Enterprise data asset value dynamic evaluation and intelligent monitoring system and method

Abstract

The application discloses a kind of enterprise data asset value dynamic evaluation and intelligent monitoring system and method, it is related to data processing and enterprise management technical field, including following module: data acquisition and pretreatment module integration multi-source interface and cleaning unit, generate high-quality data source;Data asset classification and right module realizes four-level classification and right ownership definition;Dynamic evaluation model library module is built-in multiple evaluation models;Intelligent monitoring and early warning module realizes full-dimensional monitoring and multi-channel early warning;Data asset operation analysis module outputs optimization scheme;Authority management and security protection module constructs multi-layer protection system;Visual display and report output module supports multi-scenario report generation.The application improves data asset evaluation precision and dynamic adaptability, clarifies data ownership relationship, realizes whole-process intelligent monitoring and rapid early warning, optimizes cross-enterprise collaborative evaluation efficiency, provides reliable support for enterprise data asset value release and compliance operation.

Description

Technical Field

[0001] This invention relates to the fields of data processing and enterprise management technology, and in particular to a dynamic assessment and intelligent monitoring system and method for enterprise data asset value. Background Technology

[0002] With the deepening development of the digital economy, data assets have become a core production factor for enterprises, and their value quantification and compliant operation directly affect corporate strategic decisions and market competitiveness. Currently, enterprise data asset valuation mostly adopts a static accounting model, relying on single models such as traditional cost methods and market methods. This lack of consideration for dynamic factors such as data timeliness, quality fluctuations, and adaptability to business scenarios leads to significant discrepancies between valuation results and actual value, failing to reflect the value changes of data assets throughout their entire lifecycle. Furthermore, the valuation indicator system lacks industry-specific design, and general models cannot adapt to the value composition characteristics of data assets in different industries such as finance, manufacturing, and the internet, resulting in insufficient practicality and guidance in the valuation results.

[0003] The lack of a sound data asset classification and ownership confirmation mechanism is a common pain point in the industry. Existing classifications are mostly based on simple data type divisions, failing to establish a multi-level classification system linked to business processes, value density, and sensitivity levels. This leads to vague definitions of data asset ownership, unclear distinctions between ownership, usage rights, and revenue rights, and consequently affects value distribution and transaction flow. Furthermore, data asset monitoring is largely limited to security risks, lacking comprehensive real-time monitoring of data quality, operational efficiency, and value fluctuations. Abnormal event warnings are delayed, failing to promptly detect issues such as data idleness, inefficient use, and value decay. Data asset operation optimization lacks data support.

[0004] Weak cross-enterprise data collaboration assessment and compliance adaptation capabilities hinder the release of data asset value. In multi-enterprise joint operation scenarios, there is a conflict between data privacy protection and assessment data sharing, a lack of secure and efficient collaborative computing solutions, and inconsistent value allocation standards. Meanwhile, laws and regulations concerning data security and privacy protection are constantly being updated, making it difficult for existing systems to dynamically adapt to compliance requirements. The lack of compliance verification throughout the entire process of data collection, storage, use, and transfer easily leads to legal risks. Furthermore, most systems are deployed independently, with insufficient integration with enterprise ERP, CRM, data platform, and other management systems, resulting in untimely data synchronization and affecting the real-time performance and accuracy of assessments and monitoring. Summary of the Invention

