A data asset-oriented whole life cycle dynamic traceability monitoring method and system

Abstract

The application provides a kind of data asset-oriented whole life cycle dynamic traceability monitoring method and system, applied to data processing field.This application real-time acquisition management account book, ownership registration, transfer approval and compliance audit data, extracts key node information such as record, right confirmation, approval and audit etc.Standardization arrangement and integrity check are carried out on multi-source data, invalid data is eliminated, and operation is guaranteed to be real and traceable relying on electronic signature and time stamp.Through management process correlation algorithm, traceability link map is constructed, process track, ownership change and audit node are restored.Adopting compliance quantitative evaluation method, 95% is taken as threshold to judge traceability effectiveness, and prewarning and checking are started when abnormal.Combined with compliance management evaluation model, specification fit degree is monitored, illegal behavior is corrected in real time and responsibility is defined, monitoring model parameters are optimized according to data type and scene, forming closed-loop management and control of acquisition, traceability, check and iteration.

Description

Technical Field

[0001] This invention relates to the field of data processing, and in particular to a method and system for dynamic tracking and monitoring of the entire lifecycle of data assets. Background Technology

[0002] Currently, the data asset management field generally adopts a segmented and static management model. However, this model suffers from the following technical shortcomings in the entire lifecycle management of data assets, from planning and filing, collection and processing, authorized use, operational monitoring to destruction and deregistration: Data from management ledgers, ownership registration, transfer approvals, and compliance audits are scattered across different business systems, lacking unified collection and extraction of key node information. Tracing evidence such as filing certificates, ownership confirmation documents, approval records, and audit reports is missing or disconnected, making it impossible to fully reconstruct the data asset transfer trajectory and ownership change process. Management data from different sources and in different formats has not been uniformly organized or validated for completeness; invalid, redundant, and conflicting data have not been effectively removed. The lack of use of electronic signatures and timestamps to solidify operational actions and data records poses risks of tampering, forgery, and chronological inconsistencies, making it difficult to guarantee the authenticity and traceability of operations.

[0003] Traditional methods lack the use of process association algorithms to create visualized and computable traceability graphs. They fail to perform hierarchical association and dynamic tracing based on lifecycle stages, management matters, and compliance constraints. The efficiency and accuracy of reconstructing process trajectories, ownership changes, and approval nodes are low, making it difficult to support automated traceability and overall control. Furthermore, the lack of a unified quantitative evaluation system for filing completeness, ownership clarity, approval standardization, and audit effectiveness, coupled with the absence of clear thresholds and judgment mechanisms, makes it impossible to accurately determine the effectiveness of traceability. Abnormal situations rely on manual verification, resulting in delayed warnings and inaccurate location, hindering real-time compliance verification and risk management. The absence of dynamic monitoring based on compliance management evaluation models makes it impossible to detect and intervene in abnormal behaviors such as unregistered transfers, unclear ownership, and illegal approvals in real time. The lack of reliable operation traceability and automatic association mechanisms makes it difficult to accurately define responsible parties and the scope of responsibility, and prevents the rapid correction of violations.

[0004] The existing monitoring model has fixed parameters and cannot be dynamically optimized according to the type of data asset, management scenario, and regulatory requirements. It has not formed a closed-loop control of "data collection - link traceability - compliance verification - model iteration", so the monitoring accuracy and management efficiency cannot be continuously improved, and it is difficult to adapt to the dynamic changes of data assets throughout their entire life cycle and the requirements of compliance supervision. Summary of the Invention

[0005] According to one aspect of this application, a method for dynamic traceability monitoring of the entire lifecycle of data assets is provided, comprising: real-time collection of management ledger data, ownership registration data, transfer approval data, and compliance audit data throughout the entire lifecycle of data assets; extraction of filing certificates, confirmation documents, approval records, and audit report information corresponding to key traceability nodes at each stage; standardization and integrity verification preprocessing of the collected multi-source management traceability data, elimination of invalid management data, and ensuring the authenticity and traceability of data asset management behavior by combining electronic signature and timestamp technologies; construction of a data asset management traceability link graph based on a management process association algorithm, tracing the execution trajectory of the data asset management process, ownership registration changes, and compliance audit nodes according to management specifications and approval transfer records; and using a management compliance quantitative assessment method to evaluate the filing integrity and confirmation of rights of data asset traceability. The system uses clarity, approval standardization, and audit effectiveness indicators, setting a 95% threshold for compliance with core management indicators. When the compliance rate is ≥95%, management traceability is deemed effective; otherwise, a compliance management warning is triggered, and a traceability verification procedure is initiated, generating data asset management process optimization instructions. Based on the data asset compliance management assessment model and industry regulatory compliance requirements, the system dynamically monitors the alignment between traceability data and management standards, providing real-time correction and accountability for abnormal management behaviors such as unregistered transfers, unclear ownership, and irregular approvals. Based on full lifecycle management traceability data and compliance management standards, the system dynamically optimizes management traceability monitoring model parameters according to data asset type and management scenario, providing feedback and control over the management processes of data asset registration, ownership approval, and compliance auditing. This completes a full lifecycle dynamic traceability closed-loop management monitoring system encompassing management data collection, management link tracing, compliance management verification, and model iteration.

[0006] Another aspect of this application is a dynamic tracking and monitoring system for the entire lifecycle of data assets, configured to perform a dynamic tracking and monitoring method for the entire lifecycle of data assets by executing the executable instructions.

[0007] An electronic device is characterized by comprising: a first processor; and a memory for storing executable instructions of the first processor; wherein the first processor is configured to implement the above-described dynamic traceability monitoring method for the entire lifecycle of data assets by execution.

[0008] According to another aspect of this application, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a second processor, implements the above-described method for dynamic traceability monitoring of the entire lifecycle of data assets.

[0009] The beneficial technical effects of this application are as follows: By collecting management ledgers, ownership registration, transfer approval, and compliance audit data in real time, key node information of filing, confirmation of rights, approval, and audit is extracted. After standardized organization and integrity verification, data credibility is ensured by combining electronic signatures and timestamps. A traceability link graph is constructed based on management process association algorithms to achieve complete traceability of process trajectory, ownership changes, and audit nodes. A compliance quantitative assessment method is adopted, with 95% as the threshold to determine the effectiveness of traceability. Automatic warnings and verification are initiated when anomalies occur. Based on the compliance management assessment model, the compliance degree is monitored in real time, and behaviors such as un-filed transfers, unclear confirmation of rights, and illegal approvals are corrected in real time and responsibilities are defined. At the same time, model parameters are dynamically optimized according to asset type and scenario to form a closed-loop control of data collection—link traceability—compliance verification—model iteration, realizing dynamic traceability and intelligent compliance monitoring throughout the entire life cycle.

[0010] This application achieves unified collection and standardized processing of multi-source data, resolving issues of data fragmentation and broken correlations, improving the completeness and reliability of data tracing, constructing a visualized data tracing link map, and automatically reconstructing the entire lifecycle trajectory, significantly improving tracing efficiency and accuracy. It establishes a quantitative assessment and threshold determination mechanism to achieve accurate early warning, rapid location, and closed-loop rectification of anomalies, reducing compliance risks, supporting real-time intervention and automatic responsibility identification for abnormal behavior, strengthening the controllability, traceability, and accountability of data assets throughout the entire process, forming a closed loop of model iteration and process control, adaptable to different asset types and scenarios, and continuously improving monitoring accuracy and management efficiency.

[0011] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0012] Figure 1 A flowchart of a dynamic lifecycle tracking and monitoring method for data assets, provided in an embodiment of this application, is shown. Figure 2 This illustration shows a schematic diagram of a dynamic tracking and monitoring system for the entire lifecycle of data assets, provided in an embodiment of this application. Detailed Implementation

[0013] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0014] The following is combined with Figure 1 This application describes a dynamic tracking and monitoring method for the entire lifecycle of data assets, based on exemplary embodiments thereof.

[0015] S101, collecting management ledger data, ownership registration data, circulation approval data, and compliance audit data of the full life cycle of data assets in real time, and extracting the information of filing certificates, right confirmation documents, approval records, and audit reports corresponding to key traceability nodes in each link.

[0016] In an implementation, the system collects management ledger data, ownership registration data, circulation approval data, and compliance audit data generated by data assets during the full life cycle in real time. The collection process covers all stages of data assets from planning and filing, collection and processing, authorized use, operation monitoring to destruction and cancellation, ensuring no link is omitted. The system filters and extracts the collected original information, retains the information of filing certificates, right confirmation documents, approval records, and audit reports corresponding to each key traceability node, removes redundant content unrelated to traceability, and forms a complete and usable original traceability data set.

