Cross-border salary intelligent processing method and device based on dynamic rule adaptation

Abstract

The application discloses a cross-border salary intelligent processing method and device based on dynamic rule adaptation, comprising: obtaining salary correlation data and jurisdiction information of a target employee; according to the jurisdiction information, calling a dynamically updated multi-jurisdiction tax rule set to perform taxable subject analysis and tax system matching on the salary correlation data, and generating tax system matching results corresponding to each jurisdiction after conflict resolution is performed on rules belonging to different legal systems; performing cross-jurisdiction splitting calculation on the salary correlation data according to a preset compliance constraint condition, and generating a salary payment scheme and a payment instruction meeting the requirements of withholding and payment of each jurisdiction; performing consistency verification on the payment instruction based on the salary correlation data, performing cross-border compliance verification according to the consistency verification result, and outputting a final payment instruction after passing the cross-border compliance verification. The application can realize full-process compliance management and control of cross-border salary from tax system adaptation to payment, and effectively improve the accuracy and efficiency of cross-border salary processing.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to a cross-border payroll intelligent processing method and apparatus based on dynamic rule adaptation. Background Technology

[0002] As global economic integration deepens, multinational corporations continue to expand their global staffing and cross-border payroll business. Cross-border payroll payments must simultaneously meet multiple compliance requirements in various jurisdictions, including personal income tax collection, social insurance payments, foreign exchange controls, and cross-border payment supervision. This increases the complexity of the business and the difficulty of compliance management, placing higher demands on the accuracy, compliance, and efficiency of cross-border payroll processing.

[0003] Currently, cross-border payroll processing generally adopts a pre-set static tax rule processing model. Due to significant differences in tax rules across different jurisdictions, and the varying standards for defining taxable items and applying tax calculation logic across different legal systems, static rules struggle to accurately adapt to the tax rules of multiple jurisdictions, easily leading to rule conflicts. Furthermore, the methods for splitting and calculating cross-jurisdictional payroll and verifying payment compliance are rather crude, resulting in high accounting deviation rates and difficulty in simultaneously addressing the compliance requirements of multiple jurisdictions. This leads to low efficiency and significant compliance risks in cross-border payroll processing. In summary, existing technologies are insufficient for accurate adaptation to multiple jurisdictional tax systems and efficient compliant processing of cross-border payroll, failing to adequately meet the growing cross-border payroll management needs of multinational corporations.

[0004] The preceding description is intended to provide general background information and does not necessarily constitute prior art. Summary of the Invention

[0005] This application provides a method and apparatus for intelligent cross-border payroll processing based on dynamic rule adaptation, which can realize full-process compliance control of cross-border payroll from tax system adaptation to payment, and effectively improve the accuracy and efficiency of cross-border payroll processing.

[0006] In a first aspect, embodiments of this application provide a cross-border payroll intelligent processing method based on dynamic rule adaptation, including: Obtain the target employee's salary-related data and corresponding jurisdiction information; Based on the judicial jurisdiction information, the dynamically updated multi-jurisdictional tax rule set is invoked to perform taxable item parsing and tax system matching on the salary-related data. After conflict resolution of rules belonging to different legal systems, the tax system matching results corresponding to each judicial jurisdiction are generated. Based on the tax system matching results corresponding to each jurisdiction, the salary-related data is split and calculated across jurisdictions according to preset compliance constraints to generate at least one salary payment scheme that meets the withholding and payment requirements of each jurisdiction, and a corresponding payment instruction is generated according to the salary payment scheme. The payment instruction is validated based on the salary-related data. Cross-border compliance verification is performed based on the generated validation results. The final payment instruction is output after passing the cross-border compliance verification.

[0007] Furthermore, in some embodiments of this application, the construction of a reinforcement learning-driven multi-dimensional optimization model, aimed at reducing the overall tax cost of the target employee, and the calculation of the proportional combination of the salary-related data across multiple jurisdictions in conjunction with exchange rate data, includes: Construct a state space, which includes the current tax rate, real-time exchange rate, and compliance risk score for each jurisdiction; Construct an action space, which includes the splitting ratio combination of the salary-related data across multiple jurisdictions; The splitting ratio combination for each iteration is evaluated based on the reward function, which is negatively correlated with the overall tax cost of the target employee. This drives the multi-dimensional optimization model to converge towards reducing the overall tax cost, and outputs the optimal splitting ratio combination. Furthermore, in some embodiments of this application, the step of matching the payment element information with a preset compliance rule base to determine whether the payment instruction has compliance risks includes: Semantic recognition is performed on the payment purpose field in the payment element information to extract purpose keywords; The usage keywords are compared with a preset risk keyword library, which includes risk terms associated with commissions and consulting fees. When the stated purpose keyword matches any item in the risk keyword library, the payment instruction is marked as pending review, and manual review or automatic interception is triggered.

[0008] Furthermore, in some embodiments of this application, the step of performing simulated audit verification on the payment instruction includes: Construct a virtual audit environment that simulates the regulatory review process in one or more jurisdictions; In the virtual audit environment, a compliance simulation is performed on the payment instruction and the corresponding salary payment scheme to generate a simulated review result; If the simulated audit results meet the regulatory requirements of the jurisdiction, the payment instruction is confirmed to have passed the simulated audit verification.

[0009] Furthermore, in some embodiments of this application, the method further includes: Based on the cross-border compliance verification results, a compliance report corresponding to the target employee and the jurisdiction is generated. The compliance report includes a summary of the payroll payment scheme, a compliance description of each jurisdiction, and a mark of potential risk points.

[0010] Secondly, embodiments of this application provide a cross-border payroll intelligent processing device based on dynamic rule adaptation, comprising: The data acquisition module is used to acquire the target employee's salary-related data and corresponding jurisdiction information; The rule adaptation module is used to call the dynamically updated multi-jurisdictional tax rule set to perform taxable item parsing and tax system matching on the salary-related data based on the judicial jurisdiction information, and generate tax system matching results corresponding to each judicial jurisdiction after resolving conflicts between rules belonging to different legal systems. The splitting calculation module is used to perform cross-jurisdictional splitting calculation on the salary-related data based on the tax system matching results corresponding to each judicial jurisdiction and according to preset compliance constraints, to generate at least one salary payment scheme that meets the withholding and payment requirements of each judicial jurisdiction, and to generate corresponding payment instructions based on the salary payment scheme. The compliance verification module is used to perform consistency verification on the payment instruction based on the salary-related data, perform cross-border compliance verification based on the generated consistency verification result, and output the final payment instruction after passing the cross-border compliance verification.

[0011] This application provides a method and apparatus for intelligent cross-border payroll processing based on dynamic rule adaptation. First, by acquiring the target employee's payroll-related data and corresponding jurisdictional information, the regulatory scope of multiple jurisdictions required for payroll processing can be accurately identified. Then, a dynamically updated multi-jurisdictional tax rule set is invoked to perform taxable item parsing and tax system matching, resolving conflicts between rules belonging to different legal systems. This effectively mitigates rule application conflicts arising from differences in tax system rules and legal systems across multiple jurisdictions, ensuring the accuracy and compliance of tax system matching results in each jurisdiction. Next, based on the tax system matching results and according to preset compliance constraints, cross-jurisdictional payroll splitting calculations are performed to generate payroll payment schemes and corresponding payment instructions that meet the withholding and payment requirements of each jurisdiction, ensuring that payroll splitting and calculation comply with the regulatory requirements of multiple jurisdictions. Finally, by performing consistency verification and cross-border compliance verification on the payment instructions, inconsistencies between the payment instructions and the basic payroll data are identified, and compliance risks in the cross-border payment process are simultaneously recognized, ensuring the accuracy and compliance of the final output payment instructions. This method enables full-process compliance control of cross-border payroll, from tax system adaptation to payment output, effectively improving the accuracy and overall efficiency of cross-border payroll processing. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is an application environment diagram of the cross-border payroll intelligent processing method based on dynamic rule adaptation provided in the embodiments of this application; Figure 2 This is a flowchart illustrating the intelligent cross-border payroll processing method based on dynamic rule adaptation provided in an embodiment of this application. Figure 3 This is a schematic diagram of the cross-border payroll intelligent processing device based on dynamic rule adaptation provided in the embodiments of this application; Figure 4 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0014] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of systems and methods consistent with those detailed in the appended claims or with some aspects of this application.

[0015] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover descriptions such as non-exclusive inclusion, so that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, components, features, and elements with the same names in different embodiments of this application may have the same meaning or different meanings, the specific meaning of which must be determined by its interpretation in that specific embodiment or further in conjunction with the context of that specific embodiment.

[0016] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0017] In the following description, the use of suffixes such as "module," "part," or "unit" to denote elements is solely for the purpose of illustrative purposes and has no specific meaning in itself. Therefore, "module," "part," or "unit" may be used interchangeably.

[0018] To address the aforementioned technical issues, this application provides a method and apparatus for intelligent cross-border payroll processing based on dynamic rule adaptation. This method enables full-process compliance control of cross-border payroll, from tax system adaptation to payment, effectively improving the accuracy and efficiency of cross-border payroll processing.

