Cross-transaction system accounting function separation method based on rule engine

Abstract

The invention discloses a cross-transaction system accounting function separation method based on a rule engine, and belongs to the technical field of financial accounting, in the method, the accounting function of a traditional transaction system is decoupled through the rule engine, and the agility and compliance capability of financial transaction processing are effectively improved. A core accounting rule is abstracted as an independent execution unit, so that a bottom layer system does not need to be reconstructed in business operations such as rate adjustment and account division strategy change, and the new rule online period is compressed from a week level to an hour level. The multi-level rule architecture supports a mixed processing mode of real-time transaction and batch correction, can meet millisecond-level liquidation requirements in a cross-border payment scene, can automatically adapt to dynamic compliance strategies of different supervision jurisdictions, and realizes parallel computing and accurate matching of global regional transaction rules. The continuity of key transactions is guaranteed by a geo-fence routing and intelligent traffic segmentation mechanism.

Description

Technical Field

[0001] The present invention belongs to the technical field of financial accounting, and specifically relates to a method for separating accounting functions across transaction systems based on a rule engine. Background Art

[0002] In traditional financial IT architectures, transaction processing and accounting functions are generally tightly coupled. For example, in commercial bank credit operations, after completing business processes such as customer credit approval, contract signing, and loan execution, the core credit system must parse transaction data in real time using a built-in accounting engine. Based on pre-set accounting entry configuration rules (e.g., loan principal debits the customer account and credits the loan account, and interest income credits the interest account), it generates structured accounting entries and temporarily stores them in a local database. Similarly, when processing interbank lending, fund collection and payment transactions, and other services, the fund transaction system also relies on a built-in accounting rule library to convert transaction instructions into accounting voucher data. Each business system employs different accounting logic implementation methods based on its domain characteristics: some systems implement accounting rules through database stored procedures, while others hard-code accounting logic into the business code layer.

[0003] This architectural design leads to multiple technical flaws:

[0004] First, the fragmentation of accounting rules creates data governance challenges. Because each transaction system independently maintains accounting configurations (such as account mapping tables, allocation algorithms, and tax rate calculation models), similar transactions may have different entry structures in different systems.

[0005] Second, distributed transaction consistency is difficult to ensure. Under the current architecture, transaction processing and accounting are bundled into the same database transaction. If a loan transaction succeeds but journal entry generation fails, the system must roll back the entire process, negatively impacting the customer experience.

[0006] Third, system scalability is severely limited. With each new financial product (such as green credit and supply chain finance), the development team must adapt the code at both the business logic layer and the accounting rules layer. When a city commercial bank launched its consumer finance business, configuring accounting rules accounted for 42% of the overall development cycle, and the bank also needed to coordinate rule reviews with six departments, including the head office's Finance Department, Technology Department, and external audit firm.

[0007] However, the improvement solutions currently adopted by the industry have obvious limitations:

[0008] Middleware data conversion solution: Deploy ETL tools between the transaction system and the general ledger system to standardize the format of heterogeneous journal entries. However, this solution cannot solve the problem of inconsistent accounting rules at the source, and the cost of maintaining conversion rules is high.

[0009] Microservice transformation solution: The accounting function is split into independent services, but it does not solve the fundamental problem of decentralized management of the rule base. Summary of the Invention

[0010] The purpose of the present invention is to provide a method for separating accounting functions across transaction systems based on a rule engine in order to solve the above-mentioned problems.

[0011] The technical solution adopted by the present invention is as follows: a method for separating accounting functions across transaction systems based on a rule engine, the method comprising the following steps:

[0012] S1: Establish a hierarchical model for cross-system accounting rules, dividing core billing, account write-off, and clearing and settlement logic into independent rule domains, and defining rule granularity and effectiveness priority levels based on business scenarios;

[0013] S2: Build a rules metadata center to standardize over 30 key data elements for interactions between various trading systems, including transaction flow fingerprints, multi-currency exchange rate benchmarks, and strong verification fields for the account splitting ratio matrix;

[0014] S3: Deploy a dual-mode rule engine cluster. The primary engine uses Flink+Groovy to support millisecond-level real-time accounting, and the secondary engine uses Drools to handle T+1 batch correction scenarios, achieving concurrent support of more than 20,000 TPS.