[0017] Collect ledger information from the data asset management system, and the collected content includes asset number, asset type, registration time, management department, change record, storage location, sharing scope, etc. The system extracts the certificate information related to asset filing according to the preset filing verification rules, including the data asset storage filing form, asset registration receipt, ledger change confirmation document, and asset information modification trace record. Such information is used to record the initial filing status of the asset and the basis for subsequent changes, and serves as the key basis for the starting point of traceability. For example, after a certain data asset completes storage registration, the system automatically extracts the filing form and registration receipt to form the traceability certificate of the filing node.

[0018] Collect ownership-related information from the ownership management module, and the collected content includes ownership subject, authorization scope, validity period, ownership change record, sharing permission, and responsible person information. The system extracts right confirmation documents that can prove the ownership of rights according to the ownership right confirmation rules, including data asset ownership certificate, authorized use agreement, ownership change registration document, and permission allocation confirmation sheet. Such information is used to clarify the ownership and authorization boundary of data assets, ensuring that the circulation and use process is traceable and accountable. For example, when the asset ownership is adjusted between departments, the system extracts the ownership change registration document, and records the subject information before and after the change.

[0019] The system collects information on the data asset workflow from the approval process system. This includes information on the data asset's flow through stages such as application, sharing, access, archiving, transfer, and destruction. Specifically, this information includes the operator, approval node, approval time, workflow path, operation content, and application reason. The system extracts corresponding approval records according to the workflow sequence and approval level, including the data asset usage application form, sharing workflow approval form, node review comments, approval workflow receipt, and archiving confirmation form. This information is used to completely reconstruct the entire process of data asset use from initiation to completion of approval, ensuring that every step is traceable. For example, if an asset submits a cross-departmental usage application, the system extracts the application form and review comments at each level in the approval order, forming a complete approval chain.

[0020] The system collects compliance-related information, including compliance inspection items, inspection results, audit conclusions, rectification status, verification records, and regulatory feedback. Based on compliance management standards and audit requirements, the system extracts corresponding audit report information, including compliance inspection records, special audit conclusions, regulatory verification vouchers, rectification completion reports, and audit logs. This information is used to determine whether the retrospective process complies with internal management regulations and external regulatory requirements, providing direct evidence for compliance assessments. For example, if an asset completes a quarterly compliance inspection, the system extracts the inspection records and audit conclusions as proof of retrospective compliance.

[0021] The system aggregates the above four types of information into a structured raw traceability data set. This set is ordered chronologically and by lifecycle stage, and each piece of information retains its source system, collection time, operating entity, and voucher number, ensuring that subsequent processing, verification, and traceability processes are all verifiable. The collection rules and extraction standards are formulated based on the data asset full lifecycle management specifications and are fully consistent with the actual business processes of filing, ownership confirmation, approval, and auditing, meeting the management objectives of data asset traceability, controllability, and auditability.

[0022] S102 standardizes and performs integrity verification preprocessing on the collected multi-source management traceability data, eliminates invalid management data, and combines electronic signature and timestamp technology to ensure the authenticity and traceability of data asset management behavior.

[0023] In one implementation, the system receives raw traceability data from the pre-collection stage and performs unified standardized processing on four types of multi-source data: management ledgers, ownership registration, transfer approval, and compliance audit. The processing strictly follows preset unified field specifications, converting data from different sources, styles, and expressions into a standard format that the system can recognize, compare, and calculate, ensuring the stable execution of subsequent map construction, compliance assessment, and anomaly monitoring.

[0024] The system standardizes date information, converting various date formats from different business systems into a standardized year-month-day format. It also standardizes main information, unifying department names, job titles, and operator names to internal management standards, eliminating recognition errors caused by abbreviations, colloquialisms, and foreign language spellings. Furthermore, it standardizes asset identification information, using the same coding rules for asset numbers, voucher numbers, and approval numbers to ensure uniqueness and relevance. Finally, it standardizes text content, using standardized language for operation descriptions, review comments, and usage instructions to avoid colloquial or non-standardized expressions that could negatively impact subsequent analysis.

[0025] After standardization and organization, the system performs integrity checks on all data, verifying each item according to the key elements necessary for data asset traceability. Filing-related data must include the asset number, filing date, filing department, and filing certificate number; the absence of any one of these indicates incompleteness. Ownership-related data must include the ownership entity, effective date, validity period, and scope of authorization; the absence of any one of these indicates incompleteness. Approval-related data must include the operator, approval node, approval time, review comments, and transfer result; the absence of any one of these indicates incompleteness. Audit-related data must include the inspection items, inspection results, audit conclusions, verification time, and verifier; the absence of any one of these indicates incompleteness.

[0026] The system identifies and removes data that fails integrity verification or does not comply with management standards. This includes removing blank ledger data without asset numbers or filing dates; duplicate uploads and identical approval records; process data that is in draft form and has not completed all review stages; invalid audit materials without review signatures, seals, or verification records; and expired vouchers and documents that are beyond their expiration date or irrelevant to the current data assets. After this removal process, the retained data meets traceability requirements and is valid for compliance assessment and tracing.

[0027] The system uniformly adds electronic signatures and timestamps to valid data that passes standardization and integrity verification, completing data solidification and trusted authentication. The timestamp, generated by a unified time synchronization service, marks the precise time of data generation, collection, or verification completion, clearly defining the sequence of operations and ensuring the traceability chain remains intact and nodes are traceable. The electronic signature is bound to the operating entity and data source, identifying the data-generating department, data entry personnel, and verification identity, preventing data tampering, replacement, or forgery.

[0028] The system adds electronic signatures and timestamps to approved workflow records, ensuring the immutability of approval processes. It also adds electronic signatures and timestamps to ownership documents that have been finalized, making ownership clear and verifiable. Furthermore, it adds electronic signatures and timestamps to audit results with formal conclusions, providing a basis for compliance assessments. Through these processes, each valid traceability data point possesses a reliable identifier and complete time sequence, accurately reflecting the actual management process of data assets and providing a credible data foundation for subsequent traceability mapping, compliance assessments, and anomaly identification.

[0029] S103 constructs a data asset management traceability graph based on management process association algorithms, and traces the execution trajectory of data asset management processes, ownership registration changes, and compliance review nodes according to management specifications and approval flow records.

[0030] In one implementation, the basic data for the full lifecycle management of data assets is processed and layered according to lifecycle stages and management item types. Each layer of data includes entity information, ownership information, process information, and compliance information. A dynamic monitoring window aggregates dynamic features such as operational behaviors, permission changes, workflow status, and audit results, extracting management nodes, approval chains, and compliance verification data at each level to generate corresponding subsets. The system uniformly processes the standardized and integrity-verified basic lifecycle management data, first dividing it according to the lifecycle stages of the data assets, and then further dividing it according to management item types, forming a two-layer data organization method. The lifecycle stages cover the entire process of data assets from generation to cancellation, specifically including planning and filing, collection and processing, authorized use, operational monitoring, and destruction and cancellation. Management item types correspond to the types of business operations in actual management, including ledger registration items, ownership change items, workflow approval items, and compliance audit items. After the two-layer division, each layer of data contains four types of basic information: entity information, ownership information, process information, and compliance information. Entity information is used to record the operating unit, responsible person, and department. Ownership information is used to record ownership, scope of authorization, and change history. Process information is used to record workflow steps, approval nodes, and time sequences. Compliance information is used to record inspection results, audit conclusions, and compliance status.

[0031] The system continuously aggregates data through a dynamic monitoring window, focusing on extracting four types of dynamic features: operational behavior, permission changes, workflow status, and audit results. This aggregates dynamic information scattered across different stages into a unified feature set. Based on the two-layer partitioning results, management nodes, approval links, and compliance verification data are further extracted from each level of data, forming independent and separately calculable data subsets. Ledger information, ownership information, approval information, and audit information generated during the registration and filing stage are merged into data at the same level. Registration nodes, preliminary review links, and compliance verification items within this level are extracted to form a data subset specific to the registration stage, used for subsequently constructing traceability nodes and constraint relationships related to registration. Application information, approval information, ownership change information, and audit verification information generated during the authorization and use stage are grouped into the same level. Application nodes, multi-level approval links, and compliance check results within this level are extracted to form a data subset for the authorization and use stage, used for subsequently constructing approval links and compliance verification relationships in the workflow process. Through the above-mentioned layering, aggregation, and extraction processes, the system transforms the messy multi-source data into a standardized data subset with a clear structure and well-defined hierarchy that can be directly used for graph construction, providing stable and reliable data support for subsequent node definition, relationship generation, and directed graph construction.

[0032] After completing data layering and subset generation, the system further constructs the nodes and relationships of the graph, transforming the data into a traceable and computable link structure. The system sets lifecycle stages as core nodes, corresponding management actions as control nodes, and compliance requirements as target nodes. These three types of nodes together constitute the basic building blocks of the traceability graph. Core nodes identify the management stage of the data asset, control nodes record the operational actions occurring within that stage, and target nodes constrain the compliance standards that must be met at that stage.