[0019] Figure 1 This is an application environment diagram of a cross-border payroll intelligent processing method based on dynamic rule adaptation in one embodiment. (Refer to...) Figure 1 This cross-border payroll intelligent processing method based on dynamic rule adaptation is applied to a cross-border payroll intelligent processing system based on dynamic rule adaptation. The system includes a terminal 110 and a server 120. The terminal 110 and server 120 are connected via a network. The terminal 110 can be a desktop terminal or a mobile terminal; the mobile terminal can be at least one of a mobile phone, tablet, or laptop. The server 120 can be a standalone server or a server cluster consisting of multiple servers. Terminal 110 is configured to execute the aforementioned intelligent cross-border payroll processing method based on dynamic rule adaptation, including: acquiring the payroll-related data of the target employee and the corresponding jurisdiction information; based on the jurisdiction information, calling a dynamically updated multi-jurisdictional tax rule set to perform taxable item parsing and tax system matching on the payroll-related data, and generating tax system matching results for each jurisdiction after conflict resolution for rules belonging to different legal systems; based on the tax system matching results for each jurisdiction, performing cross-jurisdictional splitting calculations on the payroll-related data according to preset compliance constraints, generating at least one payroll payment scheme that meets the withholding and payment requirements of each jurisdiction, and generating corresponding payment instructions based on the payroll payment scheme; performing consistency verification on the payment instructions based on the payroll-related data, performing cross-border compliance verification based on the generated consistency verification results, and outputting the final payment instruction after passing the cross-border compliance verification.

[0020] Please see Figure 2 , Figure 2 This is a flowchart illustrating a cross-border payroll intelligent processing method based on dynamic rule adaptation, provided in an embodiment of this application. This embodiment primarily uses the application of this method to a computer device as an example for illustration. Specifically, the cross-border payroll intelligent processing method based on dynamic rule adaptation provided in this application may include the following steps: S1. Obtain the target employee's salary-related data and corresponding jurisdiction information; Specifically, for step S1, the first step is to obtain the target employee's salary-related data and corresponding jurisdiction information. The salary-related data covers all dimensions of basic information related to the employee's salary calculation and payment, including the employee's personal identity attributes, salary composition agreement, business vouchers related to salary calculation, and historical payment records, which can fully support subsequent tax determination, breakdown calculation, and data verification. The corresponding jurisdiction information corresponds to all local regulatory areas involved in this salary transaction, covering multiple dimensions such as the employee's nationality, actual work location, and location of the salary payment entity, ensuring that subsequent rule adaptation can cover all relevant regulatory bodies and avoid regulatory omissions.

[0021] S2. Based on the judicial jurisdiction information, call the dynamically updated multi-jurisdictional tax rule set to perform taxable item parsing and tax system matching on the salary-related data, and generate the tax system matching results corresponding to each judicial jurisdiction after resolving conflicts between rules belonging to different legal systems. Specifically, for step S2, firstly, based on the defined jurisdictions, the currently effective tax collection rules for the corresponding localities are retrieved from the dynamically updated multi-jurisdictional tax rules collection platform. This rules collection platform continuously synchronizes with policy changes in each locality to ensure that the retrieved rules are all currently valid versions. Then, based on the retrieved rules, the attributes of each income component in the salary-related data are determined to determine whether each income belongs to taxable income and the corresponding tax category, thus completing the taxable item analysis. Finally, the corresponding tax rates, calculation methods, and other specific rules are matched for each taxable income that has been classified, thus completing the tax system matching.

[0022] Since different jurisdictions may belong to different legal systems, there are underlying differences in the definition of taxable income and the applicable principles of tax calculation rules. This can easily lead to conflicting rules for the same income in different jurisdictions. Therefore, this step will coordinate and handle the conflict of rules under different legal systems to ensure that the tax system in each jurisdiction complies with local regulatory requirements and that there are no conflicts of rules between multiple jurisdictions. Ultimately, a corresponding tax system matching result will be generated for each jurisdiction, which will serve as the direct tax basis for subsequent salary breakdown calculations.

[0023] S3. Based on the tax system matching results of each jurisdiction, the salary-related data is split and calculated across jurisdictions according to the preset compliance constraints to generate at least one salary payment scheme that meets the withholding and payment requirements of each jurisdiction, and the corresponding payment instruction is generated according to the salary payment scheme. Specifically, for step S3, the preset compliance constraints cover the mandatory compliance bottom lines, such as the legally mandated withholding and payment obligations and reporting requirements of each jurisdiction. The splitting and calculation process is strictly carried out within these constraints to ensure that each part of the salary after splitting meets the compliance requirements of the corresponding jurisdiction. This step uses the tax system matching results of each jurisdiction as the tax base to reasonably allocate the total amount of employee salaries among multiple jurisdictions. It calculates the total salary payable, the amount of taxes to be withheld and paid, and the final net amount for each jurisdiction separately, ensuring that the splitting results fully meet the withholding and payment supervision requirements of each jurisdiction. After the calculation is completed, at least one complete salary payment plan is generated, which clearly presents the salary calculation details of each jurisdiction. Then, based on the core elements in the plan, such as the payment amount, the payee, and the payment location, a payment instruction that can be implemented is generated.

[0024] S4. Perform consistency verification on payment instructions based on salary-related data, execute cross-border compliance verification based on the generated consistency verification results, and output the final payment instructions after passing the cross-border compliance verification. Specifically, for step S4, the consistency verification uses the original payroll-related data as a benchmark to check whether the core elements in the payment instruction match the basic business data, and to identify issues such as errors in payment amount, payment recipient, and payment cycle caused by accounting deviations. Cross-border compliance verification, on the other hand, addresses the specific regulatory requirements of cross-border payment scenarios, verifying the compliance of payment behavior and preventing compliance risks in the cross-border payment process. Only payment instructions that pass both the data consistency verification and cross-border compliance verification will be output as final executable payment instructions, ensuring the accuracy of the final payroll data and the compliance of the process.

[0025] This embodiment, through a closed-loop process of multi-jurisdictional tax system matching and conflict resolution, cross-jurisdictional payroll compliance splitting, and pre-payment double verification, can adapt to the differentiated regulatory requirements of multiple jurisdictions, effectively reduce rule application conflicts and accounting deviations, and improve the compliance and overall processing efficiency of cross-border payroll processing.

[0026] Furthermore, in some embodiments, step S1, "obtaining the target employee's salary-related data and corresponding jurisdiction information," may specifically include: S11. In response to the payroll processing trigger signal, obtain the target employee's basic information, labor contract salary structure data, attendance record data, bank statement data, historical payroll payment records, and the target employee's jurisdiction information from multiple data sources respectively; Specifically, for step S11, the payroll processing trigger signal can correspond to various business scenarios such as monthly fixed salary calculation, single cross-border payroll payment, and special payroll supplementary payment, used to accurately locate target employees and initiate corresponding data retrieval. The collection process connects to multiple independent business data sources to obtain the target employee's basic information, labor contract salary structure data, attendance record data, bank statement data, historical payroll payment records, and the corresponding judicial jurisdiction information, fully covering all dimensions of original business data required for payroll calculation and jurisdiction determination.

[0027] S12. Perform standardized format processing and missing value completion processing on the data from each data source according to the preset data format specifications to generate standardized salary source data and corresponding standardized judicial jurisdiction information; Specifically, for step S12, following preset data format specifications, two types of processing are performed on all collected data: first, format unification processing, which standardizes and converts elements such as date format, currency labeling, numerical precision, and field naming to eliminate data format differences between different business systems; second, missing value completion processing, which fills in missing fields and data gaps that occur during the collection process according to preset compliance completion rules to avoid interrupting subsequent processing due to data loss. After processing, standardized salary source data and corresponding standardized judicial jurisdiction information are generated.

[0028] S13. Based on the employee identification information of the target employee, the standardized payroll data from various data sources are linked and integrated according to a unified data association primary key, and the standardized jurisdiction information is linked with the integrated payroll data to generate payroll association data and corresponding jurisdiction information. The payroll association data includes basic employee information, labor contract salary structure data, attendance record data, bank statement data, and historical payroll payment records. The basic employee information includes the employee's nationality information and length of residence in each jurisdiction. The jurisdiction information includes the jurisdiction of the target employee's nationality, the jurisdiction of the target employee's actual workplace, and the jurisdiction of the target employee's payer. Specifically, for step S13, the integration process uses the target employee's unique employee identifier as the unified data association primary key, matching and associating standardized payroll source data from different data sources to form a complete payroll data set for the employee. Then, standardized jurisdictional information is linked and bound to the integrated payroll data to clarify the full range of regulatory jurisdictions corresponding to this payroll transaction. The final generated payroll association data comprehensively covers employee basic information, labor contract salary structure data, attendance record data, bank statement data, and historical payroll payment records. The employee basic information includes the employee's nationality and length of residence in each jurisdiction. The final output jurisdictional information includes three dimensions: the jurisdiction of the employee's nationality, the jurisdiction of the actual workplace, and the jurisdiction of the payer's location.

[0029] This embodiment, through multi-data source collection, standardized cleaning, and correlation integration, can output salary-related data and judicial jurisdiction information with uniform format and accurate correspondence, providing a standardized and reliable data foundation for subsequent salary calculation and compliance verification, and effectively avoiding calculation deviations caused by chaotic data formats, missing information, or mismatched personnel.

[0030] Furthermore, in some embodiments, the salary-related data is multi-currency salary data, and the method may further include: Before splitting the calculation steps, the multi-currency salary data is normalized according to a unified exchange rate benchmark to generate unified salary data denominated in the benchmark currency.