[0015] S4: Implement intelligent routing of transaction messages, automatically identify accounting paths through transaction type codes, dynamically divert traffic to corresponding rule execution nodes, and support parallel calculation of billing strategies for cross-border transactions in multiple regulatory jurisdictions;

[0016] S5: Develop a rule sandbox verification environment, inject full snapshot data of historical transactions into the simulation run, establish a comparison matrix of accounting result differences, and ensure that the numerical accuracy error after rule migration is less than 0.001%;

[0017] S6: Design a hot-swap mechanism for rule versions and implement the grayscale release of new rules using the ABtest traffic diversion controller. This supports traffic segmentation based on transaction amount segments and merchant categories, over 200 combinations.

[0018] S7: Build a distributed rule execution tracking chain, connect cross-system accounting steps through global transaction IDs, generate rule hit profiles in real time, and accurately count the calculation time and resource consumption of each link;

[0019] S8: Deploy an elastic rule-based decision cluster to automatically scale container instances based on transaction traffic fluctuations. Establish a rule execution circuit breaker mechanism to automatically switch to a backup rule set when the abnormal transaction ratio exceeds a threshold.

[0020] S9: Establish an intelligent rule optimization closed loop, analyze historical execution logs through machine learning, automatically identify inefficient rules and perform logical compression, and continuously output rule weight adjustment suggestions and failure warnings.

[0021] In a preferred embodiment, step S1 constructs a three-level rule architecture for transaction accounting. The basic rate model covers 12 billing scenarios, including real-time payments, cross-border remittances, and installment settlements. Each model presets over 50 billing parameters and supports gradient calculation. Fund allocation templates are divided into eight business types, including direct sales split, channel sharing, and platform service fee deduction models. Each template has 15 built-in allocation ratio verification rules.

[0022] In a preferred embodiment, in step S2, the metadata center configures a real-time verification pipeline for 34 core fields. Transaction fingerprints utilize a timestamped cryptographic hash algorithm to ensure zero duplication risk for 10 years. The exchange rate benchmark is integrated with six major international clearing house data sources, cross-validated three times per second, and abnormal fluctuations exceeding 0.5% trigger immediate manual review. A three-dimensional account splitting matrix offers over 3,000 preset combinations.

[0023] In a preferred embodiment, in step S3, the real-time processing cluster is deployed using Flink version 1.14, each computing node is configured with 32 cores and 128GB of memory, and SSD storage ensures that the rule function library is loaded within 50ms.

[0024] In a preferred embodiment, in step S4, the routing system's built-in 18-digit transaction code uses a segmented parsing mechanism. The first three digits identify the service category, the middle six digits define the billing characteristics, and the final digit uses a modulo-97 checksum to prevent tampering. The geofencing module integrates an IP library and GPS dual positioning, providing real-time analysis of regulatory policies covering over 200 countries and regions. It automatically activates a special tax rate calculation unit when a transaction involves a free trade zone.

[0025] In a preferred embodiment, in step S5, the verification environment is equipped with a historical data playback engine, injecting over 5,000 real transaction flows per second. The benchmark library contains 100,000 test cases, with 200 new exception scenarios added daily. The amount verification layer utilizes a high-precision calculation framework, retaining 8 decimal places. Cross-border transaction conversion error detection utilizes a two-way comparison mechanism, simultaneously verifying reverse calculation results in both the source and target currencies.