[0033] The system combines management process topology mapping relationships with task type identification rules to assign corresponding attribute information to each type of node. Node attributes include node name, lifecycle stage, management task type, operating entity, occurrence time, and scope of operating permissions. All attribute information comes from previously collected and standardized data to ensure consistency with actual business records. Based on process adaptability and the sequence of compliance reviews, the system generates edge association information between nodes. Edge association information clarifies the flow order, approval dependencies, and compliance verification links between nodes, enabling scattered nodes to form a coherent and traceable link.

[0034] Taking the filing and registration process as an example, the system designates filing and registration as the core node, information entry, submission for review, and review confirmation as control nodes, and filing compliance requirements as the target node. Following the actual business execution sequence, information entry must be completed before submission for review, and review is only complete after approval. Therefore, the system establishes an edge relationship where entry points to submission, and submission points to review. This relationship reflects the sequential order of the process and the correspondence between approval dependencies and compliance verification, making the traceability path of the filing process clear and traceable. Through the above node definition, attribute assignment, and relationship construction, the system transforms the originally independent management data into a structured, time-seriesd, and traceable graph structure, providing a foundation for subsequent view partitioning, model calculation, and graph generation.

[0035] The system constructs a directed graph covering the entire lifecycle of data assets based on predefined node and edge association information. This directed graph uses lifecycle stage nodes, management behavior nodes, and compliance requirement nodes as basic units, and connects them based on step sequence, approval dependencies, and verification relationships, comprehensively presenting the entire management path of data assets from planning and filing, collection and processing, authorized use, operational monitoring, to destruction and deregistration. All nodes and relationships originate from actual business records, ensuring the path is authentic and traceable. The system breaks down the overall directed graph into three independent yet interconnected views according to three dimensions: process constraints, ownership constraints, and audit constraints. The process constraint view displays management steps and flow paths, clearly reflecting the sequence and flow of operations at each stage. The ownership constraint view displays entities, permissions, and change relationships, clarifying the ownership and permission changes of data assets at each stage. The audit constraint view displays inspection nodes, verification results, and compliance status, marking each audit node and judgment conclusion. These three views present traceability information from process, ownership, and compliance perspectives, respectively, meeting the traceability and supervision needs of different dimensions.

[0036] The system strengthens the association of common nodes and critical paths between the three views through a process-based collaborative attention mechanism. This mechanism automatically identifies recurring critical nodes and core paths between views, improving the consistency and stability of cross-view edge associations. It ensures that the same node has a unified position, consistent relationship, and synchronized state across different views, avoiding link breaks or information conflicts. After completing view construction and association optimization, the system inputs various layered subsets, such as the data subsets from the filing process and the data subsets from the authorized use process, into the management process compliance coupling model. The model performs calculations using three control modes: feedforward prediction, feedback calibration, and dynamic adaptation. Feedforward prediction is used to identify potential problems such as missing processes, incorrect permissions, and audit omissions in advance. Feedback calibration is used to correct calculation deviations based on historical records. Dynamic adaptation is used to adjust the calculation logic according to different asset types and scenarios. The model ultimately outputs two quantitative results: process compliance and link integrity, intuitively reflecting the quality of traceability.

[0037] The system combines data asset ownership rules and approval timeliness requirements, and conducts feedforward planning based on model output results to predict and mark potential issues such as link breakpoints, compliance risks, and timeliness delays. The system integrates view segmentation results, cross-view association optimization results, model calculation results, and feedforward planning results to generate an initial plan for a data asset management traceability link map. This plan visually presents the entire lifecycle management trajectory, ownership registration and change records, and compliance review node locations, which can be directly used for traceability verification, compliance judgment, and process optimization, providing a reliable basis for subsequent assessment, early warning, and rectification.

[0038] S104 adopts a management compliance quantitative assessment method to evaluate the data asset traceability's filing completeness, ownership clarity, approval standardization, and audit effectiveness indicators. The management core indicator compliance threshold is set at 95%. When the indicator compliance rate is ≥95%, the management traceability is deemed effective; otherwise, a compliance management warning is triggered and the traceability verification procedure is initiated, generating a data asset management process optimization instruction.

[0039] In one implementation, the system compares and matches the completed data asset lifecycle traceability results with pre-defined compliance control rules item by item, establishing a corresponding association between each traceability piece of information and compliance requirements. The purpose of this process is to transform fragmented management records into quantifiable, verifiable, and monitorable compliance assessment objects, providing a basis for subsequent scoring, early warning, and verification. The system introduces an adaptive indicator weighting algorithm, automatically adjusting the weights of four indicators—filing completeness, ownership clarity, approval standardization, and audit effectiveness—based on the data asset's category, importance, usage scenario, and regulatory level. The algorithm differentiates weight allocation based on asset type, increasing the weight of filing and audit indicators for core business data assets and appropriately balancing the weight of approval and ownership indicators for ordinary internal data assets. The algorithm also differentiates weight allocation based on management scenario, increasing the weight of ownership and approval indicators for externally shared scenarios and emphasizing process timeliness and filing completeness for internal use scenarios. Through adaptive adjustments, the evaluation results better align with actual management needs and regulatory priorities. Based on matching and empowerment, the system establishes a comprehensive data asset traceability and compliance evaluation system covering the entire process from registration, confirmation of rights, approval, and auditing, clearly defining the evaluation dimensions, calculation methods, and judgment standards for each stage. The evaluation system covers all management activities related to data assets from registration to deregistration, ensuring no omissions or blind spots.

[0040] The system maps specific traceability information to compliance rules one by one. It correlates registration records, supporting documents, and change logs in the filing process with the filing completeness rules to determine whether the filing was timely, the materials were complete, and the information was accurate. It correlates ownership change records, authorization documents, and entity information with the rights confirmation clarity rules to determine whether ownership is clear, changes are compliant, and authorizations are valid. It correlates approval workflow records, node information, and review opinions with the approval standardization rules to determine whether the process is complete, the authority is legal, and the operations are logged. Finally, it correlates audit results, inspection records, and rectification status with the audit validity rules to determine whether the audit was comprehensive, the conclusions were truthful, and the rectification was closed-loop.

[0041] Through the above-mentioned item-by-item matching and correspondence establishment, the system transforms the original management records into quantifiable and assessable compliance data, and combines adaptive indicator weights to form a unified evaluation standard, providing a stable and reliable basis for subsequent model calculations, indicator scoring, and threshold determination.

[0042] The system treats four work components—filing verification, ownership confirmation, process approval, and compliance audit—as independent evaluation dimensions, and establishes corresponding evaluation frameworks for each. Each evaluation framework sets clear verification content, judgment conditions, and scoring methods based on internal management standards and industry regulatory requirements, achieving a quantitative transformation from process execution to compliance results. The system utilizes a pre-built full lifecycle traceability knowledge base, which contains historical traceability data, typical compliance cases, standard process templates, and anomaly judgment rules, providing a reference for model calculations. Based on the above framework and knowledge base, the system builds a dynamic compliance quantitative assessment model for the unified calculation and judgment of the compliance status of data asset traceability.

[0043] The model operates using distributed parallel computing, simultaneously calculating four indicators: completeness of record filing, clarity of rights confirmation, standardization of approval, and effectiveness of auditing. Parallel computing improves processing efficiency, ensuring that multiple indicators yield results concurrently and avoiding time-series bias. The model calculates the actual score and allowable deviation range for each indicator, then synthesizes them into a unified comprehensive judgment coefficient to reflect the overall compliance level. The system establishes a multi-indicator self-calibration and dynamic correction mechanism based on real-time calculation results. During continuous monitoring, the model automatically adjusts indicator weights, scoring formulas, and threshold parameters based on historical scoring deviations, frequency of anomalies, and changes in scenarios, making the assessment results more closely reflect actual management conditions and regulatory needs.

[0044] In its implementation, the model first calculates the completeness of the filing indicator. It scores each item based on factors such as the completeness of filing documents, the accuracy of registration information, and the completeness of change records, while determining the allowable deviation threshold for this indicator based on the asset type. After completing the completeness calculation, the model performs the same calculation process for the clarity of rights confirmation, the standardization of approval, and the effectiveness of auditing, obtaining scores and deviation thresholds for each. The model then integrates the calculation results of the four indicators using adaptive weights to derive a comprehensive judgment coefficient, which can be directly used for subsequent compliance assessment and early warning triggering. For example, a data asset might score 92 points for completeness of filing (allowable deviation 5 points), 96 points for clarity of rights confirmation (allowable deviation 4 points), 90 points for standardization of approval (allowable deviation 6 points), and 94 points for effectiveness of auditing (allowable deviation 5 points). The model combines preset weights to comprehensively calculate the four indicators, ultimately deriving a comprehensive judgment coefficient to determine whether the overall traceability meets the 95% compliance requirement.

