A financial voucher data processing method and system

Abstract

This invention discloses a method and system for processing financial voucher data, relating to the field of financial information technology and data processing. It aims to solve the technical problems of unstable access, inconsistent formats, inefficient processing, and insufficient compliance of financial data from multiple business systems. The method includes: collecting raw financial data from multiple business systems through a standardized interface pool containing various pre-built interface templates and expandable interface configuration channels; after preprocessing and preliminary field-level verification, a rule engine automatically matches and executes basic verification, business logic verification, and financial mapping rules based on the data source, type, and business scenario; completing accounting subject classification, accounting dimension matching, and other processing to form standardized accounting entry data; and then synchronizing it to the target financial accounting system through a versioned interface template to generate accounting vouchers and perform automatic compliance audits. The system includes corresponding functional modules to implement the above method.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method for processing financial voucher data between a multi-business system and a financial accounting system. Background Technology

[0002] In the field of corporate financial management, with the continuous expansion of business scale and the increasing complexity of business models, the financial data generated by various business systems has become the foundation for supporting corporate decision-making and compliant operations. This data directly affects the accuracy of financial statements, the compliance of tax declarations, and the efficiency of internal management. Therefore, how to efficiently and accurately process financial data from different business processes is particularly crucial. Currently, enterprises generally face practical difficulties in data access and processing when connecting data from multiple business systems. Different business systems adopt their own independent development logic and data organization methods, resulting in significant differences in the output of raw financial data in terms of field naming, format structure, amount precision, and time representation. For example, a sales system may record an amount as "sales revenue," while a purchasing system may use "purchasing cost" to represent the same type of amount but in the opposite direction; some systems record dates by calendar day, while others summarize by accounting period; some directly output detailed amounts, while others have pre-summarized some amounts according to internal rules. These apparent inconsistencies make it difficult to achieve stable and reliable access by relying solely on traditional interfaces or ETL (Extraction Transformation Loading) tools. Once a business system is upgraded or the meaning of a field is adjusted, the original connection relationship will be interrupted, and the adaptation logic needs to be redeveloped.

[0003] Therefore, how to achieve stable, efficient, and accurate unified access, business logic verification, precise mapping of accounts and dimensions, and seamless integration with different versions of financial accounting systems when multiple business systems continuously generate raw financial data with different formats and implicit business meanings has become a key issue facing current enterprise financial data processing. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a financial voucher data processing method and system, which solves the core issues in the prior art where adding a new business system requires redeveloping interfaces and manually maintaining rules, and where error verification relies on manual verification of each item, lacking a systematic difference tracing mechanism, leading to the recurrence of similar errors and long monthly closing cycles.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: On one hand, the present invention provides a method for processing financial voucher data, the method comprising: Raw financial data from multiple business systems is collected through a standardized interface pool. This raw financial data undergoes preprocessing and initial field-level validation. A rule engine deployed in the financial middle platform parses configurable rule scripts into executable instructions based on data source, data type, and business scenario. These executable instructions are then used to perform pattern matching on data features, automatically matching and executing basic validation rules, business logic validation rules, and financial mapping rules. Based on the financial mapping rules, the validated data is categorized by accounting subject, matched by accounting dimensions, and subjected to accounting entry template application and special business financial logic calculations to form standardized accounting entry data. This standardized accounting entry data is automatically synchronized to the target financial accounting system via a pre-configured versioned financial system interface template to generate accounting vouchers. Finally, the generated accounting vouchers undergo automatic compliance review based on pre-configured review rule links.

[0006] Furthermore, the standardized interface pool includes multiple pre-built interface templates and an extensible interface configuration channel.

[0007] Furthermore, the preset interface templates include one or more of the following: RESTful interface template, WebService interface template, SFTP file transfer template, structured database direct connection template, and table file batch import template.

[0008] Furthermore, the scalable interface configuration channel completes template selection and adaptation through parameter configuration.

[0009] Furthermore, when a new data source is added, the scalable interface configuration channel completes template selection and adaptation by configuring system identifier, authentication information, transmission frequency, and data type range through parameters.

[0010] Furthermore, the preprocessing and preliminary field-level validation include enforcing decimal place control for amount fields, standardizing the format of date fields, and performing preset encoding library matching on key encoding fields.

[0011] Furthermore, the step of parsing the configurable rule script into executable instructions includes: segmenting the rule script into token sequences through lexical analysis, parsing the token sequences into a syntax tree based on preset syntax rules through syntactic analysis, and traversing the syntax tree to generate an executable instruction sequence containing opcodes and operands.

[0012] Furthermore, the step of performing pattern matching on data features based on the executable instructions includes: extracting key feature values ​​from the input data, comparing the key feature values ​​with the conditional instructions in the executable instruction sequence one by one, and executing the corresponding action instruction to complete the verification logic or mapping logic when the key feature value satisfies all the conditional instructions.

[0013] Furthermore, the basic validation rules include data type consistency checks, field length range checks, mandatory field existence checks, and numerical reasonableness range checks.

[0014] Furthermore, the business logic verification rules include cross-field association constraint checks, dynamic calculation formula verification, and industry-specific compliance constraint checks.

[0015] Furthermore, the financial mapping rules include a financial mapping rule library, which at least includes the correspondence between business types and accounting subjects, the correspondence between business types and accounting dimensions, and the correspondence between business scenarios and journal entry templates.

[0016] Furthermore, the special business financial logic calculation includes automatic calculation of fixed asset depreciation, automatic provision of bad debts based on aging or proportion, automatic calculation of the amount of revenue and cost carried forward at the end of the period, and automatic calculation of taxes and fees based on tax rate rules; when the accounting entry template is applied, the business summary and the original business voucher association identifier are automatically populated.

[0017] Furthermore, the versioned financial system interface template includes at least the interface adaptation templates of Kingdee and / or Yonyou.

[0018] Furthermore, the automatic synchronization supports both real-time push mode and timed batch mode.

[0019] Furthermore, the automatic synchronization process includes a breakpoint resume mechanism, a bidirectional core field comparison mechanism after synchronization is completed, and an automatic rollback to the most recent consistent state when synchronization fails.

[0020] Furthermore, the pre-configured audit rule chain supports configuring one or more combinations of single-level audit, multi-level sequential audit, and conditional branch audit based on organizational level, job responsibilities, and business amount.

[0021] Furthermore, accounting vouchers that fail the compliance review are automatically associated with anomaly rule identifiers, original business data, and responsibility push nodes, and automatically enter the archiving process after the review is passed.

