Data asset identification and metrology tabulation processing method and system

Abstract

This invention discloses a method and system for data asset identification, measurement, and table entry processing, belonging to the field of measurement and table entry processing technology. It includes the following steps: multi-source data collection and standardized preprocessing; intelligent identification and value measurement of data assets; full-dimensional verification and audit recording; and financial account adaptation and automated accounting. This invention collects multi-source raw data from enterprises, including structured and unstructured data, and preprocesses it to build a standard data asset database. Then, it automatically identifies and labels assets, determines ownership, and quantifies value. Next, it performs consistency and compliance verification and audit recording, generating a list to be entered into the table. Finally, it automatically adapts financial accounts to generate compliant accounting information, achieving full-process automation. This improves the accuracy of data asset identification and measurement table entry processing and solves the problem of low accuracy in existing technologies.

Description

Technical Field

[0001] This invention relates to the field of measurement entry processing technology, and in particular to a method and system for data asset identification and measurement entry processing. Background Technology

[0002] Enterprise internal data is scattered across multiple heterogeneous systems, encompassing both structured business data and unstructured text, images, and log data. The data types are complex, the sources are diverse, and the definitions vary. This is because data assets lack unified classification standards and ownership rules. Furthermore, data possesses non-exclusive characteristics such as being reproducible, reusable, and shared by multiple entities, making it impossible to use the traditional logic of defining the boundaries between tangible and intangible assets. Manual analysis struggles to clearly delineate the asset scope of raw data, data products, and data services, and it is also impossible to accurately determine the ownership, processing, usage, and management rights of data. This directly leads to problems such as blurred data asset boundaries, omissions, misidentifications, and duplicate identifications. Based on this, existing technologies generally rely on semi-manual processing methods involving manual inventory, annotation, and verification. The problem lies in the lack of intelligent recognition methods that integrate machine learning and rule engines. Faced with massive amounts of data and complex processing chains, manual methods are insufficient for field-level lineage analysis, automatic value dimension determination, and automatic asset labeling. This not only results in long recognition cycles and high labor costs but also makes the recognition results unstable, with low coverage and poor consistency due to differences in human experience and subjective judgment, significantly reducing the efficiency of data asset recognition. Furthermore, in the measurement stage, traditional evaluation models such as the cost approach, income approach, and market approach are still used in isolation. The fundamental reason is the lack of a fusion measurement model adapted to the dynamic value of data assets and multiple accounting standards. In addition, the value of data fluctuates dynamically with timeliness, application scenarios, and quality levels, and the costs of data collection, cleaning, processing, and storage are also related to IT (Information Technology). Technology (Information Technology) operation and maintenance costs and R&D costs are intertwined and lack a unified cost collection standard and value allocation rule. At the same time, different enterprises have different understandings and implementation deviations, which ultimately leads to inconsistent measurement standards, large dispersion of valuation results, and inability to pass audit verification. The modules of data collection, AI (Artificial Intelligence) identification, measurement analysis, financial adaptation, and result visualization are disconnected and lack end-to-end process connection and consistency verification mechanism. Measurement results cannot be automatically linked to financial statement items, and compliance and accuracy are difficult to guarantee. As a result, a large number of data assets that meet the recognition conditions cannot be included in the financial statements in a compliant, accurate, and efficient manner, which seriously restricts the financial management and market monetization of enterprise data assets and results in low accuracy of data asset identification and measurement processing. Summary of the Invention

[0003] To address the low accuracy of data asset identification and measurement processing in existing technologies, this invention provides a method and system for data asset identification and measurement processing. The technical solution is as follows: On the one hand, a method for data asset identification, measurement, and table processing is provided. This method includes: S101, acquiring original multi-source data within the target enterprise, preprocessing the original multi-source data within the target enterprise to obtain internal multi-source data, in order to construct a standardized and traceable data asset base. The original multi-source data within the target enterprise includes, but is not limited to, structured business data, unstructured text data, image data, and log data; S102, automatically identifying, classifying, and labeling the internal multi-source data based on preset data asset classification rules to obtain data asset identification results, thereby clarifying data asset identification, ownership determination, and other related matters. Measurement attributes are used to quantify the value of identified data assets, resulting in a unified and auditable measurement result for data asset value. S103 involves verifying the consistency and compliance of the data asset identification results, ownership determination results, and data asset value measurement results, and recording the entire audit process to generate a list of verified data assets to be entered into the financial statements. S104 involves automatically adapting and matching the verified data assets to the pre-defined financial statement account mapping rules, generating data asset entry information that meets the requirements for financial statement preparation, thus achieving fully automated closed-loop processing of data assets from identification, measurement, verification to entry into the financial statements.

[0004] On the other hand, a data asset identification, measurement, and accounting system is provided. This system includes: a multi-source data acquisition and standardized preprocessing module, a data asset intelligent identification and value measurement module, a full-dimensional verification and auditing module, and a financial account adaptation and automated accounting module. The multi-source data acquisition and standardized preprocessing module is used to acquire original multi-source data from within the target enterprise, preprocess this data to obtain internal multi-source data, and construct a standardized and traceable data asset database. This original multi-source data includes, but is not limited to, structured business data, unstructured text data, image data, and log data. The data asset intelligent identification and value measurement module is used to automatically identify and classify the internal multi-source data based on preset data asset classification rules. The system employs several modules: classification and labeling to obtain data asset identification results, clarifying data asset identification, ownership determination, and measurable attributes; value quantification calculation for identified data assets to form a unified and auditable data asset value measurement result; a full-dimensional verification and auditing module to perform consistency verification, compliance verification, and full-process auditing of data asset identification results, ownership determination results, and data asset value measurement results, generating a list of verified data assets to be entered into the financial statements; and a financial account adaptation and automated accounting module to automatically adapt and match verified data assets to pre-defined financial statement account mapping rules, generating data asset accounting information that meets the requirements for financial statement preparation, achieving fully automated closed-loop processing of data assets from identification, measurement, verification to accounting.

