A data asset whole life cycle inventory and table-in compliance automatic mapping method

Abstract

The application discloses a kind of data asset whole life cycle inventory and table entry compliance automatic mapping methods, belong to data asset management technical field, comprising: automatically collecting the metadata information of data asset in each life cycle stage, extract technical attribute, business attribute and operation attribute, construct whole-link blood relationship diagram, generate multi-dimensional data asset list;Build table entry compliance mapping rule base, and map inventory label semantics to accounting standards condition judgment variable;By rule engine, automatically match asset confirmation condition, determine table entry category;Automatic collection development cost and calculate initial measurement amount;Data asset is checked to ownership, security, financial compliance;Output standardization table entry ledger.The mapping method of the application breaks the semantic gap between data governance and accounting standards, realizes the automatic mapping of data asset whole life cycle inventory results to accounting standards table entry information, improves the efficiency and compliance of data asset table entry.

Description

Technical Field

[0001] This invention relates to the field of data asset management technology, specifically to a method for automatic mapping of data asset lifecycle inventory and table entry compliance. Background Technology

[0002] With the development of the digital economy, data has become a core production factor for enterprises. Furthermore, data resources can be included in the enterprise's balance sheet as intangible assets or inventory. Under the current technological system, the inclusion of data assets on the balance sheet involves two relatively independent technological fields: 1. Data Governance Technology. Existing data governance platforms possess relatively mature data asset inventory capabilities, capable of generating data asset lists that include technical attributes, business attributes, operational attributes, and full-chain lineage relationships through methods such as metadata collection, data lineage analysis, and classification and labeling. Typical functions of such platforms include automated data discovery, sensitive data identification, and asset catalog construction.

[0003] 2. Financial Accounting Technology Field. Existing financial management systems have the ability to manage asset ledgers and automatically generate accounting vouchers. They can perform initial measurement, subsequent measurement and amortization of traditional intangible assets (such as software copyrights and patents) and inventory, and output standardized ledgers that meet the requirements of accounting standards according to preset rules.

[0004] However, existing technologies have obvious technical limitations and shortcomings: (1) There is a semantic gap between data governance results and accounting standards, making it impossible to automatically determine the category of data to be entered into the table. The core fields of the asset inventory results output by existing data governance platforms are technical label systems, such as data classification (structured / unstructured), security level (L1-L4), business domain affiliation, etc. There is a lack of automatically executable mapping relationship between these labels and the asset recognition conditions required by Accounting Standard No. 6—Intangible Assets and Accounting Standard No. 1—Inventories. For example, a data governance platform can label a dataset as an "external API service," but it cannot automatically determine whether this usage characteristic meets the recognition condition of intangible assets that "can be separated or divided from the enterprise and used for sale, transfer, licensing, leasing or exchange." Currently, this determination process relies entirely on manual labor, with financial personnel comparing each item based on offline materials, which is extremely inefficient and highly subjective. When the scale of data assets reaches tens of thousands of items, manual determination is basically not feasible.

[0005] (2) Development costs and expensed expenditures are difficult to distinguish automatically, and there is a lack of automated means for value measurement. The development process of data assets usually involves multiple stages such as data collection, cleaning, labeling, and modeling. The resource consumption (such as storage, computing power, and labor) of each stage is recorded in different operation and maintenance systems and project management systems. Current accounting standards require the distinction between research stage expenditures (which should be expensed) and development stage expenditures (which can be capitalized when conditions are met). However, existing technology lacks the ability to automatically collect the operational metadata of the data processing pipeline to the corresponding accounting period and automatically distinguish between expensed and capitalized expenditures. Financial personnel need to manually extract cost data from multiple systems and determine the stage attribution, which is labor-intensive and prone to errors.

[0006] (3) There is a contradiction between the dynamic nature of data assets and static ledgers, resulting in a lack of adaptive update mechanisms. Unlike traditional intangible assets, data assets are highly dynamic: their use may change from internal to external services, ownership status may change due to the expiration of licensing agreements, and new processing operations may continuously increase development costs. The existing financial system's asset ledger is a static recording model. When the asset status changes, a manual change process needs to be initiated and the entry category and measurement attributes need to be reassessed. Once omitted or delayed, it will lead to the distortion of financial statement information, constituting the risk of accounting information disclosure violations.

[0007] (4) Compliance verification relies on manual review and cannot be automated by embedding it into the data entry process. Data asset entry requires comprehensive verification of the asset's ownership compliance, security compliance, and financial compliance, involving multi-dimensional checks such as authorization agreement review, security assessment result verification, and traceability of measurement parameter sources. In existing technologies, these checks are scattered across independent processes in different departments such as legal, security, and finance, lacking a unified technical means to embed them into the automated data entry process to form a closed-loop verification across the entire chain. This not only leads to a long compliance review cycle but also makes it difficult to standardize the collection and archiving of compliance supporting materials, failing to meet the audit and regulatory requirements for the traceability of the data entry process.