[0022] Furthermore, when executing verification rules, the rule engine classifies and marks abnormal data into three levels: minor, moderate, and severe. Minor anomalies are automatically repaired by the system and a repair log is recorded. Moderate anomalies are temporarily stored and pushed to the configured responsible person's processing interface. Severe anomalies immediately block subsequent processing and trigger an alarm notification.

[0023] Furthermore, the standardized accounting entry data formation process also includes performing unified format processing on the verified data, identifying and judging the reasonableness of duplicate records, and completing missing non-critical fields according to preset rules.

[0024] On the other hand, embodiments of the present invention provide a financial voucher system, including: a standardized interface acquisition module, a preprocessing and verification module, a rule engine module, a financial mapping processing module, a financial system integration module, and a voucher generation and review module; the standardized interface acquisition module is used to generate a standardized interface pool, receive and complete the acquisition of raw financial data generated by multiple business systems, and the standardized interface pool includes various preset interface templates and expandable interface configuration channels; the preprocessing and verification module is connected to the standardized interface acquisition module, and is used to preprocess and perform preliminary field-level verification on the acquired raw financial data, and transmit the verified data to the rule engine module; the rule engine module parses the configured rule scripts into executable instructions according to the data source, data type, and business scenario, and performs data feature processing based on the executable instructions. The system performs line pattern matching, automatically matching and executing corresponding basic verification rules, business logic verification rules, and financial mapping rules, and transmits the verified data to the financial mapping processing module. The financial mapping processing module automatically performs accounting subject classification, accounting dimension matching, accounting entry template application, and special business financial logic calculations on the verified data according to the financial mapping rules, forming standardized accounting entry data and transmitting it to the financial system integration module. The financial system integration module automatically synchronizes the standardized accounting entry data to the target financial accounting system through a pre-configured versioned financial system integration template. The voucher generation and review module is connected to the financial system integration module and controls the target financial accounting system to automatically generate accounting vouchers based on the received data and execute automatic compliance rule reviews according to the pre-configured review rule chain.

[0025] Furthermore, the standardized interface pool includes one or more of the following: RESTful interface templates, WebService interface templates, SFTP file transfer templates, structured database direct connection templates, and batch import templates for tabular files; and the standardized interface acquisition module is also configured with an expandable interface configuration channel, which includes a parameter input interface for receiving system identifiers, authentication information, transmission frequency, and data type range parameters when a new data source is added to complete template selection and adaptation.

[0026] Furthermore, the rule engine module includes a rule parsing unit and an instruction execution unit. The rule parsing unit is used to parse the configurable rule script into an executable instruction sequence, and the instruction execution unit is used to perform pattern matching on data features based on the executable instruction sequence and execute the corresponding verification logic or mapping logic.

[0027] Furthermore, the basic verification rules include data type consistency checks, field length range checks, mandatory field existence checks, and numerical reasonableness range checks. The business logic verification rules include cross-field association constraint checks, dynamic calculation formula verification, and industry-specific compliance constraint checks.

[0028] Furthermore, the rule engine module also includes an anomaly classification processing unit, which is used to classify and mark abnormal data into three levels: minor, general, and severe. Minor anomalies are automatically repaired by the system and the repair log is recorded. General anomalies are temporarily stored and pushed to the responsible person's processing interface. Severe anomalies block subsequent processes and trigger alarm notifications.

[0029] Furthermore, the financial mapping processing module has a built-in financial mapping rule library, which includes a mapping sub-library from business type to accounting subject, a mapping sub-library from business type to accounting dimension, and a mapping sub-library from business scenario to journal entry template.

[0030] Furthermore, the financial system integration module includes a versioned financial system integration template, which includes interface adaptation templates for mainstream financial accounting systems. The financial system integration module supports two synchronization modes: real-time push and timed batch, and is configured with a breakpoint resume unit, a two-way core field comparison unit, and an automatic rollback unit for synchronization failure.

[0031] Thirdly, this application provides a computer device. This application is achieved through the following technical solution: A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any of the above-described financial voucher data processing methods.

[0032] Fourthly, this application provides a computer-readable storage medium. This application is achieved through the following technical solutions: A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of any of the above-described financial voucher data processing methods.

[0033] This invention parses configurable rule scripts into executable instructions using a rule engine deployed in the financial middle platform. Based on these instructions, it performs pattern matching on data features, replacing traditional manual interpretation with compilation technology and pattern matching algorithms. This makes the rule execution process controllable, solving the problems of inefficiency and difficulty in tracing object defects in existing technologies that require manual monitoring for conversion between different systems. It also improves the determinism of rule execution and system stability. By combining a standardized interface pool with pre-built templates and scalable configuration channels, it eliminates format barriers for data access from multiple business systems. Versioned interface templates with built-in multi-version financial system interface adaptation logic enable seamless integration between heterogeneous systems, overcoming the technical shortcomings of traditional hard-coded integration that requires repeated development and adaptation. In the data synchronization stage, it ensures the integrity and consistency of data synchronization through breakpoint resumption, bidirectional comparison, and automatic rollback mechanisms. Combined with a three-level anomaly classification handling mechanism, it achieves reasonable allocation of anomaly resources. This constructs a fully automated processing system from data collection, rule verification, account mapping to system integration, significantly improving processing efficiency and system reliability while ensuring data compliance. Attached Figure Description

[0034] Figure 1 This is a flowchart illustrating a data processing method for a financial voucher system provided in an embodiment of this application.

[0035] Figure 2 A flowchart illustrating the rule script parsing and instruction generation process in the rule engine provided in this application embodiment.

[0036] Figure 3 This is a structural diagram of a financial voucher system provided in an embodiment of this application. Detailed Implementation

[0037] This specific embodiment is merely an explanation of this application and is not intended to limit it. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they fall within the scope of the claims of this application.

[0038] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0039] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0040] This invention provides a method for processing financial voucher data. Figure 1 This is a schematic diagram of the overall process of the financial voucher data processing method provided in the embodiments of the present invention, as shown below. Figure 1 As shown, the method includes the following steps: Step S1: Collect raw financial data from multiple business systems through a standardized interface pool.