[0005] Beneficial effects The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: 1. The data asset identification and measurement table processing method provided by this invention, through multi-source data collection and standardized preprocessing, intelligent identification and value measurement collaborative processing, can uniformly collect and clean heterogeneous data such as structured and unstructured data of enterprises, build a standardized and traceable data asset base, and realize automatic identification, ownership verification and value quantification of data assets based on multi-level classification rules, machine learning and rule engine integration. It effectively solves the problems of traditional data asset identification relying on manual labor, chaotic classification, unclear ownership and difficulty in quantifying value, and significantly improves the accuracy, standardization and automation level of data asset identification and valuation.

[0006] 2. This invention introduces a multi-level adaptive mechanism, including dynamically adjusting the collection cycle based on identification confidence, dynamically adjusting the cross-border compliance marking threshold based on authorized scope coverage, and dynamically optimizing the multi-mapping conflict judgment threshold based on failure rate deviation. Combined with consistency verification, compliance verification, and full-process auditing, this invention achieves real-time risk control and traceable auditing of the entire data asset process. It ensures the logical consistency and regulatory compliance of asset identification, ownership determination, and measurement results, while reducing misjudgments and omissions through dynamic threshold adjustment, significantly improving the reliability and regulatory adaptability of the data asset pre-entry verification process.

[0007] 3. The verified data assets are matched with accounts and dynamically adjusted for anomalies according to preset mapping rules, and finally the accounting information that meets the requirements of financial statement preparation is generated. This realizes the full-process automated closed loop from data collection, asset identification, value measurement, compliance verification to financial accounting, effectively breaking down the barriers between business data and financial accounting, reducing the cost of manual intervention and the rate of operational errors, and improving the overall efficiency, financial compliance and audit traceability of data asset entry into the table. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0009] Figure 1 Flowchart of the data asset identification and measurement table entry processing method provided in the embodiments of this application; Figure 2 A flowchart illustrating the compliance verification process for the data asset identification and measurement table entry method provided in this application embodiment; Figure 3 This is a schematic diagram of the data asset identification and measurement table entry processing system provided in the embodiments of this application. Detailed Implementation

[0010] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0011] like Figure 1 The diagram shown is a flowchart of a data asset identification and measurement table processing method provided in this application embodiment. The method includes the following steps: S101. Obtain the original multi-source data within the target enterprise, preprocess the original multi-source data within the target enterprise to obtain the enterprise's internal multi-source data, in order to build a standardized and traceable data asset base. The original multi-source data within the target enterprise includes, but is not limited to, structured business data, unstructured text data, image data and log data.

[0012] It should be understood that acquiring raw, multi-source data from the target enterprise, including structured business data, unstructured text data, image data, and log data, and then performing preprocessing operations such as data cleaning, format normalization, noise removal, and lineage tracing on this raw, multi-source data, yields standardized and unified internal multi-source data. This allows for the construction of a standardized and traceable data asset repository, effectively integrating the enterprise's heterogeneous data resources, eliminating the problems of inconsistent definitions and disorder in multi-source data, and providing a high-quality, traceable data foundation for the subsequent automatic identification, ownership determination, and value measurement of data assets, thereby improving the accuracy and stability of subsequent processing stages.

[0013] S102, based on preset data asset classification rules, automatically identifies, classifies and labels multi-source data within the enterprise to obtain data asset identification results, so as to clarify the identification of data assets, the determination of ownership and the measurable attributes, and carry out value quantification calculation on the identified data assets to form a unified and auditable data asset value measurement result.

[0014] It needs to be explained that the specific steps for automatically identifying, classifying, and labeling multi-source data within an enterprise are as follows: A multi-level classification rule base is pre-built, which includes the target enterprise's original multi-source data, data products, and data services, based on four types of identification parameters: data source identifier, data type attribute, data quality threshold, and data link. By combining machine learning feature extraction with rule engine logic matching, the original multi-source data within the target enterprise is screened and classified layer by layer, and standardized labels with unique asset codes, category tags, and value attribute tags are assigned, outputting initial identification results containing asset details and classification information. The ownership of data assets in the initial identification results is verified and the attribution is defined in order to eliminate non-compliant data with unclear ownership or no legal authorization, and to form a list of compliant data assets with clear ownership and well-defined boundaries. Based on a preset measurability threshold, individual / combined data assets are quantitatively valued, and a full measurement process log is retained to form a unified, traceable, and auditable measurement result for the value of data assets.

[0015] In this embodiment, a multi-level classification rule base containing original multi-source data, data products, and data services within the target enterprise is pre-constructed. Based on four types of identification parameters—data source identifier, data type attribute, data quality threshold, and data link—a combination of machine learning feature extraction and rule engine logical matching is used to perform layer-by-layer screening and classification of the original multi-source data within the target enterprise. Simultaneously, standardized annotations—including unique asset codes, category labels, and value attribute labels—are assigned to output initial identification results. This enables automated and accurate classification and unique identification management of data assets, effectively improving asset identification efficiency and standardization. Based on this, the initial... The initial identification results involve verifying and defining the ownership of data assets, eliminating non-compliant data with unclear ownership or lack of legal authorization, and forming a list of compliant data assets with clear ownership and well-defined boundaries. This approach avoids ownership disputes and compliance risks from the outset, ensuring the legality and validity of assets included in the financial statements. Furthermore, based on preset measurability thresholds, individual or combined data assets are quantitatively valued and a full measurement process log is maintained. Ultimately, a unified and traceable data asset value measurement result is formed, ensuring the fairness and compliance of the valuation, while also making the measurement process verifiable and providing a true and reliable core basis for subsequent financial entries.

[0016] S103 performs consistency verification, compliance verification, and full-process auditing of the data asset identification results, ownership determination results, and data asset value measurement results, and generates a list of verified data assets to be added to the table.

