A data collaborative processing method and device and a storage medium

Abstract

This application discloses a data collaborative processing method, apparatus, and storage medium, relating to the field of financial data processing technology. The method includes: configuring a multi-level collaborative data architecture in a data processing system, the architecture comprising an interconnected data model layer, rule layer, and process layer; acquiring the original business data of the target financial asset through a data interface or optical character recognition technology, and instantiating and generating business documents based on the process layer; calling the computational logic in the rule layer to verify the document data, automatically generating a first type of structured data record to complete accounting processing; triggering deferred income tax calculation rules based on the business documents and the first type of structured data record, automatically identifying the difference between the book value and the tax base, calculating deferred income tax accordingly, and generating a second type of structured data record; establishing a full-process bidirectional index relationship between business data, accounting data, and tax data, forming a three-in-one multi-level collaborative view.

Description

Technical Field

[0001] This invention relates to the field of financial data processing technology, and in particular to a data collaborative processing method, apparatus and storage medium. Background Technology

[0002] With the deepening development of financial markets, the types of financial assets held by enterprises are becoming increasingly complex, covering various categories such as trading financial assets and debt investments. According to relevant accounting standards, the accounting for financial assets involves not only initial recognition and measurement, but also subsequent changes in fair value, interest accrual, and complex tax treatment.

[0003] In existing technologies, inconsistent database field definitions between business systems and accounting systems, coupled with a lack of underlying data anchors, prevent automatic data source tracing during complex derivative calculations (such as deferred income tax), resulting in high computational latency and difficulty in verifying data consistency. Furthermore, the management of financial assets is often fragmented within existing data processing systems. Business departments use transaction systems to record buying and selling information, finance departments use accounting systems for bookkeeping, while tax departments often rely on separate spreadsheets for ledger management. This "data silo" phenomenon leads to significant technical deficiencies: First, data acquisition primarily relies on manual entry or simple file imports, lacking the ability to automatically parse original vouchers, making it highly susceptible to inconsistencies between business and financial data due to human error.

[0004] Secondly, in the tax processing stage, especially in the calculation of deferred income tax, existing technologies exhibit significant lag and blind spots. Due to differences in the recognition of asset value between accounting standards and tax laws (for example, changes in fair value are recognized in current profit or loss, but tax law stipulates recognition only upon disposal), the "book value" and "tax base" of financial assets frequently experience temporary discrepancies due to market fluctuations. Currently, the processing method often involves finance personnel manually preparing working papers at the end of the period, comparing and calculating deferred income tax assets or liabilities item by item. This method is not only inefficient but also fails to capture tax differences caused by market fluctuations in real time, easily leading to misreporting or omissions in tax data. Furthermore, due to the lack of a unified data indexing mechanism, once anomalies are generated in the tax data, management personnel cannot quickly trace back to specific business documents or financial vouchers, resulting in weak system auditability and risk control capabilities. Summary of the Invention

[0005] This invention provides a data collaborative processing method and apparatus, which solves the problems of fragmented financial asset data, low efficiency of manual accounting, and difficulty in automating the processing of tax and accounting differences in existing technologies.

[0006] In a first aspect, embodiments of the present invention provide a data collaborative processing method, the method comprising: Configure a multi-level collaborative data architecture in the data processing system. The multi-level collaborative data architecture includes an interrelated data model layer, rule layer, and process layer. The data model layer is used to configure the data object categories, data processing scenarios, and data storage structures of financial assets. The rule layer is used to configure data operation logic, data association mapping relationships, and deferred income tax calculation rules. The process layer is used to configure the business data flow path and the calling relationship with document data formats. The system obtains the original business data of the target financial asset through data interface or optical character recognition (OCR) technology, extracts the globally unique business identifier, matches the corresponding document data format based on the call relationship in the process layer, and instantiates and generates a financial asset business document containing the globally unique business identifier and conforming to the data storage structure. Financial asset business documents are transferred along the business data flow path. The data operation logic in the rule layer is called to automatically calculate and verify the document item data in the transferred financial asset business documents. After the verification is passed, the first type of structured data record with a globally unique business identifier is automatically generated based on the data association mapping relationship to complete the accounting data processing. Based on real-time data from financial asset business documents and Type I structured data records, the deferred income tax calculation rules in the rule layer are triggered, automatically identifying the numerical differences between the book value data and the tax base data; the ending value of deferred income tax assets or deferred income tax liabilities is calculated based on the numerical differences, and combined with the beginning value of historical Type II structured data records extracted from the database, Type II structured data records carrying globally unique business identifiers are automatically generated to realize tax data processing; In the database, a bidirectional index relationship is established across the entire process between the original business data, the first type of structured data records, and the second type of structured data records based on a globally unique business identifier, forming a multi-level collaborative view of the business, accounting, and tax data of financial assets.

[0007] Preferably, the data model layer in a multi-level collaborative data architecture is configured in the following ways: The data object categories for financial assets are preset, and the data object categories must at least cover trading financial assets, debt investments, other debt investments, and other equity instrument investments; For each type of data object, pre-defined data processing scenarios covering the entire lifecycle are provided. These data processing scenarios include at least three scenarios: adding new data, adjusting data, and disposing of data. For each data processing scenario, a standardized document data structure is configured, which includes main table data items and sub-table data items. The main table data items are used to store handling information and contract overview information, while the sub-table data items are used to store attribute information, transaction details data, and accounting process data of specific financial asset projects. The document data structure is configured as a data carrier for the rule layer and the process layer.