[0005] The present invention proposes a dynamic assessment and intelligent monitoring system and method for enterprise data asset value to solve the problems mentioned in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a dynamic assessment and intelligent monitoring system for enterprise data asset value, comprising the following modules: The data acquisition and preprocessing module integrates multi-source data interfaces and cleaning and transformation units, supports structured, semi-structured and unstructured data access, and generates evaluation data sources, sets data quality verification rules, and establishes a data quality scoring system through deduplication, completion, format standardization, outlier detection and correction. The data asset classification and ownership confirmation module uses a combination of machine learning classification algorithms and manual review to classify data assets into four levels based on business scenarios, data types, value density, and sensitivity levels. This constructs a data asset classification system and establishes unique identifiers, ownership files, and a full lifecycle traceability chain for data assets. The dynamic evaluation model library module includes a built-in basic evaluation model, expands to industry-customized models, combines enterprise business attributes and data application scenarios, optimizes model parameter weights, and provides indicator weight configuration tools and model effect verification functions. The intelligent monitoring and early warning module monitors the multi-dimensional status of data assets in real time, sets early warning thresholds and trigger conditions, displays relevant trends and distributions through visual charts, and supports push notifications of early warning information via system pop-ups, emails, and SMS. The data asset operation and analysis module statistically analyzes the frequency of data asset calls, reuse rate, contribution to efficiency, and circulation efficiency; analyzes the value transformation path of data assets in business processes, upstream and downstream related value, and cross-departmental collaborative benefits; identifies data problems; and generates operation analysis reports. The access control and security protection module assigns data operation permissions based on a role-based access control mechanism and uses security technologies to build a security protection system. The visualization and report output module presents the assessment, monitoring and analysis results of data assets in a visual way through charts and graphs. It supports custom report templates, provides generation, export and sharing functions, and automatically generates regular and special analysis reports.

[0007] Furthermore, it also includes a value decay factor calibration module. This module analyzes the impact of data timeliness, completeness, accuracy, and relevance on value, constructing a multi-dimensional value decay model. The value decay calculation formula is as follows: ,in for The value of real-time data assets For initial valuation, The time decay coefficient, For data storage duration, The weighting is influenced by data quality. To improve data quality compliance, the module has a built-in dynamic optimization engine for attenuation coefficients. Based on industry data characteristics and enterprise data operation practices, the engine automatically adjusts the coefficient values ​​to quantify the value fluctuations of data assets over time and quality, and outputs value attenuation trend analysis and intervention suggestions.

[0008] Furthermore, it also includes a multi-dimensional value weight optimization module, which integrates the hierarchical analysis algorithm, entropy weight method and fuzzy comprehensive evaluation method to construct a weight influencing factor index system, dynamically adjust the cost weight, benefit weight, risk weight and strategic weight of data assets, verify the rationality of weight configuration through consistency test and sensitivity analysis, generate weight configuration scheme, support historical traceability of weight configuration, version comparison and one-click rollback, and provide weight configuration training and guidance tools to reduce the operation threshold.

[0009] Furthermore, it also includes a data asset transaction value assessment module, which constructs a multi-factor transaction value assessment model to quantify the fair value of data assets in transaction scenarios, integrates an industry transaction case database covering historical cases from different industries, data types, and transaction models; provides functions for calculating transaction pricing ranges and providing negotiation strategy suggestions; and has a built-in compliance review checklist to comprehensively verify the legality and compliance of data asset transactions.

[0010] Furthermore, it also includes a cross-enterprise data asset collaborative assessment module, which supports joint assessment, value sharing and benefit distribution of data assets among multiple enterprises. It adopts privacy computing technology to achieve assessment data sharing and collaborative computing while protecting data privacy and trade secrets. It constructs a cross-enterprise data asset association map, establishes a fair value distribution model, and provides collaborative assessment process management, result confirmation and dispute resolution tools.

[0011] Furthermore, it also includes an intelligent abnormal behavior identification module, which uses deep learning algorithms to analyze the entire data operation behavior, build user behavior profiles and normal behavior baselines, combine data asset sensitivity levels and operational risk weights, identify risky behaviors, quantify security risk values ​​and impact ranges, establish a risk behavior graded response mechanism, and generate risk behavior analysis reports and security optimization suggestions.

