Intelligent compliance and tax calculation system for multi-market cross-border e-commerce

Abstract

This invention relates to the field of cross-border e-commerce tax calculation technology, specifically to an intelligent compliance and tax calculation system for multi-market cross-border e-commerce. The system includes: a tax rule management unit, which performs structured parsing of tax regulations documents from several regions, extracts calculation conditions and parameters, and constructs a condition judgment tree and a calculation parameter library to form a tax rule tree; a transaction processing unit, which integrates product attributes, transaction context, and geographical information through multi-dimensional data fusion to generate a transaction feature dataset, dynamically matches and calculates tax results based on the tax rule tree, and returns the results to the cross-border e-commerce platform; a compliance report generation unit, which generates and verifies tax declaration reports based on a tax declaration template library and original transaction data; and a rule update unit, which continuously monitors changes to tax documents, obtains update information, and incrementally updates and verifies the integrity of the tax rule tree. This invention improves the accuracy of cross-border e-commerce tax calculations and avoids tax calculation anomalies.

Description

Technical Field

[0001] This invention relates to the field of cross-border e-commerce tax calculation technology, specifically to an intelligent compliance and tax calculation system for multi-market cross-border e-commerce. Background Technology

[0002] With the rapid development of global cross-border e-commerce, the frequency of transactions between different countries and regions is constantly increasing, and tax supervision in various markets is gradually becoming stricter. When conducting sales business, cross-border e-commerce platforms need to perform real-time tax calculations on transactions according to the tax policies of different markets and generate compliant tax declaration reports as required.

[0003] Existing technologies, such as CN115293869A which discloses a method and system for customs declaration and tax processing in cross-border e-commerce, can handle customs declaration and tax refund processes in a single market, but cannot cope with tax situations arising from rule interactions in a multi-market environment. CN120782575A discloses a cross-border tax compliance management system based on blockchain and artificial intelligence. Relying on the immutability of blockchain, it simply maps complex tax rules into smart contract code, directly stores tax regulations as hash values, and relies on policy changes in public blockchain nodes to trigger rule updates. However, it still faces the following major problems: 1. The "semantic break" crisis: Tax regulations, as natural language legal texts, are fundamentally mismatched with the required structured logical rules, resulting in a high average information loss rate when converting tax regulations into system rules, especially when dealing with vague concepts such as "location of major economic interests," where different systems interpret them very differently. 2. The "dimensional collapse" problem: Product attributes, transaction context, and geographical location should be considered as a comprehensive judgment of related multi-dimensional data, but existing technologies simplify this to a linear process. Research shows that this simplification leads to a higher error rate in boundary cases (such as transactions close to a threshold) than in regular transactions. 3. The "shadow period" phenomenon: During the "shadow period" between changes in tax regulations and the completion of system updates, expired rules continue to be used to calculate taxes. In some regions, rule changes are often implemented through informal channels first, while official announcements are delayed, causing enterprise systems to operate in a rule vacuum without the enterprise's knowledge.

[0004] Therefore, it is necessary to design an intelligent compliance and tax calculation system for cross-border e-commerce in multiple markets to address the problems existing in current technologies. Summary of the Invention

[0005] In view of this, the present invention proposes an intelligent compliance and tax calculation system for cross-border e-commerce in multiple markets, aiming to solve the problems of semantic fragmentation, simple multi-dimensional data judgment, and lagging updates of tax regulations in the existing technology.

[0006] This invention proposes an intelligent compliance and tax calculation system for multi-market cross-border e-commerce, comprising: The tax rule management unit is configured to perform structured parsing of tax regulation documents from several regions, extract tax calculation conditions and tax calculation parameters, and organize them into a condition judgment tree and a calculation parameter library to construct a tax rule tree; each node in the tax rule tree corresponds to a tax calculation condition, and each leaf node corresponds to a tax calculation result. The transaction processing unit is configured to, upon receiving a request from a cross-border e-commerce transaction platform, integrate product attribute information, transaction context information, and geographical location information through multi-dimensional data fusion technology, and correlate and match them to form a transaction feature dataset; perform dynamic rule matching based on the tax rule tree, combine multi-level judgment logic to determine the applicable tax rate and calculation method, perform tax calculation, generate tax calculation results, and return the tax calculation results to the corresponding cross-border e-commerce platform. The compliance report generation unit is configured to generate a tax declaration report based on the tax declaration template library, combined with the tax calculation results and the original transaction data in the request from the cross-border e-commerce transaction platform, through template matching and data filling; and to perform format verification on the tax declaration report. The rule update unit is configured to continuously detect changes in tax regulations documents in several regions, obtain tax update information, incrementally update the tax rule tree, and verify the integrity of the incrementally updated tax rule tree.

[0007] Furthermore, when constructing the tax rule tree, the tax rule management unit includes: Semantic analysis was performed on tax regulations documents from several regions to identify tax calculation conditions and parameters in the tax regulations documents. The tax calculation conditions are organized into a hierarchical condition judgment tree, which includes regional conditions, commodity category conditions, transaction amount conditions, and identity conditions. The tax calculation parameters are organized into a calculation parameter library, which includes tax rates, tax exemption thresholds, and special commodity classification rules. A tax rule tree is constructed based on the condition judgment tree and the calculation parameter library. Each node in the tax rule tree corresponds to a tax calculation condition, and each leaf node corresponds to a tax calculation result.

[0008] Furthermore, when constructing the tax rule tree, the tax rule management unit also includes: Assign a unique identifier to each node in the tax rule tree; record the construction timestamp and data source information of the tax rule tree; establish a version control mechanism for the tax rule tree and save historical versions of the tax rule tree; when tax regulations documents in several regions change, identify the changed tax calculation conditions and tax calculation parameters by comparing the content of the old and new documents.

[0009] Furthermore, when the transaction processing unit associates and matches data to form a transaction feature dataset, it includes: Extract product category, product description, and product value as product attribute information from requests to cross-border e-commerce platforms; Extract the transaction amount, currency, and time from the request from the cross-border e-commerce platform as transaction context information; Extract the shipping address and buyer's location as geographic location information from the request from the cross-border e-commerce transaction platform; The extracted product attribute information, transaction context information, and geographical location information are correlated and matched to form a transaction feature dataset.

[0010] Furthermore, when the transaction processing unit generates the tax calculation result, it includes: According to the hierarchical structure of the tax rule tree, the geographical location information in the transaction feature dataset is matched with the regional conditions of the tax rule tree; Once the regional conditions are successfully matched, the commodity attribute information in the transaction feature dataset will be matched with the commodity category conditions in the tax rule tree; Once the product category criteria are successfully matched, the transaction context information in the transaction feature dataset is matched with the transaction amount criteria in the tax rule tree. Based on the calculation parameters in the leaf nodes of the final matched tax rules, the tax calculation is performed on the transaction amount in the request from the cross-border e-commerce transaction platform, and the tax calculation result is generated.

[0011] Furthermore, when the transaction processing unit returns the tax calculation result to the corresponding cross-border e-commerce platform, it includes: When the transaction feature dataset successfully matches the path of the tax rule tree, the rule tree path information during the matching process is recorded as the basis for tax calculation. The basis for tax calculation includes the rule tree path, matching condition value, applicable tax rate and calculation formula. The basis for tax calculation and the tax calculation result are returned to the cross-border e-commerce platform. When the transaction feature dataset cannot match any path in the tax rule tree, the transaction processing unit generates tax calculation anomaly information and returns it to the cross-border e-commerce platform; the tax calculation anomaly information is used to explain the conditions for the matching failure.

[0012] Furthermore, when the compliance report generation unit generates a tax return report, it includes: Based on the geographical location information in the tax calculation results, select the corresponding tax declaration template from the preset tax declaration template library; fill in the tax payable, transaction details, and tax calculation basis from the tax calculation results into the corresponding positions of the tax declaration template; The transaction records in the original transaction data from the cross-border e-commerce transaction platform are organized in chronological order to form a transaction detail list; The transaction details list and tax calculation results are integrated into the tax return report to generate a formatted tax return report.