[0026] In a preferred embodiment, in step S6, the canary release system implements a three-tiered traffic gate. New rules must pass low-volume verification before being gradually released. The initial traffic ratio is 0.01%, increasing exponentially every 30 minutes. The version rollback module pre-stores three historical stable versions and simultaneously clears the rule cache during rollback, ensuring a pristine state within 10 seconds.

[0027] In a preferred embodiment, in step S7, the tracking chain generates a 32-bit global ID using a snowflake algorithm, embedding a 6-bit data center code and a 12-bit millisecond timestamp. The log collection system deploys edge computing nodes, completing rule hit marking and extracting 20 resource consumption statistics within 50ms intervals.

[0028] In a preferred embodiment, in step S8, the elastic cluster is configured with a horizontal expansion trigger. Expansion is triggered when CPU utilization exceeds 75% or memory utilization exceeds 65% for three consecutive minutes. Each newly added node does not exceed 25% of the current total. The circuit breaker mechanism implements a progressive degradation strategy: upon the first anomaly, the rate is reduced to 80% throughput. If the anomaly persists, the rate is reduced by 20% every five minutes until the backup rule is switched.

[0029] In a preferred embodiment, step S9 involves building an intelligent rule optimization engine, deploying a hybrid XGBoost and LSTM model to continuously analyze 30 days of rolling execution logs. The model input dimensions include rule hit frequency, execution time, and over 50 feature indicators such as anomaly trigger rate. The training data covers 2 billion rule execution records from the past two years, with model parameters updated every 8 hours. An impact assessment report is generated simultaneously with the output of rule weight adjustment recommendations.

[0030] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0031] 1. In the present invention, the accounting function of the traditional transaction system is decoupled through the rule engine, which effectively improves the agility and compliance capabilities of financial transaction processing. The core accounting rules are abstracted into independent execution units, so that business operations such as rate adjustments and account splitting strategy changes do not need to reconstruct the underlying system, and the new rule launch cycle is compressed from weeks to hours. The multi-level rule architecture supports a hybrid processing mode of real-time transactions and batch corrections, which can not only meet the millisecond-level clearing requirements in cross-border payment scenarios, but also automatically adapt to the dynamic compliance policies of different regulatory jurisdictions to achieve parallel calculation and precise matching of global regional transaction rules. Geographical fence routing and intelligent traffic splitting mechanisms ensure the continuity of key transactions, and can maintain more than 99.99% business availability during system upgrades.

[0032] 2. This invention establishes continuous evolution capabilities through full-chain tracking and intelligent optimization, significantly enhancing the stability of the complex trading ecosystem. The rule sandbox verification and phased release system reduces the risk of production environment changes by over 90%. The abnormal transaction circuit breaker mechanism completes policy switching within 20 seconds, minimizing business losses. Elastic resource scheduling, combined with rule execution profiling, enables system throughput to automatically scale with transaction volume, improving resource utilization while maintaining accounting accuracy to less than one ten-thousandth. The machine learning-driven rule optimization closed loop continuously streamlines inefficient logic, forming a virtuous cycle of co-evolution between business needs and technical capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a schematic diagram of the process principle of the present invention. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0035] Example:

[0036] Reference Figure 1 ,

[0037] A method for separating accounting functions across transaction systems based on a rule engine, the method comprising the following steps:

[0038] S1: Establish a hierarchical model for cross-system accounting rules, divide core billing, account write-off, and clearing and settlement logic into independent rule domains, and define rule granularity and effectiveness priority levels based on business scenarios

[0039] S2: Build a rule metadata center to standardize 30+ key data elements of the interaction between various trading systems, including transaction flow fingerprints, multi-currency exchange rate benchmarks, account split ratio matrices, and other strong verification fields

[0040] S3: Deploy a dual-mode rule engine cluster. The main engine uses Flink+Groovy to support millisecond-level real-time accounting. The auxiliary engine uses Drools to handle T+1 batch correction scenarios, achieving concurrent support of more than 20,000 TPS.

