Cross-jurisdictional data compliance dialogue intelligent governance platform for Chinese enterprises going abroad

Abstract

The application relates to the technical field of data compliance governance and natural language processing, and discloses a cross-jurisdictional data compliance speech intelligent governance platform for Chinese-funded enterprises going abroad, which comprises a data acquisition and preprocessing module, a regulation analysis and rule modeling module, a cross-jurisdictional rule mapping and conflict identification module, a multilingual semantic conversion module, a cultural context verification module, a compliance text generation module, a compliance evaluation module and an archiving and output module. By structurally modeling different jurisdictional legal rules, rule element level comparison and conflict identification are realized; on this basis, combined with multilingual semantic conversion and cultural context verification, compliance texts are automatically generated and expression optimization is realized; and through an evaluation and feedback mechanism, the generated results are dynamically optimized, and whole-process recording and traceable management are realized. The application can improve the efficiency and consistency of cross-jurisdictional data compliance text generation and reduce compliance risks caused by rule differences and language and cultural differences.

Description

Technical Field

[0001] This invention relates to the fields of data compliance governance and natural language processing technology, and more specifically, to a cross-jurisdictional data compliance discourse intelligent governance platform for Chinese enterprises going global. Background Technology

[0002] With the development of the digital economy and the accelerating pace of Chinese enterprises "going global," data collection, storage, processing, and cross-border transmission activities are becoming increasingly frequent in cross-border operations. Different countries and regions have established their own legal systems for data protection and compliance, such as the EU's General Data Protection Regulation (GDPR), relevant US data privacy laws, and the gradually improving data protection regulations in Southeast Asia and other regions. These legal systems differ significantly in terms of data classification methods, the legal basis for processing, user rights protection, and regulatory requirements, resulting in high compliance complexity for enterprises operating in multiple legal jurisdictions.

[0003] In practice, companies typically need to develop separate privacy policies, user agreements, and internal data management policies for different jurisdictions. This process requires not only a thorough understanding of local laws but also the translation of legal requirements into concrete textual expressions. However, differences in legal definitions, expressions of liability, and clause structures across jurisdictions can easily lead to misunderstandings or inconsistencies in the drafting of compliance documents. Furthermore, the drafting of compliance documents in multilingual environments faces challenges such as inaccurate translations, semantic shifts in legal terms, and expressions that do not conform to local usage, thereby increasing compliance risks.

[0004] In existing technologies, enterprises typically rely on manual methods to compile cross-jurisdictional compliance texts. This includes legal personnel interpreting legal provisions and generating corresponding texts through translation or the use of common language tools. This approach is not only inefficient but also difficult to update and manage in a timely manner when faced with complex and ever-changing regulatory requirements. Furthermore, while some existing systems incorporate natural language processing technology to process text, they primarily focus on text translation or keyword matching, lacking the ability to deeply model the structure of legal rules and making it difficult to achieve systematic comparison and conflict identification between rules from different jurisdictions.

[0005] Furthermore, in cross-cultural environments, compliance texts not only need to meet legal requirements but also need to conform to the language expression habits and cultural context of the target country or region. Existing technologies often overlook the impact of cultural factors on the understanding and acceptance of compliance texts, resulting in texts that meet formal requirements but may suffer from misunderstandings or communication barriers in practical applications. In addition, existing solutions generally lack a systematic evaluation and feedback mechanism for the generated results, making it impossible to quantitatively analyze the completeness, accuracy, and suitability of compliance texts, and also making it difficult to form a closed-loop processing flow for continuous optimization.

[0006] Therefore, how to uniformly model scattered legal rules in a multi-jurisdictional environment, automatically identify cross-jurisdictional rule differences, generate compliant texts that conform to the target language and cultural context, and establish evaluation and feedback mechanisms to improve text quality has become a pressing technical problem in the field of data compliance.

[0007] Therefore, there is an urgent need to design a cross-jurisdictional data compliance discourse intelligent governance platform for Chinese enterprises going global to solve the above problems. Summary of the Invention

[0008] The purpose of this invention is to solve the technical problems mentioned in the background section and to provide a cross-jurisdictional data compliance discourse intelligent governance platform for Chinese enterprises going global.