[0008] Preferably, the rule layer in a multi-level collaborative data architecture configures data operation logic and data association mapping relationships, specifically including: Configure the data calculation logic of the document item data, and automatically calculate the values ​​of initial cost, transaction fees, dividends receivable, fair value changes and investment income by referencing the data of previous financial asset business documents or external interface data; Configure the validation logic for document item data to automatically verify the integrity and logical correctness of the data during document flow. When the validation fails, the process will be blocked by the data processing system and an exception message will be output. Configure data record generation and write-back rules, define the mapping relationship between document item data and classification codes, and after the financial asset business document is approved, map the document item amount to the corresponding debit or credit classification code to generate the first type of structured data record, and write back the status identifier and final confirmed amount of the generated record to the corresponding field of the financial asset business document.

[0009] Preferably, the rule layer in the multi-level collaborative data architecture further includes configuring deferred income tax calculation rules, which specifically include: Using data object categories and their underlying classifications as configuration units, and embedding additional items as data detail carriers, a deferred income tax data calculation model is constructed. Configure the book value data retrieval rules, and extract the currently confirmed book value data of financial assets from the disposal order, change order or purchase order in sequence according to the priority of the data processing scenario. Configure the rules for retrieving tax base data, and calculate the tax base data of financial assets at the current point in time based on the initial cost and holding ratio at the time of purchase; Configure the difference identification and calculation logic to automatically calculate the difference between the book value data and the tax base data. If the difference is greater than zero, it is marked as a taxable temporary difference; if the difference is less than zero, it is marked as a deductible temporary difference. Configure the deferred income tax data record generation logic, multiply the temporary difference by the preset tax rate to obtain the expected value at the end of the period, compare the expected value at the end of the period with the beginning value obtained from the system database, calculate the amount to be adjusted in the current period, and automatically generate adjustment data records for deferred income tax assets or deferred income tax liabilities based on the amount to be adjusted in the current period.

[0010] Preferably, the original business data of the target financial asset is obtained through a data interface or optical character recognition (OCR) technology, and the corresponding financial asset business document is instantiated and generated based on the process layer and the document data format associated with the process layer, including: The OCR technology is used to identify the original voucher images of financial asset transactions, or to receive external transaction data packets through a data interface and parse them to obtain key field information such as transaction date, security code, transaction amount and fees. Based on key field information, the corresponding data object category is automatically matched in the data model layer, and the data processing scenario is determined based on the key field information. Initiate the business process in the process layer corresponding to the determined data processing scenario, and instantiate the corresponding financial asset business document using the document data format; The key field information and the globally unique business identifier are automatically filled into the document item data of the financial asset business document. For financial assets that are newly added projects, a new detailed file is automatically created in the system management object; for financial assets that are existing projects, historical batch information is automatically referenced and the holding status data is updated.

[0011] Preferably, when the data processing scenario involves the purchase of financial assets, the data processing logic in the rules layer is invoked to automatically calculate and verify the document item data in the financial asset business documents, specifically including: The system automatically obtains the total transaction amount as the initial cost and separates the subscription fee, transaction fee, and commission from the transaction data, summarizing them into transaction fees. If the transaction includes declared but unpaid dividends, the declared but unpaid dividends will be identified separately as a dividends receivable item. Logical verification is performed on the calculation results of initial costs, accounts receivable dividends, and transaction fees. After the verification is passed, based on the calculation results, the first type of structured data record with a globally unique business identifier is automatically generated, which contains dividend data and transaction fees. After the data record is generated, the confirmed initial cost is written back to the financial asset business document as the initial value of the tax base, serving as the benchmark data for subsequent deferred income tax calculation.

[0012] Preferably, when the data processing scenario involves changes in the fair value of financial assets during the holding period, the deferred income tax calculation rules in the rule layer are triggered, including: Receive fair value change data provided by external financial data interfaces, or enter the fair value change amount, generate a value change statement and generate fair value change profit and loss data records. On the balance sheet date or at a specific transaction trigger point, initiate the deferred income tax data retrieval task; Compare the updated book value data based on the value change statement with the tax base data that remains unchanged or is adjusted proportionally. If the increase in fair value causes the carrying amount to exceed the tax base, the increase in taxable temporary differences is calculated, and a data record is automatically generated that debits income tax expense and credits deferred income tax liability. If a decrease in fair value results in the carrying amount being less than the tax base, the increase in deductible temporary differences is calculated, and a data record is automatically generated that debits deferred tax assets and credits income tax expense.

[0013] Preferably, when the data processing scenario is financial asset disposal, the method further includes: The disposal ratio is calculated based on the disposal quantity data and holding quantity data in the financial asset business documents, and the corresponding costs and cumulative fair value changes are transferred according to the disposal ratio. Calculate the difference between disposal revenue and carried-forward costs and variable profits and losses, recognize it as investment income, and generate financial data records for the disposal business; Based on the remaining book value data and remaining tax basis data after disposal, the temporary differences are recalculated. For the deferred tax assets or deferred tax liabilities corresponding to the disposed portion, they are reversed in full or proportionally, and reversal data records are generated. The taxable income for the current period is calculated based on the difference between the disposal income and the tax base data. The income tax expense for the current period is calculated in conjunction with the income tax rate, and a tax data record is generated to confirm the income tax payable for the current period.