[0008] In summary, existing technologies in the field of data governance cannot directly serve financial reporting, while technologies in the field of finance cannot understand and process the unique dynamic attributes and processing information of data assets, resulting in a significant technological gap. Therefore, there is an urgent need to design a method that can bridge this gap to automate the mapping of data asset lifecycle inventory results to accounting standard reporting information. Summary of the Invention

[0009] To address the shortcomings of existing technologies, this invention proposes an automatic mapping method for the full lifecycle inventory and entry into financial statements of data assets. By constructing a semantic mapping rule engine between the full lifecycle inventory list of data assets and the recognition conditions of accounting standards, the method automatically completes the determination of the entry category of data assets, the value measurement based on the data collection of lineage operations, and multi-dimensional compliance verification, and finally outputs a standardized entry ledger that conforms to financial and accounting standards.

[0010] The technical solution provided by this invention is as follows: A method for automatic mapping of data asset lifecycle inventory and compliance entry includes the following steps: Step 1: Connect to various internal data storage systems of the enterprise through the data source connector, automatically collect metadata information of data assets at each stage of their life cycle, extract technical attributes, business attributes and operational attributes, and automatically construct a full-link lineage diagram of data assets through data lineage analysis technology to comprehensively generate a multi-dimensional data asset list; Step 2: Extract the accounting standard recognition conditions and transform them into structured conditional expressions. Establish the mapping relationship between data asset inventory tags and accounting standard condition judgment variables through the ontology aligner, and build an entry compliance mapping rule engine. Step 3: Identify the formation path of data assets based on the full-link lineage diagram, and then the decision-making unit determines the table entry category of data assets based on the holding purpose label and business use using the table entry compliance mapping rule engine; Step 4: For data assets that meet the compliance mapping rules for table entry, the value measurement engine automatically collects the resource consumption records of the data assets in each processing stage based on the operation attributes of each processing node on the full-link lineage diagram, and calculates the initial measurement amount and amortization parameters. Step 5: Before the entry ledger is generated, perform ownership compliance verification, security compliance verification, and financial compliance verification on the data assets. Once all the above verifications are passed, a compliance mark is generated. Step 6: Automatically generate standardized entry ledgers according to the preset ledger template.

[0011] Furthermore, in step 1, the various data storage systems within the enterprise include: relational databases, data lakes, object storage, and API interfaces; The lifecycle stages include: collection, storage, processing, circulation, and destruction. The technical attributes include data format, data volume, storage location, update frequency, and field structure; The business attributes include business domain affiliation, data usage tags, and data source type tags; The operation attributes include resource consumption records for each processing stage, processing operation timestamps, and operation subjects. The resource consumption records include storage resource usage and unit price, computing power resource consumption and unit price, and manual annotation hours and rates.

[0012] Furthermore, in step 2, the accounting standard recognition criteria include: Intangible asset recognition criteria: derived from contractual rights or other legal rights; can be separated or divided from the enterprise and used for sale, transfer, licensing, leasing or exchange; development phase expenditures attributable to the data asset can be measured; expected economic benefits related to the data asset can flow into the enterprise; Inventory recognition criteria: The enterprise holds the data asset for sale in the ordinary course of business; the cost of the data asset can be measured. The table compliance mapping rule engine includes: an input layer, a mapping parsing layer, an inference execution layer, and an output layer; The input layer is used to receive data asset inventory labels and accounting standard recognition condition library; The mapping parsing layer includes an ontology aligner, a conditional expression generator, and a dynamic rule chain configuration module. The ontology aligner is used to map the semantics of inventory tags to conditional judgment variables, the conditional expression generator is used to generate executable Boolean judgment expressions, and the dynamic rule chain configuration module is used to combine multiple conditions to form a rule chain and supports user configuration. The inference execution layer includes a rule matching engine and a decision-making unit. The rule matching engine is used to perform rule chain matching, and the decision-making unit is used to make decisions based on priorities. The output layer is used to determine the input / output table categories, recommend measurement rules, and determine the audit trail.

[0013] Furthermore, in step 3, the decision-making unit determines the data asset's entry category based on the holding purpose label and business use, using the entry compliance mapping rule engine. Specifically: If the purpose of holding the property is to sell it or to sell it, the inventory rules will be used to determine the ownership. If the purpose of holding the asset is internal use, external service, or licensing, it will be determined according to the rules for intangible assets. For data assets that do not meet any asset recognition criteria, an expense recommendation is output along with a reason label.

[0014] Furthermore, in step 4, the resource consumption records of the data asset at each processing stage are automatically collected, including: For purchased data assets, the purchase price and related taxes are extracted from the purchase contract information field as the initial measurement cost; for self-developed data assets, the development cost is automatically accumulated and collected according to the resource consumption records of each processing node in the lineage chain and the preset cost allocation weight.