[0012] Furthermore, it also includes a system adaptive upgrade module that monitors changes in industry policies, updates to evaluation standards, technological development trends, and adjustments to enterprise business in real time. It constructs an adaptability evaluation index system, automatically identifies the adaptability deviations between system functions, evaluation models, monitoring rules, index systems, and external environment and enterprise needs, predicts industry development trends and changes in enterprise needs through big data analysis, supports online iterative optimization and version management of evaluation models, monitoring rules, and index systems, and also supports seamless integration and data synchronization with other enterprise management systems.

[0013] Furthermore, the method for dynamic assessment and intelligent monitoring of enterprise data asset value includes the following steps: Multi-source data acquisition and preprocessing: Access enterprise internal multi-channel data through the system's data interface, start the cleaning and transformation unit to perform deduplication, completion, format standardization, outlier detection and correction, perform quality scoring based on data integrity, accuracy and consistency verification rules, filter low-quality data, generate standardized evaluation data sources, and establish a data quality problem ledger and rectification tracking mechanism; Data asset classification and ownership confirmation employs machine learning classification algorithms to classify preprocessed data into four levels, combined with manual review to improve the classification results, clarify data asset ownership, define the relationship between data assets and business processes, assign a unique identifier to each type of data asset, and establish ownership files and a full lifecycle traceability chain. Dynamic evaluation model selection and parameter configuration: Select appropriate models based on data asset type, evaluation purpose and application scenario; adjust indicator weights in combination with corporate strategy and industry characteristics; conduct consistency test and sensitivity analysis; calibrate results using value decay model for time-sensitive data assets; verify and optimize model parameters. Data asset value calculation and analysis: Based on the selected assessment model, configuration parameters and calibration factors, calculate the current value, historical value changes and value components of data assets, analyze the mechanism and correlation strength of value influencing factors, compare historical assessment data with industry benchmark data, identify value fluctuation trends, core driving factors and potential risks, and generate a value analysis report. The entire process of intelligent monitoring is activated, which monitors the quality status, circulation and use, security risk level and value fluctuation range of data assets in real time. It compares the data with the preset monitoring thresholds and triggering conditions. If the data quality is substandard, the value fluctuation exceeds the range, or there are risky behaviors or abnormal events, a graded early warning mechanism is triggered. Early warning information is pushed through multiple channels, and the trajectory and impact range of abnormal events are recorded. Data asset operation efficiency assessment: Statistical data on the frequency of data asset calls, reuse rate, contribution to efficiency, and circulation efficiency; analysis of the value transformation path of data assets in business processes, upstream and downstream related value, and cross-departmental collaborative benefits; output operational analysis conclusions and optimization configuration solutions. For monitoring result processing and system optimization, targeted processing solutions are developed based on early warning information and operational analysis conclusions. If the adaptability of the evaluation model is involved, the model parameters are iterated through the adaptive upgrade module to update the evaluation indicator system and weight configuration, thus forming a closed-loop optimization.

[0014] Furthermore, it also includes a data asset value prediction step, constructing a multivariate value prediction model to predict the trend of data asset value changes within a specific future period, with the prediction calculation formula being... ,in To predict the value of data assets during the forecast period, For the current assessment value, The correlation coefficient for business growth For the company's business growth rate, To predict the duration of the cycle, This represents the market risk impact coefficient. We generate value forecast reports based on market risk incidence rates.

[0015] Furthermore, it also includes dynamic adaptation steps for compliance risks, establishing a compliance assessment indicator system, tracking updates to relevant laws and regulations and revisions to industry standards in real time, dynamically updating compliance assessment indicators, risk thresholds and verification rules, automating compliance verification of the entire process of data asset collection, storage, use, transfer and destruction, quantifying compliance risk levels, generating optimization suggestions and rectification plans for high-risk links, establishing a compliance risk ledger and rectification tracking mechanism, and regularly generating compliance assessment reports.