[0045] The system constructs a multi-level traceability and compliance evaluation matrix with traceability completeness and compliance as its two core dimensions. Traceability completeness measures whether the records of data assets from filing to deregistration are complete and whether the chain is continuous. Compliance measures whether the operations at each stage comply with management standards and regulatory requirements. The matrix integrates multiple types of data, such as filing certificates, ownership change records, approval flow logs, audit conclusions, and process timeliness, consolidating scattered information into a unified and comparable evaluation structure.

[0046] The matrix is ​​hierarchically divided according to the entire lifecycle stages of data assets, covering all stages including planning and filing, collection and processing, authorized use, operational monitoring, and destruction and deregistration. Each level is further divided into sub-items based on management matters, including ledger registration, ownership change, transfer approval, and compliance audit, ensuring no management matters are omitted. The matrix includes information on the completeness of the registration and filing process, reflecting whether the filing materials are complete, the registration information is accurate, and the change records are complete. The matrix includes compliance information for the ownership process, reflecting whether the ownership entity is clear, the scope of authorization is clear, and the change process is legal. The matrix includes timeliness and standardization information for the approval process, reflecting whether the transfer nodes are complete, the operating permissions are compliant, and the review opinions are complete. The matrix includes conclusion information for the audit process, reflecting whether the scope of the inspection is comprehensive, whether the problem rectification is closed-loop, and whether the conclusions are true and valid.

[0047] Through the aforementioned multi-dimensional and multi-level combination, the matrix can intuitively present the traceability status and compliance level of each data asset throughout the entire process, providing an intuitive and reliable basis for subsequent quantitative scoring, anomaly identification, and process optimization. For example, if a data asset shows in the matrix that its filing completeness meets the standards, ownership is compliant, approval timeliness exceeds the standard, and audit conclusion is satisfactory, the system can determine the overall compliance status and locate areas for optimization in the approval process.

[0048] After constructing the traceability compliance evaluation matrix, the system activates an indicator robustness optimization model to process the data for stability. This addresses issues such as missing indicators, abnormal scoring, and data conflicts that arise during the collection, recording, and transfer of traceability data, preventing localized data defects from affecting the overall evaluation results. The indicator robustness optimization model verifies each piece of traceability data item by item. When incomplete filing information, missing rights confirmation materials, incomplete approval records, or missing audit conclusions are detected, the model uses a supplementary logic based on similar normal processes in the full lifecycle traceability knowledge base. When an indicator score significantly deviates from the normal range or does not match a process node, the model corrects the abnormal score according to a preset reasonable range. When data conflicts such as contradictions or inconsistent states are detected between different stages, the model uses reliable records of electronic signatures and timestamps to identify and correct the conflicting data.

[0049] The system combines process knowledge reasoning with management element ontology modeling to further strengthen the relationships between various types of traceability data. Process knowledge reasoning reconstructs the dependencies between nodes based on the sequential business logic of filing, authorization, approval, and auditing. Management element ontology modeling structurally associates four core elements: subject, authority, process, and compliance, forming a unified, coherent, and traceable whole from scattered data. After robustness optimization and association strengthening, the system integrates the processing results with the indicator scores, deviation thresholds, and judgment coefficients output by the dynamic compliance quantitative assessment model, forming a standardized traceability compliance evaluation dataset with unified fields, a standardized structure, and reliable status. This dataset can be directly used for subsequent feature extraction, threshold determination, and early warning triggering.

[0050] In actual operation, when an approval record lacks review comments due to a system oversight, leading to an anomaly in the approval compliance indicator score, the indicator robustness optimization model automatically identifies this data defect. The model combines the preceding and following processes, operator information, and timestamp records of that approval node, using process knowledge to infer and complete the missing review comments, and corrects the score according to normal approval standards. Then, through management element ontology modeling, the node is re-linked with relevant data from filing, confirmation of rights, and auditing, ultimately forming complete, valid, and usable evaluation data for compliance assessment.

[0051] The system extracts the indicator categories, management dimensions, traceability granularity, update frequency, and anomaly characteristics from the dataset, ranking them according to their contribution to compliance control and process optimization, and setting a comprehensive compliance threshold of 95%. The system extracts five core information categories from the dataset: 1. Indicator categories, including four core evaluation contents: completeness of filing, clarity of rights confirmation, standardization of approval, and effectiveness of audit. 2. Management dimensions, covering the entire lifecycle stages of planning and filing, data collection and processing, authorized use, operational monitoring, and destruction and deregistration. 3. Traceability granularity, distinguishing between node-level single-item verification and chain-level overall traceability. 4. Update frequency, reflecting the real-time refresh cycle of data collection, process recording, and compliance verification. 5. Anomaly characteristics, recording problem types such as missing indicators, scoring deviations, data conflicts, and exceeding time limits.

[0052] The system prioritizes various information items based on their actual contribution to compliance control and process optimization. Indicators and dimensions directly impacting regulatory compliance, ownership security, and process legality are given higher priority. Link-level data, frequently updated data, and high-risk anomaly data that support overall traceability quality are given priority attention. This prioritization clarifies key monitoring targets, enhancing the targeting of subsequent assessments and controls. The system uniformly sets a comprehensive compliance threshold of 95% as the core criterion for determining the effectiveness of traceability. This threshold is determined by comprehensively considering industry regulatory requirements, internal management standards, and historical data statistics, and is applicable to all data asset types and management scenarios.

[0053] In practice, the system extracts the completeness of the filing, the clarity of the confirmation of rights, the standardization of the approval process, and the effectiveness of the audit as core indicator categories. It also extracts lifecycle management dimensions such as planning filing, authorized use, and destruction / cancellation. Two types of traceability granularity are extracted: node-level verification and link-level tracing. After ranking according to the degree of impact on compliance control, the comprehensive compliance threshold is fixed at 95% for subsequent calculation of the comprehensive compliance rate and determination of the effectiveness of traceability.

[0054] When the overall compliance rate is greater than or equal to 95%, the system determines that the data asset's entire lifecycle traceability is authentic, complete, compliant, and effective. At this point, the system automatically includes the data asset in routine monitoring, continuously collecting data, updating link information, and verifying compliance status at fixed intervals, without triggering additional warnings or verification processes; routine monitoring is sufficient. When the overall compliance rate is below 95%, the system determines that the traceability is abnormal, indicating that the data asset has issues such as missing records, non-compliant processes, or information mismatches in the filing, ownership confirmation, approval, and auditing stages. At this point, the system immediately initiates a tiered compliance warning, categorized into general, moderate, and severe warnings based on the severity of the anomaly, and pushes them to the corresponding management positions and responsible persons. Simultaneously, an intelligent traceability verification process is initiated, verifying the traceability link in reverse, automatically locating specific link breakpoints, data mismatch locations, and operational violation nodes, clarifying the stage and specific content of the problem. After location is completed, the system automatically generates a data asset management process optimization plan and closed-loop rectification instructions, clearly defining the rectification content, rectification deadline, and responsible party, ensuring that the problem is traceable, closed-loop, and verifiable.

[0055] In practical application, the calculated overall compliance rate of a certain data asset for traceability was 90%, lower than the 95% threshold. The system immediately issued a compliance warning and initiated a traceability verification. By verifying each step of the registration, ownership confirmation, approval, and auditing processes, the system automatically identified issues in the ownership confirmation stage, such as missing ownership information and incomplete approval process nodes. The system then generated rectification instructions, requiring relevant personnel to supplement and improve ownership registration materials, standardize approval node settings, and set a deadline for rectification. After rectification, the system re-evaluated the traceability process until compliance was achieved, at which point normal monitoring resumed.

[0056] S105, based on the data asset compliance management assessment model and industry regulatory compliance requirements, dynamically monitors the consistency between traceability data and management standards, and corrects and defines responsibilities in real time for abnormal management behaviors such as unregistered transfers, unclear ownership, and illegal approvals.

[0057] In one implementation, based on a data asset compliance management assessment model and industry regulatory compliance objectives, the system structurally collects and standardizes the characteristic dimensions, time-series trajectories, and constraint relationships of full lifecycle traceability data, ownership registration data, process approval data, and compliance audit data to generate basic control data including indicator scores, deviation levels, and model input factors. The system, based on the operational requirements of the data asset compliance management assessment model and industry regulatory compliance objectives, uniformly collects and integrates the verified and solidified full lifecycle traceability data, ownership registration data, process approval data, and compliance audit data. The collection scope covers all management stages of data assets from filing and registration to deregistration and destruction, ensuring consistency with the previously constructed traceability chain map and compliance evaluation results.

[0058] The system analyzes the collected data and extracts three core types of information. The first type is feature dimensions, including the specific performance characteristics of four indicators: completeness of record-keeping, clarity of rights confirmation, standardization of approvals, and effectiveness of audits. The second type is time-series data, including the chronological order of each management step, operation time, approval timeliness, process cycle, and other time-related information. The third type is constraint relationships, including constraints on entities and permissions, constraints on processes and nodes, constraints on compliance and supervision, and the corresponding relationships between data at each stage.