[0009] The above-mentioned objective of the present invention is achieved through the following technical solution:

[0010] A cross-jurisdictional data compliance discourse intelligent governance platform for Chinese enterprises going global, including:

[0011] The system includes modules for data acquisition and preprocessing, legal analysis and rule modeling, cross-jurisdictional rule mapping and conflict identification, multilingual semantic conversion, cultural context verification, compliance text generation, compliance assessment, and evidence storage and output.

[0012] The data acquisition and preprocessing module is used to collect data compliance legal texts, multilingual compliance corpora, and corporate compliance data from different legal jurisdictions, and to perform word segmentation, entity recognition, and structured encoding on the data.

[0013] The regulatory analysis and rule modeling module is used to extract information on the data processing subject, data type, processing purpose, processing method, constraints, and legal liability from legal texts, and to construct a structured representation of the rules.

[0014] The cross-jurisdictional rule mapping and conflict identification module is used to perform element-level matching of the structured representation of rules in different jurisdictions, identify differences in the scope of application, constraints and liability requirements of rules, and output conflict marking information.

[0015] The multilingual semantic conversion module is used to convert source language compliant text into target language text based on the rule-based structured representation, while maintaining legal semantic consistency during the conversion process;

[0016] The cultural context verification module is used to perform context adaptation detection on the converted text and replace or reconstruct content that does not conform to the expression habits of the target region.

[0017] The compliance text generation module is used to generate compliance text for the target jurisdiction based on the rule structure representation, conflict marker information, and context verification results.

[0018] The compliance assessment module is used to evaluate the generated text in terms of legal consistency, semantic integrity and contextual suitability, and output the assessment results.

[0019] The evidence storage and output module is used to record the version of the generated compliance text and output the compliance text and risk warning information.

[0020] Furthermore, the data acquisition and preprocessing module is also used to: perform unified format conversion on data from different sources, and establish a multi-level index structure that includes clause number, clause category and semantic tags.

[0021] Furthermore, in the regulatory analysis and rule modeling module, the structured representation of the rules includes at least six elements: data processing subject, data type, processing purpose, processing method, constraints, and legal responsibility, and is stored in a field-based manner.

[0022] Furthermore, in the cross-jurisdictional rule mapping and conflict identification module: by matching each element in the structured representation of rules in different jurisdictions item by item, at least one of the following conflict types is identified:

[0023] Conflicts in the scope of data processing, the definition of data categories, the intensity of compliance obligations, or the manner of assuming responsibility.

[0024] Furthermore, in the multilingual semantic conversion module: each element in the rule-structured representation is mapped to a standard expression template in the target language, and the word order and sentence structure are adjusted according to the legal text expression habits of the target legal domain.

[0025] Furthermore, in the cultural context verification module: the text is detected based on a preset cultural context rule base, and when content that is inconsistent with the cultural expression of the target region is detected, the corresponding expression is replaced or reconstructed.

[0026] Furthermore, in the compliance text generation module: based on the rule structure representation and conflict marker information, the rules of different jurisdictions are integrated, and the rules with stricter constraints are selected first to generate compliance text.

[0027] Furthermore, in the compliance assessment module: by comparing the generated text with the structured representation of the rules, it is determined whether the text covers all necessary elements, and missing items and unresolved conflicts are marked.

[0028] Furthermore, in the evidence storage and output module, each generated compliance text is assigned a version number, and the corresponding rule source and processing information are recorded to achieve traceable management.

[0029] Furthermore, the conflict marker information output by the cross-jurisdictional rule mapping and conflict identification module is input to the compliance text generation module as a generation constraint. During the generation process, the compliance text generation module filters and prioritizes rule elements based on the conflict marker information, and selects rule elements that meet the preset constraint strategy for text construction.

[0030] The compliance assessment module performs rule coverage verification and conflict resolution verification on the generated results. When it detects that the preset conditions are not met, it outputs feedback information and triggers the compliance text generation module to regenerate the text.