[0014] Secondly, embodiments of the present invention provide a data collaborative processing apparatus, the apparatus comprising: The architecture configuration module is used to configure a multi-level collaborative data architecture in the data processing system. The multi-level collaborative data architecture includes an interrelated data model layer, rule layer, and process layer. The data model layer is used to configure the data object categories, data processing scenarios, and data storage structures of financial assets. The rule layer is used to configure data operation logic, data association mapping relationships, and deferred income tax calculation rules. The process layer is used to configure the business data flow path and the calling relationship with document data formats. The data acquisition and generation module is used to acquire the original business data of the target financial asset through data interface or optical character recognition (OCR) technology, extract the globally unique business identifier, match the corresponding document data format based on the call relationship in the process layer, and instantiate and generate financial asset business documents containing the globally unique business identifier and conforming to the data storage structure. The accounting processing module is used to transfer financial asset business documents along the business data flow path, call the data operation logic in the rule layer, automatically calculate and verify the document item data in the transferred financial asset business documents, and automatically generate the first type of structured data record with a globally unique business identifier based on the data association mapping relationship after the verification is passed, thus completing the accounting data processing. The tax processing module is used to trigger the deferred income tax calculation rules in the rule layer based on real-time data from financial asset business documents and first-type structured data records. It automatically identifies the numerical differences between the book value data and the tax base data; calculates the ending value of deferred income tax assets or deferred income tax liabilities based on the numerical differences; and automatically generates second-type structured data records with globally unique business identifiers by combining the beginning values ​​of historical second-type structured data records extracted from the database, thereby realizing tax data processing. The multi-level collaboration module is used to establish a bidirectional index relationship between the original business data, the first type of structured data records and the second type of structured data records in the database based on a globally unique business identifier, forming a multi-level collaborative view of the business, accounting and tax data of financial assets.

[0015] Thirdly, embodiments of the present invention provide an electronic device, the electronic device comprising: At least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by at least one processor, such that the at least one processor can perform the method proposed in the first aspect of the present invention.

[0016] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method proposed in the first aspect of the present invention.

[0017] Beneficial effects: This invention provides a data collaborative processing method that, by configuring a multi-level collaborative data architecture comprising a data model layer, a rule layer, and a process layer in the data processing system, achieves standardized and logical management of financial asset data. This method utilizes data interfaces or optical character recognition technology to acquire raw business data and instantiate it into documents, effectively solving the problems of low efficiency and poor error tolerance in traditional manual data entry, ensuring the accuracy of the source data. By calling the computational logic in the rule layer, it achieves automated generation from business documents to the first type of structured data record (accounting data). Based on real-time business data and accounting data, it can trigger deferred income tax calculation rules, automatically identify the numerical difference between the book value and the tax base, and generate the second type of structured data record (tax data) accordingly. This mechanism overcomes the limitations of existing technologies that rely on manual calculation of temporary differences, achieving accurate and dynamic measurement of deferred income tax assets or liabilities. By establishing a bidirectional index relationship across the entire process in the database, this invention forms a multi-level collaborative view integrating business, accounting, and tax data, enabling users to accurately trace back to the associated original vouchers or sources of tax differences from any type of data record, significantly improving the transparency and compliance of financial asset management. Attached Figure Description

[0018] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 This is a schematic diagram of the electronic device structure of the hardware operating environment involved in the embodiments of the present invention; Figure 2 This is a flowchart of the steps of a data collaborative processing method provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the functional modules of a data collaborative processing device provided in an embodiment of the present invention. Detailed Implementation

[0019] To more clearly illustrate the technical solutions in the embodiments of the invention or the prior art, the invention will be briefly introduced below in conjunction with the accompanying drawings and descriptions of the embodiments or the prior art. Obviously, the following description of the structure of the drawings is merely some embodiments of the invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. It should be noted that the description of these embodiments is for the purpose of aiding understanding the invention, but does not constitute a limitation on the invention. The solution of the present invention will be further described below in conjunction with the accompanying drawings.

[0020] Reference Figure 1 , Figure 1 This is a schematic diagram of the electronic device structure of the hardware operating environment involved in the embodiments of the present invention.

[0021] like Figure 1 As shown, the electronic device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (Wi-Fi interface). The memory 1005 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0022] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0023] like Figure 1 As shown, the memory 1005, as a storage medium, may include an operating system, a data storage module, a network communication module, a user interface module, and electronic programs. In the illustrated electronic device, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and memory 1005 in the electronic device of the present invention can be disposed in the electronic device, and the electronic device calls the data collaborative processing device stored in the memory 1005 through the processor 1001 and executes the data collaborative processing method provided in the embodiments of the present invention.

[0024] Reference Figure 2This embodiment provides a data collaborative processing method. This method is applied to an enterprise-level multi-level collaborative system for financial assets, aiming to solve the technical challenges in traditional financial asset management where business data, financial accounting data, and tax data are fragmented, heavily reliant on manual judgment, and difficult to accurately automate deferred income tax calculation under massive transaction volumes. This embodiment constructs a three-layer architecture of "data model-rules-process" at the bottom layer, utilizes OCR technology to capture source data, and achieves end-to-end automation from business occurrence to tax-accounting difference analysis through embedded accounting and tax engines. The data collaborative processing method of this embodiment mainly includes steps S101 to S105.

[0025] S101. Configure a multi-level collaborative data architecture in the data processing system. The multi-level collaborative data architecture includes an interrelated data model layer, rule layer, and process layer. The data model layer is used to configure the data object categories, data processing scenarios, and data storage structures of financial assets. The rule layer is used to configure data operation logic, data association mapping relationships, and deferred income tax calculation rules. The process layer is used to configure the business data flow path and the calling relationship with document data formats.

[0026] This step uses a metadata-driven approach to transform accounting standards and tax law logic into computer-executable code and data structures.

[0027] S1011, Configure Data Model Layer: Construct a multi-dimensional financial asset data warehouse.

[0028] The data model layer is used to define the "skeleton" of financial assets. A series of standardized data table structures are pre-created in a database (such as MySQL or Oracle).

[0029] Preset data object categories: In accordance with "Accounting Standard for Business Enterprises No. 22 - Recognition and Measurement of Financial Instruments", four core categories are preset in the configuration table Dict_Asset_Category: Financial Assets Held for Trading: Code FA_TRADING; Debt Investments: Code FA_DEBT; Other Debt Investments: Code FA_OTHER_DEBT; Other Equity Instrument Investments: Code FA_OTHER_EQUITY.

[0030] Based on this, users can extend the underlying categories, such as attaching subclasses under FA_TRADING: STOCK (stocks), FUND (funds), BOND (bonds), and WEALTH (financial products).