[0059] The system standardizes and processes the three types of information according to a unified format and field standards. It unifies field names, data types, and expressions, removing redundant formatting and irrelevant content to create consistent, logically clear, and directly inputtable management data for model calculations and inferences. This management data includes three fixed components: indicator scores, deviation levels, and model input factors. Indicator scores reflect the compliance evaluation results at each stage. Deviation levels reflect the gap between the actual situation and standard requirements. Model input factors provide necessary correlation parameters and logical basis for model calculations.

[0060] In its implementation, the system organizes data for the filing process. The scores for the filing completeness indicator, the deviations between this indicator and compliance standards, and the relationships between the filing process, the rights confirmation process, and the approval process are all standardized and compiled into input items that the model can directly read and calculate, forming the basic control data corresponding to the filing process. This data can be directly fed into the compliance management assessment model to determine whether the filing process meets regulatory requirements and whether there are any abnormal behaviors such as unregistered transactions.

[0061] Based on the data asset lifecycle traceability coupling characteristics and combined with the built-in judgment rules of the compliance management assessment model, the system completes the unified configuration of control parameters throughout the entire process. The system divides control verification into three levels: global verification, node verification, and end-point verification, and sets corresponding judgment thresholds, indicator dynamic response coefficients, and model weight parameters for each level, forming a complete calculation rule system covering the overall, process, and operational levels. Based on all the configuration content, a multi-level compliance control configuration specification adapted to model calculation is generated to guide subsequent model inference, anomaly identification, and control execution.

[0062] Global verification is used to determine the overall compliance status of data assets throughout their entire lifecycle. The system sets an overall compliance threshold to measure whether all stages, including filing, rights confirmation, approval, and auditing, meet comprehensive compliance requirements. This threshold is consistent with the previously set 95% comprehensive compliance threshold. Node verification is used to determine the compliance of individual management stages such as filing, rights confirmation, approval, and auditing. The system sets compliance thresholds for each stage, including approval, rights confirmation, filing, and auditing, to determine whether a single stage meets management requirements. End-point verification is used to determine the compliance of specific operational behaviors. The system sets compliance thresholds for specific operations such as filling in, submitting, reviewing, accessing, archiving, and destroying data to identify non-compliant operations.

[0063] The system simultaneously assigns corresponding weights to four indicators: completeness of record filing, clarity of ownership confirmation, standardization of approval, and effectiveness of audit. Global verification focuses on the weighted calculation of overall indicators, node verification focuses on the proportion of indicators in a single step, and end-point verification focuses on the real-time response of operational indicators. The indicator weights are adaptively adjusted based on asset type, management scenario, and regulatory level. Core assets have increased weights for ownership confirmation and approval indicators, while ordinary assets have a more balanced distribution of weights across all indicators.

[0064] By employing a three-tiered threshold system and indicator weight allocation, the system establishes hierarchical and progressively detailed compliance control standards. Global verification controls the overall compliance outcome, node verification controls the quality of execution at each stage, and end-point verification controls the compliance of operational behaviors. These three levels of standards work together to achieve comprehensive control from the overall to the specific, and from processes to operations. For example, global verification uses a 95% overall compliance rate as the threshold; node verification sets a separate 90% compliance threshold for the approval stage and a 93% compliance threshold for the authorization stage; end-point verification directly determines unapproved usage as non-compliant. Simultaneously, corresponding weights are assigned to each indicator, forming a hierarchical control standard that can be directly used for model calculations.

[0065] By combining the closed-loop monitoring architecture with the inference output requirements of the compliance management assessment model, a multi-level control mechanism is established, encompassing real-time anomaly detection, intelligent risk assessment, and tiered triggering of responses. The system, integrating the operational logic of the closed-loop monitoring architecture with the inference output requirements of the data asset compliance management assessment model, establishes a three-tiered, interconnected real-time control mechanism. This three-tiered mechanism operates automatically throughout the entire process—from problem detection and risk assessment to response execution—ensuring full controllability, traceability, and remediability of abnormal behavior throughout the entire lifecycle of data assets.

[0066] The first level is real-time anomaly detection. Based on real-time access to traceability data, the system continuously monitors operational behaviors at each stage of registration, authorization, approval, and auditing, automatically identifying various management anomalies such as unregistered transactions, unclear ownership, ambiguous authorization scope, missing approval processes, unauthorized operations, unauthorized access, and continued use despite audit failure. Anomaly detection covers all process nodes, leaving no critical operation unchecked.

[0067] The second level is intelligent risk assessment. Based on the indicator scores, deviation levels, and correlations output by the model, the system automatically determines the level of abnormal risk, clarifying the scope of risk impact, involved entities, and potential management and compliance risks. Risk levels are categorized by severity as general risk, significant risk, and major risk, with different levels corresponding to different levels of intervention.

[0068] The third level is the tiered triggering of responses. The system automatically initiates the corresponding response process based on the risk level, including alerts, deadlines for rectification, access freezes, process blocking, mandatory review, and accountability assignment. The response methods are strictly matched to the risk level to ensure timely, appropriate, and effective handling. This three-tiered mechanism forms a closed loop, automating the entire process from detection to judgment to response, enabling real-time control without human intervention.

[0069] In actual operation, the system detected in real time that a data asset had entered the cross-departmental circulation and usage stage before completing the registration and filing process. This violation was immediately captured through the real-time anomaly detection mechanism. Subsequently, based on risk assessment rules, the system classified this behavior as a moderate compliance risk, clarifying that it might lead to issues such as unclear ownership, regulatory non-compliance, and undetermined responsibility. The system then initiated the corresponding handling process, automatically issuing a reminder and rectification notice to the relevant responsible persons and temporarily restricting the circulation and usage permissions of the data asset. Normal use was restored after the registration and verification were completed, achieving real-time control and timely correction.

[0070] The system inputs the structured and standardized management data collected in the early stages into the data asset compliance management assessment model for full-process calculation and reasoning. The model uses multi-level compliance control configuration specifications as its calculation basis and multi-level control mechanisms as its execution logic, comparing the input data with compliance standards item by item, simultaneously completing compliance judgment, risk identification, and problem localization. During the calculation process, the system relies on a closed-loop monitoring architecture to obtain execution feedback in real time, comparing the model output results with the actual business status. When deviations are found between the calculation results and the actual situation, inaccurate risk judgments, or unreasonable threshold settings, the system automatically initiates parameter optimization, dynamically adjusting the indicator weights, correlation coefficients, and judgment thresholds within the model, making subsequent reasoning results more closely aligned with actual management scenarios and continuously improving the accuracy and reliability of compliance judgments.

[0071] After completing inference, the model outputs three core types of content: compliance control results, intelligent handling instructions, and dynamic adaptation solutions. Compliance control results clarify whether the data assets currently comply with regulatory and management requirements. Intelligent handling instructions specify the specific issues, rectification measures, and responsible parties. Dynamic adaptation solutions guide process optimization, parameter adjustments, and improvements to subsequent monitoring methods. These three types of outputs collectively provide direct evidence for real-time intervention in anomalies and improvements to process standards.

[0072] In specific implementation scenarios, the system inputs basic data on incomplete ownership information of a data asset into the compliance management assessment model. Based on the ownership compliance judgment rules and ownership management constraints, the model infers that the data asset has issues such as missing ownership documentation and unclear authorization scope, outputting a control result of non-compliance in ownership confirmation. Simultaneously, it generates corresponding intelligent disposal instructions, requiring the relevant responsible parties to supplement and improve ownership documentation and re-complete the ownership registration process. A dynamic adaptation plan is also generated concurrently to strengthen the verification of ownership information completeness in subsequent monitoring, preventing similar problems from recurring.

[0073] The system adopts a model-driven operation approach, combining centralized global control with distributed node verification to achieve seamless integration and collaborative operation of the two control modes. Centralized global control is used to coordinate the overall compliance status of data assets throughout their entire lifecycle, while distributed node verification is used to verify each individual node in the filing, rights confirmation, approval, and auditing processes. Both control modes share a set of traceability data and model parameters, maintaining unified judgment standards, consistent timing, and mutual recognition of results.

[0074] During operation, the system continuously reads real-time flow tracking data and synchronously tracks process compliance status, comparing real-time operational data with multi-level compliance control configuration specifications. When it detects excessive deviations in indicators, disordered relationships, or abnormal node states, the system automatically and dynamically corrects the weight parameters, judgment thresholds, and response coefficients within the model, and simultaneously adjusts the corresponding control instructions and handling strategies. This ensures that the control logic always aligns with actual business scenarios, continuously improving the accuracy of anomaly monitoring and handling. Ultimately, the system outputs accurate compliance verification results, clearly defining the compliance status of each stage and node throughout the data asset lifecycle, providing an intuitive basis for management decisions.