[0015] Furthermore, in step 5, the ownership compliance verification is as follows: automatically checking whether the source authorization agreement of the data asset is clear, whether the authorization scope covers the current use scenario, and whether there are any rights restrictions or joint ownership disputes; Security compliance verification includes: verifying whether data assets have completed the required level of security assessment, whether they have been anonymized according to classification and grading requirements, and whether operation logs fully record all access and processing behaviors; Financial compliance verification involves checking whether the entry process complies with accounting standards and whether all measurement parameters are verifiable and properly documented.

[0016] Furthermore, in step 6, the standardized entry ledger includes the following fields: asset number, asset name, data type, entry category, initial measurement amount, amortization method, amortization period, compliance status flag, associated ownership certificate index, and associated security audit report index.

[0017] Step 6 further includes: when the business use, ownership status or cost information of the data asset changes, steps 3-5 are re-executed to generate a new standardized entry ledger, and the original standardized entry ledger is recorded for verification.

[0018] Compared with the prior art, the present invention discloses the following technical effects: 1. Break down the semantic gap between data governance and accounting standards to achieve fully automated determination of the category of data to be entered into the table.

[0019] In existing technologies, there is a semantic gap between the asset inventory results output by data governance platforms (such as business tags like "external API services") and the asset recognition conditions of accounting standards (such as legal judgments like "can be separated and used for licensing"), making automatic conversion impossible and relying entirely on manual item-by-item judgment. This invention, however, innovatively introduces an ontology aligner by constructing an entry compliance mapping rule base. This automatically associates the classification and grading tags, business purpose tags, and source type tags generated during the data asset inventory process with the judgment variables corresponding to the accounting standard recognition conditions through a predefined semantic mapping table. An executable Boolean judgment expression is then automatically generated by the condition expression generator. Based on this, the rule matching engine and decision-making unit of the inference execution layer automatically determine the entry category according to priority decision logic (prioritizing inventory rules for sale purposes, and intangible asset rules for internal use or external services). Ultimately, the asset assessment process, which originally required financial professionals to spend hours or even days, is shortened to a second-level automated process. When the scale of data assets reaches tens of thousands, the problem that manual assessment is basically infeasible is fundamentally solved. At the same time, the standardized assessment logic built into the rules engine eliminates the subjective arbitrariness of manual judgment, ensuring the consistency and compliance of the assessment results.

[0020] 2. Based on lineage operation data, the automatic collection and distinction between expense and capitalization of development costs are realized, solving the problem of automated value measurement.

[0021] In existing technologies, the development costs of data assets are recorded in different operation and maintenance systems and project management systems. Financial personnel need to manually extract cost data from multiple systems and determine whether each stage should be expensed or capitalized, which is labor-intensive and prone to errors. This invention, however, innovatively extracts the operational attributes of the entire data asset processing chain during the data asset lifecycle inventory. This includes resource consumption records for each processing stage (storage resource usage and unit price, computing power resource consumption and unit price, manual annotation hours and rates), processing operation timestamps, and information on the operating entity. In the value measurement stage, the value measurement engine automatically accumulates and aggregates the entire chain cost based on the operational attributes of each processing node on the lineage diagram. Based on preset threshold parameters in the rule base (such as the proportion of development stage expenditures to total costs), it automatically distinguishes between research stage expenditures (expensed) and development stage expenditures (capitalized), only including development stage expenditures that meet the capitalization conditions in the initial asset measurement amount. This transforms the cost measurement process, which originally required manual collection, summarization, and judgment across systems, into a fully automated collection and differentiation based on lineage operation data, significantly reducing labor costs and error rates. Cost collection can be accurately traced to every processing node, and the entire measurement process is traceable, fully meeting the strict requirements of accounting standards for "reliable cost measurement".

[0022] 3. By using dynamic sensing and ledger versioning mechanisms, the contradiction between dynamic changes in data assets and static ledgers can be resolved.

[0023] In existing technologies, the asset ledgers of financial systems operate on a static recording model, failing to detect dynamic changes in the use, ownership, or cost of data assets. If the asset status changes and the ledger is not updated in a timely manner, it will lead to distorted financial statement information and pose a risk of information disclosure violations. This invention establishes a continuously operating data asset inventory mechanism that can detect changes in various attribute tags throughout the entire lifecycle of data assets in real time. When the use tag, ownership status, or cost information of a data asset changes, the inventory module automatically captures the change and triggers a re-execution of steps 3 to 5 of the judgment, measurement, and verification process. A new version of the ledger is generated while retaining the original version record, achieving versioned management and dynamic updates of the information entered into the financial statements throughout its entire lifecycle. Ultimately, this fundamentally resolves the contradiction between the dynamic nature of data assets and traditional static accounting ledgers, ensuring that the information entered into the financial statements remains synchronized with the actual asset status, effectively avoiding the risk of accounting information disclosure violations due to information lag. Simultaneously, the versioned traceability mechanism provides a complete change tracing chain for auditing.

[0024] 4. Embed ownership, security, and financial compliance checks into the automated process of the form to form a closed loop at the end of the chain.