[0031] Preset data processing scenario: In the scenario definition table Dict_Business_Scenario, define the operation types that cover the entire lifecycle of financial assets: New scenario (SC_ADD): corresponds to initial measurement, including purchase, subscription, acceptance of donation, etc.

[0032] Adjustment scenarios (SC_ADJUST): These correspond to subsequent measurements, including changes in fair value (valuation), interest accrual, dividend declarations, and impairment provisions.

[0033] Disposal scenario (SC_DISPOSE): Corresponds to termination confirmation, including sale, redemption, maturity payment, write-off, etc.

[0034] Configure a standardized document data structure: A business document model with a "master table-sub-table" structure was designed as the data carrier for the rule layer and process layer.

[0035] Main table structure (Table_Bill_Header): Used to store general information about business processes.

[0036] Bill_ID (Primary Key): A unique identifier for the document, in UUID format.

[0037] Bill_No: Business document number, such as "JYZ-20241001-001".

[0038] Operator_Info: Person in charge, department in charge, and responsible unit.

[0039] Contract_Info: Contract name, contract number, total contract amount.

[0040] Payment_Info: Payment method, payer's bank account number, and payee's bank account number.

[0041] Bill_Status: Document status (draft, under review, effective, void).

[0042] Sub-table structure (Table_Bill_Detail): Used to store specific financial asset accounting elements.

[0043] Detail_ID (primary key): A unique identifier for the detail.

[0044] Parent_ID (foreign key): Related to the main table Bill_ID.

[0045] Project_Ref: The associated financial asset project file ID (such as a specific stock).

[0046] Asset_Type: Asset category (referencing a default category).

[0047] Trans_Date: Transaction date.

[0048] Security_Code: Securities code (e.g., "600000.SH").

[0049] Trans_Price: Transaction price per unit.

[0050] Trans_Volume: Transaction volume / share.

[0051] Trans_Amount: Total transaction amount.

[0052] Fee_Details: Transaction fee details (stored in JSON format, such as {"commission": 500, "tax":100}).

[0053] Accounting-specific write-back field: Acct_Cost: The cost recorded in the accounting system.

[0054] Acct_FV_Change: Change in fair value.

[0055] Acct_Inv_Income: Investment income.

[0056] Tax_Base: Tax base.

[0057] Book_Value: Book value.

[0058] DT_Diff: Temporary difference.

[0059] DT_Asset / Liability: Amount of deferred tax assets / liabilities.

[0060] S1012, Configuration Rules Layer: Construct a business operation, audit, and accounting mapping engine.

[0061] The rules layer is responsible for transforming business data into financial and tax data.

[0062] Document item calculation logic configuration: The system has a built-in script engine (such as a Python or JavaScript engine) that allows configuration of field-level calculation formulas.

[0063] Initial cost calculation: , in, This is the initial recorded cost. To pay the total price, Cash dividends that have been declared but not yet paid.

[0064] Transaction fee calculation: , in, The total transaction cost is the sum of commission, stamp duty, transfer fee, etc.

[0065] Investment income (disposal) calculation: , in, For investment returns, To dispose of net income, For the cost to be carried forward, This refers to the fair value changes carried forward.

[0066] Audit formula configuration: Configure data integrity verification rules. If the verification fails, throw a ValidationException and block the process.

[0067] Example rule 1: if (Asset_Type == 'FA_TRADING' && Trans_Volume <= 0) return Error("Transaction count must be greater than 0"); Example rule 2: if (Tax_Rate<0 || Tax_Rate>1) return Error("Tax rate must be between 0 and 1"); Voucher push and write-back rule configuration: Create a mapping table Map_Item_Subject for "document fields - accounting subjects".

[0068] Mapping logic: Defined when Asset_Type is "Trading Financial Assets" and Scenario is "Purchase": Debit account 1: 1101 (Trading Financial Assets - Cost), the amount is taken from Acct_Cost.

[0069] Debit account 2: 1131 (Dividends Receivable), the amount is taken from Div_{declared}.

[0070] Debit account 3: 6111 (Investment Income), the amount is taken from Fee_{total}.

[0071] Credit account: 1002 (Bank Deposit), amount taken from Amt_{total}.

[0072] Write-back mechanism: After the voucher is successfully generated, the voucher's Voucher_ID and Voucher_Status are obtained and written back to Table_Bill_Header to achieve a closed loop.

[0073] Deferred income tax calculation rule configuration: Configuration unit: Establish a deferred tax model based on the underlying category (such as "stocks").

[0074] Difference recognition logic: , like This is identified as a taxable temporary difference; if It is identified as a deductible temporary difference.

[0075] Deferred tax calculation: , , in, This is the amount to be adjusted for the current period (i.e., the amount of vouchers for the current period). The beginning balance.

[0076] S1013, Configuration Process Layer: Defines the directed acyclic graph of business flow.

[0077] Define business flow paths using a workflow engine (such as Activiti).

[0078] Path definition: Start node, OCR recognition node, data verification node, manual review node, voucher generation node, deferred tax calculation node to end node.

[0079] Call Relationships: Configure the document format and rule engine interface to be called for each node. For example, the "Voucher Generation Node" automatically calls Voucher_Generation_Service.

[0080] S102. Obtain the original business data of the target financial asset through data interface or optical character recognition (OCR) technology, extract the globally unique business identifier, match the corresponding document data format based on the call relationship in the process layer, and instantiate and generate a financial asset business document containing the globally unique business identifier and conforming to the data storage structure.

[0081] This step transforms unstructured data into structured documents.

[0082] S1021. Multimodal data acquisition and preprocessing.

[0083] OCR recognition path: Image preprocessing: Denoising, binarization, and tilt correction are performed on the uploaded original voucher images (such as JPG and PDF) to improve image quality.