[0075] In its implementation, the system conducts compliance monitoring of the entire data asset transfer process at a global level, determining whether the entire process complies with the overall regulatory requirements for filing, ownership confirmation, approval, and auditing. Simultaneously, distributed verification is performed at the approval node level, conducting standardized checks on each step of the operation, including information entry, submission for review, multi-level approval, and result confirmation. When monitoring detects that a data asset is missing a necessary approval node or has an incomplete process during transfer, the system immediately identifies the violation, dynamically adjusts the process verification parameters in the model, strengthens the verification rules for the completeness of approval nodes, and immediately blocks the current non-compliant transfer process to prevent the continued execution of non-compliant operations.

[0076] Based on model-driven reasoning to locate abnormal nodes, the system provides real-time intervention and correction for management anomalies such as unregistered transfers, unclear ownership, and unauthorized approvals, and automatically defines responsibilities, achieving intelligent dynamic traceability management of the entire data asset lifecycle. The system relies on the reasoning output of the data asset compliance management assessment model to compare each node in the entire lifecycle traceability chain, accurately locating the specific links and operational nodes where management anomalies occur. For three high-frequency management anomalies—unregistered transfers, unclear ownership, and unauthorized approvals—the system initiates real-time intervention and process correction mechanisms to prevent the continued execution of unauthorized operations. Simultaneously, based on the solidified operation records, approval traces, and ownership information, it automatically traces and defines the responsible parties and the scope of responsibility, achieving fully automated and traceable intelligent dynamic traceability management.

[0077] For unregistered data transfer activities, the system verifies the data asset's entry into the usage, sharing, or transfer stages without completing registration or obtaining registration certificates. It immediately triggers a process blocking command, suspending the current operation and sending a rectification reminder, requiring completion of registration before resuming execution. For unclear ownership, the system identifies issues such as missing ownership entities, ambiguous authorization scope, and lack of basis for ownership changes. It automatically locks the data asset's usage permissions and prompts for supplementary ownership documentation, requiring re-registration and authorization confirmation. For irregular approval activities, the system identifies issues such as unauthorized approvals, missing approval nodes, missing approval opinions, and approvals exceeding time limits. It directly revokes the irregular approval results, returns the process to the compliance starting point, and requires re-initiation and review according to standard procedures.

[0078] In the responsibility determination stage, the system uses credible data with attached electronic signatures and timestamps as a basis to extract the actual operator, operation time, and operation content from the operation record; the reviewer, approval node, and approval opinion from the approval trace; and the responsible person, management department, and scope of authority from the ownership information. It accurately associates abnormal behavior with the corresponding personnel, positions, and departments, automatically classifies direct responsibility, management responsibility, and supervisory responsibility, and forms a verifiable and reviewable responsibility determination result.

[0079] In a specific implementation example, the model, through tracing and compliance verification, identifies a data asset that was used without proper approval. The system immediately issues an instruction to terminate the current operation and revoke access to the data asset. Simultaneously, based on the operation logs, it identifies the personnel who actually performed the operation, traces the corresponding approval personnel according to process settings and job permissions, and uses electronic signatures and timestamps to solidify evidence, completing the division of responsibility and recording the results. The system automatically reverts the violation process to the initial initiation node, requiring it to be re-executed according to the complete chain of filing, authorization, and multi-level approval, achieving process correction and closed-loop rectification, ensuring subsequent operations are compliant and traceable.

[0080] S106, based on full lifecycle management traceability data and compliance management standards, dynamically optimizes the parameters of the management traceability monitoring model according to data asset type and management scenario, and provides feedback to regulate the management process of data asset filing and registration, rights confirmation and approval, and compliance audit, completing the full lifecycle dynamic traceability closed-loop management and monitoring of management data collection, management link traceability, compliance management verification and model iteration.

[0081] In one implementation, the system enters the model iteration closed-loop control phase. First, it extracts and integrates three types of information: the generated full lifecycle traceability data, compliance verification results, and process execution status, removing redundant fields and invalid content. Then, it performs calculations according to preset quantitative rules, ultimately generating four core control indicators. The system extracts full lifecycle traceability data, covering all records from planning and filing, collection and processing, authorized use, operational monitoring to destruction and deregistration. Combining electronic signatures and timestamps to solidify information, it calculates the ratio of the actual number of recorded nodes to the standard number of nodes that should be stored, forming the traceability completeness. Traceability completeness directly reflects whether the records of data assets from filing to destruction are complete, whether the chain is continuous, and whether the vouchers are complete; a higher value indicates a more complete traceability chain.

[0082] The system extracts compliance verification results, summarizing the compliance status of four indicators: completeness of filing, clarity of rights confirmation, standardization of approval, and effectiveness of audit. It calculates the ratio of compliant items to the total number of verified items, forming the compliance compliance rate. The compliance compliance rate reflects the proportion of management operations that meet internal management standards and external regulatory requirements, serving as a direct measure of overall compliance level. The system extracts process execution status, collecting initiation time, review time, completion time, and workflow time for each stage. It calculates the degree of matching between actual and standard time based on standard timeliness requirements, forming a process timeliness index. The process timeliness index reflects the execution efficiency of approval, review, registration, and archiving stages; a higher index indicates more efficient process operation. The system extracts abnormal records from the entire process, counting the frequency of issues such as unfiled workflows, unclear rights confirmation, irregular approvals, and failed audits. It calculates the abnormality rate based on the total number of operations, forming the anomaly occurrence rate. The anomaly occurrence rate reflects the frequency of violations, omissions, and mismatches during traceability and management; a lower value indicates more standardized management.

[0083] In practice, indicator calculations were performed on a batch of internal business data assets. The system extracted all traceability records and compliance verification results for the assets from registration to archiving and deregistration. The statistics showed that: 20 traceability nodes were required, with 18 actually complete, resulting in a traceability completeness rate of 90%; 25 compliance verification items were required, with 23 actually meeting the standards, resulting in a compliance compliance rate of 92%; the standard timeframe for all stages was 10 working days, with an actual timeframe of 11.7 working days, resulting in a process timeliness index of 85; and a total of 50 operations and transfers occurred throughout the process, with 4 instances of incomplete approvals or missing ownership information, resulting in an anomaly rate of 80%. These four indicators were used as the basis for subsequent model fitting, parameter calibration, and process optimization, providing quantitative support for achieving dynamic closed-loop monitoring of the entire lifecycle of traceability.

[0084] After calculating four core indicators—tracing completeness, compliance rate, process timeliness index, and anomaly rate—the system conducts multi-dimensional statistical analysis and trend fitting processing on these indicators. Combining historical operational data, standard compliance thresholds, and scenario-based management rules, it generates four trend indicators for model iteration and optimization, providing a quantitative basis for parameter calibration and rule adjustment of the tracing monitoring model. The system compares the compliance assessment results output by the model with the actual compliance status obtained from actual verification item by item, calculating the difference and deviation between the two to form the model fitting deviation. The model fitting deviation is used to intuitively reflect the degree of difference between the current tracing monitoring model's calculation results and the actual compliance results. The smaller the deviation value, the higher the model's calculation accuracy and the closer the output results are to the real situation.

[0085] The system adjusts parameters such as internal model weights, decision thresholds, and correlation coefficients, and observes the magnitude of changes in the evaluation results to form parameter sensitivity. Parameter sensitivity reflects the degree of impact of changes in model parameters on the overall compliance evaluation results. Lower sensitivity indicates that the model operates more stably and is less affected by small-scale parameter adjustments.

[0086] The system matches and verifies the current model's computational logic with the operational characteristics of different types of data assets and different management scenarios to form a scenario adaptability score. The scenario adaptability score reflects the applicability and matching degree of the current model in different data types such as core data assets, ordinary internal data, and external shared data, as well as in different management scenarios such as filing and registration, authorized use, destruction and cancellation. The higher the adaptability score, the stronger the model's versatility.

[0087] The system continuously analyzes the effectiveness of closed-loop management over time periods, combining data on the rate of decrease in anomaly incidence, improvement in compliance rates, and optimization of process timeliness to generate a trend in management effectiveness. This trend reflects how the model-driven, full-lifecycle traceability closed-loop management effect changes over time, providing a clear indication of whether the management mechanism continues to function effectively.

[0088] In a specific implementation example, the system analyzes four indicators calculated in the previous step: 90% traceability completeness, 92% compliance rate, 85% process timeliness index, and 80% anomaly occurrence rate, combined with historical data and standard thresholds. A difference exists between the model's calculated evaluation results and the actual verification results, resulting in a model fit deviation of 7%. Testing by adjusting indicator weights and thresholds shows that the variation in evaluation results is within a normal range, and the sensitivity of the judgment parameters is at a moderate level. The model operates stably in all stages of filing, authorization, approval, and auditing, matching well with the current internal management scenario and demonstrating good adaptability to the judgment scenario. Statistical data over multiple consecutive periods shows a gradual decrease in the anomaly occurrence rate, a steady increase in the compliance rate, and a gradual upward trend in the effectiveness of judgment control.