[0041] S4: Implement intelligent routing of transaction messages, automatically identify accounting paths through transaction type codes, dynamically divert traffic to corresponding rule execution nodes, and support parallel calculation of billing strategies for multiple regulatory jurisdictions for cross-border transactions

[0042] S5: Develop a rule sandbox verification environment, inject full snapshot data of historical transactions for simulation, establish a comparison matrix for accounting result differences, and ensure that the numerical accuracy error after rule migration is less than 0.001%

[0043] S6: Design a hot-switch mechanism for rule versions, use the ABtest traffic diversion controller to implement grayscale release of new rules, and support traffic segmentation based on 200+ dimensional combinations such as transaction amount segments and merchant categories.

[0044] S7: Build a distributed rule execution tracking chain, connect cross-system accounting steps through global transaction IDs, generate rule hit profiles in real time, and accurately count the calculation time and resource consumption of each link

[0045] S8: Deploy an elastic rule decision cluster, automatically scale container instances based on transaction flow fluctuations, establish a rule execution circuit breaker mechanism, and automatically switch to a backup rule set when the abnormal transaction ratio exceeds the threshold.

[0046] S9: Establish an intelligent rule optimization closed loop, analyze historical execution logs through machine learning, automatically identify inefficient rules and perform logical compression, and continuously output rule weight adjustment suggestions and failure warnings.

[0047] In step S1, a three-level rule architecture for transaction accounting is constructed. The basic rate model covers 12 billing scenarios, including real-time payment, cross-border remittance, and installment settlement. Each model has 50+ pre-set billing parameters and supports gradient calculation. Fund allocation templates are divided into 8 business types, including direct sales split, channel split, platform service fee split, etc. Each template has 15 built-in allocation ratio verification rules. The 5-level account adjustment strategy sets a difference handling mechanism. The emergency level strategy response time is less than 100ms. Offline processing allows a maximum delay of 12 hours. The rule weight coefficient adopts a dynamic range of 0.1-1.0. When a cross-border transaction involves more than three regulatory regions, it automatically triggers a composite weight superposition calculation.

[0048] In step S2, the metadata center configures a real-time validation pipeline for 34 core fields. Transaction fingerprints utilize a timestamped cryptographic hash algorithm to ensure zero duplication risk for 10 years. The exchange rate benchmark is integrated with six major international clearing house data sources, cross-validated three times per second, and abnormal fluctuations exceeding 0.5% trigger immediate manual review. The three-dimensional account matrix features over 3,000 preset combinations and supports dynamic adjustments based on merchant annual transaction volume, product gross margin, and regional tax policies. Changes take effect with minute-level accuracy. The data validation layer employs a dual filtering mechanism, with format validation including 20 regular expression templates and logical pre-check coverage.

[0049] In step S3, the real-time processing cluster was deployed using Flink version 1.14. Each compute node was equipped with 32 cores and 128GB of memory. SSD storage ensured that the rule library loaded within 50ms. The Groovy scripting engine pre-installed 12 types of algorithm function packages, including core methods such as fee accumulation calculation and multi-currency instant conversion, keeping the execution time of a single script under 5ms. The batch engine cluster was configured with a 16-node Kubernetes container group. The Drools rule library implemented layered loading technology, reducing the loading time of 500,000 data volumes to less than 90 seconds, and achieving a rule matching efficiency of 8,000 records per second.

[0050] In step S4, the routing system's built-in 18-digit transaction code uses a segmented parsing mechanism. The first three digits identify the business category, the middle six digits define the billing characteristics, and the final digit uses a modulo-97 checksum to prevent tampering. The geofencing module integrates an IP library and GPS positioning, providing real-time analysis of regulatory policies covering over 200 countries and regions. When a transaction involves a free trade zone, a special tax rate calculation unit is automatically activated. The multi-strategy parallel engine is configured with eight isolated thread pools, with a single policy execution timeout threshold set to 800ms. The exchange rate lock service supports differentiated management of seven clearing channels, with a minimum lock period of 15 minutes and a maximum of 72 hours.