[0031] Specifically, before proceeding to the splitting and calculation stage, the currency of each salary item in the salary-related data is identified and statistically analyzed. When the data contains salary items denominated in two or more different currencies, it is determined to be multi-currency salary data, triggering the subsequent currency normalization process to ensure a unified numerical standard for subsequent calculations. For multi-currency conversion scenarios, a unified exchange rate benchmark rule is pre-set, clearly defining the benchmark currency type and exchange rate standard. This serves as the unified basis for all currency conversions, avoiding calculation deviations caused by inconsistent exchange rate standards. The benchmark currency can be selected according to business accounting needs, and the exchange rate can correspond to compliant standards such as officially published real-time exchange rates and fixed accounting cycle exchange rates, ensuring a unified and compliant conversion basis. Using the established unified exchange rate benchmark as the conversion basis, all salary items in the salary-related data that are not denominated in the benchmark currency are converted one by one to the amount denominated in the benchmark currency according to the corresponding exchange rate. During the conversion process, a unified numerical precision standard is maintained to ensure the accuracy of the conversion process and avoid calculation errors caused by differences in precision. After completing the conversion of all currencies, all salary items denominated in the base currency are integrated to form a complete unified salary data in a single currency, which serves as the unified data input for subsequent cross-jurisdictional split calculations.

[0032] This embodiment eliminates pricing differences between different currencies and establishes a unified accounting standard by performing unified currency normalization processing on multi-currency salary data before splitting and calculating. This effectively avoids calculation deviations caused by currency mixing, ensures the accuracy of cross-jurisdictional salary splitting and calculation, and adapts to the processing needs of multi-currency cross-border salaries.

[0033] Furthermore, in some embodiments, the dynamically updated multi-jurisdictional tax rule set covers the personal income tax calculation rules, social insurance payment rules, double taxation agreement provisions, and foreign exchange control regulations of each jurisdiction.

[0034] Furthermore, in some embodiments, step S2, "based on jurisdictional information, calling a dynamically updated multi-jurisdictional tax rule set to perform taxable item parsing and tax system matching on the payroll-related data," may specifically include: S21. Obtain multimodal tax rule source data corresponding to each jurisdiction; Specifically, for step S21, based on the target jurisdiction, all currently valid original tax-related rules and regulations for each jurisdiction are collected from compliant sources such as official channels of the corresponding local tax authorities and authoritative legal databases. These original documents exist in multiple formats, covering different forms such as formal legal texts, official policy announcements, and tax administration regulations, i.e., multimodal rule source data. This ensures that the rules are authoritative and traceable, and that the content is comprehensive and without omissions, providing complete original evidence for subsequent rule analysis.

[0035] S22. Perform structured parsing on the multimodal tax rule source data to extract the taxable rule elements of each jurisdiction. The taxable rule elements include tax type, tax rate range, taxpayer identification rules and tax treaty clauses. Specifically, for step S22, content parsing and information extraction are performed on the multimodal rule source data in different formats. Standardized taxable rule elements are identified and extracted from the original legal provisions, policy announcements, and other content. These elements include four categories: first, tax type, clarifying the types of taxes and fees corresponding to the rules and distinguishing different salary-related tax categories; second, tax rate range, extracting numerical rules such as tax rate brackets, tax bracket ranges, and tax rates for each tax type; third, rules for defining taxpayers, clarifying the criteria for determining taxpayers, such as the criteria for distinguishing between resident and non-resident taxpayers and the threshold for determining length of residence; and fourth, tax treaty clauses, extracting treaty rules for cross-border tax matters, such as clauses related to the division of taxation rights and foreign tax credits. Through structured parsing, the scattered rules expressed in natural language are transformed into structured rule data in a unified format that can be read by machines.

[0036] S23. Based on the taxable rule elements obtained from the analysis, construct a dynamic tax rule map, and update the dynamic tax rule map in real time or periodically according to the preset update strategy; Specifically, for step S23, the extracted taxable rule elements from each jurisdiction are first organized according to logical dimensions such as tax type, applicable subject, and applicable scenario to construct a structured dynamic tax rule graph. This enables the categorized storage, associated retrieval, and precise matching and retrieval of rule elements. Simultaneously, an update strategy is pre-set, using two modes to dynamically maintain the rule graph: a real-time update mode for rapid response to adjustments in local tax policies, timely rule re-analysis, and graph iteration; and a periodic update mode for reviewing and calibrating all rules at fixed intervals, identifying invalid rules, and completing content supplementation. These two update modes work together to ensure that the rule graph remains consistent with the current effective policies of each jurisdiction.

[0037] This embodiment, through multimodal rule source data collection, structured taxable element analysis, and the construction and maintenance of dynamic rule graphs, can transform various forms of local tax rules into a structured rule system that can be accurately invoked, while continuously ensuring the timeliness and accuracy of the rules, providing stable and reliable rule support for the tax system adaptation of cross-border payroll.

[0038] Furthermore, in some embodiments, step S22, "performing structured parsing on the multimodal tax rule source data," may specifically include: S221. Identify the data format types of multimodal tax rule source data, including text format, web page format, and structured data format; Specifically, for step S221, the format of each source data document of tax rules is identified and determined to identify its data format type. This includes three typical formats: first, text format, which refers to rule documents in the form of formal documents, such as written laws and official policy notices; second, webpage format, which refers to rule content published on official online channels, such as announcements on the official website of the competent authority or policy interpretation pages; and third, structured data format, which refers to rule parameters in a row-column or field-based format, such as tax rate bracket tables or contribution base parameter tables. After format identification, each source data document is labeled with a corresponding format tag, which serves as the basis for subsequent parsing strategy invocation.

[0039] S222. Invoke the corresponding parsing strategy for different data format types to extract taxable rule elements from each data source; Specifically, in step S222, for each rule source data with a marked format type, the corresponding parsing strategy is invoked to extract information. For text-format rule sources, a semantic parsing strategy is used to systematically analyze the legal provisions and extract taxable rule-related elements. For webpage-format rule sources, text purification is performed first to filter out irrelevant page elements before parsing and extracting rule elements. For structured data format rule sources, the corresponding parameters are directly read through field mapping to complete rule element extraction. Through adaptive parsing, it is ensured that rule content of different forms can be accurately decomposed into standardized rule elements.

[0040] S223. Perform consistency checks and conflict resolution on taxable rule elements from different data sources to generate structured rule data in a unified format; Specifically, for step S223, since the same rule item may exist in multiple different data sources, differences in expression or contradictions in content are likely to occur. Therefore, it is necessary to first perform a consistency check on all extracted taxable rule elements to verify whether the content of the same rule item matches in different data sources. For content conflicts found during the check, the conflicts are resolved according to the preset authority priority rules to determine the final valid rule content. After completing the check and resolution, all rule elements are organized into structured rule data with unified fields and standardized format to form standardized rule output.

[0041] This embodiment, through format recognition, differential parsing, and multi-source data verification and resolution, can adapt to various forms of tax rule sources, accurately extract taxable rule elements, effectively eliminate content conflicts between multi-source data, and output unified and standardized structured rule data, providing accurate and consistent rule basis for subsequent tax system matching.

[0042] Furthermore, in some embodiments, step S23, "constructing a dynamic tax rule graph based on the parsed taxable rule elements, and updating the dynamic tax rule graph in real time or periodically according to a preset update strategy," may specifically include: S231. Monitor tax rule change signals in each jurisdiction, including regulation publication timestamps and official announcement identifiers; Specifically, for step S231, the monitoring process continuously covers the official rule release channels of each jurisdiction, focusing on identifying two types of change signals. One type is the rule release timestamp, which is the release time identifier attached to official rule documents. By comparing the order of these timestamps, it is determined whether a new formal rule document has been released. The other type is the official announcement identifier, which is the exclusive official certification mark attached to policy adjustment announcements released through official channels. This is used to confirm the authority and formality of the rule source and avoid unofficial information mistakenly triggering the update process. By continuously monitoring these two types of signals, proactive and accurate perception of changes in tax rules in each jurisdiction is achieved.

[0043] S232. In response to the detection of a change signal, automatically trigger the re-parsing of the changed rule source data and the map update; Specifically, for step S232, when a valid change signal is detected, the system automatically responds and initiates a complete update process: first, it automatically captures the complete rule source data after the change to obtain the full text of the latest policy; then, according to the standardized structured parsing logic, it decomposes the new rule source data and extracts the updated taxable rule elements; finally, it synchronizes the parsed new rule elements to the dynamic tax rule graph, covering or supplementing the original corresponding rule entries, and completing the real-time iteration of the graph content.

[0044] S233. After completing the map update, mark the differences in rules before and after the update, and record the version change information; Specifically, for step S233, after the map update is completed, the system automatically compares the differences in the content of the same rule item before and after the update, and clearly marks the differences in specific items and adjustment ranges to facilitate quick identification of the core content of the rule change; at the same time, it fully records the version change information of this update, including the version number, update effective time, rule source channel, summary of change content and other core information, forming a traceable version management ledger.

[0045] This embodiment, through proactive monitoring of change signals, automated parsing and updating, difference marking, and version tracking, can respond promptly to changes in tax policies in various jurisdictions, achieve dynamic iteration of the tax rule map, ensure that the rule content is always consistent with the current effective policies in the jurisdiction, and retain a complete change traceability track, thereby improving the timeliness, accuracy, and compliance traceability of rule updates.

[0046] Furthermore, in some embodiments, step S2, "generating tax system matching results for each jurisdiction after resolving conflicts between rules belonging to different legal systems," may specifically include: S24. Identify the legal system type of each jurisdiction based on jurisdictional information; Specifically, for step S24, based on the legal system traditions and current rule-making basis of each jurisdiction, the legal system type to which each rule belongs is determined, covering multiple legal systems. Common law systems combine written rules and case law, with rule application emphasizing precedent and substantive judgment. Customary law systems rely heavily on long-standing local tax administration practices, and their rules have strong territorial specificity. After the determination is completed, the corresponding legal system attribute is labeled for the taxable rules of each jurisdiction, achieving the classification of rules by legal system dimension.