[0017] It should be understood that, using the unique identifier of data assets as the core associated primary key, consistency and compliance checks are simultaneously conducted on the data asset identification results, ownership determination results, and data asset value measurement results. By cross-comparing the asset information, ownership boundaries, measurement caliber, and numerical logic of the three types of results, issues such as asset mismatch, ownership conflict, and measurement deviation are identified. At the same time, the compliance of the entire process is verified in conjunction with data compliance, ownership compliance, and cross-border compliance requirements. This not only ensures the logical consistency and data accuracy of the results at each stage, but also intercepts non-compliant assets at the source and prevents the risk of data entry into the financial statements. Simultaneously, using the unique asset identifier as a traceability index, the operation information, judgment basis, parameter values, anomaly warnings, and rectification records of the entire verification process are encrypted, solidified, and audited throughout the process, generating an unalterable and independently traceable audit file. This ensures that the entire process is verifiable, verifiable, and traceable. Finally, data assets that meet both consistency and compliance standards are selected, and a list of verified data assets awaiting entry into the financial statements with standardized format and complete information is compiled. This provides a true, compliant, and reliable asset basis for subsequent financial accounting, and solidifies the compliance foundation for data asset entry into the financial statements.

[0018] It should be further explained that the specific steps for performing consistency verification are as follows: The system pre-divides and configures three threshold ranges for the automatic identification confidence level of data assets: high, medium, and low. It establishes a dynamic mapping relationship between the identification confidence level and the corresponding data asset collection cycle. This dynamic mapping relationship is used to characterize the one-to-one correspondence between the three threshold ranges for the automatic identification confidence level of data assets and the corresponding collection cycle adjustment factor, the data asset collection baseline cycle, and the collection cycle adjustment factor. Based on the confidence level, the system outputs the corresponding cycle adjustment factor, and through multiplication, obtains the target collection cycle that matches the identification quality. This enables dynamic control of extending, maintaining, or shortening the collection cycle. If the confidence level of automatic data asset identification is within the high-level threshold range, and the stability, compliance, and measurable attributes of the data asset are determined to meet the standards, then the current confidence level of automatic data asset identification is input into the dynamic mapping relationship between the identification confidence level and the corresponding data asset collection cycle. The collection cycle adjustment factor is output, and the data asset collection baseline cycle and the collection cycle adjustment factor are multiplied to obtain the target data asset collection cycle. This adaptively extends the subsequent collection cycle of this type of data asset and reduces redundant collection.

[0019] Consistency checks also include: If the confidence level of automatic identification of data assets is within the medium-level threshold range, it is determined that there is a slight risk of fluctuation in the identification results of data assets. The current data asset collection benchmark cycle is maintained to keep the benchmark collection cycle of this type of data asset unchanged. If the confidence level of automatic data asset identification is within the low-level threshold range, and the integrity, ownership clarity, and measurability of the data asset are questionable, then the current confidence level of automatic data asset identification is input into the dynamic mapping relationship between the identification confidence level and the corresponding data asset collection cycle. The collection cycle adjustment factor is output, and the data asset collection baseline cycle and the collection cycle adjustment factor are multiplied to obtain the target data asset collection cycle. This adaptively shortens the subsequent collection cycle of this type of data asset, strengthens data supplementation and real-time updates, and achieves dynamic adaptation between the data asset collection cycle and the quality of automatic identification.

[0020] In this embodiment, based on the distribution characteristics of the confidence level of data assets, historical identification error rate, data asset compliance verification deviation value, and measurement accuracy attenuation coefficient, a combined weighting algorithm of the analytic hierarchy process (AHP) and entropy weighting is used to refine and dynamically configure the threshold ranges for high, medium, and low confidence levels. The algorithm first calculates the objective weights of each evaluation indicator using the entropy weighting method, then determines the subjective weights using the AHP, and finally obtains a comprehensive weight through normalization and fusion. Based on this, a confidence level threshold determination matrix is ​​constructed, thereby establishing the relationship between the identification confidence level and the corresponding data asset collection cycle. The dynamic mapping relationship not only represents the one-to-one correspondence between the high, medium, and low confidence level threshold ranges and the data collection period adjustment factor, the data collection base period, and the data collection period adjustment factor, but also embeds an exponential smoothing correction algorithm to perform real-time iterative optimization of the period adjustment factor. Specifically, it first obtains the period adjustment factor sequence corresponding to N consecutive historical confidence levels, and then calculates the smoothing correction value using the exponential smoothing formula St=α×Ft+(1-α)×St-1 (where α is the smoothing coefficient, Ft is the current factor value, and St-1 is the smoothing value of the previous period). The corrected adjustment factor is then... The data asset collection baseline period is multiplied by a multiplicative operation, while a collection period boundary constraint algorithm is introduced to set minimum and maximum collection period thresholds. The calculation results are then clamped to correct for exceeding these thresholds, ultimately yielding a target collection period that adapts to the recognition quality. This allows for dynamic adjustment of the collection period, enabling its extension, maintenance, or shortening. If the data asset automatic recognition confidence level falls into a high-level threshold range, it is verified by a multi-dimensional compliance verification algorithm. This algorithm simultaneously verifies four core dimensions: data stability indicators, compliance label matching degree, measurable attribute error range, and ownership traceability completeness. When all four indicators... When all preset standard values ​​are met, the current confidence level is input into the dynamic mapping relationship. After exponential smoothing correction, the collection period adjustment factor is output. This factor is multiplied by the collection baseline period and subjected to boundary constraints to obtain the target collection period. This adaptively extends the subsequent collection period to reduce redundant collection. If the confidence level of the data asset automatic identification is in the medium-level threshold range, the confidence level fluctuation amplitude detection algorithm determines that the fluctuation amplitude of its identification result is within the preset safe range and there is no significant risk of distortion. In this case, the current collection baseline period is maintained, and the historical smoothing value of this period is retained through the exponential smoothing algorithm to support the subsequent mapping relationship iteration.If the confidence level of the automatic identification of data assets is in the low-level threshold range, the data asset defect assessment algorithm quantitatively analyzes the data integrity missing rate, ownership clarity ambiguity, and quantifiable deviation value. If a significant risk of data acquisition failure is determined, the current confidence level is input into a dynamic mapping relationship. After exponential smoothing correction, a data acquisition cycle adjustment factor is output. This factor is multiplied by the baseline data acquisition cycle and clamped to above the minimum data acquisition cycle threshold by a boundary constraint algorithm to obtain the target data acquisition cycle. This adaptively shortens subsequent data acquisition cycles, strengthens the frequency of data re-acquisition, and enhances real-time updates. Simultaneously, the adjustment factor corresponding to the current low-level confidence level and the target cycle are fed back to the threshold division module. This module then optimizes the weight configuration and threshold determination matrix of the analytic hierarchy process (AHP)-entropy weight method, forming a closed-loop iterative mechanism of "threshold division - confidence determination - cycle adjustment - feedback optimization." This comprehensively achieves deep dynamic adaptation between the data asset acquisition cycle and the quality of automatic identification.