[0084] Text detection and recognition: Deep learning models (such as CRNN+CTC) are used to locate text regions in images and identify the text content.

[0085] Key information extraction: Based on a pre-trained financial bill model, the key field extracted is the transaction date. ), Securities Code ( ), Transaction Amount ( ),cost( ).

[0086] API interface path: Provides a RESTful API to receive JSON data packets pushed by external trading systems, such as { "trade_date": "2024-10-01", "sec_code": "600000", "amount": 5000000 ...}.

[0087] S1022, Intelligent Matching and Scene Determination.

[0088] Based on the extracted Security_Code and transaction direction (buy / sell / dividend), the system automatically queries and matches the data model layer.

[0089] If the code does not exist in the system archive and the direction is "buy", it is determined to be a new scenario.

[0090] If the code already exists and the direction is "sell", it is determined to be a disposal scenario.

[0091] If the direction is "valuation update", it is determined to be an adjustment scenario.

[0092] S1023, Document Instantiation and File Management.

[0093] Identifier generation: The system generates a globally unique business identifier (Global_Trace_ID) for the currently acquired raw business data.

[0094] Based on the judgment result, the call relationship configured in the process layer is parsed, and the corresponding document data format template (such as Template_Purchase) is dynamically matched through the call relationship.

[0095] Data Population and Storage: Based on the judgment results, the call relationships configured in the process layer are parsed, and the corresponding document data format template is dynamically matched. The corresponding business process in the process layer is started, and the corresponding financial asset business document is instantiated using the document data format. Key field information and globally unique business identifiers are automatically populated into the document item data of the financial asset business document, and are forcibly persisted according to the preset primary and foreign key association constraints in the data model layer.

[0096] File Update: For newly added assets, a new record is automatically created in the Asset_Master_Data table, generating a unique internal asset code (Asset_UUID). For existing assets, their holding quantity and latest change date are updated.

[0097] S103. Flow financial asset business documents along the business data flow path, call the data operation logic in the rule layer, automatically calculate and verify the document item data in the flowed financial asset business documents, and automatically generate the first type of structured data record carrying a globally unique business identifier based on the data association mapping relationship after verification, and complete the accounting data processing.

[0098] After a document is instantiated, the system workflow engine strictly follows the business data flow path configured in the process layer to perform state transitions. When the flow reaches the corresponding processing node, the following calculation logic is triggered: Scenario 1: When purchasing financial assets (new scenario), the total transaction amount is automatically obtained as the initial cost and separate transaction fees. If dividends are included, they are identified as accounts receivable dividends. The system then performs logical verification on the calculation results of the initial cost, accounts receivable dividends, and transaction fees (e.g., verifying whether the initial cost is greater than zero and whether the total of various fees matches the total expenditure). After the logical verification passes, the system automatically generates a journal entry object Voucher_Data based on the verified calculation results and writes a globally unique business identifier to it.

[0099] Automatic calculation: Automatically executes the formulas configured in S1012.

[0100] Generate the first type of structured data record (financial data record): Generate a journal entry data object Voucher_Data, and write the globally unique business identifier (Global_Trace_ID) of the document passthrough into this data object to maintain data association: Assume that the OCR recognizes: transaction amount of RMB 50.5 million, including RMB 500,000 of declared dividends, RMB 20,000 of handling fees, and RMB 30,000 of stamp duty.

[0101] (10,000 yuan).

[0102] (Ten thousand yuan). (Note: This is a correction. Transaction fees are usually not included in the transaction amount, or they may need to be separated depending on the specific bill logic. Here, we assume 5050 is the total payment amount, which includes dividends. Fees may be paid separately or deducted internally, depending on the rule configuration. This example assumes that fees are included in the total payment amount for ease of understanding.)

[0103] If the fee is an additional payment, then This embodiment uses standard logic: total payment of 5050 (including dividend of 50), with an additional fee of 50,000.

[0104] Final calculation: Cost = 5000, Dividends Receivable = 50, Investment Income (Debit) = 5.

[0105] Generate the first type of structured data record (financial voucher): Generate accounting entry data object Voucher_Data: Entry 1: Dr. Trading Financial Assets - Cost 5000 Entry 2: Dr. Dividends Receivable 50 Entry 3: Dr Investment Return 5 Entry 4: Cr Bank Deposit 5055 Baseline data write-back: After the voucher is generated, Write the Tax_Base field in the document as the basis for subsequent tax processing.

[0106] Scenario 2: Changes in fair value (adjustment scenario) Data Acquisition: Obtain the closing price at the end of the period through the market data interface and calculate the latest fair value. Assuming Calculation: , Assuming the original book value is 5000, the change is 1000.

[0107] Voucher generation: Entry 1: Dr Trading Financial Assets - Fair Value Change 1000 Entry 2: Cr Fair value change gain / loss 1000 Write-back: Update the book value field Note that the tax base at this time... It remains at 5000 (tax law usually measures it at historical cost).

[0108] Scenario 3: Disposal of Financial Assets (Disposal Scenario) Proportional calculation and carry-over: Assume that 50% of the holdings are sold.

[0109] , , , Profit Calculation: Assume the disposal revenue is 3200.

[0110] , Voucher generation: Dr. Bank deposit 3200 Cr Trading financial assets - cost 2500 Cr Trading financial assets - changes in fair value 500 Cr Investment income 200 Calculation of current income tax: Tax-approved gains = Disposal income - Tax basis carryforward = .

[0111] Current period income tax payable = .

[0112] Generate voucher: Dr Income Tax Expense 175 / Cr Taxes Payable 175.

[0113] S104. Based on real-time data from financial asset business documents and first-type structured data records, the deferred income tax calculation rules are triggered, numerical differences are identified, and new second-type structured data records are generated by combining the initial values ​​of historical second-type structured data records extracted from the database, thereby realizing tax data processing.