[0013] Furthermore, when the compliance report generation unit performs format verification on the tax return report, it includes: Based on the geographical location information in the tax calculation results, determine the language requirements of the target market and translate the report content into the target language; add an electronic signature and timestamp to the translated tax return report; perform format verification on the tax return report after adding the electronic signature and timestamp, the format verification including structural verification, content verification and logical verification; when the format verification is passed, save the verified tax return report to the designated storage location.

[0014] Furthermore, when the rule update unit performs incremental updates to the tax rule tree, it includes: Regularly check and obtain the latest tax regulations documents; compare the obtained latest tax regulations documents with the currently stored tax regulations documents to identify changes; and update the tax rule tree in the tax rule management unit based on the identified changes.

[0015] Furthermore, when the rule update unit performs integrity verification on the incrementally updated tax rule tree, it includes: The updated tax rule tree is subjected to integrity verification to ensure that there are no logical conflicts. The updated tax rule tree is then applied to historical transaction data for backtesting to verify the accuracy of the update. Once the verification is successful, the updated tax rule tree is set to the effective state. A tax rule tree update notification is then sent, which includes the update content, scope of impact, and effective time.

[0016] Compared with existing technologies, the advantages of this invention are as follows: By performing semantic analysis, condition extraction, and parameter extraction on tax regulations documents from multiple regions, a hierarchical tax rule tree is constructed, achieving standardized, structured, and scalable management of tax rules. This avoids omissions and inconsistencies caused by manual rule maintenance, improving the efficiency and accuracy of rule construction. Multi-dimensional data fusion technology is used to associate product attributes, transaction context, and geographic location information to form a transaction feature dataset. Combined with multi-level matching logic, applicable tax rates can be automatically selected based on different markets, product categories, and transaction amounts, ensuring consistent, traceable, and highly accurate tax calculations. Based on the tax rule tree, path matching is performed to generate tax calculation results, while simultaneously outputting the tax calculation basis, including rule paths, matching conditions, applicable tax rates, and calculation formulas. This achieves transparency and auditability in tax calculations, improving tax compliance. Combined with a tax declaration template library, the tax calculation results and transaction details can be automatically filled into a declaration report, supporting automatic translation, electronic signatures, timestamps, and multi-level format verification. This reduces the workload of manual declarations and improves the standardization and consistency of tax declarations across different markets. By monitoring changes in tax documents and comparing the content of old and new rules to achieve incremental updates of rule paths, and by introducing a version control mechanism to record the build time, rule source, and historical versions, the rules are made traceable, auditable, and finely managed, improving the ability to respond to policy changes in multiple markets. After the rules are updated, logical conflict checks are performed, and backtesting is conducted using historical transaction data, which improves the reliability of rule changes and avoids tax calculation anomalies caused by erroneous updates. Attached Figure Description

[0017] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 This is a functional block diagram of an intelligent compliance and tax calculation system for cross-border e-commerce in multiple markets, provided as an embodiment of the present invention. Detailed Implementation

[0018] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0019] Existing technologies for cross-border e-commerce tax calculation suffer from several significant shortcomings: Firstly, because tax regulations are natural language legal texts, while tax calculation relies on structured logical rules, existing systems cannot resolve the "semantic gap" between the two. This leads to substantial information loss and misunderstandings during rule conversion, particularly when dealing with ambiguous legal expressions, resulting in significant differences in interpretation between different systems and potentially inconsistent tax results. Secondly, existing technologies generally employ linear rule processes, ignoring the relationships between multi-dimensional data such as product attributes, transaction context, and geographical location, leading to "dimensional collapse." This causes system misjudgments in borderline transaction scenarios, significantly increasing the error rate. Thirdly, existing cross-border e-commerce platforms lack effective rule update monitoring and incremental update mechanisms, generally exhibiting a "rule shadow period" between tax policy changes and system launch. This allows platforms to continuously use outdated rules to calculate taxes for extended periods, leading to large-scale potential tax discrepancies and compliance risks. These deficiencies result in serious problems for cross-border e-commerce enterprises operating in multiple markets, including insufficient tax accuracy, unstable declaration compliance, and delayed regulatory response.

[0020] For example, a region removed the VAT exemption threshold for cross-border parcel goods in its annual fiscal update. However, this change was first communicated verbally at an industry seminar, and the official document was not released until 19 days later. A cross-border e-commerce platform, relying on manual monitoring of tax document updates, failed to recognize this informal change in a timely manner. During this period, the system continued to process parcel transactions under the old rules, creating a "shadow period" of several weeks. This issue was discovered during a tax audit, leading to supplementary tax payments and retroactive filings, fully exposing the system's deficiencies in delayed rule updates, lack of automated monitoring, and lack of semantic-level parsing capabilities.

[0021] For this, please refer to Figure 1 As shown, this application proposes an intelligent compliance and tax calculation system for cross-border e-commerce in multiple markets, including: The tax rules management unit is configured to perform structured parsing of tax regulations documents from several regions, extract tax calculation conditions and parameters, and organize them into a condition judgment tree and a calculation parameter library to construct a tax rule tree; each node in the tax rule tree corresponds to a tax calculation condition, and each leaf node corresponds to a tax calculation result. The transaction processing unit is configured to, upon receiving a request from a cross-border e-commerce transaction platform, integrate product attribute information, transaction context information, and geographical location information through multi-dimensional data fusion technology, and correlate and match them to form a transaction feature dataset; perform dynamic rule matching based on a tax rule tree, combine multi-level judgment logic to determine the applicable tax rate and calculation method, perform tax calculation, generate tax calculation results, and return the tax calculation results to the corresponding cross-border e-commerce platform. The compliance report generation unit is configured to generate a tax return report based on the tax return template library, combined with the tax calculation results and the original transaction data in the request from the cross-border e-commerce transaction platform, through template matching and data filling; and to perform format verification on the tax return report. The rule update unit is configured to continuously detect changes in tax regulations documents in several regions, obtain tax update information, incrementally update the tax rule tree, and verify the integrity of the incrementally updated tax rule tree.

[0022] Specifically, this invention provides an intelligent compliance and tax calculation system for cross-border e-commerce in multiple markets. It achieves automatic parsing, dynamic matching, and compliance generation of cross-regional tax rules through a modular architecture. The system comprises four core components: a tax rule management unit, a transaction processing unit, a compliance report generation unit, and a rule update unit. The tax rule management unit performs structured parsing of tax regulations from multiple countries or regions. Through natural language semantic analysis, it identifies tax calculation conditions (such as geographical scope, product category, transaction amount threshold, buyer identity, etc.) and corresponding tax calculation parameters (such as tax rates, tax exemption rules, special provisions, etc.) in the regulations. This information is organized into a hierarchical condition judgment tree and calculation parameter library, further constructing a tax rule tree for calculation. The system can make judgments level by level according to the tree structure and output the determined tax calculation results at the leaf nodes. Upon receiving a real-time transaction request from the cross-border e-commerce platform, the transaction processing unit uses a multi-dimensional data fusion model to uniformly organize and match product attribute information (product category, description, value, etc.), transaction context information (amount, currency, time, etc.), and geographical location information (buyer address, delivery area, etc.), forming a complete transaction feature dataset. Based on the structure of the tax rule tree, it executes dynamic rule matching, progressively locking in the applicable tax rate and calculation method through multi-level judgment logic at the regional, product category, and amount condition levels. It then calculates the tax amount for the transaction, generating a tax calculation basis including the matching path, usage conditions, calculation formula, and parameter sources, and returns the tax calculation result to the cross-border e-commerce platform. The compliance report generation unit, after tax calculation, automatically selects the corresponding declaration template for the target market from the tax declaration template library. It fills the corresponding fields of the template with data such as the tax payable, transaction details, tax calculation basis, and original transaction records, generating a compliant tax declaration report through formatting. It further performs format verification processes such as language conversion, signature addition, and structural and logical checks to ensure that the declaration report meets the regulatory requirements of tax authorities in various countries. The rule update unit is responsible for periodically monitoring tax regulations documents from multiple regions, automatically identifying changes in regulations, and performing incremental updates to the tax rule tree based on the difference comparison results. Subsequently, the updated rule tree is verified for integrity, including logical conflict detection, structural consistency verification, and backtracking testing based on historical transaction data, to ensure the validity and accuracy of the updated content. Once the verification is successful, the rule update unit sets the new tax rule tree to an effective state and sends a notification containing the updated content, scope of impact, and effective time to the system and external platforms.