[0031] This leads to a compliance text generation and optimization mechanism based on conflict constraints and evaluation feedback.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] 1. This invention achieves a unified representation and systematic comparison of data compliance requirements from different countries or regions by constructing a rule-structured modeling mechanism and a cross-jurisdictional rule mapping and conflict identification mechanism. Compared to the traditional method of relying on manual interpretation and comparison of each rule, this invention can decompose and reorganize key elements such as data processing subject, data type, processing method, and constraints, and automatically identify differences between rules in terms of scope of application, intensity of obligations, and responsibility requirements. This improves the efficiency and accuracy of cross-jurisdictional compliance analysis and reduces compliance risks caused by misunderstandings of rules.

[0034] 2. This invention, through the synergistic effect of a multilingual semantic conversion module and a cultural context verification module, achieves adaptive expression of compliant texts in different linguistic environments and cultural backgrounds while maintaining legal semantic consistency. This technical solution not only adjusts word order and sentence structure according to the legal text expression habits of the target jurisdiction, but also identifies and corrects expressions that do not conform to the local cultural context, thereby improving the comprehensibility and acceptability of the generated text and avoiding communication barriers or potential risks caused by language or cultural differences.

[0035] 3. This invention, by introducing compliance assessment and feedback optimization mechanisms, as well as evidence storage and version management mechanisms, achieves assessable, optimizable, and traceable management of the compliance text generation process. The system can comprehensively evaluate the generated results from multiple dimensions, such as semantic consistency, rule coverage completeness, and cultural adaptability, and automatically trigger a regeneration process when preset conditions are not met, thus forming a closed-loop optimization mechanism. Simultaneously, by recording and storing the generated results and their processing, it facilitates subsequent auditing, version comparison, and accountability, improving the standardization and reliability of enterprise data compliance management. Attached Figure Description

[0036] Fig. 1 This is a system block diagram of the present invention;

[0037] Fig. 2 This is a diagram illustrating the structured representation of the rules in this invention. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this invention clearer, the following description is provided in conjunction with embodiments and appendices. Figs. 1-2 The present invention will be further described in detail below. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0039] Example 1: This example provides a cross-jurisdictional data compliance discourse intelligent governance platform for Chinese enterprises going global. This platform primarily addresses the difficulties faced by Chinese enterprises entering different national and regional markets due to differences in legal rules, language expression, and cultural context, including challenges in drafting data compliance texts, inconsistent compliance assessments, and untimely risk identification. This platform is not a simple text translation tool, nor is it a rule retrieval system within a single jurisdiction. Instead, it integrates data collection, legal rule analysis, cross-jurisdictional rule mapping, multilingual semantic conversion, cultural context verification, compliance text generation, result evaluation, and end-to-end evidence preservation into a unified platform, forming a continuously iterative and optimized intelligent governance mechanism.

[0040] In practical deployment, the platform can be installed on the data compliance management server at the company headquarters or deployed in the company's cloud-based compliance management environment. The platform is intended for users including corporate legal personnel, data compliance specialists, overseas business managers, external legal counsel, and internal auditors. When the platform is running, the data acquisition and preprocessing module first receives input data. This input data mainly includes three categories: the first category consists of currently effective data compliance legal texts, regulatory guidelines, enforcement case summaries, and regulatory announcements from different countries or regions; the second category consists of existing internal compliance documents such as data processing policies, privacy policies, user agreements, employee data management standards, supplier data sharing agreements, and cross-border transmission approval documents; the third category consists of pre-accumulated multilingual corpora, including legal expressions, regulatory statements, and compliance text templates in Chinese, English, and languages ​​commonly used in the countries or regions the company intends to operate in. To ensure the reliability of subsequent processing results, the data acquisition and preprocessing module does not directly send the raw data into the subsequent processing flow, but instead performs standardized processing first.

[0041] Specifically, the data acquisition and preprocessing module first converts the format of data from different sources. Content exported from PDFs, Word documents, web page texts, tables, and structured databases is uniformly converted into text and field formats recognizable by the platform. Legal texts with chapter numbers, clause numbers, and appendix descriptions retain their hierarchical structure. Documents from internal company regulations containing information such as approval dates, version numbers, and applicable departments have their corresponding metadata extracted. After format unification, this module further performs sentence segmentation, word segmentation, named entity recognition, clause boundary recognition, keyword extraction, and field annotation. For example, for a legal clause concerning "processing of sensitive personal information," the system automatically identifies the data processing subject, data type, processing behavior, processing purpose, restrictions, consent requirements, retention period, and liability for violations, and breaks this content down into structured fields. Simultaneously, the data acquisition and preprocessing module also establishes multi-level indexes such as clause number, clause category, source legal domain, effective status, language type, and semantic tags, enabling subsequent rule modeling and comparison to no longer rely on the original full-text natural language, but rather to perform more efficient and accurate processing based on structured data.