[0114] This step is a deep manifestation of multi-level collaboration and is usually triggered at the end of the month or quarter.

[0115] S1041, Data Snapshots and Difference Identification.

[0116] Perform a snapshot scan of all existing financial assets.

[0117] Obtaining book value ( ): Extracted from the latest "Change Order" or "Disposal Order". For example, in Scenario 2, the ending book value is 6000 (50% of which is 3000).

[0118] Obtaining the tax base ( ): Extracted from "Purchase Order" or "Cost Adjustment Order". For example, in Scenario 2, the tax base at the end of the period is still 5000 (50% of which is 2500).

[0119] S1042, Temporary Difference Calculation Engine.

[0120] Execute the difference analysis logic: , Scenario A (Holding Period Unrealized Profit): As in Scenario 2, It was determined to be a "taxable temporary difference".

[0121] Scenario B (Holding Period Unrealized Loss): If the market value drops to 4000, It was determined to be a "deductible temporary difference".

[0122] S1043. Automatic Calculation and Adjustment of Deferred Tax. Obtaining the Beginning Balance: Based on the asset item ID currently being processed, the system extracts historical Type II structured data records from the database for the previous accounting period and uses their ending values ​​as the beginning balance for the current period.

[0123] Generate a second type of structured data record (tax data record): The following voucher data will be automatically generated, and the globally unique business identifier (Global_Trace_ID) will be synchronously written into the associated field of the tax data record.

[0124] Take scenario A as an example: Calculate the ending balance: , Get the beginning balance: Query the database Table_Deferred_Tax_Log, assuming the beginning balance .

[0125] Calculate the adjustment amount for this period: , Generate the second type of structured data record (tax voucher): The following vouchers will be automatically generated: Debit: Income tax expense 250 Credit: Deferred income tax liability 250 Record update: Store the calculation results in the deferred tax ledger as the opening balance for the next period.

[0126] S1044, Reversal logic during disposal.

[0127] In scenario three, 50% of the issues were resolved.

[0128] Remaining assets calculation: Book value = 3000, Tax base = 2500.

[0129] New difference calculation: .

[0130] New ending balance: .

[0131] Original balance (before disposal): .

[0132] Adjustment amount (return): .

[0133] Generate vouchers: Debit: Deferred income tax liability 125 Credit: Income tax expense 125 This enables the automatic reversal of deferred income tax upon asset disposal, ensuring the real-time accuracy of tax data.

[0134] S105. Based on the globally unique business identifier, establish a full-process bidirectional index relationship between the original business data, the first type of structured data records and the second type of structured data records in the database, forming a multi-level collaborative view of the business, accounting and tax data of financial assets.

[0135] S1051. Construct a bidirectional index for the entire process.

[0136] A core relational table, Table_Holographic_Index, is maintained in the database, and physical mapping relationships are established based on a globally unique business identifier (Global_Trace_ID) used throughout each stage. The structure is as follows: Index_ID: Primary key.

[0137] Global_Trace_ID: A globally unique business identifier (used as the core cluster index key).

[0138] Bill_ID: Associated business document ID (points to the business layer via Global_Trace_ID).

[0139] Voucher_ID_Acct: Associated with the ID of the first type of structured data record (pointing to the accounting layer via Global_Trace_ID).

[0140] Voucher_ID_Tax: Associated with the ID of a second type of structured data record (pointing to the tax layer via Global_Trace_ID).

[0141] Source_Doc_ID: Associated original document image ID (points to the OCR source file).

[0142] Voucher_ID_Acct: Associated financial document ID (pointing to the accounting layer).

[0143] Voucher_ID_Tax: Associated tax voucher ID (pointing to the tax level).

[0144] Asset_Project_ID: The ID of the associated asset project.

[0145] S1052, Generation of multi-level collaborative views.

[0146] Based on the above index, the front end generates a visualized "holographic view": From a business perspective: Clicking on a transaction displays its corresponding contract, invoice, OCR recognition result, generated financial voucher number, and the resulting tax and accounting difference amount.

[0147] From a financial perspective: Click on the "Trading Financial Assets" item in the balance sheet to drill down to all detailed transaction documents, and you can also view the corresponding deferred tax calculation process horizontally.

[0148] From a tax perspective: Click "Deferred income tax liabilities" in the tax return to list all individual assets that constitute the balance and their sources of difference (i.e., specific fair value change statements), and support one-click export of tax working papers.

[0149] Through the above steps, this embodiment not only automates the accounting process for financial assets, but also improves the consistency of accounting data through strong correlation of underlying data, and solves the technical defect of "difficulty in tracing tax-accounting differences".

[0150] This invention also provides a data collaborative processing device, referring to... Figure 3 The diagram shows a functional block diagram of a data collaborative processing device 200 according to the present invention. The device may include the following modules: The architecture configuration module 201 is used to configure a multi-level collaborative data architecture in the data processing system. The multi-level collaborative data architecture includes an interrelated data model layer, rule layer, and process layer. The data model layer is used to configure the data object categories, data processing scenarios, and data storage structures of financial assets. The rule layer is used to configure data operation logic, data association mapping relationships, and deferred income tax calculation rules. The process layer is used to configure the business data flow path and the calling relationship with document data formats. The data acquisition and generation module 202 is used to acquire the original business data of the target financial asset through a data interface or optical character recognition (OCR) technology, extract the globally unique business identifier, match the corresponding document data format based on the call relationship in the process layer, and instantiate and generate a financial asset business document containing a globally unique business identifier and conforming to the data storage structure. The accounting processing module 203 is used to transfer financial asset business documents along the business data flow path, call the data operation logic in the rule layer, automatically calculate and verify the document item data in the transferred financial asset business documents, and automatically generate the first type of structured data record carrying a globally unique business identifier based on the data association mapping relationship after the verification is passed, thus completing the accounting data processing. The tax processing module 204 is used to trigger the deferred income tax calculation rules in the rule layer based on real-time data from financial asset business documents and first-type structured data records. It automatically identifies the numerical difference between the book value data and the tax base data; calculates the ending value of deferred income tax assets or deferred income tax liabilities based on the numerical difference; and automatically generates second-type structured data records with globally unique business identifiers by combining the beginning values ​​of historical second-type structured data records extracted from the database, thereby realizing tax data processing. The multi-level collaboration module 205 is used to establish a full-process bidirectional index relationship between the original business data, the first type of structured data records and the second type of structured data records in the database based on a globally unique business identifier, forming a multi-level collaborative view of the business, accounting and tax data of financial assets.