[0023] This invention solves the problems of missing rules, semantic breaks, and ambiguous calculation conditions caused by traditional manual analysis by automatically parsing and extracting structured information from tax regulations documents from different regions through a tax rule management unit. It also addresses the issues of high error rates and inaccurate boundary transaction processing caused by linear judgments in existing technologies by constructing a multi-dimensional data fusion model through a transaction processing unit. Furthermore, it solves the problems of inconsistent cross-market reporting formats, low efficiency, and error-prone manual reporting by automatically matching tax calculation results with templated reporting requirements through a compliance report generation unit. Finally, it achieves continuous monitoring, difference comparison, and incremental updates of tax regulations documents from various regions through a rule update unit, avoiding tax calculation deviations and compliance risks caused by outdated tax rules and excessively long rule shadow periods.

[0024] The working process and principle of this application are as follows: the tax rule management unit performs automated semantic parsing on tax regulation documents from several regions. It uses hierarchical natural language processing technology (sentence segmentation and dependency analysis, named entity recognition, terminology normalization and fuzzy matching) to extract tax types, tax rates, tax exemption thresholds, special clauses and applicable conditions from legal texts into structured elements. Through ontology mapping and rule normalization, similar expressions are unified into standardized calculation parameters. These structured elements are organized into hierarchical condition judgment trees and calculation parameter libraries according to dimensions such as region, commodity category, transaction amount, and identity. Finally, they are stored in the form of an indexable tax rule tree (each node is accompanied by a unique identifier, source metadata and version information). When the transaction processing unit receives a request from a cross-border e-commerce platform, it first performs data preprocessing: semantic classification and SKU merging of product descriptions, geographic and tax domain resolution of addresses, currency conversion and rounding of amounts, and associating product attributes, transaction context, and geographic location information through a multi-dimensional data fusion model (including rule matching, similarity scoring, and confidence calculation) to form a transaction feature dataset. Then, dynamic rule matching is performed hierarchically from top to bottom according to the rule tree—first matching geographic conditions, then product category, amount threshold, and identity conditions. During the matching process, precise matching, fuzzy matching, and priority conflict resolution (using weighted scoring and priority strategies) are supported. The system locates the leaf nodes and reads the corresponding calculation parameters and formulas to calculate the tax amount. The entire calculation process simultaneously generates auditable tax calculation evidence (including rule tree path, matching condition values, parameters and formulas used, rule version, and timestamp), and returns it to the calling platform as a structured response. For transactions that cannot be matched or have matching conflicts, an exception report is generated, indicating the failure conditions and suggestions for manual review. The compliance report generation unit automatically selects a template based on the tax declaration template library and the requirements of the target market. It fills the corresponding fields with the calculation results, original transaction details, and calculation basis, performs language localization, electronic signatures and timestamps, and performs structural and logical verification (field completeness, numerical consistency, and declaration rule verification). After verification, it exports the compliance declaration document and saves an audit copy. The rule update unit monitors regulatory sources (official announcements, regulatory databases, and trusted third parties) using a combination of periodic crawling and event-driven methods. It compares newly crawled documents with currently stored documents to identify change points, automatically performs incremental updates based on the differences (keeping node identifiers unchanged for traceability), and performs logical conflict detection, coverage checks, and backtesting based on historical transactions on the updated rule tree to verify accuracy. Only when completeness verification passes is the new version marked as effective and a change notification pushed out, while historical versions are retained to support rollback and dispute tracing.

[0025] Through the above-mentioned scheme, this application constructs a unified tax rule tree that spans regions and tax types, achieving structured expression and automatic calculation of complex tax regulations. This enables cross-border e-commerce businesses operating in multiple markets to obtain real-time, accurate, and traceable tax calculation results. Through multi-dimensional data fusion and dynamic rule matching mechanisms, the applicable tax rate and calculation method can be intelligently determined based on product attributes, transaction scenarios, and regional differences, reducing the workload of manual rule queries and calculations and improving tax processing efficiency and consistency. The template-driven compliance report generation method automatically adapts to different countries or regions' declaration formats and ensures data integrity, reducing the risk of human error. Continuous monitoring and incremental updates of regulatory changes through rule update units ensure that the tax rule tree remains up-to-date, while a completeness verification mechanism guarantees the stability and reliability of the updated calculation logic.

[0026] This application further proposes that when constructing a tax rule tree using a tax rule management unit, the following should be included: Semantic analysis was performed on tax regulations documents from several regions to identify tax calculation conditions and parameters in the tax regulations documents. The tax calculation conditions are organized into a hierarchical condition judgment tree, which includes regional conditions, commodity category conditions, transaction amount conditions, and identity conditions. Tax calculation parameters are organized into a calculation parameter library, which includes tax rates, tax exemption thresholds, and special commodity classification rules. A tax rule tree is constructed based on a conditional decision tree and a calculation parameter library. Each node in the tax rule tree corresponds to a tax calculation condition, and each leaf node corresponds to a tax calculation result.

[0027] Specifically, tax regulations documents from several regions undergo preprocessing and semantic analysis. Natural language processing techniques are employed—including named entity recognition (identifying entities such as region, tax type, commodity terminology, and monetary unit), relation extraction (identifying logical relationships between entities such as "applies to…", "exempt from…", and "lower / higher than…"—and semantic role labeling (labeling semantic roles such as agent, patient, and scope of conditions)—to extract clause fragments from the text into formalized tax calculation conditions and parameters. Subsequently, the extraction results undergo terminology normalization and ontology mapping, unifying synonymous or near-synonymous expressions into standardized terms and aligning them with predefined regional and commodity classification systems. The tax calculation conditions are organized into a conditional decision tree according to a predetermined hierarchy, specifically constructed layer by layer according to regional conditions (level 1), commodity category conditions (level 2), transaction amount conditions (level 3), and identity conditions (level 4). This supports composite conditions (such as scope judgments, AND / OR logic, and exception coverage) and priority parsing to ensure the expression of complex regulatory clauses. Tax calculation parameters are centrally organized into a parameter library. This library stores tax rates by region and commodity category, and tax exemption thresholds by region and transaction type. It also records the classification and processing rules for special commodities such as luxury goods, food, pharmaceuticals, and digital services in dedicated entries. Each parameter entry includes the parameter type, value / formula, applicable scope, and reference identifier. The tax rule tree, constructed based on the aforementioned conditional decision tree and parameter library, is stored in XML format. Each rule node in the XML contains fields: condition type (e.g., region / commodity / amount / identity), condition value (exact value or range description), logical operators (AND / OR / NOT / range comparison), child node reference information, and a reference identifier pointing to the parameter entry in the parameter library. Leaf nodes link to specific tax calculation result definitions (including applicable tax rates, calculation formulas, or tax exemption determinations). This XML-based rule tree facilitates hierarchical matching and rapid location through structured queries (e.g., XPath) and can be jointly parsed with entries in the parameter library through reference relationships.

[0028] As a preferred embodiment: In a certain platform, the tax rule management unit processes newly released tax regulations for a certain region, exempting goods with a value of less than 100 units from taxation. However, digital services are not eligible for this tax exemption, and special electronic devices are subject to a 20% tax rate. Semantic analysis identifies a three-layer condition structure: the first layer is the regional condition (the region itself), the second layer is the product category condition (distinguishing between physical goods, digital services, and special electronic devices), and the third layer is the transaction amount condition (the 100-unit threshold). The calculation parameter library stores three tax rate values: 0%, 15%, and 20%. In the constructed tax rule tree, a 0% tax rate is returned when the transaction meets the criteria of "region + physical goods + amount < 100 units"; a 15% tax rate is returned when the region + physical goods + amount ≥ 100 units; a 20% tax rate is returned when the region + special electronic devices; and a 15% tax rate is returned when the region + digital services.

[0029] Through the above technical solution, this application, by using a hierarchical management system of conditional decision trees and calculation parameter libraries, can differentiate the applicable tax conditions for different regions, product categories, transaction amounts, and transacting party identities, and quickly locate the corresponding tax calculation results, thus improving the accuracy and consistency of tax calculations. The use of XML format to store the rule tree gives the rule nodes traceability, scalability, and ease of version management, providing cross-border e-commerce with efficient, reliable, and automated tax processing capabilities, while reducing the risk of manual intervention and operational errors.