[0051] In step S5, the verification environment is equipped with a historical data playback engine, injecting over 5,000 real transaction flows per second. The benchmark library contains 100,000 test cases, with 200 new exception scenarios added daily. The amount verification layer utilizes a high-precision calculation framework, retaining 8 decimal places. A two-way comparison mechanism is implemented to detect cross-border transaction conversion errors, simultaneously verifying reverse calculation results in both the source and target currencies. During the stress testing phase, three levels of elastic scaling are configured, with an initial cluster processing capacity of 10,000 TPS, which automatically scales to 50,000 TPS during peak periods. System availability must reach 99.999% for the 72-hour test period.

[0052] In step S6, the grayscale release system sets up three layers of traffic gates. New rules must pass low-volume verification before being gradually released. The initial traffic ratio is 0.01%, increasing exponentially every 30 minutes. The version rollback module pre-stores three historical stable versions and simultaneously clears the rule cache during rollback, ensuring a pristine state within 10 seconds. The ABtest comparison engine implements real-time traffic diversion calculations, with data statistics latency controlled to less than 8 seconds. Key metrics include fee discrepancy rate, account splitting accuracy, and clearing time, among other 15 comparative analysis dimensions.

[0053] In step S7, the tracking chain generates a 32-bit global ID using the snowflake algorithm, embedding a 6-bit data center code and a 12-bit millisecond timestamp. The log collection system deploys edge computing nodes, extracting 20 metrics within 50ms intervals, including rule hit marking and resource consumption statistics. Columnar storage implements the ZSTD compression algorithm, achieving a compression ratio of 1:8 and a historical log retention period of 45 days. The monitoring screen integrates a rule heat map, displaying key data such as transaction volume by region, rule execution time percentiles, and abnormal transaction distribution in real time. It supports millisecond response times for 100 concurrent users.

[0054] In step S8, the elastic cluster configures a horizontal expansion trigger. When the CPU usage exceeds 75% for 3 consecutive minutes or the memory usage exceeds 65%, the expansion is triggered. The number of newly added nodes each time does not exceed 25% of the current total. The circuit breaker mechanism implements a progressive degradation strategy. The first abnormality triggers the flow limit to reduce the speed to 80% of the throughput. If the abnormality persists, the flow will be reduced by 20% every 5 minutes until the backup rules are switched. The backup rule base implements a differential synchronization mechanism, comparing the differences between the primary and backup rules every 15 seconds. The data delay tolerance is set to within 20 transaction cycles of the primary library operation to ensure that the data integrity error does not exceed 0.01% during emergency switching.

[0055] In step S9, an intelligent rule optimization engine needs to be built, and a hybrid model of XGBoost and LSTM needs to be deployed to continuously analyze the 30-day rolling execution log. The model input dimensions include more than 50 characteristic indicators such as rule hit frequency, execution time, and abnormal trigger rate. The training data covers 2 billion rule execution records in the past two years, and the model parameters are updated every 8 hours. An impact assessment report is generated simultaneously when the rule weight adjustment suggestions are output. The failure warning module sets up a three-layer detection mechanism to monitor the fluctuation of the rule matching success rate in real time. When it drops by 0.3% above the baseline value for 15 consecutive minutes or the abnormal trigger rate exceeds 2‰, an alarm is automatically pushed. The system outputs a daily rule streamlining plan, implements logical compression for the 10% of rules with the lowest execution efficiency, and reduces the average single rule condition judgment node by 40%, ensuring that the overall execution time of the rule base is optimized by no less than 15% quarter-on-quarter.

[0056] From the above we can know:

[0057] In this invention, a three-level architecture of transaction code + transaction subcode + cleaning rules is used to achieve deep decoupling of business operations and accounting rules. The transaction code defines the business category, the transaction subcode identifies the smallest operation unit, and the data cleaning rules implement field-level mapping based on dynamic SQL templates.