[0041] Multi-business systems refer to various independent business applications within an enterprise or ecosystem that generate financial data, such as e-commerce sales systems, procurement management systems, expense reimbursement systems, payroll systems, contract management systems, and asset management systems. These systems operate independently, resulting in significant differences in the format, field definitions, and transmission methods of the raw financial business data they generate. The standardized interface pool provides a unified access portal for these heterogeneous data sources, avoiding the need for the financial middleware system in the financial voucher system to directly hard-code interfaces with dozens or even hundreds of individual business systems. The financial middleware system, deployed on an enterprise's private cloud, is a middleware system with data collection, verification, mapping, and synchronization functions. It includes standardized interface pools, rule engines, and other functional modules to enable heterogeneous data interaction between multiple business systems and the financial accounting system.

[0042] In one implementation, the standardized interface pool has several pre-built interface templates. Common access methods include at least the following: RESTful interface templates are used for order sales data and payment transaction data with high real-time requirements; WebService interface templates are used for daily reconciliation statements exported from older versions of ERP systems such as Yonyou or Kingdee; SFTP file transfer templates are used for batch files of purchase receipts and scanned supplier invoices exported from supply chain systems; structured database direct connection templates are used for older system databases that have not yet been upgraded, with periodic retrieval via read-only views; and batch import templates for sporadic expense documents manually filled out by the front end are used, supporting uploads in Excel or CSV format. Specifically, the pre-built RESTful interface templates typically follow the OpenAPI 3.0 specification, providing unified request header specifications, authentication methods including OAuth2.0 or WeChat Token, a unified response structure containing code, msg, and data, and standard pagination, sorting, and filtering parameters. WebService interface templates are primarily compatible with the SOAP 1.1 / 1.2 protocol, with WSDL files automatically generated and published by the interface pool. The SFTP file transfer template supports key pair authentication or username / password authentication, and includes built-in functions for file naming rule verification, file integrity MD5 verification, and automatic decompression of compressed files.

[0043] When adding a new data source, rapid adaptation can be achieved through the scalable interface configuration channel without modifying the core program code. This scalable interface configuration channel provides a parameterized configuration interface. Business operations personnel or implementation consultants can complete the new data source access by filling in or selecting the following key fields: system unique identifier, business domain category, authentication type and key information, transmission protocol type, transmission frequency, data file storage path or interface address, expected data type range, preliminary field mapping correspondence, and email address or enterprise WeChat group for receiving anomaly alarms. For example, when a company launches a new travel expense reimbursement system and needs to integrate its generated expense reports, electronic invoices for air and train tickets, hotel accommodation invoices, etc., into the financial middle platform, operations personnel select "RESTful interface template" in the configuration interface, fill in the system identifier as "TravelExpense2025", select "Bearer Token" as the authentication method, fill in the Token acquisition address and refresh interval, set the transmission frequency to "real-time + polling every 5 minutes", and select "expense reimbursement form", "travel application form", and "invoice image" for the data type range. Then, they save and immediately test the connection. The entire process requires no developer intervention to modify the backend code such as Java, Python, or Go.

[0044] In another implementation, when accessing a supplier reconciliation system that only provides incremental data files, an SFTP file transfer template can be selected. This involves filling in the server IP, port, username, private key file upload, and the daily filename prefix rule "SUPPLIER_YYYYMMDD*.zip". The system can then be configured to automatically trigger decompression, temporary storage in the database, and downstream scheduling notifications upon file arrival. This method is particularly suitable for reconciliation scenarios with large data volumes but low update frequency.

[0045] Step S2 involves preprocessing and performing preliminary field-level validation on the collected raw financial data.

[0046] The collected raw financial business data first undergoes preprocessing and preliminary field-level validation. Preprocessing mainly completes basic tasks such as format standardization, encoding unification, and preliminary cleaning. Preliminary field-level validation involves rule checks on the fields of a single record, without involving complex relationships across records or fields. Specifically, decimal place control is enforced for amount fields, typically retaining two decimal places, rounding off or truncating any excess, and logging the differences between the original and processed values; date fields undergo format unification, converting different formats such as "2025-1-9", "20250109", and "09 / 01 / 2025" to the standard format "2025-01-09"; for key coded fields such as customer codes, supplier codes, material codes, account codes, and department codes, a preset code library matching is performed, and if a match fails, the field is marked as pending confirmation and entered into an exception pool.

[0047] In one possible implementation, preprocessing and initial field-level validation are performed in a pipeline manner. First, the character set is converted to UTF-8, and then null value filling rules, format validation rules, regular expression matching rules, and code table matching rules are executed sequentially. For example, in a sales revenue record from an e-commerce platform, the original amount is "12,345.6". The system automatically removes the thousands separator and pads it to "12345.60". The original value of the date field is "24 / 12 / 31". The system recognizes it as a year-end date and converts it to "2024-12-31". During validation, the customer code "CUST-0009123A" is found to have an extra letter "A" at the end. The system judges this as a high probability of an input error according to preset rules, marks it as a minor anomaly, and attempts to truncate and match the prefix. If no match is found, it is pushed to the responsible person for processing.

[0048] The data processed in step S2 is already relatively standardized structured data, but there may still be problems such as inaccurate business meaning, logical conflicts, and incorrect subject classification. Therefore, it is necessary to proceed to step S3, where the rule engine deployed in the financial middle platform takes over the subsequent fine-grained verification and transformation.

[0049] Step S3: Through the rule engine deployed in the financial middle platform, the configured rule scripts are parsed into executable instructions according to the data source, data type and business scenario. Based on the executable instructions, the data features are pattern matched, and basic verification rules, business logic verification rules and financial mapping rules are automatically matched and executed.

[0050] In one implementation, such as Figure 2 As shown, the specific process by which the rule engine parses the configured rule script into executable instructions is as follows: First, the rule script is segmented into token sequences through lexical analysis. For example, for the rule script "IF [amount]>10000 AND [business type]='purchasing' THEN [account]='accounts payable'", lexical analysis generates the following token sequence based on regular expressions: KEYWORD(IF), OPERATOR(>), NUMBER(10000), KEYWORD(AND), FIELD(business type), OPERATOR(=), STRING('purchasing'), KEYWORD(THEN), FIELD(account), OPERATOR(=), STRING('accounts payable'). Subsequently, the token sequence is recursively parsed using syntax analysis based on preset BNF syntax rules to generate a syntax tree containing conditional expressions, comparison expressions, and assignment expressions. Finally, the syntax tree is traversed to generate an executable instruction sequence containing opcodes and operands, which is stored in the memory instruction cache. The opcodes in the executable instruction sequence include LOAD, COMPARE, JUMP, STORE, etc., and each instruction node contains an opcode, operands, and pointers to child nodes.