[0016] Compared with existing technologies, the beneficial effects of this invention are: At the value assessment level, the system constructs a multi-dimensional dynamic assessment model library, integrating basic assessment models with industry-customized models. Combined with value decay factor calibration and multi-dimensional weight optimization, it accurately quantifies the value fluctuations of data assets over time, quality, and business scenarios, resulting in assessment results that better align with the actual operational needs of enterprises. The optimization of the classification and ownership confirmation mechanism establishes a four-level classification system and a full lifecycle traceability chain, clarifying the ownership of data assets and laying the foundation for value allocation and transaction flow.

[0017] The comprehensive intelligent monitoring system achieves full coverage from security risks to operational efficiency, monitoring the quality status, value fluctuations, and circulation and usage of data assets in real time. A multi-level early warning mechanism ensures rapid response to abnormal events, effectively reducing issues such as data idleness and inefficient use. The data asset operation analysis module provides a scientific basis for optimizing configuration schemes through multi-dimensional indicator statistics and value transformation path analysis, improving the efficiency and profitability of data asset utilization. The cross-enterprise collaborative evaluation module adopts privacy computing technology to achieve joint evaluation and value sharing while protecting data privacy. A fair and reasonable value allocation model adapts to the operational needs of multiple scenarios and cross-entity entities.

[0018] The system's adaptive upgrade and compliance dynamic adaptation capabilities are significantly enhanced. It tracks policy, regulation, and industry standard updates in real time, automatically iterates evaluation models, monitoring rules, and compliance indicators, ensuring the system's long-term adaptability to enterprise development needs. Seamless integration with existing enterprise management systems improves data synchronization efficiency, and visualization and multi-scenario reporting functions meet diverse needs such as internal management, external supervision, and collaborative collaboration. Overall, this invention constructs an intelligent system integrating dynamic evaluation, multi-dimensional monitoring, compliant operation, and collaborative sharing, providing comprehensive technical support for releasing the value of enterprise data assets and managing compliance, helping enterprises build core competitiveness in the digital economy. Attached Figure Description

[0019] Figure 1 This is a schematic block diagram of the enterprise data asset value dynamic assessment and intelligent monitoring system proposed in this invention; Figure 2 This is a schematic block diagram of the enterprise data asset dynamic assessment and intelligent monitoring method proposed in this invention; Figure 3 A comparison chart of the accuracy of data asset valuation using different valuation methods; Figure 4 A comparison chart of data asset monitoring anomaly response efficiency; Figure 5 This is a dynamic chart showing the changing compliance risk levels of data assets. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0022] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly; for example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. The invention will now be described in further detail with reference to the accompanying drawings.

[0023] Reference Figures 1 to 5 A dynamic assessment and intelligent monitoring system for enterprise data asset value, comprising the following modules: The data acquisition and preprocessing module integrates multi-source data interfaces and cleaning and transformation units, supporting structured, semi-structured, and unstructured data access. It covers multiple channels such as internal enterprise business systems, customer management platforms, supply chain data, external industry databases, and public data sources. Through deduplication, completion, format standardization, outlier detection and correction, it generates high-quality evaluation data sources, sets data integrity, accuracy, and consistency verification rules, establishes a data quality scoring system, and filters low-quality data. The data asset classification and ownership confirmation module uses a combination of machine learning classification algorithms and manual review to classify data assets into four levels based on business scenarios, data types, value density, and sensitivity levels. This constructs a data asset classification system, clarifies the ownership, usage rights, and revenue rights of data assets, defines the relationship between data assets and business processes, and establishes a unique identifier, ownership file, and full lifecycle traceability chain for data assets. The dynamic evaluation model library module includes basic evaluation models such as cost approach, market approach, and income approach, and expands to industry-customized models such as financial data asset income model and manufacturing supply chain data value model. It combines enterprise business attributes and data application scenarios to optimize model parameter weights, supports custom evaluation indicator system, provides indicator weight configuration tools and model effect verification function, and adapts to the value accounting needs of different types of data assets. The intelligent monitoring and early warning module monitors the quality status, circulation and usage, security risk level, and value fluctuation range of data assets in real time. It sets multi-level early warning thresholds and early warning trigger conditions, and displays the data asset value fluctuation trend, risk distribution, abnormal event trajectory and impact range through visual charts. It supports the push of early warning information through multiple channels, including system pop-ups, emails, SMS, and enterprise collaboration platform messages, to achieve rapid response to abnormal events. The data asset operation and analysis module statistically analyzes the frequency of data asset calls, reuse rate, contribution to efficiency, and circulation efficiency. It analyzes the value transformation path of data assets in business processes, upstream and downstream related value, and cross-departmental collaborative benefits. It identifies problems such as idle and inefficient use of data assets, generates multi-dimensional operation analysis reports, and provides data asset optimization configuration solutions and decision-making suggestions. The access control and security protection module allocates data operation permissions based on a role-based access control mechanism, refining the granularity of permissions down to specific operations such as data viewing, editing, exporting, and sharing. It employs security technologies such as encrypted transmission, encrypted storage, access log auditing, data anonymization, and operation behavior tracing to build a multi-layered security protection system, preventing the risks of data leakage, tampering, and abuse, and ensuring the security and controllability of data assets. The visualization and report output module presents data asset value assessment results, monitoring data, and operational analysis conclusions intuitively through dashboards, trend charts, heat maps, and relationship graphs. It supports custom report templates and provides report generation, export, and sharing functions. It automatically generates daily, weekly, monthly, and quarterly periodic assessment reports and special analysis reports to meet the output needs of various scenarios such as internal management, external supervision, and cooperation.