[0030] This application further proposes that when constructing a tax rule tree using a tax rule management unit, the following should also be included: Assign a unique identifier to each node in the tax rule tree; record the construction timestamp and data source information of the tax rule tree; establish a version control mechanism for the tax rule tree and save historical versions of the tax rule tree; when tax regulations documents in several regions change, identify the changed tax calculation conditions and tax calculation parameters by comparing the content of the old and new documents.

[0031] Specifically, the tax rule management unit implements a strict identification, traceability, and version management mechanism when constructing and maintaining the tax rule tree: each rule tree node is assigned a unique identifier, using the format "regional code-rule category code-version number-node sequence number" (e.g., REG-RT-0003-000124), where the regional code and rule category code are used for fast indexing, the version number increments as the rule tree is updated, and the node sequence number is unique within the same version; during construction, a construction timestamp accurate to milliseconds is recorded and complete data source metadata (including the issuing agency of the original tax regulation document, the official release date, document number, crawling time, and original text hash) is saved, and the original document and its parsed intermediate structures (such as entity tables, relation tables, and ontology mapping tables) are archived together for traceability. To ensure historical traceability and secure rollback, the tax rule tree adopts Git-based version control: each rule change generates a new commit and branch in a dedicated repository. The commit information follows a standardized template (including change summary, scope of impact, triggering source document ID, and build time). Important versions are marked as effective versions using semantic tags, and complete diff and merge records are retained. The update process can adopt a branching strategy based on regions or rule categories. Changes are merged into the main branch and tagged as effective after being automatically verified on independent branches (static logic checks, coverage checks, and backtracking tests). For detecting and identifying discrepancies in tax regulation documents, the system first preprocesses the old and new documents (normalization, sentence segmentation, term alignment). Then, a text comparison algorithm is used to precisely locate word-by-word changed sections using the longest common subsequence (LCS). Semantic similarity calculation (based on semantic embedding vectors and cosine similarity or equivalent metrics) is combined to identify clauses that have undergone semantic rewriting or textual adjustments. A similarity score is generated for each pair of sentence segments, and a strategic threshold is applied (e.g., high similarity is considered rewriting, low similarity is considered addition or deletion). This accurately maps the added, modified, and deleted clauses to the calculation conditions and parameters in the rule tree. After mapping, the system attempts to retain the original unique identifiers of unaffected nodes, writes a new version number to modified nodes, and records the reason for the change, original document reference, and difference summary in the node metadata. Newly added nodes are assigned new node sequence numbers, and corresponding parameter entries are inserted into the parameter library. Deleted or deprecated nodes are not physically deleted but are marked as "deprecated / historical" and their references are retained to support retrospective auditing. The entire incremental update process is controlled by an automated pipeline as an atomic operation: first, the change is executed in an isolated environment, logic conflict detection is performed, and coverage and backtracking tests are conducted (based on selected historical transaction samples). If all verifications pass, the branch is merged, the main version is updated, and a structured update notification (including a summary of the update content, a list of affected nodes, the expected scope of impact, and the effective time) is generated. Otherwise, the process is automatically rolled back, and the difference report is pushed to the manual review queue.

[0032] As a preferred embodiment: A region's tax regulations were updated, raising the tax exemption threshold from 100 units to 150 units, and adding "smart wearable devices" as a special product category. A unique identifier, "REG-2023-08-15-001," was automatically assigned to the new rule tree, recording the creation timestamp as August 15, 2023, at 14:30:22, with data sourced from "Official Gazette Issue No. 2023-08-15." The historical version, "REG-2023-01-01-001," was saved, and a text comparison algorithm identified the specific changes: the tax exemption threshold parameter changed from 100 to 150, and "smart wearable devices" was added to the special product category. Modified nodes were highlighted in the rule tree for easy review by the compliance team. When a transaction matches a modified rule path, it automatically records whether the new or old rule was used, ensuring the traceability of tax calculations and resolving the traceability issues previously encountered by GlobalShop due to rule changes.

[0033] Through the aforementioned technical solution, this application achieves traceability and uniqueness of rule nodes by assigning a unique identifier to each node in the tax rule tree and recording precise timestamps and complete data sources, ensuring that each rule can be traced back to the original regulatory document. Combined with a Git-based version control mechanism, historical versions are fully preserved, and each rule update generates an independent branch and retains detailed change records, achieving backtracking and secure management of the rule tree. When tax regulations change, a text comparison method combining the longest common subsequence algorithm and semantic similarity calculation identifies added, modified, and deleted clauses and automatically maps them to tax calculation conditions and parameters, thereby ensuring that the dynamic update of the rule tree is both efficient and accurate.

[0034] This application further proposes that when a transaction processing unit associates and matches data to form a transaction feature dataset, it includes: Extract product category, product description, and product value as product attribute information from requests to cross-border e-commerce platforms; Extract the transaction amount, currency, and time from the request from the cross-border e-commerce platform as transaction context information; Extract the shipping address and buyer's location as geographic location information from the request from the cross-border e-commerce transaction platform; The extracted product attribute information, transaction context information, and geographical location information are correlated and matched to form a transaction feature dataset.

[0035] Specifically, the transaction processing unit performs multi-step, multi-level data fusion processing on cross-border e-commerce transaction requests to construct a complete transaction feature dataset that can be used for tax assessment: It extracts product attribute information from transaction requests, including standardizing and mapping product categories according to the HS coding system, performing keyword extraction and semantic analysis on product descriptions to identify special attributes (such as whether they belong to fragile items, luxury goods, food, medicine, or digital services), and normalizing and clustering different descriptions of the same SKU to eliminate discrepancies in expression; simultaneously, it extracts transaction context information from the requests, including the original transaction amount, transaction currency, and transaction time. The transaction amount is transferred after interacting with a trusted exchange rate service and recording the exchange rate source and timestamp. The data is converted to the base currency of the target tax domain and standardized according to preset numerical precision and rounding rules (while also recording the net amount that may be affected by handling fees, discounts, and returns); then, geographic location information is extracted from the request, including the delivery address and the buyer's region. The delivery address is converted into a structured geocode (country / region code, administrative division code, postal code, etc.) through address resolution and standardization services, and address integrity verification and multi-source verification are performed (e.g., comparison with logistics carriers or users' historical addresses). When address information is insufficient, alternative location strategies are enabled (e.g., credibility inference based on IP or mobile phone country code). All personally identifiable information is anonymized or encrypted according to the principle of minimization during processing and storage to meet privacy compliance requirements. Subsequently, the three types of information are associated through a hybrid matching system: at the rule level, explicit mapping rules are applied (such as prioritizing the place of receipt to determine the tax domain, and directly mapping HS codes to special tax treatment rules); at the similarity level, a fuzzy matching method based on string similarity and semantic embedding is used to align product descriptions and classifications, address variants and aliases, and a confidence score is generated for each match; a strategic conflict resolver is introduced to determine the final attribution or label items requiring manual review when there are contradictory judgments, based on priority, confidence, and business rules (such as contract terms or registration identity priority). The final output transaction feature dataset is a structured record containing standardized product categories and attributes, converted transaction amounts and exchange rate metadata, standardized geocoding, transaction timestamps, matching paths and confidence levels, as well as anomaly flags and review suggestions.

[0036] As a preferred embodiment: On a certain platform, when a user purchases a product described as "wireless Bluetooth headphones with noise cancellation," the transaction processing unit extracts the product category as "electronic devices - audio devices" from the transaction request. The product description keywords include "wireless," "Bluetooth," and "noise cancellation," and the product value is 85 units. The transaction context information includes a transaction amount of 120 units and a transaction time of 10:25 AM on September 1, 2023. The geolocation information shows that the delivery address is located in a coastal city, and the buyer's location is within the city's area. Through an association matching algorithm, the keyword "noise cancellation" in the product description is analyzed, and combined with the product category, it is determined that the product belongs to the "special electronic devices" category. The transaction feature dataset completely contains all necessary information.