[0051] The specific process of pattern matching of data features based on the executable instructions is as follows: Key feature values ​​are extracted from the input data, and the key feature values ​​are compared with the conditional instructions in the executable instruction sequence one by one. First, the LOAD instruction is executed to load the amount value, and the COMPARE instruction is executed to compare it with the threshold of 10000. If the result is true, the next conditional instruction is executed; otherwise, the process jumps to the next rule. After both conditions are met, the STORE instruction is executed to write 'Accounts Payable' into the account field.

[0052] The rule engine also features a visual rule editing interface, a rule simulation testing environment, an automatic rule conflict detection mechanism, and multi-version rule storage and rollback functionality. The visual rule editing interface uses a drag-and-drop or form-based configuration method similar to low-code platforms. Rules consist of multiple condition nodes, action nodes, and branch nodes, supporting nested logic. The rule simulation testing environment allows users to input one or more simulated data sets and observe the hit status, execution path, and output results of each rule in real time, facilitating rule debugging. The automatic rule conflict detection mechanism scans for situations such as two rules having identical conditions but conflicting actions, or mutually inclusive conditions but undefined priorities, and generates a conflict report when rules are saved. The multi-version rule storage and rollback function automatically generates a new version each time a rule is changed, retains the effective time period of historical versions, and supports one-click rollback to any historical version.

[0053] The basic validation rules include data type consistency checks, field length range checks, mandatory field existence checks, and numerical reasonableness range checks. Taking sales revenue data as an example, the basic validation rules typically include: revenue amounts must be numeric values ​​greater than zero; order numbers must be between 10 and 32 digits long; sales organization codes must exist in the preset sales organization code table; customer codes cannot be empty and must be at least 6 digits long; business dates must be earlier than the current system date and later than the start date of the fiscal year, etc.

[0054] Another common basic verification is for purchase invoice data: the invoice number is fixed at 10 or 12 digits, the invoice amount must be greater than zero and less than or equal to the upper limit of the contract amount, the tax amount must be equal to the amount excluding tax multiplied by the tax rate, and the invoice date must be within the accounting period, etc.

[0055] The business logic validation rules include cross-field association constraint checks, dynamic calculation formula validation, and industry-specific compliance constraint checks. Cross-field association constraint checks include, for example: when the business type is advance payment, the advance payment account must be "2203 Advance Payments" or "1123 Advance Payments - Details"; when the document has a "Return" label, the amount field must be negative; when a refund occurs, the refund amount cannot exceed the original sales amount, etc. Dynamic calculation formula validation is common in expense-related documents. For example, in travel reimbursement, the intercity transportation subsidy standard is automatically calculated based on the administrative level of the departure and arrival points. If the subsidy amount entered by the user deviates from the system's calculated value by more than 5%, a warning is triggered. Industry-specific compliance constraint checks vary significantly across industries. Taking the pharmaceutical distribution industry as an example, validation rules may include: the proportion of gift amounts in the sales amount cannot exceed 5%; the sale of high-value consumables must be associated with a unique device code; the sale of anesthetic and psychotropic drugs must verify whether the buyer's qualifications include "Class II psychotropic drug business qualification," etc. In the retail and fast-moving consumer goods industry, the following checks may be required: discounts related to a promotion cannot be entered after the promotion ends; the amount of membership points deducted cannot exceed 30% of the order amount, etc.

[0056] When executing validation rules, the rule engine categorizes abnormal data into three levels: minor, moderate, and severe. Minor anomalies refer to formatting or non-critical field issues that have no substantial impact on business processing; these are automatically repaired by the system, and a repair log is recorded. Moderate anomalies refer to issues that may affect business accuracy but do not involve core amounts or compliance; the data is temporarily stored and pushed to the designated responsible person's processing interface. Severe anomalies refer to issues that directly affect the accuracy or compliance of financial accounting; these immediately block subsequent processing flows and trigger an alarm notification.

[0057] For example, when validating a sales revenue record, the system found that the amount field was "12345.6", which did not retain two decimal places. It was marked as a minor anomaly, automatically corrected to "12345.60", and the correction log was recorded. The system also found that the customer code "CUST009123" was not in the code table, marked as a general anomaly, temporarily stored in the anomaly pool, and pushed to the sales department manager's interface. The system further found that the difference between the debit and credit amounts was 100.00 yuan, marked as a serious anomaly, immediately blocking its entry into the financial mapping process in step S4, and sending an alert to the finance manager through the enterprise WeChat group.

[0058] Step S4: Based on the financial mapping rules, the verified data is classified into accounting subjects, matched with accounting dimensions, applied with accounting entry templates, and subjected to special business financial logic calculations to form standardized accounting entry data.

[0059] After the rule verification in step S3, the data basically meets the business logic and basic compliance requirements, but it still needs to be further converted into accounting entries that comply with financial accounting standards. Financial mapping rules are the core basis for achieving this conversion. Their function is to map business data to the correct accounting subjects, accounting dimensions, and entry templates according to accounting standards and internal accounting requirements, while performing specialized financial logic calculations for specific business scenarios.

[0060] Specifically, accounting subject classification matches transaction types in business data with a pre-defined accounting subject library. For example, "online sales revenue" is mapped to the "main business revenue - e-commerce channel" subject, and "payment for raw material purchases" is mapped to the "accounts payable - suppliers" subject. Accounting dimension matching further refines the accounting granularity based on the subjects. For example, dimensions such as "sales organization," "product line," and "customer type" are added to revenue subjects, and dimensions such as "cost center" and "project number" are added to cost subjects. Accounting entry template application automatically selects debit / credit directions, amount field sources, and summary generation rules based on the business type to form complete accounting entry records. Special business financial logic calculation automatically completes amount splitting or accrual for specific scenarios, such as fixed asset depreciation and bad debt provision. The financial mapping rule library at least includes the correspondence between business types and accounting subjects, the correspondence between business types and accounting dimensions, and the correspondence between business scenarios and entry templates.