[0021] It should be understood that, such as Figure 2 The diagram shows a compliance verification flowchart for the data asset identification and measurement table processing method provided in this application embodiment. The specific process is as follows: Based on the authorized scope coverage value of a single data asset, it is first classified and judged according to a preset upper limit, reference interval, and lower limit: If the coverage is greater than or equal to the preset upper limit, the authorized boundary is determined to be clear and the cross-border compliance risk is low. A gain coefficient is output through a positive dynamic mapping rule, which is multiplied by the benchmark threshold and rounded up to obtain the target threshold, thereby tightening the labeling acceptance standard; If the coverage is within the preset reference open interval, the asset scope is determined to be compliant, and the cross-border compliance labeling accuracy benchmark threshold is directly maintained unchanged; If the coverage is less than or equal to the preset lower limit, the authorization is determined to be incomplete and the cross-border compliance risk is high. A decay coefficient is output through a positive dynamic mapping rule, which is multiplied by the benchmark threshold and rounded down to obtain the target threshold, thereby relaxing the labeling acceptance standard and triggering an anomaly review mechanism, ultimately achieving dynamic adaptation between the cross-border compliance labeling judgment accuracy and the authorization compliance level.

[0022] It should be further explained that the specific process for compliance verification is as follows: By statistically analyzing the ratio of the actual authorized application scenarios, authorized data fields, and authorized geographical scope of a single data asset to the preset legal authorization scope, the corresponding authorization scope coverage value is obtained; A positive dynamic mapping rule is established between the authorized scope coverage rate and the data cross-border compliance mark accuracy threshold. This rule is used to characterize the determination relationship between the authorized scope coverage rate value of a single data asset and its preset scope coverage reference upper limit, reference interval, and reference lower limit value, as well as the corresponding compliance mark accuracy threshold gain coefficient or attenuation coefficient. The functional correspondence between the coverage rate value and the target threshold is obtained by multiplying it by the benchmark threshold and rounding it down. If the corresponding authorized scope coverage value of a single data asset is greater than or equal to the preset upper limit of the scope coverage reference, it is determined that the data asset's authorization boundary is clear and the cross-border compliance risk is low. Then, the corresponding authorized scope coverage value is input into the positive dynamic mapping rule between the authorized scope coverage and the data cross-border compliance marking accuracy threshold, and the compliance marking accuracy threshold gain coefficient is output. The result of multiplying the data cross-border compliance marking accuracy benchmark threshold and the compliance marking accuracy threshold gain coefficient and then rounding up is taken as the target data cross-border compliance marking accuracy threshold, so as to tighten the marking acceptance standard.

[0023] Compliance verification also includes: If the corresponding authorized scope coverage value of a single data asset is within the preset scope coverage reference range, the data asset scope is determined to be compliant, and the current data cross-border compliance mark accuracy benchmark threshold is maintained. The preset scope coverage reference range represents the open interval formed by the preset scope coverage reference lower bound and the preset scope coverage reference upper bound. If the corresponding authorized scope coverage value of a single data asset is less than or equal to the preset lower limit of the scope coverage reference, and the data asset scope authorization is deemed incomplete and the cross-border compliance risk is high, then the corresponding authorized scope coverage value is input into the positive dynamic mapping rule between the authorized scope coverage and the data cross-border compliance marking accuracy threshold. The compliance marking accuracy threshold decay coefficient is output. The result of multiplying the data cross-border compliance marking accuracy benchmark threshold and the compliance marking accuracy threshold decay coefficient and then rounding down is used as the target data cross-border compliance marking accuracy threshold. This relaxes the marking acceptance standard and simultaneously triggers the abnormal review mechanism, thereby achieving dynamic adaptation between the accuracy of cross-border compliance marking judgment and the degree of authorization compliance.

[0024] In this embodiment, firstly, based on four core dimensions—the matching degree of the actual authorized application scenario of a single data asset, the completeness of the authorized data field, the compliance of the authorized geographical scope, and the conformity of the legal authorization terms—the CRITIC objective weighting algorithm is used to calculate the distinguishing power and conflict degree of each dimension indicator. The comprehensive weight of each dimension is determined, and a weighted summation model is constructed. The weighted ratio of the actual authorized application scenario, authorized data field, authorized geographical scope, and preset legal authorization scope of a single data asset is statistically analyzed to obtain the accurate authorized scope coverage value after dimension weight correction. Subsequently, a positive dynamic mapping rule is established between the authorized scope coverage value and the data cross-border compliance marking accuracy threshold, embedded with a Kalman filter dynamic correction algorithm. This rule not only represents the multi-level judgment relationship between the authorized scope coverage value of a single data asset and its preset scope coverage reference upper bound, reference interval, and reference lower bound, but also outputs the compliance marking accuracy threshold gain coefficient or attenuation coefficient through a nonlinear fitting function of the coverage value and the benchmark threshold. Simultaneously, the Kalman filter algorithm is used to optimally estimate the historical threshold coefficient sequence, eliminating coefficient fluctuation noise and outputting the corrected adjustment coefficient. Finally, the coefficient is multiplied by the benchmark threshold and rounded. The beam operation obtains the functional correspondence of the target threshold. If the corresponding authorized scope coverage value of a single data asset is greater than or equal to the preset upper limit of the scope coverage reference, the authorization link closed loop, scenario adaptation uniqueness, and territorial jurisdiction exclusiveness are verified by the cross-border authorization boundary topology verification algorithm. If it is determined that the data asset authorization boundary is clear and the cross-border compliance risk is low, the current authorized scope coverage value corrected by the CRITIC algorithm is input into the positive dynamic mapping rule embedded with Kalman filtering. The filtered and corrected compliance marking accuracy threshold gain coefficient is output. The cross-border compliance marking accuracy benchmark threshold of data is multiplied by the gain coefficient and then rounded up. The threshold upper limit clamping function is used to constrain it to the preset maximum threshold range, which is used as the target data cross-border compliance marking accuracy threshold to achieve dynamic tightening of the marking acceptance standard. If the corresponding authorized scope coverage value of a single data asset is within the preset scope coverage reference open range, the coverage steady-state detection algorithm determines that its authorization compliance fluctuation range is within the preset steady-state range. If the overall scope of the data asset is deemed compliant, the current cross-border compliance marking accuracy benchmark threshold of data iteratively optimized by Kalman filtering is maintained to ensure the stability of the marking judgment standard.If the corresponding authorization scope coverage value of a single data asset is less than or equal to the preset lower bound of the scope coverage reference value, the authorization incompleteness topology evaluation algorithm quantitatively analyzes the authorization scenario missingness, field out-of-bounds risk value, regional violation probability, and clause conflict index. If it is determined that the data asset scope authorization is incomplete and the cross-border compliance risk is high, the current authorization scope coverage value is input into the positive dynamic mapping rule. The output is the compliance mark accuracy threshold attenuation coefficient after Kalman filtering and noise reduction. The cross-border compliance mark accuracy benchmark threshold and the attenuation coefficient are multiplied and then rounded down. The threshold lower limit protection function ensures that it is not lower than the preset minimum credible threshold. This is used as the target data cross-border compliance mark accuracy threshold to relax the mark acceptance standard and simultaneously trigger the hierarchical anomaly review mechanism based on authorization incompleteness. At the same time, the target threshold and coverage value are fed back to the CRITIC weighting module and the Kalman filtering module to achieve closed-loop iterative optimization of the mapping rule coefficient and dimension weight, and finally achieve full-dimensional dynamic adaptation of cross-border compliance mark judgment accuracy and authorization compliance degree.