[0047] S25. For taxable rule elements belonging to different legal systems, extract the definition criteria and calculation rules for the same taxable item for each legal system separately; Specifically, for step S25, for the specific taxable item to be matched, the taxable rule elements marked with legal system attributes are extracted according to legal system category, and the complete rule content for the taxable item under different legal systems is sorted out one by one, including the definition of taxable income, the criteria for determining the tax base, the applicable rules for tax calculation, the applicable conditions for tax reduction and exemption, etc., clearly showing the rule differences of different legal systems for the same taxable matter and identifying the points of conflict.

[0048] S26. Based on the attention mechanism, determine the weight allocation of taxable rule elements of each legal system type during fusion, and perform weighted fusion of rules of different legal system types according to the weight allocation to generate tax system matching results corresponding to each jurisdiction after conflict resolution. Specifically, for step S26, an attention mechanism is introduced. Combining the current cross-border payroll scenario with multiple constraints such as the basis for determining tax obligations, bilateral tax treaty agreements, territorial tax administration priority, and income source attributes, the weight ratio of different legal system rules in the fusion process is automatically calculated. Higher weights are assigned to the core applicable territorial rules, and corresponding weights are assigned to reference and harmonized rules. Subsequently, the taxable rule elements of different legal systems are weighted and fused according to the weight allocation results to harmonize differences in rule interpretation and resolve application conflicts. Finally, a tax system matching result that meets local regulatory requirements and has no cross-jurisdictional rule application contradictions is generated for each jurisdiction.

[0049] This embodiment effectively resolves the underlying logical differences and application conflicts of tax rules under different legal systems by identifying the legal system affiliation, extracting the differences in rules between different legal systems, and performing weighted fusion processing based on an attention mechanism. This improves the accuracy and compliance of cross-jurisdictional tax system matching, ensuring that the application of tax systems in each jurisdiction not only meets the requirements of local supervision but also achieves cross-jurisdictional rule coordination and unification.

[0050] Furthermore, in some embodiments, step S3, "based on the tax system matching results corresponding to each jurisdiction, performing cross-jurisdictional splitting calculations on the salary-related data according to preset compliance constraints, generating at least one salary payment scheme that meets the withholding and payment requirements of each jurisdiction, and generating corresponding payment instructions based on the salary payment scheme," may specifically include: S31. Obtain the results of the attribution of taxable income of the target employee in each jurisdiction and the corresponding applicable tax rate information from the dynamic tax rule map; Specifically, for step S31, all valid tax rules for the corresponding jurisdiction are retrieved from the dynamic tax rule map. Combined with core information such as the target employee's nationality, length of residence in each jurisdiction, source of income, and payee, the tax attribution of each salary income in different jurisdictions is determined one by one according to the local tax administration rules and tax treaty agreements, and the tax liability of each part of the income is determined. At the same time, the applicable tax rate standard and taxation rules of the corresponding jurisdiction are matched for each taxable income that has been attributed, to ensure that the tax calculation of subsequent breakdowns fully complies with the local regulatory requirements.

[0051] S32. Construct a reinforcement learning-driven multi-dimensional optimization model with the goal of reducing the overall tax cost for target employees. Combine exchange rate data to calculate the splitting ratio combination of salary-related data across multiple jurisdictions. In each iteration, the multi-dimensional optimization model updates the splitting ratio combination for the next iteration based on the overall tax cost and compliance risk score corresponding to the current iteration's splitting ratio combination. The optimization dimensions in this step include the tax cost of each jurisdiction, exchange rate fluctuation risk, and compliance risk score of cross-border payment channels. Specifically, for step S32, a reinforcement learning-driven multi-dimensional optimization model is first built, with the core optimization objective of reducing the overall tax cost for target employees. At the same time, three optimization dimensions are included as evaluation and constraint indicators: first, the tax cost of each jurisdiction, i.e., the total amount of withheld and paid taxes in each jurisdiction under different split ratios; second, the exchange rate fluctuation risk, i.e. the cost fluctuation risk caused by exchange rate changes during the conversion and transfer of multi-currency salaries; and third, the compliance risk score of cross-border payment channels, i.e. the compliance risk level corresponding to different fund transfer paths.

[0052] S33. Based on the calculation results of the split ratio combination, generate at least one salary payment plan that meets the compliance requirements of each jurisdiction. Each salary payment plan includes the amount payable, the amount withheld and paid on behalf of others, and the amount actually paid in each jurisdiction. Specifically, for step S33, the accounting process strictly follows the withholding and payment supervision requirements of each jurisdiction, calculating the total amount of salary payable, the statutory withholding amount (including all statutory withholding items such as personal income tax and statutory social insurance) and the final amount paid in each jurisdiction. The final output of each salary payment plan fully lists the above three core amounts for each jurisdiction, which can be directly used for subsequent salary disbursement and declaration.

[0053] This embodiment, by retrieving precise local tax rules and relying on reinforcement learning models to carry out multi-dimensional splitting optimization, can reasonably optimize the splitting ratio of salaries across multiple jurisdictions while strictly meeting the compliance requirements of each jurisdiction, effectively reducing the overall tax costs for employees, while also taking into account exchange rate fluctuation risks and cross-border payment compliance risks.

[0054] Furthermore, in some embodiments, step S32, "constructing a reinforcement learning-driven multi-dimensional optimization model with the goal of reducing the overall tax cost of target employees, and combining exchange rate data to calculate the splitting ratio of salary-related data across multiple jurisdictions," may specifically include: S321. Construct a state space that includes the current tax rate, real-time exchange rate, and compliance risk score for each jurisdiction; Specifically, for step S321, the state space includes the following three types of environmental parameters: first, the current tax rate of each jurisdiction, i.e., the applicable tax rate bracket, taxation ratio, and other tax calculation rules parameters for salary income in each jurisdiction; second, the real-time exchange rate, i.e., the real-time exchange rate between different currencies involved in cross-border salary payments; and third, the compliance risk score, i.e., the quantitative score of the compliance risk level corresponding to each cross-border payment channel. All parameters are dynamically updated with the external environment to ensure the timeliness and accuracy of the model's decisions.

[0055] S322. Construct an action space, which includes the proportions and combinations of splitting salary-related data across multiple jurisdictions; Specifically, for step S322, the core component of the action space is the combination of the splitting ratio of salary-related data across multiple jurisdictions. Each complete set of allocation ratios corresponds to an executable decision action. The splitting ratios must meet the basic constraints that the allocation ratios of each jurisdiction are non-negative and the sum of the allocation ratios of all jurisdictions is 1, while also complying with the compliance requirements such as the minimum base for salary declaration in each jurisdiction.

[0056] S323. Evaluate the split ratio combination for each iteration based on the reward function. The reward function is negatively correlated with the overall tax cost of the target employee, driving the multi-dimensional optimization model to converge in the direction of reducing the overall tax cost, and outputting the optimal split ratio combination. Specifically, for step S323, the value of the reward function is negatively correlated with the overall tax cost of the target employee. That is, the lower the overall tax cost of the employee corresponding to a certain split ratio, the higher the reward value obtained by the model after executing the action. The model iterates round by round during operation. In each round, a set of split ratios is selected from the action space, and the corresponding overall tax cost is calculated based on the parameters of the current state space, thereby calculating the reward value for this action. The subsequent ratio selection strategy is then adjusted based on the reward feedback, continuously optimizing towards higher reward values. After multiple iterations, the model converges to a stable optimal strategy, ultimately outputting the corresponding optimal split ratio combination.

[0057] This embodiment builds a reinforcement learning framework that includes a state space, action space, and reward function. It can automatically conduct multiple rounds of iterative optimization with the clear goal of reducing overall tax costs, and efficiently output the optimal cross-jurisdictional salary split ratio. This not only improves the optimization accuracy of the splitting scheme, but also improves the decision-making efficiency of cross-border salary splitting.

[0058] Furthermore, in some embodiments, step S33, "generating at least one payroll payment scheme that meets the compliance requirements of each jurisdiction based on the calculation results of the split ratio combination," may specifically include: S331. Rank at least one payroll payment scheme according to preset evaluation indicators, including comprehensive tax costs, payment timeliness and compliance risk level; S332. The best-ranked salary payment plan is selected as the recommended salary payment plan.

[0059] Specifically, during the ranking process, standardized scoring rules are used to assign values ​​to three indicators for each plan, and then the comprehensive scoring results are combined to rank all plans in order of merit, forming an orderly plan tier. The comprehensive tax cost corresponds to the total amount of all withheld and paid taxes, including personal income tax and statutory social insurance contributions, that the target employee is required to pay in all relevant jurisdictions. The lower this indicator value, the more prominent the plan's tax cost advantage. The payment timeliness corresponds to the entire process time from payment initiation to actual receipt of salary funds in the employee's personal account. Affected by factors such as payment path, foreign exchange review, and local settlement cycle, the shorter this indicator time, the higher the plan's fund transfer efficiency. The compliance risk level corresponds to the stability of a plan in terms of tax declaration standards and cross-border fund transfers in compliance with local regulatory requirements. It is usually divided into different levels according to risk level; the lower the level, the more stable the plan's compliance and the lower the probability of triggering regulatory verification.