[0061] In one implementation, the financial mapping rule base uses a multi-level mapping table structure for storage and management. The first layer is the correspondence between business types and accounting subjects. For example, "e-commerce platform sales revenue" corresponds to "6001 Main Business Revenue - E-commerce Channel," and "employee travel reimbursement" corresponds to "6602 Management Expenses - Travel Expenses." The second layer is the correspondence between business types and accounting dimensions. For example, "Main Business Revenue - E-commerce Channel" must be accompanied by three dimensions: "Sales Organization," "Channel Type," and "Product Category." "Management Expenses - Travel Expenses" must be accompanied by two dimensions: "Department" and "Expense Attribution Item." The third layer is the correspondence between business scenarios and journal entry templates. For example, the scenario "e-commerce platform sales revenue - cash on delivery" corresponds to the template "Debit: Accounts Receivable - E-commerce Platform; Credit: Main Business Revenue - E-commerce Channel," and automatically extracts the "Order Amount" field from the business data as the journal entry amount and extracts "Order Number + Payment Time" as the summary content.

[0062] For example, when processing sales revenue data from an e-commerce platform, the system first matches the business type "online sales - cash on delivery" to the accounting subject "6001 Main Business Revenue - E-commerce Channel". Then, based on the accounting dimension requirements, it extracts the sales organization as East China Region, the channel type as a third-party platform, and the product category as home appliances. Finally, it applies the journal entry template to generate a debit record "Accounts Receivable - E-commerce Platform Amount 12345.60 Summary: Order No. EC20251001001 Payment Confirmation" and a credit record "Main Business Revenue - E-commerce Channel Amount 12345.60 Summary: Order No. EC20251001001 Payment Confirmation", and appends the dimension information to the credit record. In another implementation, for multi-store sales scenarios of chain retail enterprises, the financial mapping rule base will dynamically adjust the accounting dimensions based on the "store number" field. For example, when the store number belongs to the scope of directly operated stores, the revenue account will be supplemented with the dimension of directly operated store number; when it belongs to the scope of franchised stores, the revenue account will be supplemented with the dimension of franchisee number. The summary format in the journal entry template will be automatically adjusted to "directly operated store sales revenue - store number" or "franchise store sales revenue - franchisee number" to achieve refined accounting.

[0063] The special business financial logic calculations include automatic calculation of fixed asset depreciation, automatic provision for bad debts based on aging or proportion, automatic calculation of the amount of revenue and cost carried forward at the end of the period, and automatic calculation of taxes and fees according to tax rate rules.

[0064] In one possible implementation, the automatic calculation of fixed asset depreciation is performed on fixed assets marked as "in use" in the company's asset ledger. Based on the pre-defined depreciation method and depreciation period for the asset category, the system automatically calculates the depreciation amount to be accrued in the current period and generates the journal entry: "Debit: Administrative Expenses - Depreciation; Credit: Accumulated Depreciation". For example, a production piece of equipment with an original value of 100,000.00 yuan, a depreciation period of 5 years, and using the straight-line method, automatically calculates the monthly depreciation amount as 1,666.67 yuan, and adds the production workshop dimension based on the department to which the equipment belongs.

[0065] In another implementation, bad debt provision is automatically calculated based on aging or percentage. For overdue records in the accounts receivable details table, the bad debt provision amount is automatically calculated based on a preset provision ratio according to the aging range or customer risk level. For example, the provision ratio for accounts receivable aged 30-60 days is 5%, and the provision ratio for accounts receivable aged 61-90 days is 10%. The system iterates through all overdue records, calculates the total provision amount to be 2350.00 yuan, generates the journal entry "Debit: Credit Impairment Loss Credit: Bad Debt Provision", and records the detailed aging distribution in the log for auditing purposes.

[0066] In one implementation, the system automatically calculates the cost of goods sold at the end of the period for sales revenue records that have not yet been transferred. It matches the corresponding cost rate or cost amount based on the product category and automatically calculates the cost to be transferred. For example, if the sales revenue of a batch of home appliances is 50,000.00 yuan, the system matches the cost rate to 60% based on the product category "Home Appliances - Small Appliances," calculating the cost to be transferred to be 30,000.00 yuan, and generating the journal entry "Debit: Cost of Goods Sold - Small Appliances; Credit: Inventory - Small Appliances."

[0067] Furthermore, taxes are automatically calculated according to tax rate rules. For taxes such as value-added tax and consumption tax, the system automatically calculates the tax amount based on the business type and tax rate table. For example, for a sales revenue record of 10,000.00 yuan, with an applicable tax rate of 13%, the system automatically calculates the tax amount as 1,300.00 yuan, generating the journal entry "Debit: Accounts Receivable; Credit: Main Business Revenue; Credit: Taxes Payable - Value-Added Tax Payable - Output Tax," ensuring accurate tax accounting. When the accounting entry template is applied, it automatically includes the business summary and the original business voucher association identifier. Specifically, the business summary is an important component of the accounting entry, used to briefly describe the economic transaction content of the entry. It is usually automatically generated by the system based on the business type and key fields, such as "E-commerce Sales Revenue - Order No. EC20251001001." The original business voucher association identifier establishes a link between the entry and the original business document, such as binding the entry record with unique identifiers like order number, invoice number, and payment serial number, facilitating subsequent traceability and auditing. For example, when processing a purchase payment entry, the system automatically generates a summary as "Purchase Payment - Supplier No. SUPP001 - Payment Document No. PAY20251001" and stores the entry in association with the payment document number and the supplier invoice number.

[0068] For data that passes validation, further processing is performed, including format standardization, duplicate record identification and reasonableness assessment, and completion of missing non-critical fields according to preset rules. Format standardization involves further standardizing the validated data, such as uniformly retaining two decimal places for all amount fields and standardizing date fields to the "YYYY-MM-DD" format. Duplicate record identification and reasonableness assessment uses a combination of fields such as order identifier, amount, and business date to determine whether it constitutes reasonable duplication across multiple channels. If it does, a master record is retained and associated with the multi-source identifier; otherwise, duplicate records are automatically removed. Completion of missing non-critical fields according to preset rules means that if a non-critical field is empty, it is filled according to rules. For example, if the summary field is empty, it is filled with "Sales Revenue - Automatically Generated" based on the business type.

[0069] Step S5: The standardized accounting entry data is automatically synchronized to the target financial accounting system through the pre-configured versioned financial system interface template to generate accounting vouchers.

[0070] After standardized accounting entries are generated in step S4, they need to be synchronized to the enterprise's core financial accounting system for subsequent financial statement preparation. Versioned financial system interface templates are a key tool for achieving this synchronization. Their function is to shield the interface differences between different financial system versions, ensuring accurate data transmission to the target system and automatic generation of accounting vouchers. Versioned financial system interface templates include interface adaptation templates for at least several mainstream versions of Kingdee and Yonyou systems.