[0025] It should be further explained that the specific process for full-process audit documentation is as follows: Using the unique identifier of a single data asset as the traceability index, the system synchronously collects operation information and judgment data throughout the entire process of consistency verification and compliance verification, and records in real time the verification operator, operation timestamp, verification node type, parameter value, judgment rule, verification conclusion, abnormal warning information and rectification review record; The aforementioned audit trail information is encrypted and stored in a solidified manner, establishing a one-to-one binding relationship between the audit trail information and the corresponding data asset identification results, ownership determination results, and value measurement results, thereby generating an unalterable and independently traceable audit trail code; Data assets that have passed verification and rectification review will be archived and retained as a complete set of audit records, forming an audit file that is retrievable, verifiable, and traceable, providing compliance evidence for the list of data assets to be included in the table.

[0026] In this embodiment, a unique identifier for each data asset is used as the traceability index. Operational information and judgment data from the entire process of consistency and compliance verification are collected simultaneously. Real-time recording of operators, timestamps, node types, parameter values, judgment rules, verification conclusions, anomaly warnings, and rectification review records comprehensively covers key information across all verification stages, ensuring no omissions and traceability in the operation process. The aforementioned trace information is encrypted and stored, and a one-to-one binding relationship is established between the trace information and the corresponding asset identification results, ownership determination results, and value measurement results. This generates an immutable and independently traceable audit trace code, effectively preventing tampering or forgery of the trace information and ensuring the authenticity and security of the audit data. For data assets that pass verification and rectification review, a complete set of audit trace information is archived, forming a retrieveable, verifiable, and traceable audit file. This provides sufficient and reliable compliance evidence for the data asset entry list and meets the traceability and verification requirements of external audits and internal supervision, further solidifying the compliance foundation for data asset entry.

[0027] S104 automatically adapts and matches the verified data assets to the preset financial statement account mapping rules, generating data asset accounting information that meets the requirements for financial statement preparation, realizing the fully automated closed-loop processing of data assets from identification, measurement, verification to accounting.

[0028] It should be understood that retrieving the list of data assets that have passed previous verification, have clear ownership, and are compliant and traceable, and then automatically adapting and accurately matching the accounts for individual or combined data assets within the list according to preset financial statement account mapping rules that conform to accounting standards, combines core attributes such as asset type, measurement attributes, and useful life to complete account alignment and positioning. This not only replaces the tedious manual matching operation, significantly improving the efficiency of account matching, but also avoids the risks of mismatch and omission caused by manual operation, ensuring the compliance and accuracy of the recorded accounts. For abnormal situations such as multiple mapping conflicts and matching failures that occur during the matching process, preset thresholds are used to address these issues. Dynamic adjustment of judgment criteria ensures timely interception and optimization correction of anomalies, further guaranteeing the uniqueness and accuracy of account matching. Subsequently, based on the matched account information, asset value, accounting period, auxiliary accounting, and other elements are integrated to generate data asset accounting information that fully complies with financial statement preparation standards and can be directly connected to the financial system. Ultimately, this establishes a complete data asset process chain from front-end identification, value measurement, compliance verification to back-end financial accounting, achieving fully automated closed-loop processing. This completely breaks down the barriers between business data and financial accounting, improving the overall efficiency, financial compliance, and audit traceability of data asset accounting.

[0029] It needs to be explained that the specific process for automatic adaptation and subject matching is as follows: The number of failed data asset account matching attempts, the proportion of failed types, and the magnitude of failed assets within a single period are collected in advance. The real-time failure rate deviation is calculated in combination with the preset account matching failure judgment threshold. An inverse correlation is established between the failure rate deviation and the multi-mapping conflict judgment threshold. The inverse correlation between the failure rate deviation and the multi-mapping conflict judgment threshold is used to characterize the judgment correspondence between the real-time failure rate deviation and its preset failure rate deviation upper limit, critical interval, and critical lower limit. The multi-mapping conflict judgment threshold correction factor or compensation factor corresponding to the deviation value is output. The functional correspondence of the target threshold is obtained by combining it with the multi-mapping conflict judgment benchmark threshold. If the real-time failure rate deviation exceeds the preset failure rate deviation threshold, it is determined that the current subject matching rule is too strict and the misjudgment rate is too high. Then, the current real-time failure rate deviation is input into the inverse correlation between the failure rate deviation and the multi-mapping conflict judgment threshold, and the multi-mapping conflict judgment threshold correction factor is output. The multi-mapping conflict judgment benchmark threshold and the multi-mapping conflict judgment threshold correction factor are combined to obtain the target multi-mapping conflict judgment threshold. The multi-mapping conflict judgment threshold is lowered to relax the conflict judgment standard for matching multiple subjects of a single asset and reduce invalid conflict interception.