[0037] Through the above technical solutions, this application uses standardized HS codes to uniformly classify commodity categories, and combines keyword extraction and semantic analysis to identify special commodity attributes, distinguishing the tax treatment rules for different commodities; it uniformly converts transaction amounts into the target tax domain's base currency and records exchange rate metadata, ensuring the consistency and traceability of calculations; and it achieves accurate tax domain determination through standardized coding of geographical location information. In the association matching process, a combination of rule-based matching and similarity-based fuzzy matching is used to comprehensively verify and intelligently align commodity, transaction, and address information, reducing matching errors and handling boundary and fuzzy cases.

[0038] This application further proposes that when the transaction processing unit generates tax calculation results, it includes: According to the hierarchical structure of the tax rule tree, the geographical location information in the transaction feature dataset is matched with the regional conditions of the tax rule tree; Once the regional conditions are successfully matched, the commodity attribute information in the transaction feature dataset will be matched with the commodity category conditions in the tax rule tree; Once the product category criteria are successfully matched, the transaction context information in the transaction feature dataset is matched with the transaction amount criteria in the tax rule tree. Based on the calculation parameters in the leaf nodes of the final matched tax rules, the tax calculation is performed on the transaction amount in the request from the cross-border e-commerce transaction platform, and the tax calculation result is generated.

[0039] Specifically, the transaction processing unit performs dynamic rule matching and completes tax calculation based on the tax rule tree in a hierarchical, traceable, and fault-tolerant pipeline: The input transaction feature dataset is preprocessed and normalized (including currency conversion and recording exchange rate metadata, applying tax base consolidation rules for discounts / freight / insurance, etc., to the transaction amount, and standardizing geographical addresses into administrative codes / postal codes / multi-level boundary representations). Then, matching is performed hierarchically from top to bottom according to the tax rule tree. At the first level, the geographical location information in the transaction features is matched with the regional conditions of the rule tree. The matching logic not only supports precise matching of administrative division codes but also considers inclusion relationships (e.g., regional coverage, multi-level jurisdiction) and exception clauses (e.g., special customs zones, outlying islands, or duty-free ports), and uses confidence scoring to handle address uncertainties. In cases of multi-source address conflicts, once the region is matched, the second level of product category matching is performed, using a hierarchical matching algorithm based on HS codes—refining from chapters and major categories down to subheadings and tariff items. This algorithm supports precise coding matching and semantic-based fuzzy matching (for records of product descriptions or non-standardized HS codes), and can identify and apply coverage or exclusion logic for special product classification rules (such as luxury goods, food, pharmaceuticals, digital services, etc.). The third level handles transaction amount-related conditions, judging according to the interval divisions and thresholds defined in the rules (including "≤ / > / interval / cumulative amount" types), and supports multiple tax calculation methods such as progressive tax rate interval accumulation calculation (applying marginal tax rates segment by segment), single proportional tax rate calculation, and fixed tax rate calculation, while also considering the processing of minimum / maximum limits, tax exemption thresholds, and deductible items. Finally, upon reaching a leaf node, the system reads the associated calculation parameters (including tax rate, tax base formula, tax exemption or preferential rules and applicable conditions), calculates the tax amount on the normalized transaction amount according to the selected calculation method, follows preset rounding and precision rules, and records all intermediate values. The entire matching and calculation process generates a complete audit record (including rule tree path, matching condition value, parameter ID, rule version number, timestamp and confidence score). In case of matching failure or conflict, an exception code can be returned according to the strategy and manual review can be triggered, or a conservative default (such as applying the highest tax rate) can be adopted to avoid compliance risks.

[0040] As a preferred embodiment: On a certain platform, for a transaction, the geographical location information is first matched to determine that the transaction destination belongs to "high-tax area A". Next, the product attributes are matched to confirm that the product category is "special electronic device" (due to its noise reduction function). Then, the transaction amount is matched, and it is found that the transaction amount of 120 units exceeds the tax-free threshold of 100 units for this category. Finally, the leaf node of the tax rule tree is matched, and the 20% tax rate for special electronic devices is applied, calculating the tax payable to be 24 units. The entire matching process is completed within 200 milliseconds, and the complete matching path is recorded. This strict hierarchical matching mechanism solves the rule coverage problem previously caused by improper matching order.

[0041] Through the above technical solution, this application uses hierarchical matching to match the geographical location information in the transaction feature dataset with the regional conditions of the rule tree, considering the inclusion relationship and exceptions of administrative divisions to ensure the accuracy of tax domain determination; commodity attribute information is matched step by step from major category to minor category according to HS codes, combined with special commodity classification rules, to identify the tax treatment requirements of different commodities; transaction context information (such as transaction amount) is matched with the transaction amount conditions of the rule tree, supporting interval division and progressive tax rate calculation, and realizing accurate processing of boundary transactions and complex amount structures. Based on the calculation parameters of the finally matched leaf nodes, multiple calculation methods such as proportional tax rate, progressive tax rate or fixed tax rate can be executed to generate traceable and structured tax calculation results, and record complete matching paths and parameter information, providing real-time, reliable and compliant tax calculation services for cross-border e-commerce platforms.

[0042] This application further proposes that when the transaction processing unit returns the tax calculation results to the corresponding cross-border e-commerce platform, it includes: When the transaction feature dataset successfully matches the path of the tax rule tree, the rule tree path information during the matching process is recorded as the basis for tax calculation. The basis for tax calculation includes the rule tree path, matching condition value, applicable tax rate and calculation formula. The basis for tax calculation and the tax calculation result are returned to the cross-border e-commerce platform. When the transaction feature dataset cannot match any path in the tax rule tree, the transaction processing unit generates tax calculation exception information and returns it to the cross-border e-commerce platform; the tax calculation exception information is used to explain the conditions for the matching failure.

[0043] Specifically, when the transaction processing unit returns the tax calculation results to the cross-border e-commerce platform, if the transaction feature dataset successfully matches a path in the tax rule tree, the complete matching process will be returned as the basis for tax calculation. This basis is encapsulated in a structured data format (such as JSON or XML) and includes at least: the rule tree path (unique identifiers of nodes listed hierarchically), the condition type and matching condition value of each matching node, the rule version number and build timestamp used, the original document ID or source information referenced, the applicable tax rate and its parameter ID, the calculation formula and intermediate calculation steps used (including tax base, discount / shipping / handling fee processing, currency conversion and the exchange rate and timestamp used), rounding and precision rules, the final calculated tax amount, confidence score and audit hash value. This tax calculation basis and the tax calculation results (tax amount, tax details) are returned to the caller through the same response body, along with response metadata (transaction ID, request time, processing time, rule version effective time and associated audit record location) so that the calling platform can directly save, display or use it for declaration. If the transaction feature dataset cannot match any path in the rule tree, the transaction processing unit will generate and return tax calculation anomaly information. This anomaly information is also provided in a structured format, including a list of unmatched condition types and their corresponding transaction feature values, a textual explanation of the failure reason (e.g., "address resolution failed," "HS code not recognized," "amount range not covered," "rule missing or conflicting"), anomaly classification code and priority, suggested processing actions (e.g., triggering manual review, using a conservative default tax rate, temporarily adopting the most recent similar rule, or retrying data completion), and debugging information for manual or automated processes (e.g., snapshots of relevant rule nodes, summaries of input transaction features, confidence distribution of the most recent matching attempts, external data source status, and timestamps). Regardless of success or anomaly, an immutable log record (including complete request / response payload, rule version, timestamp, and processing node) is written to the internal audit storage. The results can be pushed to the compliance report generation unit or alarm / monitoring module according to the policy to support subsequent declaration, review, and regulatory audit.

[0044] As a preferred embodiment: On a certain platform, for normal transactions, a tax calculation result of 24 units is returned, along with the tax calculation basis: (“Rule Path”: “High Tax Rate Region A” -> “Special Electronic Devices” -> Amount ≥ 100 units”, “Matching Condition Value”: (“Region”: “High Tax Rate Region A”, “Product Category”: “Special Electronic Devices”, “Amount”: 120}, “Applicable Tax Rate”: 20%, “Calculation Formula”: “Amount × Tax Rate”). When encountering a new product, “Smart Health Monitoring Bracelet”, no matching item can be found in the product category conditions, and an error message is returned: “Matching Failed: Product Category “Health Monitoring Devices” is not defined in the rule tree. Please check the product category or update the rule library.” This prompts the seller to improve the product information or triggers the rule update process.