[0151] Based on the same inventive concept, another embodiment of the present invention provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus. Memory, used to store computer programs; The processor, when executing a program stored in memory, implements the data collaborative processing method of the present invention.

[0152] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used in the diagram, but this does not indicate that there is only one bus or one type of bus. The communication interface is used for communication between the aforementioned terminal and other devices. The memory can include Random Access Memory (RAM), or non-volatile memory, such as at least one disk storage device. Optionally, the memory can also be at least one storage device located remotely from the aforementioned processor.

[0153] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0154] Furthermore, to achieve the above objectives, embodiments of the present invention also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the data collaborative processing method of the embodiments of the present invention.

[0155] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, embodiments of the present invention can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of the present invention can take the form of computer program products implemented on one or more computer-usable hardware devices (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0156] The embodiments of the present invention are described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (apparatus), and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0157] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1The function specified in one or more boxes.

[0158] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0159] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. "And / or" indicates that either one or both can be chosen. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes the element.

[0160] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method of data syndication processing, the method comprising: The method includes: A multi-level collaborative data architecture is configured in the data processing system. The multi-level collaborative data architecture includes an interrelated data model layer, a rule layer, and a process layer. The data model layer is used to configure the data object categories, data processing scenarios, and data storage structures of financial assets. The rule layer is used to configure data operation logic, data association mapping relationships, and deferred income tax calculation rules. The process layer is used to configure the business data flow path and the calling relationship with document data formats. The original business data of the target financial asset is obtained through data interface or optical character recognition (OCR) technology, a globally unique business identifier is extracted, and the corresponding document data format is matched based on the call relationship in the process layer. A financial asset business document containing the globally unique business identifier and conforming to the data storage structure is instantiated and generated. The financial asset business documents are transferred along the business data transfer path. The data operation logic in the rule layer is called to automatically calculate and verify the document item data in the transferred financial asset business documents. After the verification is passed, a first type of structured data record carrying the globally unique business identifier is automatically generated based on the data association mapping relationship to complete the accounting data processing. Based on the real-time data of the financial asset business documents and the first type of structured data records, the deferred income tax calculation rules in the rule layer are triggered to automatically identify the numerical difference between the book value data and the tax base data; the ending value of the deferred income tax asset or deferred income tax liability is calculated according to the numerical difference, and the beginning value of the historical second type of structured data records extracted from the database is used to automatically generate a second type of structured data record carrying the globally unique business identifier, thereby realizing tax data processing; Based on the globally unique business identifier, a full-process bidirectional index relationship is established in the database between the original business data, the first type of structured data records, and the second type of structured data records, forming a multi-level collaborative view of the business, accounting, and tax data of financial assets.

2. The data syndication method of claim 1, wherein, The data model layer in the multi-level collaborative data architecture can be configured in the following ways: The data object categories for financial assets are preset, and the data object categories at least cover trading financial assets, debt investments, other debt investments, and other equity instrument investments; For each of the data object categories, a data processing scenario covering the entire lifecycle is preset, and the data processing scenario includes at least a new addition scenario, an adjustment scenario, and a disposal scenario; For each of the aforementioned data processing scenarios, a standardized document data structure is configured, which includes a main table data item and a sub-table data item; wherein, the main table data item is used to store handling information and contract overview information, and the sub-table data item is used to store attribute information, transaction details data and accounting process data of specific financial asset projects; The document data structure is configured as the data carrier for the rule layer and process layer.

3. The data syndication method of claim 2, wherein, The rule layer in the multi-level collaborative data architecture configures the data operation logic and the data association mapping relationship, specifically including: Configure the data calculation logic of the document item data, and automatically calculate the values ​​of initial cost, transaction fees, dividends receivable, fair value changes and investment income by referencing the data of the financial asset business documents mentioned above or external interface data; Configure the validation logic for document item data to automatically verify the integrity and logical correctness of the data during document flow. When the validation fails, the process will be blocked by the instructions of the data processing system and an exception prompt will be output. Configure data record generation and write-back rules, define the mapping relationship between document item data and classification codes, and after the financial asset business document is approved, map the document item amount to the corresponding debit or credit classification code to generate the first type of structured data record, and write back the status identifier and final confirmed amount of the generated record to the corresponding field of the financial asset business document.

4. The data syndication method of claim 3, wherein, The rule layer in the multi-level collaborative data architecture also includes configuring the deferred income tax calculation rules, which specifically include: Using the data object category and its underlying classification as configuration units, and embedding additional items as data detail carriers, a deferred income tax data calculation model is constructed. Configure the book value data retrieval rules, and extract the currently confirmed book value data of financial assets from the disposal order, change order or purchase order in sequence according to the priority of the data processing scenario. Configure the rules for retrieving tax base data, and calculate the tax base data of financial assets at the current point in time based on the initial cost and holding ratio at the time of purchase; Configure the difference identification and calculation logic to automatically calculate the difference between the book value data and the tax base data. If the difference is greater than zero, it is marked as a taxable temporary difference; if the difference is less than zero, it is marked as a deductible temporary difference. Configure the deferred income tax data record generation logic, multiply the temporary difference by the preset tax rate to obtain the expected value at the end of the period, compare the expected value at the end of the period with the beginning value obtained from the system database, calculate the amount to be adjusted in the current period, and automatically generate adjustment data records for deferred income tax assets or deferred income tax liabilities based on the amount to be adjusted in the current period.