[0024] This invention also includes a value decay factor calibration module. This module analyzes the impact of data timeliness, completeness, accuracy, and relevance on value, constructs a multi-dimensional value decay model, and dynamically calibrates the evaluation results. The value decay calculation formula is as follows: ,in for The value of real-time data assets For initial valuation, The time decay coefficient, For data storage duration, The weighting is influenced by data quality. To ensure data quality compliance, the module has a built-in dynamic optimization engine for attenuation coefficients. Based on industry data characteristics and enterprise data operation practices, the coefficient values ​​are automatically adjusted, while also supporting manual fine-tuning. This accurately quantifies the value fluctuations of data assets over time and with changes in quality, and outputs value attenuation trend analysis and intervention suggestions.

[0025] This invention also includes a multi-dimensional value weight optimization module, which integrates the hierarchical analysis algorithm, entropy weight method, and fuzzy comprehensive evaluation method. It combines factors such as corporate strategic goals, business priorities, market competition environment, policy and regulatory requirements, and the importance of data application scenarios to construct a weight influencing factor index system. It dynamically adjusts the cost weight, benefit weight, risk weight, and strategic weight of data assets, verifies the rationality of weight configuration through consistency testing and sensitivity analysis, generates the optimal weight configuration scheme, supports historical traceability of weight configuration, version comparison, and one-click rollback, improves the relevance and practicality of value assessment results, and provides weight configuration training and guidance tools to reduce the operational threshold.

[0026] This invention also includes a data asset transaction value assessment module. Based on core factors such as market supply and demand, data scarcity, data integrity, compliance risk level, application scenario adaptability, and the bargaining power of both parties, it constructs a multi-factor transaction value assessment model to quantify the fair value of data assets in transaction scenarios. It integrates an industry transaction case database, covering historical cases from different industries, data types, and transaction models, and supports case retrieval, comparative analysis, and assessment result correction. It provides functions for calculating transaction pricing ranges and providing negotiation strategy suggestions. It also has a built-in compliance review checklist to comprehensively verify the legality and compliance of data asset transactions, providing full-process pricing support for data asset transfer, authorized use, joint operation, and other transaction behaviors.