[0045] Through the above technical solution, when the transaction feature dataset successfully matches the tax rule tree path, this application not only returns accurate tax calculation results but also includes complete tax calculation basis, including the rule tree path, each matching condition value, applicable tax rate, and specific calculation formula. This ensures that the platform can clearly trace the source of tax calculation and supports auditing, review, and declaration operations. All matching processes are recorded in internal logs, guaranteeing data traceability and reliability. For transactions that cannot be matched, tax calculation anomaly information is automatically generated, clearly identifying the conditions and possible reasons for the matching failure. This enables the platform to take timely measures such as manual intervention, default tax rates, or rule completion, avoiding tax evasion or incorrect declarations.

[0046] This application further proposes that when the compliance report generation unit generates tax return reports, it includes: Based on the geographical location information in the tax calculation results, select the corresponding tax declaration template from the preset tax declaration template library; fill in the tax payable, transaction details, and tax calculation basis from the tax calculation results into the corresponding positions of the tax declaration template; The transaction records in the original transaction data from the cross-border e-commerce transaction platform are organized in chronological order to form a transaction detail list; The transaction details list and tax calculation results are integrated into the tax return report to generate a formatted tax return report.

[0047] Specifically, the compliance report generation unit uses template-driven and rule-validation as its core when generating tax return reports. Based on the geographic location information in the tax calculation results, it automatically selects the target template from the tax return template library, which is classified and managed by country / region and tax type. Each template in the library contains format definitions (field order, data type, pagination / attachment rules), field mapping (mapping rules from source fields to template fields), localization settings (date / number / time zone / language format), and executable validation rules (required fields, numerical range, cross-consistency checks, and business logic constraints). After selecting a template, the template system maps and populates the tax payable, transaction details, and tax calculation basis (including audit information such as rule path, parameter ID, and rule version) from the tax calculation results into the template fields. The template system supports conditional judgment (if / else), loop (for each), and aggregation operations (grouping and summarizing by tax rate, and accumulating in segments) to adapt to the complex structure of different templates. At the same time, the original transaction data is organized and formatted into a transaction detail list in chronological order (including transaction date, standardized transaction amount, commodity category code, tax base, applicable tax rate, single tax amount, and remarks). Before filling, the amount is subjected to necessary currency conversion, rounding, and pre-tax / post-tax distinction processing. When processing refunds and adjustments, corresponding negative records are also generated according to the template requirements. After the data is populated, the compliance report generation unit performs multi-level format verification on the generated file—structural verification (field types and required fields), content verification (consistency of total amount and tax rate matching), logical verification (time range and reporting period coverage), and localization verification (language and encoding, date / decimal separator). Reports that pass verification are exported in both PDF and XML formats (or PDF + localized XML / CSV) according to target market requirements, and machine-readable appendices (such as JSON or XML snapshots of the calculation basis) can be attached when necessary. During report generation, electronic signatures and timestamps (supporting X.509 certificates or compliance seals) are automatically added, checksums / hashes are generated to ensure file integrity, and the report, its corresponding original transaction snapshot, calculation basis, and rule version information are written to an immutable audit storage (supporting encryption and access control). For reports that fail verification, a structured error list is generated, triggering a manual review workflow or a dry-run submission for correction. The compliance report generation unit can push verified declaration documents to the destination (such as a tax submission interface, third-party compliance services, or the company's internal archiving system) through a configured transmission adapter, and record the submission result, return code, and receipt from the receiving end after the push.

[0048] As a preferred embodiment: On a certain platform, the compliance report generation unit selects the "HTA-VAT-2023" template from the template library based on the geographical location information "High Tax Rate Area A" in the tax calculation results. The unit then fills in the designated locations in the template with the tax payable of 24 units, transaction details (product name, quantity, and amount), and complete tax calculation basis. Simultaneously, it extracts all transaction records for the current month from the original transaction data and organizes them into a transaction detail list in chronological order, containing 128 transactions, a total transaction amount of 15,600 units, and a total tax payable of 3,120 units. The final generated tax return report includes four parts: a cover page, a transaction summary table, a detailed transaction list, and tax calculation instructions.

[0049] Through the above technical solutions, this application intelligently selects the corresponding tax declaration template based on geographic location information, ensuring that the declaration formats and field requirements of different countries or regions are strictly followed, avoiding the risk of declaration failure or penalties due to format or field errors; by using the template system to perform conditional judgment and cyclical filling on tax calculation results and original transaction data, accurate matching of transaction details and tax payable is achieved, ensuring that the declaration data is complete, accurate, and compliant with tax rules; transaction records are organized in chronological order and a standardized transaction detail list is generated, which is integrated into the report along with the tax calculation basis, making the declaration process transparent, traceable, and easy to audit and review.

[0050] This application further proposes that when the compliance report generation unit performs format verification on tax return reports, it includes: Based on the geographical location information in the tax calculation results, determine the language requirements of the target market and translate the report content into the target language; add an electronic signature and timestamp to the translated tax return report; perform format verification on the tax return report after adding the electronic signature and timestamp, including structural verification, content verification, and logical verification; once the format verification is passed, save the verified tax return report to the designated storage location.

[0051] Specifically, when performing format verification on tax return reports, the compliance report generation unit first determines the localization requirements (language, character encoding, date / number format, currency symbols, etc.) of the target market based on the geographical location information in the tax calculation results, and then performs localization conversion on the report content through a machine translation system that prioritizes translation memory and terminology database support; for high-risk or low-confidence paragraphs, items that require manual proofreading can be marked to enable the manual review process. After translation, an electronic signature and timestamp are added to the report. The electronic signature supports PKI-based X.509 certificates or signature methods compliant with target market standards, and records the immutable signing time through a Trusted Timestamp Service (TSA). Subsequently, multi-level format verification is performed on the signed document: at the structural verification level, XSD / Schema validation, element order, and required field checks are performed on XML; for PDFs, compliance checks (e.g., PDF / A or local acceptance format requirements), attachment integrity, and metadata consistency checks are performed; at the content verification level, the data type and value range of fill fields are verified (e.g., amount fields are numeric and decimal places meet requirements), the sum of totals matches the sum of each transaction, currency and exchange rate metadata are matched, and the existence and format of required identification numbers and taxpayer identification information are verified; at the logical verification level, the tax amount is recalculated and compared with the original tax calculation result (including tax rates, progressive or fixed-amount calculation logic, rounding rules, and deductible item processing), the consistency between the reporting period covered by the report and the transaction detail time interval is verified, and exception clauses or local incentives are checked for correct application. If all verifications pass, a cryptographic hash will be generated for the verified report, and the report, along with its calculation basis, rule version number, signature certificate chain, and timestamp metadata, will be saved to a designated compliant storage location (supporting version control, write-once-read-many WORM settings, access control, and crypto-at-rest). Complete audit entries and a searchable index will be recorded. Simultaneously, the subsequent submission process will be triggered, or a success notification will be sent to the relevant systems and users. If verification fails, a structured error list will be generated explaining the failure (including failure rules, field examples, and suggested corrective actions), and the report will be marked as "verification failed." This will trigger an automatic correction strategy (such as feasible data adjustments, rerunning exchange rates, or format correction) or transfer the report to a manual review queue.

[0052] As a preferred embodiment: On a certain platform, for tax return reports targeting a specific language region, the report content is automatically translated into that language, ensuring accurate expression of technical terminology. An electronic signature compliant with the region's laws and a timestamp accurate to the second are added to the end of the report. Subsequently, a triple verification process is performed: structural verification confirms that the report contains all necessary sections in the correct order; content verification checks the completeness and accuracy of key fields (such as tax ID and amount); logical verification ensures that the total transaction amount matches the sum of the details, and that the tax rate application complies with regulations. After successful verification, the report is saved to an encrypted storage area, generating a unique access link.

[0053] Through the above technical solutions, this application automatically translates the report content and adds electronic signatures and timestamps according to the language requirements of the target market, ensuring the legal validity and traceability of the report; through multi-level format checks of structure verification, content verification and logic verification, it ensures that the tax declaration report fully meets the requirements of the target market in terms of format, data integrity and calculation logic, reducing the risk of errors caused by manual filling or rule errors.

