties. Contracts, as the core document of transactions, need to strictly comply with the regulatory requirements of the target market. Enterprises will face huge fines and reputation losses once they violate the regulations. Traditional manual auditing methods rely on the experience and knowledge of compliance personnel, which is not only inefficient, but also difficult to fully cover the legal differences of different countries, resulting in the risk of missed or misjudged contract audits.

[0003] The prior art has significant defects in cross-border contract compliance audit. On the one hand, the multi-language contract analysis capability is insufficient. The semantic expression and grammatical structure of legal texts in different languages are very different. For example, the rigor of German legal provisions, the speciality of Arabic writing direction, and the ambiguity of professional terms in translation all pose obstacles to contract understanding. Traditional machine translation tools cannot accurately handle professional terms in the legal field, and manual translation is costly and inefficient, making it difficult for enterprises to quickly respond to cross-border business needs. On the other hand, the contradiction between dynamic regulation updates and real-time contract audits is prominent. Trade policies of various countries are frequently adjusted, and if enterprises cannot incorporate new regulations into contract audits in a timely manner, they will easily fall into compliance difficulties. However, existing audit systems are mostly based on static rule bases, lacking the ability to automatically monitor and intelligently adapt to changes in regulations.

[0004] In addition, there is a lack of multi-dimensional risk assessment mechanism for cross-border contract audits. In addition to legal compliance, contracts also involve complex risks such as customs calculation, intellectual property protection, and data privacy. Traditional methods are difficult to establish a multi-factor correlation analysis model, and existing technologies cannot meet the urgent needs of cross-border e-commerce enterprises for efficient, accurate and comprehensive compliance audits. SUMMARY

[0005] The cross-border e-commerce compliance intelligent audit platform and multi-language contract analysis method proposed by the present application aim to solve the problems mentioned in the prior art.

[0006] To achieve the above purpose, the present application adopts the following technical solutions: a cross-border e-commerce compliance intelligent audit platform and a multi-language contract analysis method, comprising:

[0007] Multi-modal data access step: Build multi-language data interface, use natural language processing technology to preprocess unstructured contract text; Deploy Tesseract OCR engine, recognize scanned documents through CRNN+CTC algorithm; Built-in flow control algorithm, start fuse mechanism to trigger flow limiting alarm when concurrent request exceeds threshold; Use confidence weighted algorithm Handle data conflicts, C i The confidence of the i-th data source is w i The weight coefficient is w

[0008] Cross-language semantic alignment step: Build multilingual compliance knowledge graph, use XLM-RoBERTa-large pre-training model, calculate semantic similarity through contrastive learning method; Entity alignment uses GCN-Align algorithm; According to the target market regulation update frequency, automatically adjust the weight, the formula is T update The last update of the regulation is T reference The regulation is cited in the history audit, and a is 0.6 and b is 0.4.

[0009] Compliance risk reasoning step: Build a three-layer reasoning model based on Drools rule engine, use multi-agent collaborative reasoning to solve compliance problems, communicate through KQML language; When processing EU GDPR compliance, automatically extract contract entities and check privacy policy clauses; Use confidence propagation algorithm, the formula is p i The confidence of the i-th reasoning step is p

[0010] Dynamic risk assessment step: Build a dynamic risk assessment model, input risk coefficient parameters, output risk change curve, use LSTM network to predict compliance risk change trend; Analyze risk transmission, calculate influence path through graph neural network, identify potential risk points; Risk assessment results use five-level classification method, different levels trigger different audit processes;

[0011] Multi-language report generation step: Build multi-format document generation engine based on Apache POI and OpenOffice SDK, use template engine technology to customize report format, select adaptive font, and compare key compliance clauses in multiple languages; Built-in watermark encryption function, use AES-256 algorithm to encrypt document content.

[0012] Further, it also includes:

[0013] Intelligent translation quality control steps: build a translation quality evaluation model to score the quality of machine translation results; calculate the semantic similarity of translated contract terms, set a similarity threshold with the original text, and use a term library to force alignment of professional terms; trigger the manual translation process when the translation quality score is below the threshold, and use an inverted index structure to store translated terms.