[0025] In existing technologies, compliance work required for data entry, such as ownership review, security assessment verification, and source verification of measurement parameters, is scattered across independent processes in different departments such as legal, security, and finance. The lack of a unified technical means to connect and embed these processes into an automated data entry workflow leads to long review cycles and difficulties in standardizing the collection and archiving of supporting documents. This invention innovatively constructs an automatic compliance verification module that automatically performs three verifications before the ledger is generated: ownership compliance verification (checking the completeness of the authorization agreement and the restriction of rights), security compliance verification (verifying the security assessment level and the status of de-identification processing), and financial compliance verification (checking the data entry determination and the traceability of the measurement process). All verifications are automatically executed based on collected objective data, and the verification results are marked in a structured manner in the final ledger, with the verification basis stored in association with the ledger records. This transforms the previously scattered compliance review work, requiring manual processing across multiple departments, into an automated verification closed loop embedded in the data entry workflow, significantly shortening the compliance review cycle. The verification results are output as an integrated whole with the data entry ledger, with a high degree of standardization, directly meeting the audit and regulatory requirements for traceability of the data entry process and significantly reducing compliance risks.

[0026] 5. The overall solution achieves end-to-end automation, filling the technological gap between data governance and financial reporting.

[0027] This invention provides a complete end-to-end automated solution: starting with a full lifecycle inventory of data assets, it uses a semantic mapping rule engine to determine data entry, performs value measurement based on lineage operation data, embeds triple compliance checks, and finally outputs a standardized entry ledger that can be directly integrated with ERP systems. The entire process requires no manual intervention, bridging the technical gap between the fields of data governance and financial accounting. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 Flowchart of the method for automatic mapping of data asset lifecycle inventory and table entry compliance provided in this embodiment of the invention; Figure 2 A flowchart illustrating the process architecture of the data asset lifecycle inventory and automatic mapping method for compliance in table entry provided in this embodiment of the invention; Figure 3 This is a schematic diagram of the internal structure of the table entry compliance mapping rule engine provided in an embodiment of the present invention. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] This invention provides a method for automatic mapping of data asset lifecycle inventory and compliance entry, such as... Figure 1-2 As shown, it includes the following steps: Step 1: Conduct a full lifecycle inventory of data assets and generate a multi-dimensional data asset list.

[0032] Specifically: 1. Connect to various internal data storage systems of the enterprise (including relational databases, data lakes, object storage, API interfaces, etc.) through data source connectors to automatically collect metadata information of data assets at each stage of their lifecycle (collection, storage, processing, circulation, and destruction).

[0033] 2. Extract technical attributes, business attributes, and operational attributes based on metadata information. Technical attributes include, but are not limited to: data format, data volume, storage location, update frequency, field structure, etc.; extracted business attributes include, but are not limited to: business domain affiliation, data usage tags (such as "internal operation", "external API service", "data product sale"), data source type tags (such as "purchased", "self-developed", "outsourced processing"), etc.; extracted operational attributes include, but are not limited to: resource consumption records for each processing stage (storage resource usage and unit price, computing power resource consumption and unit price, manual annotation hours and rates), processing operation timestamps, operation entities, etc.

[0034] 3. Through data lineage analysis technology, automatically construct the full-link lineage diagram of data assets, recording the complete formation path of data from the source system to the current state, including data source, fusion and processing process, downstream dependencies, etc.

[0035] 4. Based on the above information, generate a multi-dimensional data asset list. Each asset is accompanied by a structured record of its technical attributes, business attributes, operational attributes, and full-chain lineage.

[0036] Step 2: Build a compliance mapping rule library for table entries.

[0037] Specifically: 1. Extract the recognition conditions of accounting standards and form conditional expressions.

[0038] The recognition criteria for intangible assets extracted from Accounting Standard No. 6—Intangible Assets include: ① derived from contractual rights or other legal rights; ② capable of being separated or divided from the enterprise and used for sale, transfer, licensing, leasing or exchange; ③ the development stage expenditures attributable to the asset can be reliably measured; and ④ the expected economic benefits related to the asset are likely to flow into the enterprise.

[0039] The inventory recognition criteria extracted from Accounting Standard No. 1—Inventories include: ① The enterprise holds the asset for sale in the ordinary course of business; ② The cost of the asset can be reliably measured.

[0040] The above confirmation conditions are transformed into structured condition expressions. For example, condition 2 for intangible assets can be expressed as "{separability} = yes AND {holding purpose} IN {sale, transfer, license, lease, exchange}".

[0041] 2. Construct an ontology aligner to establish a mapping relationship between data asset inventory labels and accounting standard condition judgment variables.

[0042] A predefined mapping table maps semantic labels in the inventory model to accounting standard condition variables. For example, "External API service" in the data usage label is mapped to holding purpose = "external service", thus satisfying the "granting license" judgment branch in condition 2 of intangible assets; "sale of data products" is mapped to holding purpose = "sale"; "purchased from outside" in the source type label is mapped to acquisition method = "purchased from outside"; self-developed and with clear cost records is mapped to "reliable cost measurement" = yes; and the licensing agreements and contract information in the lineage extract the "separability" judgment criteria.