[0042] After data preprocessing, the regulatory parsing and rule modeling module begins its work. This module transforms the loosely structured natural language expressions in the original legal texts and corporate documents into a rule-based expression that can be uniformly processed by the system. The module first reads the preprocessed legal text and corporate compliance documents, then parses the text according to a pre-defined rule extraction template, breaking down each rule into multiple core elements. These typically include: the data processing entity, the data category involved, the processing purpose, the processing method, the accompanying preconditions, restrictions, notification obligations, or exceptions, and the liability for violations. For multiple obligations or exceptions appearing in the same legal clause, the system further breaks them down into multiple sub-rules to avoid treating complex and logically overlapping clauses as a single, coarse whole. After extraction, the regulatory parsing and rule modeling module saves the rules as unified field objects, forming a rule set. This allows rules from different jurisdictions, languages, and sources to be stored and accessed within the same representation framework.

[0043] After rule modeling is completed, the cross-jurisdictional rule mapping and conflict identification module begins to play a crucial role. One of the core challenges faced by Chinese companies going global lies in the fact that different countries and regions may have different, even conflicting, requirements regarding the same data processing matters. For example, one jurisdiction emphasizes explicit user consent, while another allows for non-exclusive consent in certain legally obligated scenarios; one jurisdiction classifies biometric data as highly sensitive information, while another uses a different classification logic; some jurisdictions require local data storage, while others focus more on cross-border transmission assessment and safeguards. The cross-jurisdictional rule mapping and conflict identification module does not simply display these rules side by side, but rather performs element-level comparisons of corresponding rules under different jurisdictions. The comparison process not only focuses on whether the wording is consistent, but also on whether there are differences in the applicable objects, scope of application, intensity of obligations, exceptions, and methods of liability.

[0044] In its implementation, this module first searches for rule items with consistent themes or similar processing scenarios across different legal jurisdictions, based on the field-based rules output by the rule modeling module. Examples include "obligation to inform users of personal information collection," "conditions for cross-border data transmission," "disclosure requirements for third-party sharing," and "requirements for the protection of minors' data." For each found rule, the system checks whether its data type definition, processing purpose constraints, processing method requirements, authorization basis, retention period, audit requirements, and liability for violations are consistent. If significant differences exist in these fields, the system marks them as conflicting items. For conflicting items, the system further provides a conflict type description, such as "conflict in processing scope," "conflict in data classification," "conflict in the intensity of obligations," or "conflict in the way liability is assumed." During this process, the platform not only generates a list of conflicting rules but also retains the source clause, clause number, applicable legal jurisdiction, conflict description, and suggested processing level for each conflict, providing a basis for subsequent compliance text generation and risk warnings.

[0045] After identifying rule conflicts, the multilingual semantic conversion module is responsible for converting the company's existing source language compliance text into a semantically accurate expression acceptable to the target legal jurisdiction. The core task of this module is not ordinary translation, but rather cross-language reconstruction while maintaining "legal semantic consistency." Often, the company's original texts are formed within the Chinese local compliance context, and their wording, order of obligations, disclaimer structures, and risk warning conventions may not be suitable for overseas target markets. Therefore, the multilingual semantic conversion module first reads the company's text to be processed and the corresponding structured representation of the rules, then combines this with commonly used wording in the target legal jurisdiction's legal texts to establish a standard expression template. During the conversion process, the module prioritizes maintaining the legal meaning of the clauses; for example, it must not weaken "shall obtain separate consent" to "suggest obtaining consent," nor should it misrepresent "has the right to delete" as "may consider deleting." Simultaneously, the module adjusts the word order, sentence structure, logical connectors, and clause structure according to the legal text expression habits of the target language, ensuring that the final text maintains both legal semantic consistency and conforms to the legal text writing norms of the target language.