[0030] Automatic adaptation and subject matching also include: If the real-time failure rate deviation is within the preset failure rate deviation critical interval, the current subject matching rule is deemed to meet the standard, and the multi-mapping conflict judgment benchmark threshold remains unchanged. The preset failure rate deviation critical interval represents the closed interval formed by the preset failure rate deviation lower limit and the preset failure rate deviation upper limit. If the real-time failure rate deviation is lower than the preset failure rate deviation threshold, it is determined that the current subject matching rule is too lenient and the missed judgment rate is too high. Then, the current real-time failure rate deviation is input into the inverse correlation between the failure rate deviation and the multi-mapping conflict judgment threshold, and the multi-mapping conflict judgment threshold compensation factor is output. The multi-mapping conflict judgment benchmark threshold and the multi-mapping conflict judgment threshold compensation factor are combined to obtain the target multi-mapping conflict judgment threshold. The multi-mapping conflict judgment threshold is adjusted upward to tighten the conflict judgment standard for multi-subject matching of a single asset, strengthen the uniqueness control of subject matching, and realize the dynamic synergy between subject matching failure control and multi-mapping conflict control.

[0031] In this embodiment, the number of failed data asset account matching attempts, the proportion of failed types, and the magnitude of failed assets within a single period are pre-collected. Combined with a preset account matching failure judgment threshold, the real-time failure rate deviation is calculated. An inverse correlation is established between the failure rate deviation and the multi-mapping conflict judgment threshold. This correlation clearly represents the correspondence between the deviation and preset critical upper limit, critical closed interval, and critical lower limit, as well as the functional relationship between the corresponding output correction factor, compensation factor, and the target threshold obtained by combining them with the benchmark threshold. This provides a quantitative adjustment basis for account matching anomaly control, improving the scientific and targeted nature of threshold regulation. If the real-time failure rate deviation exceeds the preset critical upper limit, the current account matching rule is deemed to be overly stringent and the misjudgment rate too high. The deviation is substituted into the inverse correlation to output a correction factor, which is then combined with the benchmark threshold to obtain the target threshold, and the multi-mapping conflict judgment threshold is lowered. By relaxing the conflict judgment threshold for matching multiple accounts for a single asset, the invalid conflict interception can be effectively reduced, and the asset misjudgment interception rate can be lowered. If the real-time failure rate deviation is within the preset critical closed range, the subject matching rule is deemed to meet the adaptability standard, and the benchmark threshold is directly maintained unchanged, balancing the accuracy of matching control and processing efficiency, and avoiding system losses caused by over-adjustment. If the real-time failure rate deviation is lower than the preset critical lower limit, the current matching rule is deemed to be too lenient and the omission rate is too high. The deviation value is substituted into the correlation relationship to output the compensation factor, and after combination calculation, the multi-mapping conflict judgment threshold is raised, tightening the conflict judgment standard, strengthening the uniqueness control of subject matching, and eliminating the problem of multi-subject mismatch and omission. Ultimately, the dynamic synergy between subject matching failure control and multi-mapping conflict control is achieved, continuously optimizing the accuracy of subject matching and ensuring the accuracy and compliance of financial entries.

[0032] like Figure 3The diagram shown is a structural schematic of the data asset identification and measurement system provided in this application embodiment, including: a multi-source data acquisition and standardization preprocessing module, a data asset intelligent identification and value measurement module, a full-dimensional verification and auditing module, and a financial account adaptation and automated accounting module. The multi-source data acquisition and standardization preprocessing module is used to acquire original multi-source data within the target enterprise, preprocess the original multi-source data within the target enterprise to obtain internal multi-source data, and construct a standardized and traceable data asset database. The original multi-source data within the target enterprise includes, but is not limited to, structured business data, unstructured text data, image data, and log data. The data asset intelligent identification and value measurement module is used to automatically perform multi-source data identification and value measurement based on preset data asset classification rules. The system identifies, classifies, and labels data assets to determine their identification, ownership, and measurable attributes. It then performs value quantification on the identified data assets, resulting in a standardized and auditable measurement of their value. A full-dimensional verification and auditing module verifies the consistency and compliance of the data asset identification, ownership determination, and value measurement results, and provides full-process auditing, generating a list of verified data assets awaiting entry into the financial statements. Finally, a financial account adaptation and automated accounting module automatically adapts and matches verified data assets to pre-defined financial statement account mapping rules, generating accounting information that meets financial statement preparation requirements. This achieves fully automated closed-loop processing of data assets from identification, measurement, verification to accounting entry.

[0033] In this embodiment, it consists of four core modules: a multi-source data acquisition and standardized preprocessing module, a data asset intelligent identification and value measurement module, a full-dimensional verification and auditing module, and a financial account adaptation and automated accounting module. These modules work together in a progressive manner to achieve fully automated control of the entire process. Specifically, the multi-source data acquisition and standardized preprocessing module is responsible for acquiring raw multi-source data from the target enterprise, including structured business data, unstructured text data, image data, and log data, and performing specialized preprocessing operations on it. This establishes a standardized and traceable data asset database, effectively integrating heterogeneous data resources and eliminating data caliber differences, thus laying a high-quality data foundation for subsequent asset processing. The data asset intelligent identification and value measurement module automatically identifies, classifies, and determines the ownership of the preprocessed compliant data based on preset classification rules, while also classifying qualified assets. The system performs quantitative valuation and generates standardized value measurement results, enabling the transformation of data into compliant assets and improving the automation and accuracy of asset identification and valuation. The full-dimensional verification and auditing module conducts dual verification of consistency and compliance for asset identification results, ownership determination results, and value measurement results, simultaneously completing encrypted auditing throughout the process. It also generates a list of compliant assets to be entered into the financial statements, ensuring data accuracy and compliance, controlling entry risks, and achieving full-process traceability and audit compliance. The financial account matching and automated accounting module automatically matches and adapts verified assets to financial mapping rules, generating compliant accounting information. This connects the entire chain of asset identification, measurement, verification, and accounting, achieving a fully automated closed loop, significantly reducing manual operation costs and error rates, and improving the overall efficiency and financial compliance of data asset entry into the financial statements.