[0054] This application further proposes that when the rule update unit performs incremental updates to the tax rule tree, it includes: Regularly check and obtain the latest tax regulations documents; compare the obtained latest tax regulations documents with the currently stored tax regulations documents to identify changes; and update the tax rule tree in the tax rule management unit based on the identified changes.

[0055] Specifically, when continuously monitoring and incrementally updating tax regulations documents in several regions, the rule update unit employs an automated and controllable pipeline-style processing flow to ensure the timeliness, accuracy, and traceability of the rules: First, the monitoring layer uses configurable crawlers and subscribers, combined with an event-driven mechanism, to periodically or in real-time detect and obtain new documents from target regulatory sources. The detection frequency is driven by regional metadata—high-frequency crawling is enabled for regions with frequent regulatory changes, while a low-frequency strategy is adopted for regions with fewer changes, and the timestamp and source identifier of each crawl are recorded. After obtaining the document, the source verifier verifies the credibility of the document source, accepting only sources that have been verified through a whitelist or official certificate (such as official announcement sites, government regulatory databases, or trusted third-party publishing channels), and performs integrity verification on the document (hash, signature, and HTTPS certificate checks). Entries that do not comply with the trust policy are isolated and marked as requiring manual verification. After the preprocessing module normalizes the old and new documents (formatting, sentence segmentation, term alignment), the difference identification module uses a semantic-level comparison algorithm (combining traditional character-by-character or longest common subsequence localization with similarity comparison based on semantic embedding vectors) to identify added, modified, and deleted clauses, and generates a confidence score and change summary for each difference. Subsequently, the change mapper maps the identified clause differences to specific nodes and parameters in the tax rule tree (labeled as added / modified / deprecated). During the mapping process, the unique identifiers of the original nodes of the unchanged parts are preserved as much as possible, and only the affected nodes are generated with new version numbers or new node sequence numbers, thereby achieving incremental updates rather than overall reconstruction. After mapping is complete, the update process performs automated integrity verification in an isolated verification environment. This includes static logical conflict detection (mutual exclusion / override / priority checks), structural consistency checks, dependency checks, and backtracking tests on selected historical transaction samples (replaying historical transactions to compare calculation differences under the old and new rules and calculate impact metrics). If all automatic verifications pass, the update generates a new branch / commit in the version control system (e.g., Git) and produces change records and a backtrackable difference package. Subsequently, it is implemented in stages according to a configurable release strategy (e.g., first in a small-scale or canary environment before full rollout), and a structured update notification (including change summary, scope of impact, list of affected nodes, and effective time) is pushed to relevant systems and users. If the semantic similarity of a change is below a threshold or the backtracking test shows significant differences, the change is marked as low confidence and a manual review workflow is triggered, or it is not allowed to take effect before manual confirmation. At the same time, the rule update unit retains complete historical versions, original document copies, change audit logs, and build timestamps (accurate to milliseconds), supporting rollback operations and dispute tracing.

[0056] As a preferred embodiment: In a certain platform, the rule update unit is configured to automatically detect tax regulation documents for each target market every morning. On a certain day, an update to the tax regulations for a certain region is detected, with the tax exemption threshold adjusted from 100 units to 150 units. After obtaining the latest document, a semantic-level comparison is performed with the currently stored document to identify the specific changes: changes to the tax exemption threshold parameter and the addition of special commodity categories. Based on these changes, only the affected parts of the tax rule tree are updated, while the structure and data of the unchanged parts remain unchanged. The entire update process is completed within 30 minutes without downtime.

[0057] Through the above technical solutions, this application flexibly adjusts the detection strategy according to the frequency of regulatory changes in different regions, ensuring that regulatory changes can be captured in a timely manner; through semantic-level comparison algorithms, it can identify newly added, modified, or deleted clauses in documents and accurately map them to the corresponding nodes in the tax rule tree, realizing incremental updates without destroying the structure and data of the unchanged parts; through the source credibility verification mechanism, it avoids interference from unofficial or unreliable information, ensuring the reliability and legality of rule tree updates.

[0058] This application further proposes that when the rule update unit performs integrity verification on the incrementally updated tax rule tree, it includes: Perform integrity verification on the updated tax rule tree to ensure there are no logical conflicts; apply the updated tax rule tree to historical transaction data for backtesting to verify the accuracy of the update; once the verification is successful, set the updated tax rule tree to the effective state; and send a tax rule tree update notification, which includes the update content, scope of impact, and effective time.

[0059] Specifically, when the rule update unit performs integrity verification on the incrementally updated tax rule tree, it first runs an automated consistency check algorithm on the rule tree to discover potential logical conflicts. This algorithm includes condition coverage checks (checking whether all leaf nodes and their upstream conditions are fully covered by the rule set, identifying uncovered cases or combinations of conditions that may lead to "empty paths"), mutual exclusion condition checks (identifying cases where different branches are logically mutually exclusive but have no priority or reconciliation strategy), and integrity checks (verifying that the referenced calculation parameters, thresholds, and external dependencies all exist and are in the correct format, and verifying that references between nodes do not produce circular dependencies); for each All anomalies generate locatable diagnostic entries and are graded by severity. After passing static checks, the updated rule tree is applied to historical transaction data covering the most recent 12 months in an isolated playback environment to perform backtracking tests. The backtracking tests recalculate and statistically analyze the tax differences before and after the update at the transaction granularity (including absolute differences, relative percentage differences, grouping summaries by region / commodity category / transaction type, and a list of high-impact transactions). At the same time, key indicators such as the overall difference rate, the proportion of abnormal transactions, and the maximum single transaction difference are calculated. If the difference exceeds the preset threshold (configurable), it will be automatically marked as a "high-risk change" and trigger a manual review or rollback process. The update process only allows new rules to be made effective when there are no fatal conflicts in the static consistency check and the impact metrics of the backtracking test are within an acceptable range. The activation adopts a phased strategy—first, the rules are deployed to the test state (test environment / small batch canary traffic) and several runtime metrics (match success rate, anomaly rate, computation latency and backtracking difference comparison) are observed. Then, the rules are automatically or manually switched to the production state. The switching operation generates an explicit commit in version control and the version is marked with a semantic tag. The logs, difference reports and input / output snapshots generated throughout the verification and switching process are recorded and signed for auditing purposes. After the switchover is completed, a structured tax rule tree update notification will be sent. The notification will list the specific update content (summary of added / modified / deprecated clauses, affected rule node IDs and snapshots before and after the change), the scope of impact (affected regions, product categories, transaction types and estimated range of transaction volume or tax amount), and the effective time accurate to the minute in the form of a change log. The notification will also include key statistical summaries of backtesting and information on the person in charge of review, and will include rollback plans and emergency contact information if necessary, to ensure that rule updates are fast, controllable, traceable and safe to roll back in scenarios with frequent changes in regulations.

[0060] As a preferred embodiment: In a certain platform, after the rule update unit completes an incremental update of the tax rules for a certain region, it first performs integrity verification, using a rule consistency check algorithm to ensure that the updated rule tree has no logical conflicts. Then, the new rules are applied to historical transaction data from the past 30 days for backtesting, calculating the difference in tax amounts before and after the update. The test shows that 2.3% of the transaction tax amounts changed after the update, all within a reasonable range with no abnormal differences. After verification, the updated rule tree is set to "test state," and after running for 24 hours in a low-traffic environment to confirm its correctness, it is switched to "production state." At the same time, an update notification is sent to the compliance team, detailing that "the tax exemption threshold has been adjusted from 100 units to 150 units, affecting approximately 15% of transactions, and the new rule will take effect at 00:00 on August 16, 2023."

[0061] Through the above technical solutions, the rule consistency check algorithm of this application can ensure that there are no logical conflicts in the updated tax rule tree, avoiding omissions of conditions, mutual exclusion conflicts or incorrect node references, thereby reducing the risk of calculation anomalies; by backtracking the historical transaction data covered by the test, the difference in tax amount before and after the update can be accurately assessed, verifying the accuracy and feasibility of the update, and ensuring that the rule change will not have a negative impact on the historical transaction processing results; the phased implementation strategy (first verifying in the test environment, and then switching to the production environment) can effectively control the risk of rule application and ensure that the new rules are fully verified before going live.