[0071] In one implementation, for Kingdee financial software, the system includes built-in interface adaptation templates for multiple versions such as Kingdee K / 3, Kingdee EAS, and Kingdee Cloud Starry Sky. Each template includes the target system's authentication method, data format requirements, field mapping relationships, and error code handling logic. For example, the Kingdee K / 3 template supports pushing journal entry data via a WebService interface, with the data format being XML. The amount field must retain two decimal places, and the account code must have spaces removed. For UFIDA financial software, the system includes built-in interface adaptation templates for versions such as UFIDA U8, UFIDA NC, and UFIDA U9. For example, the UFIDA U8 template supports writing journal entry data via a database intermediate table, requiring an additional operator number field. For example, in a medium-sized manufacturing enterprise, the financial accounting system is Kingdee Cloud Starry Sky. The system automatically selects the corresponding interface adaptation template and pushes standardized accounting entry data through the RESTful interface. Before pushing, the amount field is uniformly formatted as "12345.60" and the account code is converted to the "6001.01" format required by the target system. After receiving the data, the target financial accounting system automatically generates accounting vouchers. The system records the success mark and voucher number returned by the target system to facilitate subsequent reconciliation.

[0072] In another implementation, for the Yonyou NC version, the system writes the journal entry data into the intermediate table of the target system through a pre-configured direct database connection. Before writing, it automatically verifies whether the target system already has an entry with the same business identifier. If it does, it is marked as duplicate and synchronization is paused. After writing, the system automatically triggers the voucher generation storage process of the target system to ensure data integrity and automatically generate accounting vouchers.

[0073] The automatic synchronization supports real-time push mode and scheduled batch mode. Real-time push mode is suitable for business scenarios with high timeliness requirements, such as sales revenue entries and payment transaction entries on e-commerce platforms. After generating standardized accounting entries in step S4, the system immediately pushes them to the target financial accounting system via an interface and triggers voucher generation. Scheduled batch mode is suitable for scenarios with large data volumes but lower timeliness requirements, such as month-end fixed asset depreciation entries and expense allocation entries. The system temporarily stores the entry data in a buffer and pushes it in batches at fixed times daily or weekly, such as pushing the cumulative entry data of the previous day at 2:00 AM daily. For example, in a chain retail enterprise, for the tens of thousands of sales revenue entries generated daily, the system uses real-time push mode, immediately pushing each entry generated in step S4 to the Kingdee K / 3 system and generating vouchers, ensuring that financial data and business data are updated synchronously. For the hundreds of depreciation entries generated at the end of the month, the system uses scheduled batch mode, uniformly pushing them to the target system at 11:00 PM on the last day of each month and generating vouchers in batches, avoiding impact on system performance during peak periods.

[0074] The automatic synchronization process includes a breakpoint resume mechanism, a bidirectional core field comparison mechanism after synchronization is completed, and an automatic rollback to the most recent consistent state when synchronization fails.

[0075] Specifically, the breakpoint resume mechanism means that if transmission fails during synchronization due to network interruption or target system unavailability, the system records the identifier of the last successfully synchronized entry. Upon connection restoration, transmission resumes from that identifier, avoiding duplicate transmission of already successfully synchronized data. The bidirectional core field comparison mechanism after synchronization means that after synchronization, the system reads back the synchronized entry data from the target financial accounting system and compares it with the source data for core fields such as amount, account code, and business identifier. If inconsistencies are found, an anomaly is marked and the responsible party is notified for handling. The automatic rollback to the most recent consistent state mechanism in case of synchronization failure means that if synchronization fails and cannot be recovered, the system rolls back a portion of the synchronized data in the target system to its pre-synchronization state, ensuring data consistency between the two systems.

[0076] For example, during a batch synchronization process, the system pushed 1000 journal entries to the Yonyou U8 system. However, the synchronization was interrupted at the 800th entry due to network fluctuations. The system recorded the interruption point as the business identifier of the 799th entry. After the network was restored, the transmission resumed from the 800th entry, avoiding duplicate pushes of the first 799 entries. After synchronization was completed, the system read back the journal entries from the target system and found that the amount of the 850th entry was inconsistent with the source data. It was automatically marked as an anomaly and pushed to the finance manager's processing interface. In another synchronization, a database lock conflict in the target system caused synchronization failure. The system automatically rolled back the 50 journal entries already written to the target system to ensure data consistency between the two systems.

[0077] Step S6: Perform automatic compliance audit on the generated accounting vouchers according to the pre-configured audit rule chain.

[0078] After generating accounting vouchers in step S5, the system performs compliance checks through pre-configured audit rules to ensure that the vouchers comply with accounting standards and internal management requirements. The automatic compliance audit includes at least checks on the balance of debit and credit amounts, the standardization of accounting subject usage, the completeness of required fields for accounting dimensions, and the reasonableness of key monetary fields.

[0079] In one implementation, the debit / credit balance check checks whether the total debit amount equals the total credit amount on each accounting voucher. If they do not equal, the voucher is marked as abnormal and subsequent processes are blocked. The accounting subject usage compliance check checks whether the accounting subjects used in the voucher are within the company's chart of accounts and comply with the subject usage rules; for example, negative balances are not allowed for bank deposit accounts. The mandatory accounting dimension completeness check checks whether the accounts in the voucher have all the required accounting dimensions attached; for example, the management expense account must have the department dimension attached. If any dimension is missing, the voucher is marked as incomplete. The key amount field reasonableness check checks whether the amount field is within a reasonable range; for example, the total amount of a single voucher must not exceed the company's preset single-transaction limit. For example, when processing a sales revenue voucher, the system first checks whether the debit amount of accounts receivable of RMB 12,345.60 and the credit amount of main business revenue of RMB 12,345.60 are balanced. Then it checks whether the main business revenue account is in the company's valid account table and whether the sales organization and product category dimensions have been added. Finally, it checks whether the amount exceeds the single sales revenue limit of RMB 500,000.00 to ensure the compliance of the voucher.