[0089] Based on model fitting bias, parameter sensitivity, scenario adaptability, and control effectiveness trend information, the system fits and calibrates the traceability monitoring model against compliance management standards, generating optimized parameters for the management traceability monitoring model. The system uses the generated analysis results of model fitting bias, parameter sensitivity, scenario adaptability, and control effectiveness trends to match and calibrate the currently running traceability monitoring model against established compliance management standards item by item. The calibration process is based on full lifecycle management specifications, regulatory compliance requirements, and process execution logic, dynamically adjusting the model's internal calculation parameters, weight ratios, judgment thresholds, and verification rules to make the model's output results closer to the actual management state. Ultimately, it generates optimized parameters for the management traceability monitoring model that can be directly used for iterative updates. The calibration focuses on four trend information: reducing fitting bias, balancing parameter sensitivity, improving scenario adaptability, and strengthening control effectiveness.

[0090] The system calibrates for model fitting bias by analyzing the difference between the model-calculated evaluation results and the actual verification results to pinpoint the links and indicators causing the bias. The system then reduces the magnitude of the bias by adjusting the weights of compliance indicators. The system also calibrates for parameter sensitivity by smoothing thresholds and coefficients with significant impact and optimizing threshold determination logic to prevent large fluctuations in evaluation results caused by small parameter changes. Furthermore, the system calibrates for scenario adaptability by adjusting the model's adaptation rules at each stage of filing, rights confirmation, approval, and auditing based on data asset types and management scenarios, improving cross-scenario applicability. Finally, the system calibrates for control effectiveness trends by combining historical control results to strengthen rules related to anomaly identification, process interruption, and responsibility definition, thereby enhancing overall closed-loop control capabilities.

[0091] In practice, when the model fitting deviation is large, the system identifies that the deviation mainly stems from unreasonable weighting of the two indicators: completeness of filing and effectiveness of audit. It then increases the calculation weights of the filing and audit stages while decreasing the weights of non-critical indicators, bringing the model output closer to the actual compliance status. When parameter sensitivity is high, the system identifies that even slight changes in the approval timeliness threshold can cause significant score fluctuations. It then optimizes the threshold determination logic, adding interval determination rules and changing the single threshold to an interval threshold to reduce sensitive fluctuations and improve model stability. Through these targeted adjustments, the system generates complete model parameter optimization results for subsequent model iterations and process control.

[0092] The optimization results of the management traceability monitoring model parameters are integrated and quantified to generate model iteration correction results. After completing the optimization of single-dimensional parameters, the system performs unified integration, summarization, and quantification of the optimization results of all management traceability monitoring model parameters. During the processing, the system systematically reviews the optimization items in each stage of filing, confirmation of rights, approval, and auditing, clarifying the adjustment range, applicable scope, effective conditions, and priority of each parameter. The scattered optimization content is merged into structured, standardized, and directly loadable model iteration correction results, which are used to replace the original operating parameters of the model and complete the model version upgrade.

[0093] During the integration process, the system automatically identifies and eliminates duplicate, conflicting, and redundant optimization items, ensuring that only one set of final adjustment rules is retained for the same parameter, thus avoiding confusion in the model's computational logic caused by multi-source optimization. Simultaneously, the system quantifies and normalizes all adjustment values, unifying percentages, coefficients, thresholds, etc., into a standard format that the model can directly read, ensuring that the parameters can be immediately put into operation after loading without secondary conversion.

[0094] The integration scope covers key parameters across the entire process, including but not limited to: the weight of the filing integrity indicator, the threshold for determining the clarity of rights confirmation, the timeliness coefficient of the approval process, the rules for verifying the effectiveness of audits, the sensitivity of anomaly identification, and the coefficient of process correlation strength. The system categorizes the above optimizations according to the lifecycle stages, forming a complete, consistent, and stable set of next-generation model operating parameters.

[0095] In its implementation, the system integrates several optimizations derived from this round of optimization, including adjusting the filing integrity weight from 0.2 to 0.25, increasing the qualification threshold for rights confirmation clarity from 90% to 93%, revising the approval process timeliness coefficient from 1.0 to 0.85, and increasing the audit verification frequency to twice per quarter. After removing duplicates and conflicts and standardizing the quantification format, the system generates the model iteration and correction results. This result is then directly written into the model configuration layer, replacing the original parameters, completing the model upgrade, and ensuring that the next round of traceability monitoring and compliance assessment is executed according to the optimized rules.

[0096] After generating and loading the model iteration and correction results, the system uses the optimized content, such as indicator weights, judgment thresholds, verification rules, and process constraints included in the iteration and correction results, to reverse-regulate and standardize the three core management processes of data asset filing and registration, rights confirmation and approval, and compliance audit. This ensures that the actual management processes are consistent with the optimized traceability monitoring model, achieving synchronous iteration and collaborative optimization of the model and business processes. In the filing and registration process, the system optimizes and improves the existing filing rules based on the iteration and correction results. Specifically, this includes: standardizing filing fields and adding mandatory fields such as asset number, registration time, ownership entity, and scope of use; strengthening the pre-filing verification logic and automatically verifying the integrity of information during the submission process; and adding requirements for electronic archiving and signature solidification of filing certificates to ensure that filing data is traceable and tamper-proof. Through these adjustments, the data quality and completeness at the starting point of traceability are improved from the source.

[0097] In the rights confirmation and approval process, the system improves the process structure and access control based on the model iteration results. Specifically, this includes: supplementing missing approval nodes and standardizing the multi-level approval flow sequence; clarifying the review authority and responsibility boundaries of different positions; adding a cross-verification step for rights confirmation information to avoid problems such as ownership conflicts and unclear subjects; and mandating the recording of approval opinions, timeliness, and flow status to improve the standardization and traceability of the approval process.

[0098] In the compliance audit process, the system enhances audit depth and verification standards according to model optimization. Specifically, this includes: expanding audit inspection points to cover high-frequency anomaly types such as unregistered transfers, unauthorized operations, exceeding time limits, and data conflicts; increasing audit frequency and verification accuracy; establishing a linkage mechanism between audit conclusions and traceability results, directly linking audit issues to corresponding process nodes; and forming a closed-loop mechanism of audit-rectification-review to enhance compliance control.

[0099] By simultaneously regulating and standardizing the three processes of filing, confirmation of rights, and auditing, the system translates the results of model iteration into executable, superviseable, and traceable actual business rules. Ultimately, it forms a dynamic, closed-loop monitoring system for the entire lifecycle, which is fully integrated, automatically operated, and continuously optimized, encompassing data collection, management link traceability, compliance management verification, and model iteration.

[0100] In a specific implementation example, the system adjusts the workflow based on the results of this round of model iterations: automatic validation of three mandatory fields—asset number, filing date, and ownership department—is added to the registration process; a three-tiered review process—departmental preliminary review, management review, and compliance final review—is added to the ownership confirmation approval process to fill gaps in the workflow; and approval compliance and ownership confirmation integrity are listed as high-frequency inspection items in the compliance audit process to increase the frequency of anomaly identification. After workflow optimization, traceability completeness and compliance compliance rates are improved simultaneously, model output is more accurate, control is more effective, and overall dynamic traceability monitoring capabilities are significantly enhanced.

[0101] In one implementation, such as Figure 2 As shown, this application also provides a dynamic traceability and monitoring system for the entire lifecycle of data assets, including: The full lifecycle traceability data acquisition module 201 is used to collect data on the management ledger, ownership registration, transfer approval, and compliance audit data of data assets in real time throughout the entire lifecycle. It extracts the filing certificates, confirmation documents, approval records, and audit report information corresponding to key traceability nodes in each stage, and constructs a data asset traceability perception data source. The data preprocessing module 202 is used to standardize, verify the integrity of, and remove invalid data from multi-source management data. It combines electronic signature and timestamp technology to ensure the authenticity and traceability of data asset management activities. The traceability graph construction module 203 is used to construct a data asset management traceability graph based on the management process association algorithm, and to trace the execution trajectory of the management process, ownership registration changes and compliance review nodes according to management specifications and approval flow records. The compliance quantitative assessment module 204 is used to assess the integrity of the filing, the clarity of the confirmation of rights, the standardization of approval, and the effectiveness of the audit of the traceability using the management compliance quantitative assessment method. The effectiveness of the traceability is judged with a 95% pass rate. When there is an abnormality, an early warning and verification are triggered and a process optimization instruction is generated. The compliance anomaly control module 205 is used to dynamically monitor the conformity between the data traceability data and management standards based on the data asset compliance management assessment model and industry regulatory requirements, and to correct and define responsibilities for abnormal behaviors such as unregistered transfers, unclear ownership, and illegal approvals in real time. The model iteration closed-loop control module 206 is used to dynamically optimize the monitoring model parameters based on the data asset type and management scenario according to the full life cycle traceability data and compliance management standards. It provides feedback and control for the filing and registration, rights confirmation and approval, and compliance audit processes, and completes the closed-loop management and monitoring of data collection, management link traceability, compliance management verification and model iteration.