[0060] Based on the aforementioned ranking results, the top-ranked plan with the best overall performance is designated as the recommended plan and will be the primary implementation plan for payroll processing. The recommended plan will fully present core information such as the amount due, details of withholding and payment, and net payout for each jurisdiction, while also highlighting its advantages in various evaluation indicators, making it easy for staff to adopt directly. The remaining plans will be retained as alternatives and can be flexibly retrieved according to specific business needs.

[0061] This embodiment ranks payroll payment schemes based on three categories of indicators: comprehensive tax cost, payment timeliness, and compliance risk level, and outputs the optimal recommended scheme. It can select the best-performing scheme from multiple dimensions such as cost, efficiency, and compliance, reducing the workload of manual comparison and screening, and improving the efficiency of cross-border payroll scheme decision-making.

[0062] Furthermore, in some embodiments, step S4, "performing consistency verification of payment instructions based on salary-related data," may specifically include: S41. Extract labor contract salary structure data, bank transaction data, and attendance record data from salary-related data; Specifically, for step S41, the three types of data correspond to different dimensions of payroll management. The labor contract salary structure data serves as the agreed-upon basis for payroll disbursement, including salary components, amount standards for each component, disbursement cycle, and calculation rules. Bank statement data serves as the historical basis for payroll disbursement, recording the actual amount, currency, and arrival time of payments in past periods. Attendance record data serves as the business basis for current payroll calculation, including actual business information affecting payroll calculation, such as attendance days, leave duration, and overtime hours within the calculation period. By extracting these three core data types, a complete verification benchmark system can be built from the three dimensions of contractual agreements, historical execution, and current business.

[0063] S42. Align labor contract salary structure data, bank transaction data, and attendance record data across modalities using time and currency dimensions as alignment benchmarks to generate cross-modal aligned data; Specifically, for step S42, since the three types of data differ in their recording format, statistical period, and pricing currency, belonging to different modalities of business data, alignment needs to be achieved through a two-dimensional benchmark. In the time dimension, the three types of data are uniformly matched to the same payroll calculation period, ensuring that contractual standards, historical payment records, and current attendance data all correspond to the same payroll period, avoiding comparison discrepancies caused by time mismatches. In the currency dimension, various types of data priced in different currencies are uniformly converted to the same benchmark currency, eliminating inconsistencies in numerical standards caused by currency differences. After completing the two-dimensional alignment, the three types of data form cross-modal aligned data with unified standards that can be directly cross-compared.

[0064] S43. Based on the cross-modal alignment data, perform cross-consistency checks on the payment amount in the payment instruction, the agreed amount in the labor contract salary structure data, the historical payment amount in the bank transaction data, and the actual attendance information in the attendance record data, and generate consistency check results; Specifically, for step S43, the verification process uses the total payment amount and itemized amounts in the payment instruction as the verification objects, and conducts three types of cross-verification in sequence: First, it compares with the amount stipulated in the labor contract to verify whether the payment items and payment standards conform to the salary structure and calculation rules stipulated in the contract; second, it compares with historical payment amounts to verify whether the fluctuation of the current payment amount compared with the same period in history is within a reasonable range and whether there are any abnormally large deviations; third, it compares with actual attendance information to verify whether the salary adjustment items corresponding to attendance (such as deductions for absence, overtime pay, and calculation of expatriate subsidies) are accurately reflected in the payment amount. If all comparison items match, a consistency verification result is generated; if there are data differences, the corresponding difference items and deviation ranges are marked, and a verification result is generated.

[0065] This embodiment extracts multi-dimensional business benchmark data and performs multi-dimensional cross-validation after cross-modal alignment in both time and currency dimensions. This enables a comprehensive verification of the matching degree between payment instructions and basic payroll data, effectively identifying problems such as accounting deviations and amount mismatches, ensuring the numerical accuracy and business rationality of payment instructions, and reducing the error risk of cross-border payroll disbursement.

[0066] Furthermore, in some embodiments, step S4, "performing cross-border compliance verification based on the generated consistency verification result, and outputting the final payment instruction after passing the cross-border compliance verification," may specifically include: S44. Perform semantic parsing on payment instructions to extract payment element information, including payee identification, payment amount, payment purpose, and fund flow; Specifically, for step S44, the parsing process performs semantic recognition and information extraction on the complete content of the payment instruction, extracting the following four types of payment elements: First, the payee identifier, i.e., the identity information of the entity corresponding to the payee account, used to verify the compliance qualifications of the payee; second, the payment amount, i.e., the total amount and corresponding currency of this payment, used to verify whether it complies with the regulatory requirements for cross-border payment limits; third, the purpose of payment, i.e., a description of the business nature of this fund payment, used to determine whether it falls within the scope of compliant cross-border payments; and fourth, the fund flow, i.e., the complete transfer path and nodes of funds from the payer to the payee, used to verify the compliance of the payment channel. After extraction, all elements are organized into a standardized format for easy rule matching in the subsequent process.

[0067] S45. Match payment element information with a pre-set compliance rule base to determine whether the payment instruction has compliance risks. The compliance rule base includes fund compliance rules and sanctions list rules. Specifically, for step S45, a pre-defined compliance rule base includes two types of regulatory rules: one is fund compliance rules, covering regulatory norms such as compliance requirements for the use of cross-border payments, fund flow control requirements, and large transaction monitoring standards; the other is sanctions list rules, which includes authoritative global lists of sanctioned entities, used to check whether the payee is a restricted entity. The matching process compares each extracted payment element with the rules in the rule base to verify whether the payee is restricted, whether the payment purpose is compliant, whether the fund path complies with regulatory requirements, and whether the payment amount triggers a large transaction warning, ultimately determining whether the payment instruction has compliance risks.

[0068] S46. After the payment instruction passes the compliance risk assessment and the consistency verification result is passed, a simulated audit verification is performed on the payment instruction, and the payment instruction is confirmed as the final payment instruction after the simulated audit verification is passed; Specifically, for step S46, this step requires that the payment instruction simultaneously meet two conditions: first, the aforementioned compliance risk assessment result is qualified; and second, the consistency verification result of the payment instruction is passed. Once these conditions are met, a simulated audit verification process is initiated. This simulates the review standards and verification procedures of the corresponding regulatory authority for cross-border payroll payments, conducting a full-process simulation and verification of the payroll tax calculation, reporting requirements, and fund flow related to the payment instruction. If the simulated review result meets the regulatory requirements of the corresponding jurisdiction, the payment instruction is confirmed to have passed the simulated audit verification and is thus determined as a final, officially executable payment instruction.

[0069] This embodiment, through multi-level compliance verification including payment instruction semantic parsing, compliance rule base risk matching, and simulated audit verification, combined with data consistency verification results, can comprehensively identify the subject risks, usage risks, and path risks of cross-border payroll payments, proactively resolve potential regulatory audit risks, and effectively improve the compliance and approval rate of cross-border payments.

[0070] Furthermore, in some embodiments, step S45, "matching the payment element information with a preset compliance rule base to determine whether the payment instruction has compliance risks," may specifically include: S451. Perform semantic recognition on the payment purpose field in the payment element information and extract purpose keywords; Specifically, in step S451, the natural language description of the payment purpose is precisely broken down to extract core words that reflect the true nature of the funds' business. The processing involves semantic analysis of the text content of the payment purpose field, removing redundant modifying and supplementary expressions, and identifying and extracting core keywords representing the business type and nature of the funds. It also supports the recognition of diverse expression forms, including abbreviations, synonyms, and vague descriptions, avoiding missed risk assessments due to differences in expression methods. The extracted purpose keywords will serve as the core judgment object for subsequent risk comparison.

[0071] S452. Compare the usage keywords with a preset risk keyword library, which includes risk terms associated with commissions and consulting fees; Specifically, for step S452, a pre-set risk keyword database is built around high-risk illegal uses in cross-border payroll payment scenarios. It primarily includes various risk terms directly related to or associated with commissions and consulting fees. This includes core risk terms such as "commission" and "consulting fee," as well as related derivative terms and synonyms commonly used to disguise payroll, evade taxes and foreign exchange regulations, such as "service fee," "advisory fee," "intermediary fee," and "labor fee." The comparison process uses semantic matching logic, not only matching completely identical terms but also identifying synonymous and near-synonymous variations, comprehensively covering various forms of disguised risky uses.

[0072] S453. When the purpose keyword matches any item in the risk keyword library, mark the payment instruction as pending review and trigger manual review or automatic interception. Specifically, for step S453, when the purpose keyword matches any item in the risk keyword library, the system first marks the corresponding payment instruction as a "pending review" risk status, distinguishing it from normal and compliant payment instructions; then, it executes the corresponding handling according to the preset risk level rules. For high-risk items, automatic interception is triggered directly, suspending the payment process; for medium-risk items, it is pushed to the compliance position to trigger manual review. The compliance personnel verify the true business background, transaction vouchers, and tax payment status of the payment to confirm whether it belongs to illegal splitting of salary, evasion of individual income tax collection, or violation of foreign exchange control, and then decide whether to release the payment.

[0073] This embodiment, through semantic recognition and extraction of payment purposes, matching and comparison with a risk keyword database, and tiered handling after a match, can accurately identify high-risk cross-border payments disguised as commissions, consulting fees, etc., effectively preventing compliance risks such as evading salary and personal income tax collection and violating foreign exchange management regulations by changing the purpose of payment, and improving the accuracy of cross-border payment compliance screening and the efficiency of risk interception.