Claims

1. A method for identifying, measuring, and recording data assets, characterized in that: Includes the following steps: S101, acquire the original multi-source data within the target enterprise, preprocess the original multi-source data within the target enterprise to obtain the enterprise's internal multi-source data, so as to build a standardized and traceable data asset base. The original multi-source data within the target enterprise includes structured business data, unstructured text data, image data and log data. S102, based on the preset data asset classification rules, automatically identify, classify and label multi-source data within the enterprise to obtain data asset identification results, so as to clarify the identification of data assets, the determination of ownership and the measurable attributes, carry out value quantification calculation on the identified data assets, and form a unified and auditable data asset value measurement result; S103, perform consistency verification, compliance verification and full-process auditing of data asset identification results, ownership determination results and data asset value measurement results, and generate a list of data assets that have passed verification and are ready to be entered into the table; S104 automatically adapts and matches the verified data assets to the preset financial statement account mapping rules, generating data asset accounting information that meets the requirements for financial statement preparation, realizing the fully automated closed-loop processing of data assets from identification, measurement, verification to accounting.

2. The data asset identification and measurement table processing method as described in claim 1, characterized in that: The specific steps for automatically identifying, classifying, and labeling multi-source data within an enterprise are as follows: A multi-level classification rule base is pre-built, which includes the target enterprise's original multi-source data, data products, and data services, based on four types of identification parameters: data source identifier, data type attribute, data quality threshold, and data link. By combining machine learning feature extraction with rule engine logic matching, the original multi-source data within the target enterprise is screened and classified layer by layer, and standardized labels with unique asset codes, category tags, and value attribute tags are assigned, outputting initial identification results containing asset details and classification information. The ownership of data assets in the initial identification results is verified and the attribution is defined in order to eliminate non-compliant data with unclear ownership or no legal authorization, and to form a list of compliant data assets with clear ownership and well-defined boundaries. Based on a preset measurability threshold, individual / combined data assets are quantitatively valued, and a full measurement process log is retained to form a unified, traceable, and auditable measurement result for the value of data assets.

3. The data asset identification and measurement table processing method as described in claim 1, characterized in that: The specific steps for performing consistency verification are as follows: The system pre-divides and configures three levels of confidence thresholds for automatic identification of data assets: high, medium, and low. It establishes a dynamic mapping relationship between the identification confidence level and the corresponding data asset collection cycle. This dynamic mapping relationship is used to characterize the one-to-one correspondence between the three levels of confidence thresholds for automatic identification of data assets, the corresponding collection cycle adjustment factor, the data asset collection baseline cycle, and the collection cycle adjustment factor. Based on the confidence level, the system outputs the corresponding cycle adjustment factor, and obtains the target collection cycle that matches the identification quality through a multiplication operation. This enables dynamic control of extending, maintaining, or shortening the collection cycle. If the confidence level of automatic data asset identification is within the high-level threshold range, and the stability, compliance, and measurable attributes of the data asset are determined to meet the standards, then the current confidence level of automatic data asset identification is input into the dynamic mapping relationship between the identification confidence level and the corresponding data asset collection cycle. The collection cycle adjustment factor is output, and the data asset collection baseline cycle and the collection cycle adjustment factor are multiplied to obtain the target data asset collection cycle. This adaptively extends the subsequent collection cycle of this type of data asset and reduces redundant collection.

4. The data asset identification and measurement table processing method as described in claim 3, characterized in that: The consistency verification also includes: If the confidence level of automatic identification of data assets is within the medium-level threshold range, it is determined that there is a slight risk of fluctuation in the identification results of data assets. The current data asset collection benchmark cycle is maintained to keep the benchmark collection cycle of this type of data asset unchanged. If the confidence level of automatic data asset identification is within the low-level threshold range, and the integrity, ownership clarity, and measurability of the data asset are questionable, then the current confidence level of automatic data asset identification is input into the dynamic mapping relationship between the identification confidence level and the corresponding data asset collection cycle. The collection cycle adjustment factor is output, and the data asset collection baseline cycle and the collection cycle adjustment factor are multiplied to obtain the target data asset collection cycle. This adaptively shortens the subsequent collection cycle of this type of data asset, strengthens data supplementation and real-time updates, and achieves dynamic adaptation between the data asset collection cycle and the quality of automatic identification.

5. The data asset identification and measurement table processing method as described in claim 1, characterized in that: The specific process for compliance verification is as follows: The corresponding authorization coverage rate is obtained by statistically analyzing the ratio of the actual authorized application scenarios, authorized data fields, and authorized geographical scope of a single data asset to the preset legal authorization scope. A positive dynamic mapping rule is established between the authorized scope coverage rate and the data cross-border compliance mark accuracy threshold. The positive dynamic mapping rule between the authorized scope coverage rate and the data cross-border compliance mark accuracy threshold is used to characterize the judgment relationship between the authorized scope coverage rate value of a single data asset and the preset scope coverage reference upper limit, reference interval, and reference lower limit value, as well as the corresponding compliance mark accuracy threshold gain coefficient or attenuation coefficient of the coverage rate value. The functional correspondence of the target threshold is obtained by multiplying it with the benchmark threshold and performing rounding operations. If the corresponding authorized scope coverage value of a single data asset is greater than or equal to the preset upper limit of the scope coverage reference, it is determined that the authorized boundary of the data asset is clear and the cross-border compliance risk is low. Then, the corresponding authorized scope coverage value is input into the positive dynamic mapping rule between the authorized scope coverage and the data cross-border compliance mark accuracy threshold, and the compliance mark accuracy threshold gain coefficient is output. The result of multiplying the data cross-border compliance mark accuracy benchmark threshold and the compliance mark accuracy threshold gain coefficient and then rounding up is taken as the target data cross-border compliance mark accuracy threshold, so as to tighten the mark acceptance standard.