[0014] Further, it also includes:

[0015] Regulatory dynamic monitoring steps: deploy a distributed crawler cluster to regularly crawl the official websites of global customs, tax, and trade departments, use a text classification model to identify regulatory updates, automatically extract new regulatory key information, and infer the impact through a knowledge graph; when there is a conflict in regulations, the system automatically marks and provides risk avoidance suggestions, and regulatory monitoring data is stored in a time series database.

[0016] Further, in the multi-modal data access step, the customs code library API uses an incremental update mechanism to synchronize changes in global customs codes to the review system through a message queue; image format contracts use OCR technology to extract features through a feature pyramid network, automatically detect text orientation, and correct the text.

[0017] Further, in the cross-language semantic alignment step, the multi-language compliance knowledge graph uses graph embedding technology to map legal entities to a low-dimensional vector space, calculates the semantic distance of entities through the TransE algorithm, and adapts to unregistered terms using meta-learning algorithms; during entity alignment, dynamically adjust language feature weights through an attention mechanism, and update entity embedding vectors through graph convolution operations when new regulations are added.

[0018] Further, in the compliance risk reasoning step, the Drools rule engine uses a hybrid reasoning strategy, known risk points use forward reasoning to match rules, and compliance issues use backward reasoning to backtrack risk paths; the system automatically retrieves global patent databases to handle intellectual property clauses and check for infringement risks; generate reasoning paths during the reasoning process, use natural language generation technology to convert logical reasoning processes, and automatically trigger multi-level approval processes for major compliance risks.

[0019] Further, in the dynamic risk assessment step, the LSTM network uses a bidirectional structure combined with an attention mechanism to prevent overfitting through Dropout; use historical contract data to train the model and use mean squared error loss function to predict error rates; use PageRank algorithm to calculate risk propagation probability in risk transmission analysis, and automatically alert associated enterprises when risk values exceed the threshold, and provide response plans based on risk levels.

[0020] Further, the multilingual report generation step document generation engine supports 256 template combinations, customizes report content according to industry needs; adopt XSL-FO technology cross-platform conversion format, generate PDF / A-3 standard documents; the data visualization module uses the ECharts library to generate risk distribution radar charts and compliance trend line charts, bilingual reports automatically adjust the layout, align English and Chinese content; dynamic encryption watermark, generate watermark information according to the viewing identity.

[0021] Further, it also includes:

[0022] Intelligent interaction feedback step: build a multi-turn dialogue system, query compliance problems through natural language, and use a hybrid architecture of retrieval and generation to retrieve similar problems or generate answers; the dialogue system interacts in multiple languages, automatically identifies the user's language and switches the corresponding knowledge base, collects and modifies suggestions during the audit process, analyzes user preferences using collaborative filtering algorithms, and optimizes audit strategies; build a portrait based on user historical operation behavior, provide personalized audit services.

[0023] Further, it also includes:

[0024] Security and privacy protection step: adopt zero trust architecture design, access through identity authentication, authorization and encrypted channel request; user data uses federated learning technology to train models, sensitive information uses homomorphic encryption technology to process; deploy multi-level firewall, use intrusion detection and defense system to monitor network traffic in real time, data backup uses multi-active architecture, controls backup recovery time target and backup recovery point target; record user operation and system behavior, generate audit report.

[0025] Compared with the existing technology, the beneficial effects of the present application are:

[0026] Build a multi-modal data access system, integrate global customs codes, international treaties and target market regulations, accurately analyze multiple language contract texts, efficiently process scanned OCR recognition and complex legal term translation, and greatly improve contract audit efficiency.

[0027] In terms of compliance risk management, the platform uses knowledge graph and intelligent reasoning technology to perform semantic alignment between contract clauses and dynamically updated regulation library, identify syntax and logic level problems, and deeply analyze the matching degree of clauses and regulations to accurately locate potential risks. In the EU GDPR compliance audit, the system automatically extracts data processing clauses from the contract and checks whether it meets the regulatory requirements, with high risk identification accuracy, effectively avoiding penalties for compliance loopholes.