[0080] The pre-configured audit rule chain supports one or more combinations of single-level audit, multi-level sequential audit, multi-person parallel audit, and conditional branch audit based on organizational level, job responsibilities, and business amount. Single-level audit is suitable for vouchers with small amounts and low risk, such as miscellaneous expense vouchers less than 1,000.00 yuan, which only require approval from the finance specialist before archiving. Multi-level sequential audit is suitable for vouchers with larger amounts or involving multiple departments, such as purchase payment vouchers exceeding 100,000.00 yuan, which require sequential approval from the finance specialist, finance manager, and finance director. Multi-person parallel audit is suitable for urgent business vouchers, such as revenue recognition vouchers before month-end closing, which require simultaneous approval from the finance manager and the business department head. Conditional branch audit dynamically selects the audit path based on voucher attributes; for example, vouchers involving fixed assets require additional approval from the asset management department. For example, in a manufacturing company, for an equipment maintenance expense voucher of 50,000.00 yuan, the system automatically assigns it to a multi-level sequential review path based on the amount and the type of account. It first pushes it to the financial specialist's review interface, then to the financial manager's interface after the review is approved, and finally to the financial director's interface. Each level of review requires the entry of review comments and electronic signatures to ensure that the review process is traceable.

[0081] Accounting vouchers that fail the review are automatically associated with anomaly rule identifiers, original business data, and responsibility push nodes. Once approved, they automatically enter the archiving process. Specifically, if a voucher fails the review, the system automatically records the specific rule triggering the anomaly, such as "imbalance between debit and credit amounts - debit side exceeds 500.00 yuan," and associates the voucher with original business data such as order number and payment document number. Simultaneously, the abnormal voucher is pushed to the pre-configured responsible person's processing interface, such as the finance specialist or business initiator. Approved vouchers automatically enter the archiving process, generating a voucher number, archiving timestamp, and archiving operator identifier. The voucher data is backed up to a long-term storage repository for subsequent querying and auditing. For example, if a purchase payment voucher fails the review due to "accounting dimension - department missing," the system automatically records the anomaly reason as "accounting dimension required completeness check failed - accounts payable account without attached department dimension," associates the voucher with the original purchase order number PO20251001001, and pushes it to the purchasing department head's processing interface. It is then resubmitted for review after the missing information is completed. After another sales revenue voucher is approved, the system automatically generates voucher number PZ20251001001, and the record archiving time is 2025-10-01 15:30:25. The archiving operator is the system to automatically archive and back up the voucher data to the audit database.

[0082] Please see Figure 3Another embodiment of the present invention provides a financial voucher system, including a standardized interface acquisition module, a preprocessing and verification module, a rule engine module, a financial mapping processing module, a financial system integration module, and a voucher generation and approval module. The standardized interface acquisition module is used to generate a standardized interface pool, receive and collect raw financial business data generated by multiple business systems, and the standardized interface pool includes various preset interface templates and expandable interface configuration channels. The preprocessing and verification module is used to preprocess and perform preliminary field-level verification on the collected raw financial business data, and transmit the verified data to the rule engine module. The rule engine module is used to parse the configured rule scripts into executable instructions based on the data source, data type, and business scenario, and perform data feature analysis based on the executable instructions. Pattern matching automatically matches and executes corresponding basic verification rules, business logic verification rules, and financial mapping rules, transmitting verified data to the financial mapping processing module. The financial mapping processing module automatically performs accounting subject classification, accounting dimension matching, accounting entry template application, and special business financial logic calculations on the verified data according to the financial mapping rules, forming standardized accounting entry data and transmitting it to the financial system integration module. The financial system integration module automatically synchronizes the standardized accounting entry data to the target financial accounting system using a pre-configured versioned financial system integration template. The voucher generation and review module controls the target financial accounting system to automatically generate accounting vouchers based on the received data and executes automatic compliance rule reviews according to a pre-configured review rule chain.

[0083] The specific limitations of the financial voucher system provided in this embodiment can be found in the above embodiment regarding the data processing method of a financial voucher system. Each module in the aforementioned financial voucher system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0084] If the technical solution of this application involves the collection, processing, or application of personal information, the relevant products have strictly complied with the requirements of the "Personal Information Protection Law of the People's Republic of China" and other laws and regulations before implementing any personal information processing activities, clearly and explicitly informing individuals of the rules for personal information processing and obtaining their independent and voluntary authorization and consent. Specifically, if the information involved is sensitive personal information, the product has not only obtained the individual's separate consent before processing, but this consent is also an explicit consent made on the basis of full knowledge. For example, in areas where personal information collection devices such as cameras are deployed, prominent and eye-catching signs have been set up to clearly inform users that entering the area is considered as consenting to the collection of their personal information; or, on the personal information processing interface (such as applications, web pages, etc.), through pop-ups, checkboxes, or active uploads, the user is required to actively authorize the process after clearly displaying key rules such as the identity of the personal information processor, the purpose of processing, the processing method, and the types of information involved.

[0085] The above description of the embodiments is only for the purpose of helping to understand the technical solutions and core ideas of this application; those skilled in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for processing financial voucher data, characterized in that, include: Raw financial data from multiple business systems is collected through a standardized interface pool; Preprocess and perform preliminary field-level validation on the collected raw financial data; By deploying a rule engine in the financial middle platform, the configured rule scripts are parsed into executable instructions based on the data source, data type and business scenario. Based on the executable instructions, pattern matching is performed on the data features, and basic verification rules, business logic verification rules and financial mapping rules are automatically matched and executed. Based on the financial mapping rules, the verified data is classified into accounting subjects, matched with accounting dimensions, applied with accounting entry templates, and subjected to special business financial logic calculations to form standardized accounting entry data. Standardized accounting entries are automatically synchronized to the target financial accounting system through a pre-configured versioned financial system interface template to generate accounting vouchers; Automatic compliance audits are performed on the generated accounting vouchers based on pre-configured audit rule links.

2. The method as described in claim 1, characterized in that, The standardized interface pool includes a variety of pre-built interface templates and an extensible interface configuration channel.

3. The method as described in claim 2, characterized in that, The preset interface templates include one or more of the following: RESTful interface template, WebService interface template, SFTP file transfer template, structured database direct connection template, and batch import template for tabular files.

4. The method as described in claim 2, characterized in that, The scalable interface configuration channel completes template selection and adaptation through parameter configuration.

5. The method as described in claim 4, characterized in that, When a new data source is added, the scalable interface configuration channel completes template selection and adaptation by configuring system identifier, authentication information, transmission frequency, and data type range.

6. The method as described in claim 1, characterized in that, The preprocessing and preliminary field-level validation include enforcing decimal place control for amount fields, standardizing the format of date fields, and performing preset encoding library matching on key encoding fields.

7. The method as described in claim 1, characterized in that, The step of parsing the configurable rule script into executable instructions includes: segmenting the rule script into token sequences through lexical analysis, parsing the token sequences into a syntax tree based on preset syntax rules through syntactic analysis, and traversing the syntax tree to generate an executable instruction sequence containing opcodes and operands.