[0074] Furthermore, in some embodiments, step S46, "performing simulated audit verification of the payment instruction," may specifically include: S461. Construct a virtual audit environment that simulates the regulatory review process in one or more jurisdictions; Specifically, for step S461, the virtual audit environment is based on the official regulatory requirements of the corresponding jurisdiction, fully replicating the review logic, verification points, judgment standards, and compliance requirements of local tax, foreign exchange, and other regulatory departments for cross-border payroll payments. It supports both independent review simulations within a single jurisdiction and joint review simulations across multiple jurisdictions, comprehensively adapting to the business characteristics of cross-border payroll involving multiple jurisdictional regulations. The review rules within the environment are synchronized with the latest updates to local regulatory policies, ensuring that the simulation standards remain consistent with current regulatory requirements.

[0075] S462. Perform compliance simulations on payment instructions and corresponding payroll payment schemes in a virtual audit environment to generate simulated audit results; Specifically, for step S462, the simulation process takes the payment instructions to be verified and the corresponding payroll payment scheme as the verification objects, and inputs them completely into the virtual audit environment. According to the regulatory review process built into the environment, it verifies item by item whether the payroll calculation rules are accurate, whether the declaration documents are complete, whether the flow of funds and the purpose of payment are compliant, and whether the tax treaty is applicable, etc. All regulatory verification items are verified. During the simulation process, the judgment results of each verification point are recorded in detail, and items with compliance doubts or non-compliance with regulatory requirements are marked. Finally, a complete simulation review result is output, clearly presenting the compliance status and potential risk points.

[0076] S463. If the simulated audit results meet the regulatory requirements of the jurisdiction, the payment instruction is confirmed to have passed the simulated audit verification; Specifically, for step S463, the judgment process is based on the results of the simulated review and verifies the results of all verification items: when all core verification items comply with the current regulatory provisions of the corresponding jurisdiction and there are no major compliance risks or substantive reporting defects, the payment instruction is judged to have passed the simulated audit verification and can enter the final payment output stage; if there are items that do not meet regulatory requirements, it is judged to have failed, and specific issues are simultaneously fed back for scheme adjustment and optimization.

[0077] This embodiment constructs a virtual audit environment that replicates the local regulatory logic, and conducts a full-process pre-compliance simulation of cross-border payroll payment schemes and instructions. It can identify and resolve compliance risks in advance before formal declaration and payment, improve the regulatory declaration approval rate of cross-border payroll payments, reduce the compliance risks of subsequent regulatory verification and penalties, and reduce the compliance costs of cross-border payroll business.

[0078] Furthermore, in some embodiments, the method may further include: S5. Based on the cross-border compliance verification results, generate a compliance report corresponding to the target employees and jurisdictions. The compliance report includes a summary of the payroll payment scheme, a compliance description for each jurisdiction, and a mark of potential risk points.

[0079] Specifically, for step S5, firstly, all conclusion data of this cross-border compliance verification are fully collected, including data consistency verification results, compliance rule matching judgment results, simulated audit verification conclusions, and other compliance verification information throughout the entire process; at the same time, the target employee entity information corresponding to this salary payment is matched and associated with all judicial jurisdictions covered by the business, to ensure that the compliance conclusion, employee entity, and jurisdiction correspond one-to-one, avoid information mismatch, and provide accurate and complete data support for the generation of subsequent report content.

[0080] The summary outlines the core elements of this salary payment plan, including the salary calculation cycle, total salary and income composition, cross-jurisdictional splitting ratio, the amount due and actual paid in each jurisdiction, the total amount of taxes withheld and paid on behalf of others, payment channels and expected arrival arrangements, etc. The information is presented concisely and clearly, and can intuitively reflect the overall framework of this salary payment plan.

[0081] Each jurisdiction is treated as an independent unit, and the compliance status of this salary payment in the local regulatory rules is explained one by one, covering core dimensions such as tax calculation compliance, statutory social security payment compliance, cross-border fund receipt and payment compliance, and bilateral tax treaty compliance. Each compliance conclusion is marked with the applicable regulatory rules in the locality, clearly explaining the specific logic of this payment in compliance with local regulatory requirements, ensuring that the compliance conclusions are based on evidence.

[0082] Each item identified during the compliance verification process that does not currently affect the compliance of this payment but poses risks of future regulatory changes or uncertainties in the execution process is marked, and its corresponding risk level and focus are clearly defined. The risk points include both policy change risks and execution process risks, helping managers to anticipate future compliance trends and reserve room for response.

[0083] This embodiment generates standardized localized compliance reports based on cross-border compliance verification results, which can completely retain the compliance verification trajectory of cross-border payroll payments, clearly present the full picture of the payroll scheme and the compliance basis in each locality, and at the same time provide advance warning of potential compliance risks, meet the internal control and regulatory review requirements of cross-border payroll business, and improve the transparency and risk prediction capabilities of cross-border payroll compliance management.

[0084] To facilitate understanding of the intelligent cross-border payroll processing method based on dynamic rule adaptation provided in this embodiment, a specific scenario will be used as an example to further illustrate the method. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0085] In a specific embodiment, the cross-border payroll intelligent processing method based on dynamic rule adaptation provided in this embodiment is applied to the cross-border payroll intelligent management system of multinational enterprises. The specific implementation process of this method is as follows: Step 1. Collection, standardization, and integration of salary-related data and jurisdictional information. Once the system receives the trigger signal for monthly cross-border payroll calculation, it connects with multiple internal business systems to collect raw data: It retrieves basic employee information from the Human Resources Management System, including nationality, length of residence in China or abroad, and social security registration location; it retrieves the salary structure, payment cycle, and calculation rules stipulated in the labor contract from the Contract Management System; it retrieves the monthly attendance records from the Attendance Management System, showing the employee had perfect attendance with no overtime or absences; it retrieves the employee's bank statements for payroll payments over the past three months from the Finance System; and it retrieves the relevant jurisdiction information from the Compliance Management System.

[0086] Subsequently, standardization processing was performed on all raw data: the date format of different systems was unified to the "YYYY-MM-DD" standard format; currency normalization conversion was performed on salary data of different currencies, and all data were converted to the same currency accounting standard; and missing non-core fields were supplemented in accordance with local tax collection rules, ultimately generating standardized salary source data and judicial jurisdiction information with a unified format.

[0087] Finally, using the employee's unique employee ID as the primary key, all standardized salary data is matched to the same employee entity, while binding two types of jurisdictional attributes: nationality and the location of the payer as location A, and the actual place of work as location B, forming a complete salary association data package, which serves as the data foundation for subsequent full-process processing.

[0088] Step 2. Construction and Dynamic Maintenance of a Multi-Jurisdictional Dynamic Tax Rule Map The system first collects multimodal tax rule source data from various jurisdictions, and then performs structured parsing on the multimodal rule source data: first, it identifies the data format of each rule source; for text-formatted rules, it uses a semantic parsing model to decompose the legal provisions; for webpage-formatted rules, it first purifies the main text and removes redundant content such as page navigation; for structured table-formatted tax rate tables, it directly reads parameters through field mapping; then, it extracts standardized taxable rule elements from all rules, including four core categories of elements: tax type (individual income tax, employee social insurance), tax rate range, rules for defining taxpayers, and tax treaty clauses.

[0089] After extraction, a multi-source data consistency check is performed. The descriptions of the same rule item are compared with those of different data sources. When content conflicts occur, the official written legal provisions shall prevail to resolve the conflicts, and finally a structured rule dataset with a unified format is generated.

[0090] Based on structured rule data, a dynamic tax rule graph is built according to the logical association of "judicial jurisdiction - tax type - taxpayer type" to achieve classified storage and precise retrieval of rules. At the same time, the system continuously monitors rule change signals from official channels in various jurisdictions, and judges policy updates by identifying the timestamp of the regulation release and the official announcement identifier. When it detects that a social security contribution ratio adjustment announcement has been issued in location B, it automatically captures the source data of the new rule and re-parses it, and updates the corresponding entries in the rule graph synchronously. After the update is completed, it automatically marks the differences between the old and new rules, records the version number, effective time and rule source link, and forms a traceable version management ledger.

[0091] In this embodiment, the multi-jurisdictional tax rule set fully covers four core categories of rules: personal income tax calculation rules, social insurance payment rules, double taxation agreement provisions, and foreign exchange control regulations, comprehensively supporting payroll calculation and payment verification.

[0092] Step 3. Cross-legal system tax system matching and rule conflict resolution First, the corresponding legal system is identified based on the information of the jurisdiction. Then, for the same taxable item, the corresponding definition and calculation rules are extracted according to the legal system.

[0093] The system employs an attention mechanism to perform weighted fusion of cross-legal system rules. The tax administration rules of the country of income source serve as the core applicable basis, with a weight of 0.6; the tax administration rules of the country of residence serve as a coordinating reference, with a weight of 0.4. After weighted fusion, conflicts in rule application are resolved, and the system outputs the tax system matching results for each of the two locations.

[0094] Step 4. Cross-jurisdictional salary breakdown optimization and solution generation First, the employee's taxable income attribution result and corresponding applicable tax rate are retrieved from the dynamic tax rule graph. Then, a reinforcement learning-driven multi-dimensional optimization model is constructed to optimize the splitting ratio. State space: Includes three types of real-time environmental parameters: the effective tax rate in both locations, the real-time exchange rate, and the compliance risk score (low risk, score 2 / 10) of the registered cross-border payment channel; Action space: Covers all salary splitting ratio combinations that meet the minimum social security contribution base in both locations; Reward function: The reward value is negatively correlated with the employee's comprehensive tax cost, which includes the total personal income tax in both locations, the total social security contributions paid by the individual in both locations, and the cross-border exchange rate conversion cost.