[0028] The future compliance risk trend is predicted through the LSTM model to provide forward-looking decision support for enterprises; and multi-language reports conforming to different market specifications are automatically generated, covering risk assessment results and rectification suggestions, with a standard format and detailed content, which significantly improves the global competitiveness and risk response ability of enterprises. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 A schematic block diagram of a cross-border e-commerce compliance intelligent auditing platform and a multi-language contract analysis method is provided for the present application.

[0030] Figure 2 A comparison diagram of multi-language OCR recognition accuracy is provided.

[0031] Figure 3 A comparison diagram of cross-language semantic alignment accuracy is provided.

[0032] Figure 4 A comparison diagram of compliance risk reasoning efficiency is provided.

[0033] Figure 5 A comparison diagram of dynamic risk assessment prediction error is provided.

[0034] Figure 6 A comparison diagram of different language report generation speed is provided. DETAILED DESCRIPTION

[0035] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0036] In the description of the present application, it should be understood that the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and therefore cannot be understood as indicating or implying that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application.

[0037] In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the said features. In the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined. In addition, the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. The present invention will be further described in detail below with reference to the accompanying drawings.

[0038] Reference Figures 1 to 6 A cross-border e-commerce compliance intelligent audit platform and multi-language contract analysis method, including:

[0039] Multimodal data access steps: Build a three-level heterogeneous data access system to comprehensively collect cross-border e-commerce contract and compliance data. At the customs code database access level, the system establishes API interfaces with the coding systems of 18 countries and regions, including the General Administration of Customs of China, the EU Taric, and the US HTS, obtaining the latest coding data through an incremental synchronization mechanism. A dedicated parsing engine has been developed based on the 10-digit subdivision rules of the Chinese Customs HS code, identifying the differences between "8517121000" (smartphones) and "8517129000" (other mobile phones) and automatically associating corresponding regulatory requirements (such as 3C certification and endangered species certification). Regarding the international commercial treaty database, the system includes 32 core WTO agreements, including the Agreement on Technical Barriers to Trade and the Agreement on Trade-Related Aspects of Intellectual Property Rights. Using natural language processing technology, the system extracts the obligations contained therein. For example, it parses the specific requirement of "regional value content of origin ≥ 40%" from the Regional Comprehensive Economic Partnership (RCEP). A layered parsing strategy is employed to process multilingual contract texts. In the document format processing stage, for PDF format contracts, Apache PDFBox is used to extract text layer information, and for scanned PDFs, the Tesseract OCR engine is called for text recognition. The system incorporates special corpus for cross-border contracts during the training process. In the language processing stage, the system adopts a hybrid word segmentation strategy for eight mainstream languages, including Chinese, English, French, German, Spanish, Arabic, and Portuguese. The English text uses spaCy's en-core-web-lg model to accurately identify the legal term "forcema jeure" (force majeure); the Chinese text combines jieba and HanLP to achieve accurate word segmentation for legal concepts such as "joint liability" and "performance bond"; the Arabic text uses the Farasa word segmenter to solve the problem of no space separation in Arabic. The data access module also includes an intelligent quality verification mechanism. The system performs a completeness check on the imported contract text and automatically identifies contracts that are missing key terms (such as payment methods, dispute resolution). For data conflict issues, such as inconsistent HS codes for the same product in different data sources, a confidence weighted algorithm is used for processing, and the calculation formula is: Handling data conflicts, C i is the confidence of the i-th data source, w i is the weight coefficient.

[0040] Cross-lingual semantic alignment step: XLM-RoBERTa-large is used as the base model, which is fine-tuned on 1.5 TB cross-border e-commerce domain corpus, covering international trade contracts, General Administration of Customs announcements, International Chamber of Commerce (ICC) publications, etc. During the fine-tuning process, the system adopts a contrast learning strategy, by constructing legal term pairs (such as "anti-dumping duty" and "anti-dumping duty"), so that the model learns the cross-lingual semantic association. A bottom-up approach is used to build the knowledge graph, and entity recognition technology is used to extract legal entities from multiple sources, including 1.2 million entities such as "tariff rate", "origin rule", "intellectual property protection", etc. The joint modeling strategy is used for entity relationship extraction, combining BiLSTM and attention mechanism to identify 380 million relationships such as "apply", "contain", "conflict", etc. For example, the system can identify the "protection" relationship between "EU GDPR" and "personal data", and the "mandatory requirement" relationship between "US FDA certification" and "medical devices". To address the differences between different legal systems, the system has developed a dynamic weight adjustment mechanism. For civil law countries (such as Germany), the system increases the weight of "legal provision citation"; for common law countries (such as the United States), it increases the weight of "judicial cases". The weight calculation formula is T update F is the number of days since the last update of the regulation, F reference is the citation frequency of the regulation in historical audits, α is 0.6, and β is 0.4. When dealing with the EU Digital Services Act (DSA), since the regulation is updated frequently and has a high citation rate, the system automatically increases its weight to 0.85, ensuring that relevant provisions are given priority in audits.