[0102] The various embodiments in this application are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments for evaluating the dynamic tracking and monitoring method, electronic device, electronic device, and readable storage medium for the entire lifecycle of data assets are basically similar to the above-described embodiments for the dynamic tracking and monitoring method for the entire lifecycle of data assets, and therefore are described relatively simply. Relevant parts can be referred to in the descriptions of the above-described embodiments for the dynamic tracking and monitoring method for the entire lifecycle of data assets.

Claims

1. A method for dynamic traceability and monitoring of the entire lifecycle of data assets, characterized in that, include: Real-time collection of management ledger data, ownership registration data, transfer approval data, and compliance audit data throughout the entire lifecycle of data assets; extraction of filing certificates, ownership confirmation documents, approval records, and audit report information corresponding to key traceability nodes in each stage; The collected multi-source management traceability data is standardized, organized, and preprocessed for integrity verification to remove invalid management data. Electronic signature and timestamp technologies are used to ensure the authenticity and traceability of data asset management activities. Based on the management process association algorithm, a data asset management traceability graph is constructed. According to the management specifications and approval flow records, the execution trajectory of the data asset management process, ownership registration changes and compliance review nodes are traced. The management compliance quantitative assessment method is used to evaluate the data asset traceability indicators such as the completeness of filing, the clarity of rights confirmation, the standardization of approval, and the effectiveness of auditing. The threshold for the compliance of the core management indicators is set at 95%. When the compliance rate of the indicators is ≥95%, the management traceability is deemed to be effective. Otherwise, a compliance management warning is triggered and the traceability verification procedure is initiated. Data asset management process optimization instructions are generated, including: matching and mapping the traceability results of the entire life cycle of data assets with compliance control rules, and introducing an adaptive indicator weighting algorithm and a data asset traceability compliance evaluation system. By integrating a multi-dimensional evaluation framework encompassing filing verification, ownership confirmation, process approval, and compliance auditing with a full lifecycle traceability knowledge base, a dynamic compliance quantitative assessment model is constructed. This model uses distributed parallel computing to determine indicator scores, deviation thresholds, and judgment coefficients, establishing a multi-indicator self-calibration and dynamic correction mechanism. With traceability completeness and compliance as core dimensions, it integrates filing certificates, ownership change records, approval flow logs, audit conclusions, and process timeliness data to construct a multi-dimensional, multi-level traceability compliance evaluation matrix. A robust indicator optimization model addresses missing indicators, scoring anomalies, and conflicting defects, combined with process knowledge... The system enhances the traceability correlation features through reasoning and management element ontology modeling, and integrates them with the output of the dynamic compliance quantitative assessment model to generate a standardized traceability compliance evaluation dataset. It extracts the indicator categories, management dimensions, traceability granularity, update frequency, and abnormal features of the dataset, sorts them according to their contribution to compliance control and process optimization, and sets a comprehensive compliance threshold of 95%. When the comprehensive compliance rate is ≥95%, the traceability is deemed effective and enters routine monitoring. If it is below the threshold, a hierarchical compliance warning is triggered and intelligent traceability verification is initiated, automatically locating breakpoints, mismatches, and violation nodes, and outputting data asset management process optimization and closed-loop rectification instructions. Based on the data asset compliance management assessment model and industry regulatory compliance requirements, we dynamically monitor the consistency between the traceability data and management standards, and make real-time corrections and define responsibilities for abnormal management behaviors such as unregistered transfers, unclear ownership, and illegal approvals. Based on full lifecycle management traceability data and compliance management standards, the parameters of the management traceability monitoring model are dynamically optimized according to data asset type and management scenario. The model provides feedback and regulation to control the management process of data asset filing and registration, rights confirmation and approval, and compliance audit, thus completing the full lifecycle dynamic traceability closed-loop management and monitoring of management data collection, management link tracing, compliance management verification, and model iteration.

2. The method as described in claim 1, characterized in that, A data asset management traceability graph is constructed based on a management process association algorithm. According to management standards and approval records, the execution trajectory of the data asset management process, ownership registration changes, and compliance review nodes are traced, including: The basic data for the full lifecycle management of data assets is processed and layered according to lifecycle stage and management item type. Each layer of data includes subject information, ownership information, process information, and compliance information. Dynamic features of operation behavior, permission changes, flow status, and audit results are aggregated through a dynamic monitoring window. Management nodes, approval links, and compliance verification data at each level are extracted to generate corresponding subsets. With lifecycle stages as core nodes, management behaviors as control nodes, and compliance requirements as target nodes, node attribute information is generated by combining management process topology mapping and event type identification, and edge association information is generated according to process adaptability and compliance review sequence. A directed graph is constructed that includes nodes at all stages of the lifecycle, nodes for various types of management behaviors, and nodes for differentiated compliance requirements. It is divided into three views based on process constraints, ownership constraints, and audit constraints. The strength of cross-view edge associations is optimized through a process collaboration attention mechanism. Based on the management process compliance coupling model, combined with feedforward prediction, feedback calibration, and dynamic adaptation control modes, the process compliance and link integrity are calculated by inputting corresponding subset data. Feedforward planning is carried out based on data asset ownership rules and approval timeliness requirements to generate an initial scheme for the data asset management traceability link graph.

3. The method as described in claim 1, characterized in that, Based on the data asset compliance management assessment model and industry regulatory compliance requirements, the system dynamically monitors the alignment between traceability data and management standards, and provides real-time correction and accountability for abnormal management behaviors such as unregistered transfers, unclear ownership, and illegal approvals, including: Based on the data asset compliance management assessment model and industry regulatory compliance objectives, the characteristic dimensions, time-series trajectory, and constraint relationships of full life cycle traceability data, ownership registration data, process approval data, and compliance audit data are collected and standardized in a structured manner to generate basic control data that includes indicator scores, deviation degree, and model input factors. Based on the data asset lifecycle traceability coupling characteristics and compliance management assessment model rule configuration, the judgment thresholds, indicator dynamic response coefficients, and model weight parameters of global verification, node verification, and end verification are clarified, and multi-level compliance control configuration specifications adapted to model calculation are generated. By combining the closed-loop monitoring architecture with the inference output requirements of the compliance management assessment model, a multi-level control mechanism is set up for real-time anomaly detection, intelligent risk judgment, and graded triggering of handling. The basic data for control is input into the data asset compliance management assessment model for calculation and reasoning. It is then integrated and executed in conjunction with multi-level compliance control standards and multi-level control mechanisms. Through a closed-loop system, the model weights and judgment thresholds are optimized in real time, generating data asset compliance control results that include model reasoning results, intelligent disposal instructions, and dynamic adaptation solutions. Achieve seamless integration of model-driven centralized global control and node-distributed verification; dynamically correct model parameters and adjust control instructions based on real-time traceability and process compliance status; and output accurate compliance verification results. Based on model reasoning to locate abnormal nodes, real-time intervention and correction are carried out for management abnormal behaviors such as unregistered transfers, unclear ownership, and illegal approvals, and responsibility is automatically defined, thus completing the dynamic traceability and intelligent management of data assets throughout their entire lifecycle.

4. The method as described in claim 3, characterized in that, Based on full lifecycle management traceability data and compliance management standards, the parameters of the management traceability monitoring model are dynamically optimized according to data asset type and management scenario. This provides feedback and control over the management processes of data asset registration, ownership approval, and compliance auditing, completing a closed-loop management and monitoring system for the entire lifecycle of dynamic traceability, including: management data collection, management link tracing, compliance management verification, and model iteration. Extract and calculate the full lifecycle traceability data, compliance verification results, and process execution status to generate information on traceability completeness, compliance compliance rate, process timeliness index, and anomaly occurrence rate. The system processes data on traceability completeness, compliance rate, process timeliness index, and anomaly rate to generate information on model fitting deviation, parameter sensitivity, scenario adaptability, and control effectiveness trends. Based on model fitting deviation, parameter sensitivity, scenario adaptability, and control effectiveness trend information, the traceability monitoring model is fitted and calibrated with compliance management standards to generate optimized parameter results for the management traceability monitoring model. The optimization results of the management traceability monitoring model parameters are integrated and quantified to generate model iteration correction results; Based on the feedback of model iteration and correction results, the data asset filing and registration, ownership approval, and compliance audit management processes are adjusted to form a dynamic closed-loop monitoring system covering the entire lifecycle of data collection, management link traceability, compliance management verification, and model iteration.

5. A dynamic traceability and monitoring system for the entire lifecycle of data assets, characterized in that, The system is configured to execute the dynamic tracking and monitoring method for the entire lifecycle of data assets as described in any one of claims 1 to 4 by executing executable instructions.

6. An electronic device, characterized in that, include: First processor; The processor also includes a memory for storing executable instructions of the first processor; wherein the first processor is configured to execute the dynamic lifecycle tracking and monitoring method for data assets according to any one of claims 1 to 4 by executing the executable instructions.

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