[0095] The model converges after 120 rounds of iteration and optimization, outputting the optimal split ratio.

[0096] The system simultaneously generates two alternative splitting schemes, which are then uniformly scored and ranked according to three preset indicators: comprehensive tax cost, payment timeliness, and compliance risk level. The scheme with the highest comprehensive score is output as the recommended salary payment scheme, while the remaining schemes are archived as alternatives.

[0097] Step 5. Payment Instruction Consistency Verification After generating the corresponding payment instruction based on the recommended scheme, the consistency verification process is initiated. First, three types of benchmark verification data are extracted from the salary-related data: the salary structure and calculation standard stipulated in the labor contract, the employee's bank statements for salary payments over the past three months, and the attendance records for the current month with perfect attendance.

[0098] Subsequently, cross-modal data alignment was performed: in the time dimension, the three types of data were uniformly matched to the current accounting month to eliminate statistical cycle differences; in the currency dimension, historical transaction data were uniformly converted into the same currency to eliminate currency differences, ultimately forming a cross-modal alignment benchmark dataset with unified standards.

[0099] Next, three cross-validations were conducted: First, a comparison was made with the labor contract to confirm that the individual amounts of basic salary, housing allowance, and performance bonus fully matched the calculation rules; second, a comparison was made with historical payment records, showing that the actual amount paid in the current month fluctuated by 3.2% compared to the previous month, which is within a reasonable fluctuation range; and third, a comparison was made with attendance records, confirming that there were no deductions for absences or overtime pay in the current month, consistent with the payment calculation logic. All three validations passed, generating a consistency validation result.

[0100] Step 6. Cross-border payment compliance verification and simulated audit First, semantic parsing is performed on the two payment instructions to extract the core payment elements.

[0101] The payment elements were then matched and verified against a pre-set compliance rule database: the payee's identity information was compared with the global sanctions list, and no match was found; the payment purpose "salary and wages" was compared with a risk keyword database (which includes high-risk related terms such as commissions, consulting fees, and brokerage fees), and no risk item was found; the payment channel was verified to be an officially registered and compliant cross-border payroll channel, and the payment amount did not trigger the large transaction warning threshold. Ultimately, the payment instruction was determined to be compliant with risk standards.

[0102] With consistency verification and compliance risk assessment both passed, a simulated audit was initiated to fully replicate the review points, judgment standards, and compliance requirements in both jurisdictions. The payroll payment plan and payment instructions were input into the virtual audit environment to simulate the entire regulatory review process, verifying item by item the accuracy of taxable income calculation, compliance with tax treaty application conditions, completeness of tax declaration requirements, and compliance of cross-border fund flows. The simulation results showed that all core matters complied with the regulatory requirements of both jurisdictions, with no substantial compliance flaws. The payment instruction was thus confirmed to have passed the simulated audit verification and was output as the final executable payment instruction.

[0103] Step 7. Compliance Report Generation Based on the results of this full-process cross-border compliance verification, a dedicated compliance report corresponding to the employee and the two jurisdictions is automatically generated. The report contains three core parts: Salary Payment Plan Summary: List the month of this calculation, total salary amount, split ratio between the two locations, amount payable in each jurisdiction, details of withholding and payment, actual amount paid, payment channel and expected arrival time; Compliance explanations for each jurisdiction: The compliance basis and conclusions are explained for each jurisdiction. For jurisdiction A, the compliance of individual income tax calculation, social security payment, and foreign exchange purchase and payment and the corresponding legal basis are explained. For jurisdiction B, the compliance of federal and state individual income tax calculation, social security payment, and cross-border fund remittance are explained. The applicable compliance of the bilateral tax treaty between A and B is also specifically explained. Potential risk points are marked: Two non-veto risks are marked. First, region B plans to adjust the tax rate in the next year, and there may be a small difference in tax payment or refund for employees' annual individual income tax settlement (risk level: low); second, the annual cumulative quota of the currently used cross-border payment channel has been used up to 58%, and the remaining quota needs to be checked in advance for centralized payments in the second half of the year (risk level: medium).

[0104] The generated compliance report is archived in the employee's personal salary compliance file and can be accessed in scenarios such as internal compliance audits, regulatory review, and business debriefing.

[0105] In summary, compared with existing technologies, the cross-border payroll intelligent processing method based on dynamic rule adaptation provided in this embodiment achieves full-process compliance control of cross-border payroll from tax system adaptation to payment through closed-loop processing of multi-jurisdictional tax system matching and conflict resolution, cross-jurisdictional payroll compliance splitting, and pre-payment double verification. It can adapt to the differentiated regulatory requirements of multiple jurisdictions, effectively improve the compliance and processing efficiency of cross-border payroll processing, and ensure the accuracy and compliance of cross-border payroll payments.

[0106] It should be understood that, although Figure 2The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 2 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0107] To facilitate better implementation of the cross-border payroll intelligent processing method based on dynamic rule adaptation according to the embodiments of this application, this application also provides a cross-border payroll intelligent processing device based on dynamic rule adaptation based on the above-described cross-border payroll intelligent processing method based on dynamic rule adaptation. The meanings of the terms used are the same as in the above-described cross-border payroll intelligent processing method based on dynamic rule adaptation, and specific implementation details can be found in the descriptions in the method embodiments.

[0108] Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of a cross-border payroll intelligent processing device based on dynamic rule adaptation provided in an embodiment of this application. Specifically, the device may include a data acquisition module 201, a rule adaptation module 202, a splitting calculation module 203, and a compliance verification module 204, as follows: The data acquisition module 201 is used to acquire the salary-related data of the target employees and the corresponding judicial jurisdiction information; The rule adaptation module 202 is used to call the dynamically updated multi-jurisdictional tax rule set to perform taxable item parsing and tax system matching on the salary-related data based on the judicial jurisdiction information, and generate the corresponding tax system matching results for each judicial jurisdiction after resolving conflicts between rules belonging to different legal systems. The splitting calculation module 203 is used to perform cross-jurisdictional splitting calculation on salary-related data based on the tax system matching results of each judicial jurisdiction and according to preset compliance constraints, to generate at least one salary payment scheme that meets the withholding and payment requirements of each judicial jurisdiction, and to generate corresponding payment instructions based on the salary payment scheme. The compliance verification module 204 is used to perform consistency verification on payment instructions based on salary-related data, execute cross-border compliance verification based on the generated consistency verification results, and output the final payment instruction after passing the cross-border compliance verification.

[0109] Specific limitations regarding the intelligent cross-border payroll processing device based on dynamic rule adaptation can be found in the limitations of the intelligent cross-border payroll processing method based on dynamic rule adaptation mentioned above, and will not be repeated here. Each module in the aforementioned intelligent cross-border payroll processing device based on dynamic rule adaptation can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0110] The cross-border payroll intelligent processing device based on dynamic rule adaptation provided in this embodiment achieves full-process compliance control of cross-border payroll from tax system adaptation to payment through closed-loop processing of multi-jurisdictional tax system matching and conflict resolution, cross-jurisdictional payroll compliance splitting, and pre-payment double verification. It can adapt to the differentiated regulatory requirements of multiple jurisdictions, effectively improve the compliance and processing efficiency of cross-border payroll processing, and ensure the accuracy and compliance of cross-border payroll payments.

[0111] Furthermore, embodiments of this application also provide an electronic device, such as... Figure 4 As shown, it illustrates a structural schematic diagram of the electronic device involved in the embodiments of this application, specifically: The electronic device may include components such as a processor 301 with one or more processing cores, a memory 302 with one or more computer-readable storage media, a power supply 303, and an input unit 304. Those skilled in the art will understand that... Figure 4 The electronic device structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein: The processor 301 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines, and performs various functions and processes data by running or executing software programs and / or modules stored in the memory 302, and by calling data stored in the memory 302, thereby providing overall monitoring of the electronic device. Optionally, the processor 301 may include one or more processing cores; preferably, the processor 301 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 301.

[0112] The memory 302 can be used to store software programs and modules. The processor 301 executes various functional applications and a cross-border payroll intelligent processing method based on dynamic rule adaptation by running the software programs and modules stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 302 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 302 may also include a memory controller to provide the processor 301 with access to the memory 302.

[0113] The electronic device also includes a power supply 303 that supplies power to various components. Preferably, the power supply 303 can be logically connected to the processor 301 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply 303 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0114] The electronic device may also include an input unit 304, which can be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0115] Although not shown, the electronic device may also include a display unit, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 301 in the electronic device loads the executable files corresponding to the processes of one or more applications into the memory 302 according to the following instructions, and the processor 301 runs the applications stored in the memory 302 to realize various functions, as follows: The system acquires the target employee's salary-related data and corresponding jurisdiction information. Based on the jurisdiction information, it invokes a dynamically updated multi-jurisdictional tax rule set to perform taxable item parsing and tax system matching on the salary-related data. After resolving conflicts between rules belonging to different legal systems, it generates tax system matching results for each jurisdiction. Based on the tax system matching results for each jurisdiction, it performs cross-jurisdictional splitting and calculation on the salary-related data according to preset compliance constraints, generating at least one salary payment scheme that meets the withholding and payment requirements of each jurisdiction, and generates corresponding payment instructions based on the salary payment scheme. Based on the salary-related data, it performs consistency verification on the payment instructions, performs cross-border compliance verification based on the generated consistency verification results, and outputs the final payment instruction after passing the cross-border compliance verification.