[0041] Compliance risk inference step: A three-layer compliance inference model is built based on the Drools rule engine to achieve comprehensive review from syntax checking to regulation verification. In the syntax layer checking, the system checks the style of contract text in different languages. For example, German law requires that contract clauses must use formal style. The system automatically identifies expressions that do not meet the requirements by analyzing indicators such as vocabulary complexity (Flesch-Kincaid grade level ≥ 12) and sentence length (average ≤ 25 words). The logic layer checking mainly checks the logical consistency between contract clauses. The system uses a combination of propositional logic and modal logic to build a logical relationship graph between clauses. For example, the payment clause states "pay within 30 days after delivery", while the delivery clause states "the buyer must pick up the goods within 5 days after payment". The system identifies a circular dependency contradiction between the two clauses through temporal logic analysis. In processing complex contracts, a multi-agent collaborative reasoning strategy is adopted, with each agent focusing on logical verification in a specific field. For example, the tariff agent is responsible for checking whether the price clause contains the tariff calculation method, and the intellectual property agent verifies whether the technology license clause complies with the regulations of the target market. The regulation layer checking is the core of the entire inference process. The system compares contract clauses with regulations in the target market. Taking the European Union GDPR as an example, the system first extracts key entities such as "data processor", "data controller", and "personal data type" from the contract, and then checks whether the clauses contain necessary content required by GDPR, such as data subject rights declaration and data cross-border transmission mechanism. The system uses the belief propagation algorithm to calculate the reliability of each compliance conclusion, with the formula p i being the confidence of the i-th inference step. When the calculation result is below the threshold of 0.9, the system automatically triggers a secondary review process. In the audit of a cross-border e-commerce platform's user data processing contract, the system found that the compliance confidence of the data cross-border transmission clause was only 0.82, prompting the addition of a standard contract clause (SCC). Through natural language generation technology, the complex logical reasoning process is converted into a user-understandable text explanation. For example, for the conclusion "the contract clause violates the United States Foreign Corrupt Practices Act (FCPA)", the system generates a detailed explanation: "According to FCPA Section 3(a), it is prohibited to provide anything of value to a foreign official to obtain or retain business. Your contract contains a'market promotion fee' clause, but does not explicitly state the purpose of the fee and the monitoring mechanism, which poses a compliance risk." This explainability allows enterprise compliance personnel to quickly understand the problem and improve rectification efficiency.

[0042] Dynamic risk assessment step: Construct a time series analysis model based on LSTM to realize dynamic prediction and evaluation of cross-border e-commerce compliance risks. The input layer of the model contains 50 feature dimensions, including historical violation records, target market regulation change frequency, commodity category risk coefficient, etc. In the training process, 100,000 historical contract data are used, the mean square error (MSE) loss function is adopted, the parameters are updated by Adam optimizer, the learning rate is set to 0.001, the batch size is 64, and the number of training rounds is 100. On the test set, the prediction error rate of the model is 14.3%, which can accurately predict the compliance risk trend in the next 12 months. Adopt a five-level risk classification system to evaluate the contract, low risk (0-20 points), lower risk (21-40 points), medium risk (41-60 points), higher risk (61-80 points), high risk (81-100 points). Different risk levels trigger different review processes, for example, high-risk contracts automatically enter the manual review stage and generate detailed risk assessment reports. When evaluating an electronic cigarette export contract, the system considers the strict supervision of electronic cigarettes by the FDA (regulation risk coefficient is 3.8), the historical violation records of the enterprise (2 violations in the past 12 months), and the health risks of the commodity itself (risk coefficient is 2.5), and finally assesses it as a high-risk level of 85 points, preventing possible legal disputes. Adopt graph neural network (GNN) to build a risk propagation model between enterprises. The model is based on the supply chain network, with nodes representing enterprises and edges representing trade relationships. The weight of the edge represents the transaction amount and frequency. Through the PageRank algorithm, the risk propagation probability is calculated, and when the enterprise risk value exceeds the threshold, the system automatically warns the associated enterprises. For example, when the system detects that a European importer has been blacklisted due to intellectual property rights issues, it will immediately notify the 12 Chinese suppliers that have trade relations with it, reminding them to re-evaluate the cooperation risks.