[0062] In summary, by performing semantic analysis, condition extraction, and parameter extraction on tax regulations documents from multiple regions, a hierarchical tax rule tree is constructed, achieving standardized, structured, and scalable management of tax rules. This avoids omissions and inconsistencies caused by manual rule maintenance, improving the efficiency and accuracy of rule construction. Multi-dimensional data fusion technology links product attributes, transaction context, and geographic location information to form a transaction feature dataset. Combined with multi-level matching logic, applicable tax rates can be automatically selected based on different markets, product categories, and transaction amounts, ensuring consistent, traceable, and highly accurate tax calculations. Based on the tax rule tree, path matching is executed to generate tax calculation results, while simultaneously outputting the tax calculation basis, including rule paths, matching conditions, applicable tax rates, and calculation formulas. This achieves transparency and auditability in tax calculations, improving tax compliance. Combined with a tax declaration template library, the system can automatically fill in tax calculation results and transaction details to form a declaration report, supporting automatic translation, electronic signatures, timestamps, and multi-level format verification. This reduces the workload of manual declarations and improves the standardization and consistency of tax declarations across different markets. By monitoring changes in tax documents and comparing the content of old and new rules to achieve incremental updates of rule paths, and by introducing a version control mechanism to record the build time, rule source, and historical versions, the rules are made traceable, auditable, and finely managed, improving the ability to respond to policy changes in multiple markets. After the rules are updated, logical conflict checks are performed, and backtesting is conducted using historical transaction data, which improves the reliability of rule changes and avoids tax calculation anomalies caused by erroneous updates.

[0063] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A smart compliance and tax calculation system for multi-market cross-border e-commerce, characterized in that, include: The tax rules management unit is configured to perform structured parsing of tax regulations documents from several regions, extract tax calculation conditions and parameters, organize them into a condition judgment tree and a calculation parameter library, and construct a tax rules tree. Each node in the tax rule tree corresponds to a tax calculation condition, and each leaf node corresponds to a tax calculation result. The transaction processing unit is configured to, upon receiving a request from a cross-border e-commerce transaction platform, integrate product attribute information, transaction context information, and geographical location information through multi-dimensional data fusion technology, and correlate and match them to form a transaction feature dataset; perform dynamic rule matching based on the tax rule tree, combine multi-level judgment logic to determine the applicable tax rate and calculation method, perform tax calculation, and generate tax calculation results; The tax calculation results are then returned to the corresponding cross-border e-commerce platform. The compliance report generation unit is configured to generate a tax declaration report based on the tax declaration template library, combined with the tax calculation results and the original transaction data in the request from the cross-border e-commerce transaction platform, through template matching and data filling; and to perform format verification on the tax declaration report. The rule update unit is configured to continuously detect changes in tax regulations documents in several regions, obtain tax update information, incrementally update the tax rule tree, and verify the integrity of the incrementally updated tax rule tree.

2. The intelligent compliance and tax calculation system for multi-market cross-border e-commerce as described in claim 1, characterized in that, When constructing the tax rule tree, the tax rule management unit includes: Semantic analysis was performed on tax regulations documents from several regions to identify tax calculation conditions and parameters in the tax regulations documents. The tax calculation conditions are organized into a hierarchical condition judgment tree, which includes regional conditions, commodity category conditions, transaction amount conditions, and identity conditions. The tax calculation parameters are organized into a calculation parameter library, which includes tax rates, tax exemption thresholds, and special commodity classification rules. A tax rule tree is constructed based on the condition judgment tree and the calculation parameter library. Each node in the tax rule tree corresponds to a tax calculation condition, and each leaf node corresponds to a tax calculation result.

3. The intelligent compliance and tax calculation system for multi-market cross-border e-commerce as described in claim 2, characterized in that, When constructing the tax rule tree, the tax rule management unit also includes: Assign a unique identifier to each node in the tax rule tree; record the construction timestamp and data source information of the tax rule tree; establish a version control mechanism for the tax rule tree and save historical versions of the tax rule tree; when tax regulations documents in several regions change, identify the changed tax calculation conditions and tax calculation parameters by comparing the content of the old and new documents.

4. The intelligent compliance and tax calculation system for multi-market cross-border e-commerce as described in claim 3, characterized in that, When the transaction processing unit associates and matches data to form a transaction feature dataset, it includes: Extract product category, product description, and product value as product attribute information from requests to cross-border e-commerce platforms; Extract the transaction amount, currency, and time from the request from the cross-border e-commerce platform as transaction context information; Extract the shipping address and buyer's location as geographic location information from the request from the cross-border e-commerce transaction platform; The extracted product attribute information, transaction context information, and geographical location information are correlated and matched to form a transaction feature dataset.

5. The intelligent compliance and tax calculation system for multi-market cross-border e-commerce as described in claim 4, characterized in that, When the transaction processing unit generates tax calculation results, it includes: According to the hierarchical structure of the tax rule tree, the geographical location information in the transaction feature dataset is matched with the regional conditions of the tax rule tree; Once the regional conditions are successfully matched, the commodity attribute information in the transaction feature dataset will be matched with the commodity category conditions in the tax rule tree; Once the product category criteria are successfully matched, the transaction context information in the transaction feature dataset is matched with the transaction amount criteria in the tax rule tree. Based on the calculation parameters in the leaf nodes of the final matched tax rules, the tax calculation is performed on the transaction amount in the request from the cross-border e-commerce transaction platform, and the tax calculation result is generated.

6. The intelligent compliance and tax calculation system for multi-market cross-border e-commerce as described in claim 5, characterized in that, When the transaction processing unit returns the tax calculation results to the corresponding cross-border e-commerce platform, it includes: When the transaction feature dataset successfully matches the path of the tax rule tree, the rule tree path information during the matching process is recorded as the basis for tax calculation. The basis for tax calculation includes the rule tree path, matching condition value, applicable tax rate and calculation formula. The basis for tax calculation and the tax calculation result are returned to the cross-border e-commerce platform. When the transaction feature dataset cannot match any path in the tax rule tree, the transaction processing unit generates tax calculation anomaly information and returns it to the cross-border e-commerce platform; the tax calculation anomaly information is used to explain the conditions for the matching failure.

7. The intelligent compliance and tax calculation system for multi-market cross-border e-commerce as described in claim 6, characterized in that, When the compliance report generation unit generates a tax return report, it includes: Based on the geographical location information in the tax calculation results, select the corresponding tax declaration template from the preset tax declaration template library; fill in the tax payable, transaction details, and tax calculation basis from the tax calculation results into the corresponding positions of the tax declaration template; The transaction records in the original transaction data from the cross-border e-commerce transaction platform are organized in chronological order to form a transaction detail list; The transaction details list and tax calculation results are integrated into the tax return report to generate a formatted tax return report.

8. The intelligent compliance and tax calculation system for multi-market cross-border e-commerce as described in claim 7, characterized in that, When the compliance report generation unit performs format verification on the tax return report, it includes: Based on the geographical location information in the tax calculation results, determine the language requirements of the target market and translate the report content into the target language; add an electronic signature and timestamp to the translated tax return report; perform format verification on the tax return report after adding the electronic signature and timestamp, the format verification including structural verification, content verification and logical verification; when the format verification is passed, save the verified tax return report to the designated storage location.

9. The intelligent compliance and tax calculation system for multi-market cross-border e-commerce as described in claim 8, characterized in that, When the rule update unit performs incremental updates to the tax rule tree, it includes: Regularly check and obtain the latest tax regulations documents; compare the obtained latest tax regulations documents with the currently stored tax regulations documents to identify changes; and update the tax rule tree in the tax rule management unit based on the identified changes.

10. The intelligent compliance and tax calculation system for multi-market cross-border e-commerce as described in claim 9, characterized in that, When the rule update unit performs integrity verification on the incrementally updated tax rule tree, it includes: The updated tax rule tree is subjected to integrity verification to ensure that there are no logical conflicts. The updated tax rule tree is then applied to historical transaction data for backtesting to verify the accuracy of the update. Once the verification is successful, the updated tax rule tree is set to the effective state. A tax rule tree update notification is then sent, which includes the update content, scope of impact, and effective time.

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