[0046] After multilingual semantic conversion, the cultural context verification module further checks the cultural compatibility of the output text. Even if legally equivalent, the acceptance of a text in some countries or regions may be influenced by cultural expression. For example, some regions prefer direct, explicit, and obligation-oriented expressions, while others emphasize transparency, rights disclosure, and procedural fairness; certain words that are standard regulatory terms in one country may be misinterpreted in another due to historical, religious, or social contexts. To address this, the cultural context verification module has established a cultural context rule base, which includes common risky expressions, taboo words, unsuitable word structures, and more appropriate alternative expression templates for the target region. The module examines the multilingual semantically converted text sentence by sentence. If it finds content that clearly does not conform to the expression habits of the target region, it automatically marks it and provides modification suggestions. For content that can be automatically corrected, the system will call replacement or reconstruction rules for adjustment; for content with high risk and for which the optimal expression cannot be automatically determined, it prompts legal personnel for manual confirmation. Through this step, the platform avoids the problem of "being basically correct in law, but easily causing misunderstanding or aversion in the cultural context," making the final text more easily accepted by local users, regulatory agencies, and partners.

[0047] After completing cultural verification, the compliance text generation module begins to integrate and process various types of information, outputting compliance text under the target legal jurisdiction. This module does not directly concatenate legal provisions; instead, it systematically generates the target text based on the previously completed rule modeling, conflict identification, semantic conversion, and cultural verification results. Specifically, this module first reads the core business information from the company's existing compliance texts, then reads the set of rules that must be met in the current business scenario, and, combined with cross-jurisdictional rule conflict results, prioritizes the rules that should be applied first. For situations where requirements differ across jurisdictions, the system prioritizes the stricter rules that cover more comprehensive risks to avoid significant gaps in the generated text under any particular jurisdiction. For clauses identified as conflicting but with reconcilable solutions, the system uses layered explanations or supplementary conditions in the text to meet higher requirements without altering the original business logic. For example, in cross-border transmission scenarios, the system can simultaneously add "processing purpose explanation," "transmission basis explanation," "recipient protection measures explanation," "user rights explanation," and "regulatory inquiry channel explanation" to the same compliance text to ensure a complete text structure and logical coherence.

[0048] The compliance text generation module does not output a single, fixed text, but rather multiple candidate versions. For example, in the same business scenario, the system can generate a "regulatory robust version," a "user-friendly version," and a "generally balanced version." The regulatory robust version emphasizes covering stricter rules and more conservative wording, suitable for initial entry into markets with regulatory uncertainty. The user-friendly version optimizes the reading experience and rights explanations without compromising compliance requirements, making it suitable for public display to consumers. The generally balanced version strikes a balance between compliance rigor and readability. Corporate legal personnel can directly select from the candidate versions based on actual business needs, or fine-tune them with the platform's assistance.

[0049] After text generation, the compliance assessment module performs multi-dimensional checks on the results. This module aims to prevent the system from simply "generating text" and ending the process; instead, it ensures thorough verification of the results. The compliance assessment module evaluates at least three aspects: First, it checks the consistency between the generated text and the target semantics, preventing substantive deviations during semantic conversion and cultural adjustments. Second, it checks the text's adaptation to the cultural context, confirming the absence of content that clearly contradicts local expression habits. Third, it checks the generated text's coverage of the rule set, confirming that all necessary elements are reflected, conflicts are resolved, and key obligations are not omitted. Upon completion of the assessment, the system outputs a comprehensive evaluation result, specifically listing which paragraphs passed the check, which paragraphs are missing, which clauses require supplementary explanation, and which expressions are still recommended for replacement. For cases where the evaluation results are unsatisfactory, the compliance assessment module does not merely issue warnings but feeds the results back to the compliance text generation module, triggering a new round of generation and correction. This ensures that the platform's output continuously approaches a more optimal level of compliance expression under the target jurisdiction.

[0050] After the entire process is completed, the evidence storage and output module performs version management and process logging on the final text. This module first assigns a version number to each generated compliance text and saves the corresponding generation time, source rules, applicable legal jurisdiction, applicable business scenario, key rule items involved in the processing, and major modification nodes during the system's generation process. Subsequently, the system calculates a unique identifier for the final output text and stores this identifier along with the rule source and processing logs. This way, when enterprises subsequently face regulatory inspections, internal audits, cross-departmental accountability, or version comparison requirements, they can clearly trace which legal rules a particular version of the compliance text was based on, when it was formed, and through which conflict resolution and contextual correction steps. For deployment scenarios requiring high-reliability management, this module can also record the text and summary information of key versions in an immutable manner to improve the platform's overall auditability and evidence preservation capabilities.