[0043] Multilingual report generation step: Build a multilingual report generation engine based on Apache POI and OpenOffice SDK, support bilingual reports in Chinese and English, PDF / A-3 electronic archive format. The report generation process is divided into three stages: data aggregation, template rendering and format conversion. In the data aggregation stage, the audit results are collected from each module, including the total score of compliance evaluation, the score details of each dimension, the risk point marking and the rectification suggestion. In the template rendering stage, Freemarker template engine is used to customize the report format for different markets. For example, the report for the US market highlights the FDA certification requirements, and the report for the Japanese market emphasizes the JIS standard compliance. The system has 256 built-in template combinations that can automatically select the appropriate template based on contract type, target market, product category, etc. When processing bilingual reports in Chinese and English, a two-column layout design is used, with Chinese content on the left and English translation on the right, ensuring that the content of the two languages is aligned and beautiful. For key compliance clauses, different colors are used for marking, such as highlighting the clauses that violate regulations in red and marking the clauses that need to be modified in yellow. In the format conversion stage, XSL-FO technology is used to convert the rendered content into PDF / A-3 format, which meets the ISO 19005 standard and supports long-term archiving and digital signature. Metadata is embedded in the PDF file, including contract number, audit time, auditor signature, etc., to ensure the integrity and traceability of the document. To enhance the visual effect of the report, ECharts library is used to generate risk distribution radar chart, compliance trend line chart and other charts. Report generation speed is an important indicator of system performance. In the test environment, the system processes 50-page contract report generation time ≤30 seconds, supporting concurrent generation of 50 reports. For reports containing complex charts, the generation time is controlled within 60 seconds. To ensure data security, the system encrypts the generated report using AES-256 algorithm, with a key validity period of 72 hours.

[0044] In the present application, the following steps are also included:

[0045] Intelligent translation quality control step: Build a translation quality evaluation model based on BERT to score the quality of machine translation results. The model is trained on the WMT multilingual parallel corpus and fine-tuned for cross-border contract field, with evaluation indicators including accuracy, fluency and professionalism. The system calculates the semantic similarity of the translated contract clauses, with a similarity threshold of 0.9 compared to the original text. For professional terms (such as "Certificate of Origin"), a term library alignment mechanism is used to ensure translation consistency. When the translation quality score is < 85 points, an automatic human translation process is triggered. This step also includes translation memory management, which uses an inverted index structure to store translated clauses, with a retrieval response time ≤ 100 ms and a reuse rate ≥ 40%, significantly improving translation efficiency.

[0046] In the present application, the following steps are also included:

[0047] Regulatory dynamic monitoring step: Deploy a distributed crawler cluster to crawl the official websites of customs, tax, and trade departments in 62 countries and regions around the world every day, with an update frequency of ≥2 times per day. Use a text classification model (based on RoBERTa) to automatically identify regulatory updates, with a classification accuracy of ≥97%. For new regulations, the system automatically extracts key information (such as effective time, scope of application, and punishment measures) and uses knowledge graph reasoning to determine its impact on existing contracts. For example, when the EU updates the battery directive, the system automatically identifies in-transit contracts involving battery products and assesses compliance risks. This step also includes regulatory conflict detection, which automatically marks and provides risk avoidance suggestions when there is a conflict between two countries' regulations (e.g., A country requires local testing, while B country prohibits such testing). Regulatory monitoring data is stored in a time series database, supporting backtracking queries for 36 months of historical regulations.