[0043] 3. Construct an inbound compliance mapping rule engine, which consists of an input layer, a mapping parsing layer, an inference execution layer, and an output layer (see reference). Figure 3 Meanwhile, users can adjust the mapping relationship and judgment thresholds through a visual interface. For example, users can set "development phase expenditures account for more than X% of total costs" as an auxiliary threshold parameter to distinguish between the research phase and the development phase.

[0044] The input layer receives data asset inventory tags and accounting standard confirmation condition libraries from step 1. The mapping and parsing layer includes an ontology aligner, a conditional expression generator, and a dynamic rule chain configuration module. The ontology aligner maps the semantics of inventory tags to conditional judgment variables, the conditional expression generator generates executable Boolean judgment expressions, and the dynamic rule chain configuration module combines multiple conditions to form rule chains and supports user configuration. The inference and execution layer includes a rule matching engine and a decision-making unit. The rule matching engine performs rule chain matching, and the decision-making unit makes decisions based on priority. The output layer outputs and outputs table category determinations, measurement rule recommendations, and audit trail determinations.

[0045] Step 3: Determine if an asset is classified as an asset, and automatically output the category to be entered into the table.

[0046] Specifically: 1. Identify the formation path of data assets based on lineage diagrams. Mark whether the data asset is from a single source or a multi-source integration, whether it has undergone substantial processing, and whether there is a clear licensing agreement.

[0047] 2. The decision-making unit prioritizes decisions based on the purpose tags and business uses, utilizing a rules engine: If the purpose of holding is "for sale" or "held for sale", then the inventory rules will be used to determine the appropriate method. If the purpose of holding the data is "internal use," "external service," or "licensing," it is determined according to the intangible asset rules. For example, for a data asset labeled "Financial Anti-Fraud Model Training Dataset," its business use is tagged as "external API service," and its source type is "self-developed." The rule engine matches it against the intangible asset conditions one by one: Condition ①, the lineage information shows that the data was legally authorized for collection, and the validity period of the authorization agreement covers the usage period, satisfying "derived from contractual rights"; Condition ②, the dataset has an independent API service interface and can provide services externally independently, satisfying "can be separated and used for licensing"; Condition ③, the development cost of the data asset has been separately measured, and satisfies "costs can be reliably measured"; Condition ④, the external API service has generated continuous revenue, satisfying "expected economic benefits are likely to flow in." If all four conditions are met, the rule engine determines that the asset is recognized as an intangible asset.

[0048] For data assets that do not meet any asset recognition criteria, output an expense recommendation with a reason label. For example, "expected economic benefits cannot be reliably inflowed", "costs cannot be separately identified", "no explicit authorization agreement", etc.

[0049] Step 4: Value measurement, calculate the initial measurement amount and amortization parameters.

[0050] For data assets identified as intangible assets or inventory, the value measurement engine automatically collects resource consumption records for each processing node from the operational attributes generated in step 1.

[0051] For purchased data assets, the purchase price and related taxes are automatically extracted from the purchase contract information fields as the initial measurement cost. For self-developed data assets, the development cost is automatically accumulated and collected according to the resource consumption records (storage resources, computing resources, manual annotation hours, etc.) of each processing node in the lineage chain, based on preset cost allocation weights.

[0052] Based on set threshold parameters, the system automatically distinguishes between research phase expenditures (expensed) and development phase expenditures (capitalized), including development phase expenditures that meet the capitalization criteria in the initial asset measurement amount. For example, the aforementioned "Financial Anti-Fraud Model Training Dataset" consumed a total of RMB 482,000 in storage resources, RMB 120,000 in computing resources, and RMB 330,000 in manual annotation time costs across the collection, cleaning, labeling, and model training stages. It was determined that all of the above stages belonged to the development phase and met the capitalization criteria; therefore, the initial measurement amount was RMB 482,000.

[0053] Furthermore, this invention also determines the measurement model: based on the configuration in the rule base and the asset usage characteristics, it automatically determines whether to use the cost model or the fair value model for subsequent measurement. For data assets with active market trading prices (such as similar datasets having public quotes on data trading platforms), the system can automatically collect reference market price or revenue sharing data to generate a fair value reference range.

[0054] The amortization period is determined by automatically setting a pre-defined lifespan assessment model based on the data type, timeliness characteristics, and business iteration cycle. In this embodiment, since the financial dataset involves financial risk control scenarios and the data timeliness cycle is approximately 5 years, the amortization period is also set to 5 years, and the amortization method is the straight-line method.

[0055] Step 5: Automatic compliance verification.

[0056] Before the data asset entry ledger is generated, the automatic compliance verification module performs triple compliance verification on the data assets: Ownership compliance verification. Automatically checks whether the source authorization agreement for data assets is clear, whether the scope of authorization covers the current use case, and whether there are any restrictions on rights or disputes over shared ownership. The check results are marked as "compliant" or "non-compliant," and the specific check items and supporting evidence are recorded.