[0027] This invention also includes a cross-enterprise data asset collaborative assessment module, which supports joint assessment, value sharing, and benefit distribution of data assets among multiple enterprises. It adopts privacy computing technologies such as federated learning and secure multi-party computation to achieve assessment data sharing and collaborative computation while protecting data privacy and trade secrets. It constructs a cross-enterprise data asset association graph, clarifies the contribution assessment standards for joint data assets, establishes a fair and reasonable value distribution model, supports multiple distribution methods such as contribution, input cost, and agreement, and is adaptable to cross-entity data operation scenarios such as industry chain collaboration, data alliances, and cross-border data cooperation. It also provides collaborative assessment process management, result confirmation, and dispute resolution tools.

[0028] This invention also includes an intelligent abnormal behavior identification module, which uses deep learning algorithms such as recurrent neural networks and graph neural networks to analyze the entire process of data access, transmission, use, modification, and deletion. It constructs user behavior profiles and normal behavior baselines, and combines data asset sensitivity levels and operational risk weights to identify risky behaviors such as unauthorized access, high-frequency downloads, abnormal time period operations, cross-regional logins, and batch data export. It quantifies security risk values ​​and impact ranges, establishes a risk behavior graded response mechanism, supports automatic blocking of high-risk operations, triggering manual review, recording violations, and other handling methods, generates risk behavior analysis reports and security optimization suggestions, and realizes pre-event warning, in-event control, and post-event traceability of security risks.

[0029] This invention also includes a system adaptive upgrade module, which monitors changes in industry policies, updates to evaluation standards, technological development trends, and adjustments to enterprise business in real time. It constructs an adaptability evaluation index system, automatically identifies the adaptability deviations between system functions, evaluation models, monitoring rules, index systems, and the external environment and enterprise needs, predicts industry development trends and changes in enterprise needs through big data analysis, and pushes personalized upgrade suggestions. It supports online iterative optimization and version management of evaluation models, monitoring rules, and index systems, provides upgrade effect verification tools and rollback mechanisms, and ensures that the system adapts to enterprise development needs in the long term. At the same time, it supports seamless integration and data synchronization with other enterprise management systems such as ERP, CRM, and data platforms, improving system integration and scalability.

[0030] This invention includes the following steps: Multi-source data acquisition and preprocessing: Through the system's data interface, data is accessed from multiple channels such as the enterprise's internal business system, customer management platform, supply chain data, external industry databases, and public data sources. The cleaning and transformation unit is activated to perform deduplication, completion, format standardization, outlier detection and correction. Based on the data integrity, accuracy, and consistency verification rules, quality scores are performed, low-quality data is filtered out, standardized evaluation data sources are generated, and a data quality problem ledger and rectification tracking mechanism are established. Data asset classification and ownership confirmation employs machine learning classification algorithms to classify preprocessed data into four levels based on business scenarios, data types, value density, and sensitivity levels, constructing a data asset classification spectrum. The classification results are then refined through manual review, clarifying the ownership, usage rights, and revenue rights of data assets, defining the relationship between data assets and business processes, assigning a unique identifier to each type of data asset, and establishing ownership files and a full lifecycle traceability chain. Dynamic evaluation model selection and parameter configuration: Based on the data asset type, evaluation purpose and application scenario requirements, select a suitable basic evaluation model or industry-customized model from the model library. Combined with corporate strategic goals, business priorities and industry characteristics, adjust the indicator weights through the multi-dimensional value weight optimization module, conduct consistency verification and sensitivity analysis. If time-sensitive data assets are involved, enable the value decay model to calibrate the initial evaluation results, verify the model effect and optimize the parameters. Data asset value calculation and analysis: Based on the selected assessment model, configuration parameters and calibration factors, calculate the current value, historical value changes and value components of data assets, analyze the mechanism and correlation strength of value influencing factors, compare historical assessment data with industry benchmark data, identify value fluctuation trends, core driving factors and potential risks, and generate a detailed value analysis report. The entire process of intelligent monitoring is activated, which monitors the quality status, circulation and use, security risk level and value fluctuation range of data assets in real time. It compares the data with the preset monitoring thresholds and triggering conditions. If the data quality is substandard, the value fluctuation exceeds the range, or there are risky behaviors or abnormal events, a graded early warning mechanism is triggered. Early warning information is pushed through multiple channels, and the trajectory and impact range of abnormal events are recorded. Data asset operation efficiency assessment: Statistical data asset call frequency, reuse rate, contribution to efficiency, and circulation efficiency; analyze the value transformation path of data assets in business processes, upstream and downstream related value, and cross-departmental collaborative benefits; identify problems such as idle data assets and inefficient use; and output multi-dimensional operation analysis conclusions and optimization configuration solutions. For monitoring result processing and system optimization, targeted processing plans are formulated based on early warning information and operational analysis conclusions. Rectification measures are implemented and their effects are tracked. Data asset operation strategies and system monitoring rules are optimized. If there are issues related to the adaptability of the evaluation model, the model parameters are iterated through the adaptive upgrade module, and the evaluation indicator system and weight configuration are updated to form a closed-loop optimization.