[0051] In this embodiment, scenario-based applications can be further demonstrated through the application decision support module. This module outputs customized compliance recommendations for different business types. For example, when a company engages in cross-border e-commerce, the platform will pay special attention to user personal information collection and notification, payment information processing, order fulfillment data sharing, after-sales service record retention, and data transmission risks in the international logistics chain; when a company engages in overseas sales of new energy vehicles, the platform will pay additional attention to vehicle networking data, location information, driving behavior data, after-sales remote diagnostic data, and data sharing obligations of overseas local service partners; when a company conducts overseas recruitment and localization, the platform will focus on checking the scope of employee information collection, background check boundaries, attendance data retention, and the legality of information flow in cross-border human resource management. After reading the compliance assessment results, the application decision support module can generate output results of different granularities, including concise risk warnings, detailed compliance recommendations, clause revision suggestions, jurisdictional difference reports, and lists awaiting manual review, thereby helping companies quickly make implementation decisions in different scenarios.

[0052] For example, in a specific application scenario, a Chinese-funded cross-border e-commerce company plans to enter multiple markets in the EU and Southeast Asia, and needs to update its official website's privacy policy, APP user agreement, and third-party advertising cooperation data disclosure terms. The company first imports its existing Chinese version of the privacy policy, historical English versions, and regulatory texts for the regions it intends to enter into the platform. After the data collection and preprocessing module cleans and fields the raw documents, the regulatory analysis and rule modeling module extracts rule content related to user registration, order processing, advertising, after-sales communication, cross-border logistics, and customer service recordings. Subsequently, the cross-jurisdictional rule mapping and conflict identification module identifies that EU jurisdictions have stricter requirements for basic user authorization statements and notification of user deletion rights, while some Southeast Asian regions have higher requirements for the explanation methods used in cross-border transmission and the comprehensibility of local languages. Based on this, the multilingual semantic conversion module converts the company's original Chinese text into target language versions for different jurisdictions, adjusting the word order and sentence structure according to the common legal expressions used in the target markets. The cultural context verification module further discovers that some expressions with a strong management flavor in the company's original text are not suitable for direct use in European consumer scenarios, and therefore automatically adjusts them to expressions that emphasize transparency and users' right to know. The compliance text generation module ultimately produces multiple versions of the text for the official website, app, and cooperation disclosure terms. The compliance assessment module checks each version and points out that one version is incomplete in its disclosure of the scope of third-party sharing. The system then returns the missing text and regenerates the supplementary paragraphs. Finally, the platform outputs official text versions suitable for different jurisdictions and usage scenarios, while the evidence preservation and output module archives and records the process. Through this complete process, companies can generate structurally complete, clearly expressed, and jurisdiction-appropriate data compliance texts in a short time without relying on translation agencies, external lawyers, or multiple rounds of internal manual editing.

[0053] Therefore, the platform described in this embodiment is not a single-point tool, but an integrated intelligent governance system built around "rule extraction, cross-jurisdictional comparison, language conversion, cultural adaptation, text generation, evaluation feedback, and full-process traceability." Its technical effects are: it can unify the scattered, complex, and differently expressed data compliance requirements under different jurisdictions into a structured processing framework; it can automatically identify cross-jurisdictional rule differences and form an operable conflict list; it can complete the expression reconstruction for the target language and target cultural environment while maintaining consistency in legal meaning; it can continuously optimize the generated results through an evaluation feedback mechanism, improving text coverage, accuracy, and adaptability; and it can enhance the traceability and auditability of enterprise compliance work through version management and evidence storage mechanisms. Based on the above implementation methods, those skilled in the art will understand that this invention is not only applicable to scenarios such as cross-border e-commerce, new energy vehicles, internet platforms, multinational manufacturing, and overseas recruitment, but can also be extended to other business areas involving cross-border data processing, cross-jurisdictional text compliance, and multilingual compliance governance.