[0057] Security and compliance verification. Verify whether data assets have completed the required level of security assessment, whether they have been anonymized according to classification and grading requirements, and whether operation logs fully record all access and processing activities.

[0058] Financial compliance verification. Check whether the entry process complies with accounting standards, and whether all measurement parameters (cost accumulation details, amortization period determination basis, fair value reference sources) are verifiable and properly documented.

[0059] Once all inspection items pass, a compliance mark "compliant" is generated; if any inspection item fails, it is marked "pending rectification" with rectification suggestions.

[0060] Step 6: Generate ledgers and output standardized entry ledgers.

[0061] The ledger generation engine receives the entry category determination result from step 3, the value measurement result from step 4, and the compliance mark from step 5, and automatically assembles and generates a standardized entry ledger according to the preset ledger template.

[0062] The ledger records include the following fields: asset number, asset name, data type, entry category, initial measurement amount, amortization method, amortization period, compliance status flag, associated ownership certificate index, and associated security audit report index.

[0063] The standardized entry ledger of this invention also supports versioned management. When the usage label, ownership status, or cost information of data assets changes, steps 3 to 5 are executed again to generate a new version of the ledger record, while the original version record is retained for verification, realizing dynamic updating and full-process traceability of entry information.

[0064] The ledger generation engine provides a standard API interface that can be connected to an enterprise's ERP general ledger system or financial management system to automatically push ledger data to generate accounting vouchers and complete the automatic accounting of data assets.

[0065] In summary, this invention achieves an automated closed loop for data assets, from technical management to accounting confirmation, reducing the average processing time for a single asset to be entered into the financial statements from several days to minutes. Furthermore, because all judgment, measurement, and verification processes are based on traceable objective data and a configurable rule engine, the system-generated entry ledgers have high consistency and compliance, and can be directly used as audit working papers, enabling enterprises to meet the policy requirements for data asset entry into the financial statements under low-cost and high-efficiency conditions.

[0066] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0067] Example 1: A dataset developed in-house and used for external services is classified as an intangible asset.

[0068] This example uses a "Financial Anti-Fraud Model Training Dataset" developed by a fintech company. This dataset is formed by cleaning, feature engineering, and labeling raw data such as user transaction records and device fingerprints collected by the company from legitimate channels. It is provided to third-party financial institutions for anti-fraud scoring services through an API interface, and its purpose is "external API service".

[0069] Step 1: Data asset lifecycle inventory.

[0070] By connecting to the enterprise's Hadoop data lake and MySQL database through a data source connector, the system automatically collects the dataset's technical attributes (data format: Parquet, data volume: 2.3TB, update frequency: daily increment), business attributes (business domain: "risk control," data usage tag: "external API service," source type tag: "self-developed," holding purpose tag: "external service"), operational attributes (collection stage consumes 15,000 yuan in computing resources, cleaning and feature engineering consumes 28,000 yuan, manual annotation costs 79,000 yuan, model training consumes 200,000 yuan, totaling 322,000 yuan), and a complete chain lineage diagram (recording the complete processing path from raw transaction logs → data lake entry → cleaning task → feature engineering task → annotation task → model training task → API service library). Based on this information, a structured list of this data asset is generated.

[0071] Step 2: Construct a compliance mapping rule base for table entry and perform assetization determination.

[0072] Four recognition criteria were extracted from Accounting Standard No. 6 – Intangible Assets to form a conditional expression. The ontology aligner mapped the inventory labels to judgment variables: “External API services” was mapped to holding purpose = “external services”, corresponding to the legal judgment branch of “granting licenses”; “self-developed and with clear cost records” was mapped to “reliable cost measurement” = yes; the licensing agreement information in the lineage extracted “separability” = yes, and “derived from contractual rights” = yes.

[0073] Step 3: Determine if an asset is classified as an asset, and automatically output the category to be entered into the table.

[0074] The rule matching engine receives the above instantiated variables and performs intangible asset condition matching: Condition ① "Originating from contractual rights or other legal rights," the lineage shows that the original data collection has a legally authorized agreement, which is satisfied; Condition ② "Can be separated and used for licensing," the dataset has an independent API interface that can provide services externally, which is satisfied; Condition ③ "Development phase expenditures can be reliably measured," the operational attributes have recorded the cost details of each processing node, which is satisfied; Condition ④ "Expected economic benefits are likely to flow in," the API service has generated continuous subscription revenue, which is satisfied. The decision-making unit combines the four results and determines that the category to be included in the table is "intangible assets."

[0075] Step 4: Value Measurement.

[0076] The value measurement engine aggregates the entire cost chain from operational attributes. There were no capitalizable expenditures during the research phase (data exploration, demand analysis), while the total expenditures during the development phase (collection, cleaning, annotation, model training) amounted to RMB 322,000, all meeting the capitalization criteria and thus recognized as the initial measurement amount of RMB 322,000. Considering the data timeliness characteristics of the financial risk control scenario (approximately 3 years), the system recommends an amortization period of 3 years using the straight-line method.