[0116] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0117] This application's embodiments achieve full-process compliance control of cross-border payroll from tax system adaptation to payment through closed-loop processing of multi-jurisdictional tax system matching and conflict resolution, cross-jurisdictional payroll compliance splitting, and pre-payment dual verification. It can adapt to the differentiated regulatory requirements of multiple jurisdictions, effectively improve the compliance and efficiency of cross-border payroll processing, and ensure the accuracy and compliance of cross-border payroll payments.

[0118] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0119] To this end, embodiments of this application provide a storage medium storing multiple instructions that can be loaded by a processor to execute steps in any of the cross-border payroll intelligent processing methods based on dynamic rule adaptation provided in embodiments of this application. For example, the instructions can execute the following steps: The system acquires the target employee's salary-related data and corresponding jurisdiction information. Based on the jurisdiction information, it invokes a dynamically updated multi-jurisdictional tax rule set to perform taxable item parsing and tax system matching on the salary-related data. After resolving conflicts between rules belonging to different legal systems, it generates tax system matching results for each jurisdiction. Based on the tax system matching results for each jurisdiction, it performs cross-jurisdictional splitting and calculation on the salary-related data according to preset compliance constraints, generating at least one salary payment scheme that meets the withholding and payment requirements of each jurisdiction, and generates corresponding payment instructions based on the salary payment scheme. Based on the salary-related data, it performs consistency verification on the payment instructions, performs cross-border compliance verification based on the generated consistency verification results, and outputs the final payment instruction after passing the cross-border compliance verification.

[0120] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0121] The storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0122] Since the instructions stored in the storage medium can execute the steps of any of the cross-border payroll intelligent processing methods based on dynamic rule adaptation provided in the embodiments of this application, the beneficial effects that any of the cross-border payroll intelligent processing methods based on dynamic rule adaptation provided in the embodiments of this application can achieve can be realized. For details, please refer to the previous embodiments, which will not be repeated here.

[0123] The above provides a detailed description of a cross-border payroll intelligent processing method and apparatus based on dynamic rule adaptation provided in the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A cross-border payroll intelligent processing method based on dynamic rule adaptation, characterized in that, include: Obtain the target employee's salary-related data and corresponding jurisdiction information; Based on the jurisdictional information, a dynamically updated multi-jurisdictional tax rule set is invoked to perform taxable item parsing and tax system matching on the salary-related data. After conflict resolution of rules belonging to different legal systems, tax system matching results corresponding to each jurisdiction are generated. This includes: obtaining multimodal tax rule source data corresponding to each jurisdiction; performing structured parsing on the multimodal tax rule source data to extract taxable rule elements for each jurisdiction, including tax type, tax rate range, taxpayer identification rules, and tax treaty clauses; constructing a dynamic tax rule graph based on the parsed taxable rule elements, and updating the dynamic tax rule graph in real-time or periodically according to a preset update strategy. Based on the tax system matching results corresponding to each jurisdiction, the salary-related data is split and calculated across jurisdictions according to preset compliance constraints to generate at least one salary payment scheme that meets the withholding and payment requirements of each jurisdiction, and a corresponding payment instruction is generated according to the salary payment scheme. The payment instruction is validated based on the salary-related data. Cross-border compliance verification is performed based on the generated validation results. The final payment instruction is output after passing the cross-border compliance verification.

2. The intelligent cross-border payroll processing method based on dynamic rule adaptation according to claim 1, characterized in that, The acquisition of the target employee's salary-related data and corresponding jurisdiction information includes: In response to a payroll processing trigger signal, the system obtains the target employee's basic information, labor contract salary structure data, attendance record data, bank statement data, historical payroll payment records, and the target employee's jurisdiction information from multiple data sources. The data from each data source is processed according to the preset data format specifications, and missing value completion is performed to generate standardized salary source data and corresponding standardized judicial jurisdiction information. Based on the employee identification information of the target employee, the standardized salary source data from each data source are linked and integrated according to a unified data association primary key, and the standardized judicial jurisdiction information is linked with the integrated salary data to generate the salary association data and the corresponding judicial jurisdiction information.

3. The cross-border payroll intelligent processing method based on dynamic rule adaptation according to claim 1, characterized in that, The structured parsing of the multimodal tax rule source data includes: Identify the data format type of the multimodal tax rule source data, including text format, web page format, and structured data format; The corresponding parsing strategy is invoked for different data format types to extract taxable rule elements from various data sources; Consistency checks and conflict resolution are performed on the taxable rule elements from different data sources to generate structured rule data in a unified format.

4. The intelligent cross-border payroll processing method based on dynamic rule adaptation according to claim 1, characterized in that, The step of constructing a dynamic tax rule graph based on the taxable rule elements obtained from the analysis, and updating the dynamic tax rule graph in real time or periodically according to a preset update strategy, includes: Monitor tax rule change signals in various jurisdictions, including regulation publication timestamps and official announcement identifiers; In response to the detection of the change signal, the re-parsing and map update of the changed rule source data are automatically triggered; After the map update is completed, the differences in rules before and after the update are marked, and the version change information is recorded.

5. The intelligent cross-border payroll processing method based on dynamic rule adaptation according to claim 1, characterized in that, The process of resolving conflicts in the enforcement of rules belonging to different legal systems and generating tax system matching results for each jurisdiction includes: Identify the legal system type of each jurisdiction based on the aforementioned jurisdictional information; For taxable rule elements belonging to different legal systems, extract the definition standards and calculation rules for the same taxable item for each legal system. The weight allocation of taxable rule elements of each legal system type is determined based on the attention mechanism during fusion, and the rules of different legal systems types are weighted and fused according to the weight allocation to generate tax system matching results corresponding to each jurisdiction after conflict resolution.

6. The intelligent cross-border payroll processing method based on dynamic rule adaptation according to claim 1, characterized in that, Based on the tax system matching results corresponding to each jurisdiction, the salary-related data is split and calculated across jurisdictions according to preset compliance constraints to generate at least one salary payment scheme that meets the withholding and payment requirements of each jurisdiction, and a corresponding payment instruction is generated according to the salary payment scheme, including: Obtain the results of the attribution of the target employee's taxable income in each jurisdiction and the corresponding applicable tax rate information from the dynamic tax rules map; A reinforcement learning-driven multi-dimensional optimization model is constructed to reduce the overall tax cost of the target employees. The model combines exchange rate data to calculate the splitting ratio combination of the salary-related data across multiple jurisdictions. In each iteration, the multi-dimensional optimization model updates the splitting ratio combination for the next iteration based on the overall tax cost and compliance risk score corresponding to the splitting ratio combination of the current iteration. Based on the calculation results of the split ratio combination, at least one salary payment plan that meets the compliance requirements of each jurisdiction is generated, and each salary payment plan includes the amount due, the amount withheld and paid on behalf of others, and the amount actually paid in each jurisdiction.

7. The intelligent cross-border payroll processing method based on dynamic rule adaptation according to claim 1, characterized in that, The consistency verification of the payment instruction based on the salary association data includes: Extract labor contract salary structure data, bank statement data, and attendance record data from the aforementioned salary-related data; The labor contract salary structure data, the bank statement data, and the attendance record data are aligned across modally using time and currency dimensions as alignment benchmarks to generate cross-modal aligned data. Based on the cross-modal alignment data, cross-consistency checks are performed on the payment amount in the payment instruction, the agreed amount in the labor contract salary structure data, the historical payment amount in the bank transaction data, and the actual attendance information in the attendance record data to generate consistency check results.

8. The cross-border payroll intelligent processing method based on dynamic rule adaptation according to claim 1, characterized in that, The step of performing cross-border compliance verification based on the generated consistency verification result, and outputting the final payment instruction after passing the cross-border compliance verification, includes: The payment instruction is semantically parsed to extract payment element information, which includes payee identifier, payment amount, payment purpose, and fund flow. The payment element information is matched with a preset compliance rule base to determine whether the payment instruction has any compliance risks. The compliance rule base includes fund compliance rules and sanctions list rules. After the payment instruction passes the compliance risk assessment and the consistency verification result is passed, a simulated audit verification is performed on the payment instruction, and the payment instruction is confirmed as the final payment instruction after the simulated audit verification is passed.

9. A cross-border payroll intelligent processing device based on dynamic rule adaptation, executing the cross-border payroll intelligent processing method based on dynamic rule adaptation as described in any one of claims 1-8, characterized in that, include: The data acquisition module is used to acquire the target employee's salary-related data and corresponding jurisdiction information; The rule adaptation module is used to call the dynamically updated multi-jurisdictional tax rule set to perform taxable item parsing and tax system matching on the salary-related data based on the judicial jurisdiction information, and generate tax system matching results corresponding to each judicial jurisdiction after resolving conflicts between rules belonging to different legal systems. The splitting calculation module is used to perform cross-jurisdictional splitting calculation on the salary-related data based on the tax system matching results corresponding to each judicial jurisdiction and according to preset compliance constraints, to generate at least one salary payment scheme that meets the withholding and payment requirements of each judicial jurisdiction, and to generate corresponding payment instructions based on the salary payment scheme. The compliance verification module is used to perform consistency verification on the payment instruction based on the salary-related data, perform cross-border compliance verification based on the generated consistency verification result, and output the final payment instruction after passing the cross-border compliance verification.

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