[0058] In this invention, through the rule hot loading mechanism, rule changes can take effect without restarting the service. The new rule uses the version number + effective identification double field to control the effective scope, supporting grayscale verification based on business system and traffic ratio.

[0059] This invention uses a dual-scaling solution: Redis shard locks and dynamic thread pools. Sharding is based on business system ID and date hash. A single shard supports 5,000 transactions per second (TPS), and capacity can be increased by horizontally scaling the number of shards. Parallelism is dynamically adjusted based on CPU load, resulting in a 40% increase in resource utilization.

[0060] This invention significantly reduces operation and maintenance costs through rule self-maintenance and intelligent optimization algorithms. A visual drag-and-drop interface generates rule code, reducing manual intervention by 86%. High-frequency rules are analyzed based on historical data and automatically loaded into the Redis cache, reducing response time from 15ms to 0.5ms.

[0061] This invention, through three major technological innovations: rule engine decoupling, elastic architecture expansion, and an intelligent verification engine, builds a next-generation financial infrastructure that separates transactions from accounting. Verified by the company's production, the system maintains 99.99% availability even with tens of millions of transactions, while reducing the cost of changing a single rule by 95%. This provides a standardized solution for financial institutions to address accounting standard iterations and business expansion, with significant industry-wide adoption potential.

[0062] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprises" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further limitations, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device that includes the element.

[0063] The above description is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for separating accounting functions across trading systems based on a rule engine, characterized by: The method comprises the following steps: S1: Establish a hierarchical model for cross-system accounting rules, dividing core billing, account write-off, and clearing and settlement logic into independent rule domains, and defining rule granularity and effectiveness priority levels based on business scenarios; S2: Build a rules metadata center to standardize over 30 key data elements for interactions between various trading systems, including transaction flow fingerprints, multi-currency exchange rate benchmarks, and strong verification fields for the account splitting ratio matrix; S3: Deploy a dual-mode rule engine cluster. The primary engine uses Flink+Groovy to support millisecond-level real-time accounting, and the secondary engine uses Drools to handle T+1 batch correction scenarios, achieving concurrent support of more than 20,000 TPS. S4: Implement intelligent routing of transaction messages, automatically identify accounting paths through transaction type codes, dynamically divert traffic to corresponding rule execution nodes, and support parallel calculation of billing strategies for cross-border transactions in multiple regulatory jurisdictions; S5: Develop a rule sandbox verification environment, inject full snapshot data of historical transactions into the simulation run, establish a comparison matrix of accounting result differences, and ensure that the numerical accuracy error after rule migration is less than 0.001%; S6: Design a hot-swap mechanism for rule versions and implement the grayscale release of new rules using the ABtest traffic diversion controller. This supports traffic segmentation based on transaction amount segments and merchant categories, over 200 combinations. S7: Build a distributed rule execution tracking chain, connect cross-system accounting steps through global transaction IDs, generate rule hit profiles in real time, and accurately count the calculation time and resource consumption of each link; S8: Deploy an elastic rule-based decision cluster to automatically scale container instances based on transaction traffic fluctuations. Establish a rule execution circuit breaker mechanism to automatically switch to a backup rule set when the abnormal transaction ratio exceeds a threshold. S9: Establish an intelligent rule optimization closed loop, analyze historical execution logs through machine learning, automatically identify inefficient rules and perform logical compression, and continuously output rule weight adjustment suggestions and failure warnings.

2. The method for separating accounting functions across trading systems based on a rule engine according to claim 1, characterized in that: In step S1, a three-level rule architecture for transaction accounting is constructed. The basic rate model covers 12 billing scenarios, including real-time payment, cross-border remittance, and installment settlement. Each model presets 50+ billing parameters and supports gradient calculation. The fund allocation template is divided according to 8 business types, including direct sales account sharing, channel sharing, and platform service fee deduction models. Each template has 15 built-in allocation ratio verification rules.