[0077] Step 5: Automatic compliance verification.

[0078] Ownership compliance: Verification passed; the original data collection authorization agreement's validity period covers the usage period; the API service authorization terms are complete. Security compliance: Verification passed; the dataset has passed the Level 3 Information Security Protection Assessment; the API interface has deployed a dynamic data masking module. Financial compliance: Verification passed; the entire process of table entry determination and cost collection is traceable. All check items are marked as "compliant".

[0079] Step 6: Generate ledger.

[0080] Based on the above results, the ledger generation engine automatically generates the first ledger record as shown in Table 1: Asset ID DA-2025-001, Asset Name "Financial Anti-Fraud Model Training Dataset", Data Type "Structured Dataset", Category "Intangible Assets", Initial Measurement Amount 322,000 RMB, Amortization Method "Straight-Line Method", Amortization Period "3 Years", Compliance Status "Compliant", and associates the ownership certificate index PROOF-2025-089 and the security audit index AUDIT-2025-056. This ledger is pushed to the ERP general ledger system via API, automatically generating accounting vouchers for intangible asset entry.

[0081] Table 1 Standardized Entry Ledger

[0082] Example 2: Purchased datasets awaiting sale – identified as inventory This example uses a "regional consumer profile dataset" purchased by a data service provider from a third-party data supplier. The dataset is a structured CSV file, with a purchase cost of 80,000 yuan (including tax). The purchase contract clearly stipulates that ownership can be bought out and resold, and the company's purpose in holding it is "to process and then sell it."

[0083] Step 1: Data asset inventory information shows that the business purpose label is "data product sale", the source type label is "external purchase", the holding purpose label is "sale", the operation attributes record the purchase contract price of 80,000 yuan and taxes of 1,600 yuan, and the lineage is single external purchase with no internal processing.

[0084] Step 2: The rules engine, based on the holding purpose tag "for sale," initiates a priority decision, prioritizing the matching of inventory rules. It checks condition ① "held for sale in the ordinary course of business," which is consistent with data product transactions in the company's daily operations, thus satisfying the condition; and condition ② "costs can be reliably measured," which is also satisfied because the purchase contract price is clear. The entry category is determined to be "inventory."

[0085] Step 3: The value measurement engine extracts the purchase price and taxes totaling RMB 81,600 from the contract field as the initial cost of inventory, and manages it by batch. Since inventory does not involve amortization, the amortization-related fields are empty.

[0086] Step 4: Verify ownership compliance by confirming the contract contains a buy-out / resale clause and has no restrictions on rights; verify security compliance by confirming the data has been anonymized; verify financial compliance by marking it as "compliant".

[0087] Step 5: The ledger generation engine outputs the second record as shown in Table 1: Asset number DA-2025-002, entry category "inventory", initial measurement amount 81,600 yuan, amortization method and period marked as "—" (not applicable), associated ownership certificate index PROOF-2025-102.

[0088] Example 3: Multi-source integration but expected economic benefits cannot reliably flow in – determined as expensing. This example uses a "competitive product dynamic monitoring dataset" crawled from multiple public channels by a company for internal research purposes. The company's technical team cleaned and integrated the data, labeling it as "internal research."

[0089] Step 1: Data asset inventory information shows that the business purpose is "internal research", the source type is "self-collected", the cost of cleaning and integrating operation attribute records is about RMB 12,000, the lineage shows integration from multiple public channels, and there is no independent authorization agreement.

[0090] Step 2: The rule engine first matches the intangible asset condition. The condition "expected economic benefits are likely to flow in" is not met—this dataset is only for internal auxiliary research and has no direct path to generate revenue. It then matches the inventory condition, but the purpose of holding it is not "sale," so it is not met. The entry category is determined to be "expensive," with the reason label "expected economic benefits cannot reliably flow in."

[0091] Step 3: Expensing treatment, without calculating the initial measurement amount of the asset, the expenditure of RMB 12,000 is included in the current profit and loss.

[0092] Step 4: Ownership compliance is marked as "compliant" (data from public channels); security and financial compliance are passed.

[0093] Step 5: The ledger generation engine outputs the third record as shown in Table 1: Asset number DA-2025-003, entry category "Expense", initial measurement amount marked as "—", and the reason for expense is explained in the remarks.

[0094] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for automatic mapping of data asset lifecycle inventory and compliance entry, characterized in that, Includes the following steps: Step 1: Connect to various internal data storage systems of the enterprise through the data source connector, automatically collect metadata information of data assets at each stage of their life cycle, extract technical attributes, business attributes and operational attributes, and automatically construct a full-link lineage diagram of data assets through data lineage analysis technology to comprehensively generate a multi-dimensional data asset list; Step 2: Extract the accounting standard recognition conditions and transform them into structured conditional expressions. Establish the mapping relationship between data asset inventory tags and accounting standard condition judgment variables through the ontology aligner, and build an entry compliance mapping rule engine. Step 3: Identify the formation path of data assets based on the full-link lineage diagram, and then the decision-making unit determines the table entry category of data assets based on the holding purpose label and business use using the table entry compliance mapping rule engine; Step 4: For data assets that meet the compliance mapping rules for table entry, the value measurement engine automatically collects the resource consumption records of the data assets in each processing stage based on the operation attributes of each processing node on the full-link lineage diagram, and calculates the initial measurement amount and amortization parameters. Step 5: Before the entry ledger is generated, perform ownership compliance verification, security compliance verification, and financial compliance verification on the data assets. Once all the above verifications are passed, a compliance mark is generated. Step 6: Automatically generate standardized entry ledgers according to the preset ledger template.