[0054] Example 2: This example provides a cross-jurisdictional data compliance discourse intelligent governance platform for Chinese enterprises going global. It enables the automatic generation, conflict resolution, and dynamic optimization of data compliance texts in multi-jurisdictional regulatory environments. The platform forms a closed-loop processing mechanism through modules such as data collection, rule modeling, conflict identification, multilingual conversion, cultural verification, text generation, evaluation feedback, and evidence output.

[0055] First, multi-source data is acquired through the data acquisition and preprocessing module, and the original dataset is defined as follows: ; in, Represents the original data set. Indicates the first One piece of raw text data, The total number of data points indicates that the original data is preprocessed. The data preprocessing function is defined as follows: , thus obtaining a structured data set: ; in, This represents the processed data set. Indicates the first The semantic feature vector corresponding to each data point This refers to preprocessing functions, including word segmentation, named entity recognition, field extraction, and normalization encoding. , For the feature dimension, all components are dimensionless normalized values.

[0056] In the regulatory analysis and rule modeling module, rules are extracted from structured data to construct a rule set: ; in, Represents a set of rules. Indicates the first Rule 1 Indicates the number of rules.

[0057] Each rule is represented as: ; in, Indicates the main code of the data processing entity. Indicates data type encoding, Indicates the processing destination encoding, Indicates the processing method encoding. Represents the constraint vector (already normalized). This represents the level of legal liability, with values ​​ranging from [0,1]. A larger value indicates a stricter constraint. For ease of calculation, the rules are mapped to a vector form: ; in, Represents a regular vector. This is the encoding vectorization function, used to map discrete codes to continuous vector representations, where all vectors are dimensionless normalized quantities.

[0058] In the cross-jurisdictional rule mapping and conflict identification module, rules are compared pairwise, and the conflict degree is defined as: ; in, These represent two rules respectively. Indicates the number of dimensions of the rule elements. For the first Dimensional weights, satisfying , Representation rule number One element, This is a difference function; it takes the value 1 when the elements are inconsistent, and 0 otherwise. Indicates the degree of rule conflict.

[0059] When the following conditions are met: ; in, When the threshold for conflict is reached, the rule is determined to be conflicting, and a conflict set is constructed: ; in, This represents the set of conflict rules.

[0060] In the multilingual semantic conversion module, the current semantic vector to be processed is selected as... Transformation is performed using a semantic mapping matrix: ; in, Represents the semantic vector of the target language. This is a semantic mapping matrix, and all variables are dimensionless.

[0061] In the cultural context validation module, the cultural adaptation function is defined as follows: ; in, Indicates cultural compatibility. Represents semantic vector components, This is the cultural weighting coefficient. This is a culture adaptation function with an output range of [0,1].

[0062] When the following conditions are met: ; in, To adapt to the threshold, the semantics are corrected: ; in, This is the corrected semantic vector. The correction value is generated by the cultural context verification module based on the low-fit component.

[0063] In the compliance text generation module, semantic information is fused with the rule set to generate a text representation vector: ; in, To generate text vectors, For semantic fusion coefficients, This refers to the rule weight. The rule weight is defined as follows: ; in, Indicates the strength of the rule constraint. The conflict adaptation factor is determined by the conflict set. The calculation is as follows. In the compliance assessment module, the comprehensive assessment function is defined as: ; in, This is a comprehensive score.

[0064] Each sub-indicator is defined as follows: ; Indicates semantic consistency, where The cosine similarity function; ; Indicates cultural compatibility; ; This represents the rule coverage, where, To generate the subset of rules that are actually covered in the text, Indicates the size of the set.

[0065] When the following conditions are met: ; in, When evaluating the threshold, the error is defined as: ; And update the rule weights: ; in, To update the coefficients.

[0066] Finally, in the evidence storage and output module, the generated vector is decoded into the final text. And calculate the hash value: ; in, It serves as a unique identifier and is stored together with the rule source and processing records to achieve traceable management.

[0067] Through the above implementation methods, unified modeling of cross-jurisdictional data compliance rules, automatic conflict identification, consistent multilingual conversion, and cultural adaptation are achieved. The generated results are continuously optimized through an evaluation and feedback mechanism, thereby constructing a complete intelligent compliance governance system.