[0048] In this invention, in the multi-modal data access step, the customs coding library API uses an incremental update mechanism to synchronize global customs coding changes at 3 a.m. every day, pushing the data to the review system through a message queue (Kafka) with a processing delay of ≤5 minutes. For image format contracts, an OCR technology with multi-scale feature fusion is used to enhance feature extraction capability in small font scenarios through a feature pyramid network (FPN), increasing the recognition accuracy of 6pt fonts to 96.7%. The system automatically detects and corrects text direction for different languages (e.g., Arabic from right to left), with a correction success rate of ≥99%. The data access module supports breakpoint continuation, allowing data transmission to continue from the breakpoint position after network interruption, ensuring data integrity.

[0049] In this invention, in the cross-language semantic alignment step, the multilingual compliance knowledge graph uses graph embedding technology to map legal entities to a low-dimensional vector space and calculates the semantic distance between entities through the TransE algorithm. For unregistered terms (e.g., emerging technology nouns), the meta-learning (MAML) algorithm is used for rapid adaptation, achieving an 80% recognition accuracy with only 5 samples. During entity alignment, the attention mechanism dynamically adjusts the weights of different language features, such as focusing on the "responsibility assumption" clause in Chinese contracts and the "termination" clause in English contracts. The knowledge graph supports incremental updates, updating related entity embedding vectors through graph convolution operations of graph neural networks when new regulations are added, with an update time of ≤10 minutes.

[0050] In the invention, the Drools rule engine in the compliance risk inference step adopts a hybrid reasoning strategy combining forward reasoning and backward reasoning. For known risk points, forward reasoning is used to quickly match rules; for complex compliance problems, backward reasoning is used to backtrack possible risk paths. When dealing with intellectual property clauses, the system automatically retrieves global patent databases (covering 120 countries / regions) to check whether the technology involved in the contract has infringement risks. An interpretable reasoning path is generated during the reasoning process, and natural language generation technology is used to convert the logical reasoning process into a user-understandable text explanation, with an explanation accuracy of ≥92%. For major compliance risks, the system automatically triggers a multi-level approval process to ensure that risks are controllable.

[0051] In the invention, the LSTM network in the dynamic risk assessment step adopts a bidirectional structure combined with an attention mechanism, the input layer contains 50 feature dimensions, the hidden layer is set to 128 neurons, and Dropout (ratio 0.2) is used to prevent overfitting. The model is trained on 100,000 historical contract data, using a mean square error loss function, with a prediction error rate of 14.3% on the test set. Risk transmission analysis uses the PageRank algorithm to calculate the risk transmission probability between enterprises, and when the risk value of a certain enterprise exceeds the threshold, the system automatically warns associated enterprises. This step also includes risk mitigation measures recommendation, providing different solutions according to risk levels, such as purchasing export credit insurance, adjusting supply chain layout, etc.

[0052] In the invention, the multi-language report generation step uses a document generation engine that supports 256 template combinations, allowing customization of report content according to different industries (such as machinery, electronics, clothing). XSL-FO technology is used to achieve cross-platform format conversion, and the generated PDF documents meet the PDF / A-3 standard, supporting long-term archiving. The data visualization module in the report uses the ECharts library to generate risk distribution radar charts, compliance trend line charts, etc., with a rendering speed of ≤2 seconds per chart. For bilingual reports, the system automatically adjusts the layout to ensure that Chinese and English content are aligned and aesthetically pleasing. The watermark encryption function supports dynamic watermarks, generating unique watermark information based on the viewer's identity to prevent unauthorized dissemination.

[0053] In the invention, the following steps are also included:

[0054] Intelligent interaction feedback step: build a multi-turn dialogue system based on Transformer, support users to query compliance questions through natural language, such as "what certifications are required for exporting medical devices to the United States?". The system adopts a hybrid architecture of retrieval and generation, first retrieves similar questions from the knowledge base (recall rate ≥ 90%), and generates answers if not found (perplexity ≤ 20). The dialogue system supports multilingual interaction, automatically identifies user language and switches to the corresponding knowledge base. The user feedback module collects modification suggestions during the audit process, uses collaborative filtering algorithm to analyze user preferences, and optimizes subsequent audit strategies. For example, when multiple users propose modification suggestions for the audit standards of a certain clause, the system automatically adjusts the weight of the relevant rules. This step also includes user portrait analysis, which builds a portrait based on user historical operation behavior, and provides personalized audit services for different users.