3. The method for separating accounting functions across trading systems based on a rule engine as claimed in claim 1, characterized in that: In step S2, the metadata center configures a real-time verification pipeline for 34 core fields, and the transaction flow fingerprint adopts a timestamp-based encrypted hash algorithm to ensure no duplication risk within 10 years. The exchange rate benchmark value is connected to the data sources of six major international clearing houses, and cross-validation is performed three times per second. Abnormal fluctuations exceeding 0.5% immediately trigger manual review. The three-dimensional account matrix is ​​set with 3000+ preset combinations.

4. The method for separating accounting functions across trading systems based on a rule engine according to claim 1, wherein: In step S3, the real-time processing cluster is deployed using Flink version 1.

14. Each computing node is configured with 32 cores and 128 GB of memory. SSD storage ensures that the rule function library is loaded within 50 ms.

5. The method for separating accounting functions across trading systems based on a rule engine as claimed in claim 1, wherein: In step S4, the 18-digit transaction code built into the routing system adopts a segmented parsing mechanism, with the first three digits identifying the business category, the middle six digits defining the billing characteristics, and the last digit check code implementing the modulo 97 algorithm to prevent tampering; the geo-fence module integrates the IP library and GPS dual positioning, and parses the regulatory policies covering more than 200 countries and regions in real time, and automatically activates the special tax rate calculation unit when the transaction involves a free trade zone.

6. The method for separating accounting functions across trading systems based on a rule engine as claimed in claim 1, characterized in that: In step S5, the verification environment is equipped with a historical data playback engine, which injects 5,000+ real transaction flows per second. The benchmark library contains 100,000 test cases and 200 new abnormal scenarios are added every day. The amount verification layer adopts a high-precision calculation framework, retaining 8 digits of calculation accuracy after the decimal point. The cross-border transaction conversion error detection implements a two-way comparison mechanism, and simultaneously verifies the reverse calculation results of the original currency and the target currency.

7. The method for separating accounting functions across trading systems based on a rule engine according to claim 1, wherein: In step S6, the grayscale release system sets a three-layer traffic gate. New rules must be gradually opened after passing small-volume verification. The initial traffic ratio is 0.01%, which increases exponentially every 30 minutes. The version rollback module pre-stores three historical stable versions and synchronously clears the rule cache during rollback to ensure that it is restored to a pure state within 10 seconds.

8. The method for separating accounting functions across trading systems based on a rule engine according to claim 1, wherein: In step S7, the tracking chain generates a 32-bit global ID using a snowflake algorithm, embedding a 6-bit data center code and a 12-bit millisecond timestamp; the log collection system deploys edge computing nodes, and completes rule hit marking and resource consumption statistics within 50ms intervals, extracting 20 indicators.

9. The method for separating accounting functions across trading systems based on a rule engine as claimed in claim 1, wherein: In step S8, the elastic cluster configures a horizontal expansion trigger, which triggers expansion when the CPU usage exceeds 75% for 3 consecutive minutes or the memory usage exceeds 65%. The number of newly added nodes each time does not exceed 25% of the current total. The circuit breaker mechanism implements a progressive degradation strategy. The first abnormality triggers the flow limit to reduce the throughput to 80%. If the abnormality continues, the flow will be reduced by 20% every 5 minutes until the backup rule is switched.

10. The method for separating accounting functions across trading systems based on a rule engine according to claim 1, wherein: In step S9, an intelligent rule optimization engine needs to be built, and an XGBoost and LSTM hybrid model needs to be deployed to continuously analyze the 30-day rolling execution log. The model input dimensions include rule hit frequency, execution time, and abnormal trigger rate 50+ feature indicators; The training data covers 2 billion rule execution records in the past two years. The model parameters are updated every 8 hours, and an impact assessment report is generated simultaneously when outputting rule weight adjustment suggestions.

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