2. The method for automatic mapping of data asset lifecycle inventory and table entry compliance as described in claim 1, characterized in that, In step 1, the various data storage systems within the enterprise include: relational databases, data lakes, object storage, and API interfaces; The lifecycle stages include: collection, storage, processing, circulation, and destruction.

3. The method for automatic mapping of data asset lifecycle inventory and table entry compliance as described in claim 2, characterized in that, The technical attributes include data format, data volume, storage location, update frequency, and field structure; The business attributes include business domain affiliation, data usage tags, and data source type tags; The operation attributes include resource consumption records for each processing stage, processing operation timestamps, and operation subjects. The resource consumption records include storage resource usage and unit price, computing power resource consumption and unit price, and manual annotation hours and rates.

4. The method for automatic mapping of data asset lifecycle inventory and table entry compliance as described in claim 1, characterized in that, In step 2, the accounting standards recognition criteria include: Intangible asset recognition criteria: derived from contractual rights or other legal rights; can be separated or divided from the enterprise and used for sale, transfer, licensing, leasing or exchange; development phase expenditures attributable to the data asset can be measured; expected economic benefits related to the data asset can flow into the enterprise; Inventory recognition criteria: The enterprise holds the data assets for sale in the ordinary course of business; the cost of the data assets can be measured.

5. The method for automatic mapping of data asset lifecycle inventory and table entry compliance as described in claim 4, characterized in that, The table compliance mapping rule engine includes: an input layer, a mapping parsing layer, an inference execution layer, and an output layer; The input layer is used to receive data asset inventory labels and accounting standard recognition condition library; The mapping parsing layer includes an ontology aligner, a conditional expression generator, and a dynamic rule chain configuration module. The ontology aligner is used to map the semantics of inventory tags to conditional judgment variables, the conditional expression generator is used to generate executable Boolean judgment expressions, and the dynamic rule chain configuration module is used to combine multiple conditions to form a rule chain and supports user configuration. The inference execution layer includes a rule matching engine and a decision-making unit. The rule matching engine is used to perform rule chain matching, and the decision-making unit is used to make decisions based on priorities. The output layer is used to determine the input / output table categories, recommend measurement rules, and determine the audit trail.

6. The method for automatic mapping of data asset lifecycle inventory and table entry compliance as described in claim 1, characterized in that, In step 3, the decision-making unit determines the data asset's entry category based on the holding purpose label and business use, using the entry compliance mapping rule engine. Specifically: If the purpose of holding the property is to sell it or to sell it, the inventory rules will be used to determine the ownership. If the purpose of holding the asset is internal use, external service, or licensing, it will be determined according to the rules for intangible assets. For data assets that do not meet any asset recognition criteria, an expense recommendation is output along with a reason label.

7. The method for automatic mapping of data asset lifecycle inventory and table entry compliance as described in claim 1, characterized in that, In step 4, the resource consumption records of the data asset at each processing stage are automatically collected, including: For purchased data assets, the purchase price and related taxes are extracted from the purchase contract information field as the initial measurement cost; for self-developed data assets, the development cost is automatically accumulated and collected according to the resource consumption records of each processing node in the lineage chain and the preset cost allocation weight.

8. The method for automatic mapping of data asset lifecycle inventory and table entry compliance as described in claim 1, characterized in that, In step 5, the ownership compliance verification is as follows: automatically checking whether the source authorization agreement of the data asset is clear, whether the authorization scope covers the current use scenario, and whether there are any rights restrictions or joint ownership disputes; Security compliance verification includes: verifying whether data assets have completed the required level of security assessment, whether they have been anonymized according to classification and grading requirements, and whether operation logs fully record all access and processing behaviors; Financial compliance verification involves checking whether the entry process complies with accounting standards and whether all measurement parameters are verifiable and properly documented.

9. The method for automatic mapping of data asset lifecycle inventory and table entry compliance as described in claim 1, characterized in that, In step 6, the standardized entry ledger includes the following fields: asset number, asset name, data type, entry category, initial measurement amount, amortization method, amortization period, compliance status flag, associated ownership certificate index, and associated security audit report index.

10. The method for automatic mapping of data asset lifecycle inventory and table entry compliance as described in claim 8, characterized in that, Step 6 further includes: when the business use, ownership status or cost information of the data asset changes, steps 3-5 are re-executed to generate a new standardized entry ledger, and the original standardized entry ledger is recorded for verification.

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