[0055] In the present application, the following steps are also included:

[0056] Security and privacy protection step: adopt zero trust architecture design, all access requests need to pass through identity authentication (support OAuth 2.0+SAML), authorization and encrypted channel (TLS1.3). User data adopts federated learning technology for model training, ensures that data does not leave the local. Sensitive information (such as bank card number) is processed by homomorphic encryption technology, supports compliance calculation in ciphertext state. The system deploys multi-level firewall, uses intrusion detection system (IDS) and intrusion prevention system (IPS) to monitor network traffic in real time, the abnormal traffic interception rate ≥ 99.9%. Data backup adopts multi-active architecture, backup recovery time target (RTO) ≤ 30 minutes, backup recovery point target (RPO) ≤ 5 minutes. This step also includes compliance audit function, records all user operations and system behavior, generates audit report in accordance with SOX Act requirements.

[0057] The above is only the preferred specific embodiment of the present application, but the protection scope of the present application is not limited to this, any skilled person in the art can make equivalent replacement or change according to the technical scheme and the inventive concept of the present application within the technical range disclosed by the present application, which should be covered in the protection scope of the present application.

Claims

1. A cross-border e-commerce compliance intelligent audit platform and multi-language contract parsing method, characterized by: The following steps are involved: Multimodal data access steps: Build multi-language data interfaces, use natural language processing technology to segment and pre-process unstructured contract texts; deploy the Tesseract OCR engine to recognize scanned documents using the CRNN+CTC algorithm; use a built-in flow control algorithm to trigger a flow limit alarm when the number of concurrent requests exceeds the threshold; and use a confidence weighted algorithm. Handling data conflicts, C i is the confidence of the i-th data source, w i is the weight coefficient; Cross-language semantic alignment steps: Build a multilingual compliance knowledge graph, use the XLM-RoBERTa-large pre-training model, and calculate semantic similarity through contrastive learning. Use the GCN-Align algorithm for entity alignment. Automatically adjust weights based on the frequency of regulatory updates in the target market. The formula is: T update The number of days since the regulation was last updated. reference is the frequency of citation of regulations in historical reviews, α is taken as 0.6, and β is taken as 0.4; Compliance risk reasoning steps: Build a three-layer reasoning model based on the Drools rule engine, use multi-agent collaborative reasoning for compliance issues, and communicate through the KQML language; automatically extract contract entities when handling EU GDPR compliance and check privacy policy terms; use the confidence propagation algorithm, the formula is p i is the confidence level of the i-th reasoning step, and the conclusion below the threshold triggers the secondary review process; Dynamic risk assessment steps: Build a dynamic risk assessment model, input risk coefficient parameters, output a risk change curve, and use an LSTM network to predict compliance risk change trends; analyze risk transmission, calculate the impact path through a graph neural network, and identify potential risk points; Risk assessment results are classified using a five-level classification method, with different levels triggering different review processes; Multilingual report generation steps: Build a multi-format document generation engine based on Apache POI and OpenOffice SDK, use template engine technology to customize report formats, select appropriate fonts, and compare key compliance clauses in multiple languages; Built-in watermark encryption function, using AES-256 algorithm to encrypt document content.

2. The cross-border e-commerce compliance intelligent audit platform and multilingual contract parsing method according to claim 1 is characterized in that: Also includes: Intelligent translation quality control steps: Build a translation quality assessment model to score the quality of machine translation results; Calculate the semantic similarity of translated contract terms, set a similarity threshold with the original text, and use a forced alignment mechanism with the terminology database for professional terms. When the translation quality score falls below the threshold, the manual translation process is triggered, and an inverted index structure is used to store the translated terms.