6. The data asset identification and measurement table processing method as described in claim 5, characterized in that: The compliance verification also includes: If the corresponding authorized scope coverage value of a single data asset is within the preset scope coverage reference interval, the data asset is determined to be compliant, and the current data cross-border compliance mark accuracy benchmark threshold is maintained. The preset scope coverage reference interval represents the open interval formed by the preset scope coverage reference lower bound and the preset scope coverage reference upper bound. If the corresponding authorized scope coverage value of a single data asset is less than or equal to the preset lower limit of the scope coverage reference, it is determined that the scope authorization of the data asset is incomplete and the cross-border compliance risk is high. Then, the corresponding authorized scope coverage value is input into the positive dynamic mapping rule between the authorized scope coverage and the data cross-border compliance mark accuracy threshold, and the compliance mark accuracy threshold decay coefficient is output. The result of multiplying the data cross-border compliance mark accuracy benchmark threshold and the compliance mark accuracy threshold decay coefficient and then rounding down is used as the target data cross-border compliance mark accuracy threshold. This relaxes the mark acceptance standard and triggers the abnormal review mechanism simultaneously, so as to achieve dynamic adaptation between the cross-border compliance mark judgment accuracy and the degree of authorization compliance.

7. The data asset identification and measurement table processing method as described in claim 1, characterized in that: The specific process for full-process auditing and record keeping is as follows: Using the unique identifier of a single data asset as the traceability index, the system synchronously collects operation information and judgment data throughout the entire process of consistency verification and compliance verification, and records in real time the verification operator, operation timestamp, verification node type, parameter value, judgment rule, verification conclusion, abnormal warning information and rectification review record; The aforementioned audit trail information is encrypted and stored in a solidified manner, establishing a one-to-one binding relationship between the audit trail information and the corresponding data asset identification results, ownership determination results, and value measurement results, thereby generating an unalterable and independently traceable audit trail code; Data assets that have passed verification and rectification review will be archived and retained as a complete set of audit records, forming an audit file that is retrievable, verifiable, and traceable, providing compliance evidence for the list of data assets to be included in the table.

8. The data asset identification and measurement table processing method as described in claim 1, characterized in that: The specific process for automatic adaptation and subject matching is as follows: The number of failed data asset account matching attempts, the proportion of failed types, and the magnitude of failed assets within a single period are collected in advance. The real-time failure rate deviation is calculated in combination with the preset account matching failure judgment threshold. An inverse correlation is established between the failure rate deviation and the multi-mapping conflict judgment threshold. The inverse correlation between the failure rate deviation and the multi-mapping conflict judgment threshold is used to characterize the judgment correspondence between the real-time failure rate deviation and the preset failure rate deviation upper limit, critical interval, and critical lower limit. The deviation value corresponds to the output of the multi-mapping conflict judgment threshold correction factor or compensation factor. The function correspondence of the target threshold is obtained by combining it with the multi-mapping conflict judgment benchmark threshold. If the real-time failure rate deviation exceeds the preset failure rate deviation threshold, it is determined that the current subject matching rule is too strict and the misjudgment rate is too high. Then, the current real-time failure rate deviation is input into the inverse correlation between the failure rate deviation and the multi-mapping conflict judgment threshold, and the multi-mapping conflict judgment threshold correction factor is output. The multi-mapping conflict judgment benchmark threshold and the multi-mapping conflict judgment threshold correction factor are combined to obtain the target multi-mapping conflict judgment threshold. The multi-mapping conflict judgment threshold is lowered to relax the conflict judgment standard for matching multiple subjects of a single asset and reduce invalid conflict interception.

9. The data asset identification and measurement table processing method as described in claim 8, characterized in that: The automatic adaptation and subject matching also includes: If the real-time failure rate deviation is within the preset failure rate deviation critical interval, it is determined that the current subject matching rule is suitable and the multi-mapping conflict judgment benchmark threshold remains unchanged. The preset failure rate deviation critical interval represents the closed interval formed by the preset failure rate deviation lower limit and the preset failure rate deviation upper limit. If the real-time failure rate deviation is lower than the preset failure rate deviation threshold, it is determined that the current subject matching rule is too lenient and the missed judgment rate is too high. Then, the current real-time failure rate deviation is input into the inverse correlation between the failure rate deviation and the multi-mapping conflict judgment threshold, and the multi-mapping conflict judgment threshold compensation factor is output. The multi-mapping conflict judgment benchmark threshold and the multi-mapping conflict judgment threshold compensation factor are combined to obtain the target multi-mapping conflict judgment threshold. The multi-mapping conflict judgment threshold is adjusted upward to tighten the conflict judgment standard for multi-subject matching of a single asset, strengthen the uniqueness control of subject matching, and realize the dynamic synergy between subject matching failure control and multi-mapping conflict control.

10. A system applying the data asset identification and measurement table processing method as described in any one of claims 1-9, characterized in that, include: Multi-source data acquisition and standardized preprocessing module, intelligent identification and value measurement module for data assets, full-dimensional verification and audit recording module, and financial account adaptation and automated accounting module; The multi-source data acquisition and standardization preprocessing module is used to acquire original multi-source data within the target enterprise, preprocess the original multi-source data within the target enterprise to obtain internal multi-source data, so as to build a standardized and traceable data asset base. The original multi-source data within the target enterprise includes structured business data, unstructured text data, image data and log data. The data asset intelligent identification and value measurement module is used to automatically identify, classify and label multi-source data within the enterprise based on preset data asset classification rules, obtain data asset identification results, clarify data asset identification, ownership determination and measurable attributes, carry out value quantification calculation on the identified data assets, and form a unified and auditable data asset value measurement result. The full-dimensional verification and auditing module is used to perform consistency verification, compliance verification, and full-process auditing of data asset identification results, ownership determination results, and data asset value measurement results, and to generate a list of verified data assets to be entered into the table. The financial account adaptation and automated accounting module is used to automatically adapt and match verified data assets according to preset financial statement account mapping rules, generate data asset accounting information that meets the requirements of financial statement preparation, and realize the fully automated closed-loop processing of data assets from identification, measurement, verification to accounting.

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