8. The method as described in claim 7, characterized in that, The step of performing pattern matching on data features based on the executable instructions includes: extracting key feature values ​​from the input data, comparing the key feature values ​​with the conditional instructions in the executable instruction sequence one by one, and executing the corresponding action instruction to complete the verification logic or mapping logic when the key feature value satisfies all the conditional instructions.

9. The method as described in claim 1, characterized in that, The basic validation rules include data type consistency checks, field length range checks, required field existence checks, and numerical reasonableness range checks.

10. The method as described in claim 1, characterized in that, The business logic validation rules include cross-field association constraint checks, dynamic calculation formula validation, and industry-specific compliance constraint checks.

11. The method as described in claim 1, characterized in that, The financial mapping rules include a financial mapping rule library, which at least includes the correspondence between business types and accounting subjects, the correspondence between business types and accounting dimensions, and the correspondence between business scenarios and journal entry templates.

12. The method as described in claim 1, characterized in that, The special business financial logic calculation includes automatic calculation of fixed asset depreciation, automatic provision of bad debts based on age or proportion, automatic calculation of the amount of revenue and cost carried forward at the end of the period, and automatic calculation of taxes and fees based on tax rate rules. When the accounting entry template is applied, the business summary and the original business voucher association identifier are automatically populated.

13. The method as described in claim 1, characterized in that, The versioned financial system interface templates include at least the interface adaptation templates of Kingdee and / or Yonyou.

14. The method as described in claim 1, characterized in that, The automatic synchronization supports both real-time push mode and scheduled batch mode.

15. The method as described in claim 1, characterized in that, The automatic synchronization process includes a breakpoint resume mechanism, a bidirectional core field comparison mechanism after synchronization is completed, and an automatic rollback to the most recent consistent state when synchronization fails.

16. The method as described in claim 1, characterized in that, The pre-configured audit rule chain supports configuring one or more combinations of single-level audit, multi-level sequential audit, and conditional branch audit based on organizational level, job responsibilities, and business amount.

17. The method as described in claim 1, characterized in that, Accounting vouchers that fail the compliance review are automatically associated with anomaly rule identifiers, original business data, and responsibility push nodes. Once the review is passed, they automatically enter the archiving process.

18. The method as described in claim 1, characterized in that, When executing verification rules, the rule engine classifies and marks abnormal data into three levels: minor, moderate, and severe. Minor anomalies are automatically repaired by the system and a repair log is recorded. Moderate anomalies are temporarily stored and pushed to the designated responsible person's processing interface. Severe anomalies immediately halt subsequent processing and trigger an alarm notification.

19. The method as described in claim 1, characterized in that, The standardized accounting entry data formation process also includes uniform format processing of verified data, identification and reasonableness judgment of duplicate records, and completion of missing non-critical fields according to preset rules.

20. A financial voucher data processing system, characterized in that, include: The system includes a standardized interface acquisition module, a preprocessing and verification module, a rules engine module, a financial mapping processing module, a financial system integration module, and a voucher generation and approval module. The standardized interface acquisition module is used to generate a standardized interface pool, receive raw financial data generated by multiple business systems and complete the acquisition. The standardized interface pool includes a variety of pre-set interface templates and an extensible interface configuration channel. The preprocessing and verification module is connected to the standardized interface acquisition module and is used to preprocess and perform preliminary field-level verification on the raw financial data acquired, and transmit the verified data to the rule engine module. The rule engine module parses the configured rule scripts into executable instructions based on the data source, data type, and business scenario. Based on the executable instructions, it performs pattern matching on the data features, automatically matches and executes the corresponding basic verification rules, business logic verification rules, and financial mapping rules, and transmits the verified data to the financial mapping processing module. The financial mapping processing module automatically completes accounting subject classification, accounting dimension matching, accounting entry template application, and special business financial logic calculation for the verified data according to the financial mapping rules, forming standardized accounting entry data and transmitting it to the financial system interface module. The financial system integration module automatically synchronizes standardized accounting entries to the target financial accounting system through a pre-configured versioned financial system integration template. The voucher generation and review module is connected to the financial system interface module and is used to control the target financial accounting system to automatically generate accounting vouchers based on the received data and to perform automatic compliance rule review according to the pre-configured review rule link.

21. The system as claimed in claim 20, characterized in that, The standardized interface pool includes one or more of the following: RESTful interface templates, WebService interface templates, SFTP file transfer templates, structured database direct connection templates, and batch import templates for tabular files. The standardized interface acquisition module is also configured with an expandable interface configuration channel, which includes a parameter input interface for receiving system identifiers, authentication information, transmission frequency, and data type range parameters when a new data source is added to complete template selection and adaptation.

22. The system as claimed in claim 20, characterized in that, The rule engine module includes a rule parsing unit and an instruction execution unit. The rule parsing unit is used to parse the configurable rule script into an executable instruction sequence. The instruction execution unit is used to perform pattern matching on data features based on the executable instruction sequence and execute the corresponding verification logic or mapping logic.

23. The system as described in claim 20, characterized in that, The basic validation rules include data type consistency checks, field length range checks, mandatory field existence checks, and numerical reasonableness range checks. The business logic validation rules include cross-field association constraint checks, dynamic calculation formula validation, and industry-specific compliance constraint checks.

24. The system as claimed in claim 20, characterized in that, The rule engine module also includes an anomaly classification processing unit, which classifies and marks abnormal data into three levels: minor, general, and severe. Minor anomalies are automatically repaired by the system and a repair log is recorded. General anomalies are temporarily stored and pushed to the responsible person's processing interface. Severe anomalies block subsequent processes and trigger alarm notifications.

25. The system as described in claim 20, characterized in that, The financial mapping processing module has a built-in financial mapping rule library, which includes a mapping sub-library from business type to accounting subject, a mapping sub-library from business type to accounting dimension, and a mapping sub-library from business scenario to journal entry template.

26. The system as described in claim 20, characterized in that, The financial system integration module includes a versioned financial system integration template, which includes interface adaptation templates for mainstream financial accounting systems. The financial system integration module supports two synchronization modes: real-time push and scheduled batch, and is equipped with a breakpoint resume unit, a two-way core field comparison unit, and an automatic rollback unit for synchronization failure.

27. A computer device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method according to any one of claims 1 to 19.

28. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1 to 19.

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