3. The cross-border e-commerce compliance intelligent audit platform and multilingual contract parsing method according to claim 1 is characterized in that: Also includes: Steps for dynamic monitoring of regulations: Deploy a distributed crawler cluster to regularly crawl the official websites of customs, taxation, and trade departments around the world, use text classification models to identify regulatory updates, automatically extract key information about new regulations, and infer their impact through knowledge graphs; when there are conflicts in regulations, the system automatically marks them and provides risk avoidance suggestions, and regulatory monitoring data is stored in a time series database.

4. The cross-border e-commerce compliance intelligent audit platform and multilingual contract parsing method according to claim 1 is characterized in that: In the multimodal data access step, the customs code library API adopts an incremental update mechanism to synchronously change the global customs code and push it to the review system through the message queue; the image format contract uses OCR technology to extract features through the feature pyramid network, automatically detect the text direction and correct the text.

5. The cross-border e-commerce compliance intelligent audit platform and multilingual contract parsing method according to claim 1 is characterized in that: In the cross-language semantic alignment step, the multilingual compliance knowledge graph uses graph embedding technology to map legal entities into a low-dimensional vector space. The entity semantic distance is calculated using the TransE algorithm, and the unregistered terms are adapted using a meta-learning algorithm. During the entity alignment process, the language feature weights are dynamically adjusted through the attention mechanism, and the entity embedding vector is updated through graph convolution operations when new regulations are added.

6. The cross-border e-commerce compliance intelligent audit platform and multi-language contract parsing method according to claim 1 is characterized in that: In the compliance risk reasoning step, the Drools rule engine adopts a hybrid reasoning strategy, using forward reasoning matching rules for known risk points and reverse reasoning to trace the risk path for compliance issues; the system automatically searches the global patent database to process intellectual property clauses and check infringement risks; the reasoning path is generated during the reasoning process, and natural language generation technology is used to transform the logical reasoning process. Major compliance risks automatically trigger a multi-level approval process.

7. The cross-border e-commerce compliance intelligent audit platform and multi-language contract parsing method according to claim 1 is characterized in that: In the dynamic risk assessment step, the LSTM network adopts a bidirectional structure combined with an attention mechanism, and uses Dropout to prevent overfitting; the model is trained using historical contract data, and the mean square error loss function is used to predict the error rate; the risk transmission analysis uses the PageRank algorithm to calculate the risk propagation probability, and automatically warns related companies when the risk value exceeds the threshold, and provides response plans based on the risk level.

8. The cross-border e-commerce compliance intelligent audit platform and multi-language contract parsing method according to claim 1 is characterized in that: The document generation engine in the multilingual report generation step supports 256 template combinations, allowing report content to be customized according to industry needs; XSL-FO technology is used to convert formats across platforms and generate documents that comply with the PDF / A-3 standard. The data visualization module uses the ECharts library to generate risk distribution radar charts and compliance trend line charts. Bilingual reports automatically adjust the layout and align the Chinese and English content. Dynamically encrypted watermark, generating watermark information based on viewing identity.

9. The cross-border e-commerce compliance intelligent audit platform and multilingual contract parsing method according to claim 1 is characterized in that: Also includes: Intelligent interactive feedback steps: Build a multi-round dialogue system, query compliance issues through natural language, and use a hybrid search and generation architecture to retrieve similar questions and generate answers; The dialogue system has multi-language interaction, automatically identifies the user's language and switches to the corresponding knowledge base. During the review process, it collects modification suggestions, uses collaborative filtering algorithms to analyze user preferences, and optimizes the review strategy. Build profiles based on users' historical operating behaviors and provide personalized review services.

10. The cross-border e-commerce compliance intelligent audit platform and multi-language contract parsing method according to claim 1 is characterized in that: Also includes: Security and privacy protection steps: Adopt a zero-trust architecture design and request access through identity authentication, authorization, and encrypted channels; User data is trained using federated learning technology, and sensitive information is processed using homomorphic encryption technology; Deploy multi-level firewalls, use intrusion detection and prevention systems to monitor network traffic in real time, and use a remote multi-active architecture for data backup to control backup recovery time objectives and backup recovery point objectives; Record user operations and system behaviors and generate audit reports.

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