5. The data syndication method of claim 2, wherein, The process of acquiring the original business data of the target financial asset through a data interface or optical character recognition (OCR) technology, and instantiating and generating the corresponding financial asset business document based on the process layer and the document data format associated with the process layer, includes: The OCR technology is used to identify the original voucher images of financial asset transactions, or to receive external transaction data packets through a data interface and parse them to obtain key field information such as transaction date, security code, transaction amount and fees. Based on the key field information, the corresponding data object category is automatically matched in the data model layer, and the data processing scenario is determined based on the key field information. Initiate the business process in the process layer corresponding to the determined data processing scenario, and instantiate the corresponding financial asset business document using the document data format; The key field information and the globally unique business identifier are automatically filled into the document item data of the financial asset business document. For financial assets that are newly added projects, a new detailed file is automatically created in the system management object; for financial assets that are existing projects, historical batch information is automatically referenced and the holding status data is updated.

6. The data syndication method of claim 3, wherein, When the data processing scenario involves the purchase of financial assets, the invocation of the data processing logic in the rule layer to automatically calculate and verify the document item data in the financial asset business document specifically includes: The system automatically obtains the total transaction amount as the initial cost and separates the subscription fee, transaction fee, and commission from the transaction data, summarizing them into transaction fees. If the transaction includes declared but unpaid dividends, the declared but unpaid dividends shall be separately identified as dividends receivable data; Logical verification is performed on the initial cost, the dividend receivable data, and the transaction fee calculation results. After the verification is passed, based on the verified calculation results, a first-class structured data record carrying the globally unique business identifier is automatically generated, containing the dividend receivable data and the transaction fee. After the data record is generated, the confirmed initial cost is written back to the financial asset business document as the initial value of the tax base, serving as the benchmark data for subsequent deferred income tax calculation.

7. The data syndication method of claim 4, wherein, When the data processing scenario involves changes in the fair value of financial assets during the holding period, the rules for triggering the deferred income tax calculation in the rule layer include: Receive fair value change data provided by external financial data interfaces, or enter the fair value change amount, generate a value change statement and generate fair value change profit and loss data records. On the balance sheet date or at a specific transaction trigger point, initiate the deferred income tax data retrieval task; The updated book value data based on the aforementioned value change statement is compared with the tax base data that remains unchanged or is adjusted proportionally. If the increase in fair value causes the book value data to be greater than the tax base data, calculate the increase in taxable temporary differences and automatically generate a data record of debiting income tax expense and crediting deferred income tax liability. If a decrease in fair value results in the carrying amount being less than the tax base, the increase in deductible temporary differences is calculated, and a data record is automatically generated that debits deferred tax assets and credits income tax expense.

8. The data syndication method of claim 4, wherein, When the data processing scenario involves the disposal of financial assets, the method further includes: The disposal ratio is calculated based on the disposal quantity data and holding quantity data in the financial asset business documents, and the corresponding costs and cumulative fair value changes are transferred according to the disposal ratio. Calculate the difference between disposal revenue and carried-forward costs and variable profits and losses, recognize it as investment income, and generate financial data records for the disposal business; Based on the remaining book value data and remaining tax basis data after disposal, the temporary differences are recalculated. For the deferred tax assets or deferred tax liabilities corresponding to the disposed portion, they are reversed in full or proportionally, and reversal data records are generated. The taxable income for the current period is calculated based on the difference between the disposal income and the tax base data. The income tax expense for the current period is calculated in conjunction with the income tax rate, and a tax data record is generated to confirm the income tax payable for the current period.

9. A data cooperative processing apparatus characterized by comprising: The device includes: The architecture configuration module is used to configure a multi-level collaborative data architecture in the data processing system. The multi-level collaborative data architecture includes an interconnected data model layer, rule layer, and process layer. The data model layer is used to configure the data object categories, data processing scenarios, and data storage structures of financial assets. The rule layer is used to configure data operation logic, data association mapping relationships, and deferred income tax calculation rules. The process layer is used to configure the business data flow path and the calling relationship with document data formats. The data acquisition and generation module is used to acquire the original business data of the target financial asset through a data interface or optical character recognition (OCR) technology, extract the globally unique business identifier, match the corresponding document data format based on the call relationship in the process layer, and instantiate and generate a financial asset business document containing the globally unique business identifier and conforming to the data storage structure. The accounting processing module is used to transfer the financial asset business documents along the business data flow path, call the data operation logic in the rule layer, automatically calculate and verify the document item data in the transferred financial asset business documents, and automatically generate a first type of structured data record carrying the globally unique business identifier based on the data association mapping relationship after verification, thus completing the accounting data processing. The tax processing module is used to trigger the deferred income tax calculation rules in the rule layer based on the real-time data of the financial asset business documents and the first type of structured data records, automatically identify the numerical difference between the book value data and the tax base data; calculate the ending value of the deferred income tax asset or deferred income tax liability according to the numerical difference, and automatically generate the second type of structured data record carrying the globally unique business identifier by combining the beginning value of the historical second type of structured data records extracted from the database, thereby realizing tax data processing. The multi-level collaboration module is used to establish a full-process bidirectional index relationship between the original business data, the first type of structured data records and the second type of structured data records in the database based on the globally unique business identifier, forming a multi-level collaborative view of the business, accounting and tax data of financial assets.

10. 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 data collaborative processing method as described in any one of claims 1 to 8.

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