[0068] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, but the present invention is not limited to these embodiments. Equivalent modifications made by those skilled in the art without departing from the principles of the present invention should fall within the protection scope of the present invention.

[0069] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A cross-jurisdictional data compliance discourse intelligent governance platform for Chinese enterprises going global, characterized by: include: The system includes modules for data acquisition and preprocessing, legal analysis and rule modeling, cross-jurisdictional rule mapping and conflict identification, multilingual semantic conversion, cultural context verification, compliance text generation, compliance assessment, and evidence storage and output. The data acquisition and preprocessing module is used to collect data compliance legal texts, multilingual compliance corpora, and corporate compliance data from different legal jurisdictions, and to perform word segmentation, entity recognition, and structured encoding on the data. The regulatory analysis and rule modeling module is used to extract information on the data processing subject, data type, processing purpose, processing method, constraints, and legal liability from legal texts, and to construct a structured representation of the rules. The cross-jurisdictional rule mapping and conflict identification module is used to perform element-level matching of the structured representation of rules in different jurisdictions, identify differences in the scope of application, constraints and liability requirements of rules, and output conflict marking information. The multilingual semantic conversion module is used to convert source language compliant text into target language text based on the rule-based structured representation, while maintaining legal semantic consistency during the conversion process; The cultural context verification module is used to perform context adaptation detection on the converted text and replace or reconstruct content that does not conform to the expression habits of the target region. The compliance text generation module is used to generate compliance text for the target jurisdiction based on the rule structure representation, conflict marker information, and context verification results. The compliance assessment module is used to evaluate the generated text in terms of legal consistency, semantic integrity and contextual suitability, and output the assessment results. The evidence storage and output module is used to record the version of the generated compliance text and output the compliance text and risk warning information.

2. The platform according to claim 1, characterized in that, The data acquisition and preprocessing module is also used to: perform unified format conversion on data from different sources, and establish a multi-level index structure that includes clause number, clause category and semantic tags.

3. The platform according to claim 1, characterized in that, In the regulatory analysis and rule modeling module, the structured representation of the rules includes at least six elements: data processing subject, data type, processing purpose, processing method, constraints, and legal responsibility, and is stored in a field-based manner.

4. The platform according to claim 1, characterized in that, In the cross-jurisdictional rule mapping and conflict identification module: by matching each element in the structured representation of rules in different jurisdictions item by item, at least one of the following conflict types is identified: Conflicts in the scope of data processing, the definition of data categories, the intensity of compliance obligations, or the manner of assuming responsibility.

5. The platform according to claim 1, characterized in that, In the multilingual semantic conversion module, each element in the rule-structured representation is mapped to a standard expression template in the target language, and the word order and sentence structure are adjusted according to the legal text expression habits of the target jurisdiction.

6. The platform according to claim 1, characterized in that, In the cultural context verification module: the text is detected based on a preset cultural context rule base. When content that is inconsistent with the cultural expression of the target region is detected, the corresponding expression is replaced or reconstructed.

7. The platform according to claim 1, characterized in that, In the compliance text generation module: based on the structured representation of rules and conflict marker information, rules from different jurisdictions are integrated, and rules with stricter constraints are selected first to generate compliance text.

8. The platform according to claim 1, characterized in that, In the compliance assessment module: by comparing the generated text with the structured representation of the rules, it is determined whether the text covers all necessary elements, and missing items and unresolved conflicts are marked.

9. The platform according to claim 1, characterized in that, In the evidence storage and output module, each generated compliance text is assigned a version number, and the corresponding rule source and processing information are recorded to achieve traceable management.

10. The platform according to claim 1, characterized in that, The conflict marker information output by the cross-jurisdictional rule mapping and conflict identification module is input to the compliance text generation module as a generation constraint. During the generation process, the compliance text generation module filters and prioritizes rule elements based on the conflict marker information, and selects rule elements that meet the preset constraint strategy for text construction. The compliance assessment module performs rule coverage verification and conflict resolution verification on the generated results. When it detects that the preset conditions are not met, it outputs feedback information and triggers the compliance text generation module to regenerate, thereby forming a compliance text generation and optimization mechanism based on conflict constraints and assessment feedback.

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