[0031] This invention also includes a data asset value prediction step. Based on historical value data, business development plans, market environment changes, policy and regulatory adjustments, and technological innovation trends, a multivariate value prediction model is constructed. The model is trained using algorithms such as time series analysis and regression analysis to predict the trend of data asset value changes within a specific future period. The prediction calculation formula is as follows: ,in To predict the value of data assets during the forecast period, For the current assessment value, The correlation coefficient for business growth For the company's business growth rate, To predict the duration of the cycle, This represents the market risk impact coefficient. We generate value forecast reports based on market risk incidence rates, including forecast values, confidence intervals, analysis of key influencing factors, risk warnings, and response suggestions, providing forward-looking support for long-term planning, investment decisions, and operational strategy adjustments for data assets.

[0032] This invention also includes a dynamic adaptation step for compliance risks, establishing a compliance assessment indicator system that covers the entire process of data collection compliance, storage compliance, usage compliance, circulation compliance, and destruction compliance. It tracks updates to relevant laws and regulations and industry standard revisions related to data security, privacy protection, and data transactions in real time, dynamically updates compliance assessment indicators, risk thresholds, and verification rules, and performs automated compliance verification on the entire process of data asset collection, storage, use, circulation, and destruction. It quantifies compliance risk levels, generates specific compliance optimization suggestions and rectification plans for high-risk links, establishes a compliance risk ledger and rectification tracking mechanism, and regularly generates compliance assessment reports to ensure that data asset operations comply with legal norms, industry standards, and internal corporate regulations.

[0033] The following two examples further illustrate specific embodiments of the present invention: Example 1: Application of Customer Data Asset Valuation and Monitoring in the Financial Industry This embodiment addresses the high timeliness and sensitivity of customer data assets in the financial industry by employing a dynamic valuation and intelligent monitoring system and method for enterprise data assets. This system enables dynamic valuation and full-process monitoring of data assets such as customer basic information, transaction records, and risk ratings. The system is deployed on the enterprise's local server and connects to internal business systems such as credit management systems, customer relationship management systems, and risk control systems. It also accesses external data sources such as central bank credit data and industry risk databases.

[0034] After system startup, the data acquisition and preprocessing module synchronously accesses various data types, including structured customer transaction data tables, semi-structured customer complaint texts, and unstructured customer survey audio, through multi-source data interfaces. The cleaning and transformation unit performs deduplication, completion, format standardization, outlier detection and correction. Based on data integrity, accuracy, and consistency verification rules, it performs quality scoring, filters low-quality data with missing key fields and logical errors, generates standardized evaluation data sources, and establishes a data quality issue ledger and rectification tracking mechanism. The data asset classification and ownership confirmation module uses machine learning classification algorithms to classify data into four levels based on business scenarios, data types, value density, and sensitivity levels, constructing a data asset classification spectrum. Combined with manual review, the classification results are refined, clarifying that the ownership of customer transaction data belongs to the financial enterprise, the right to use it is allocated to the credit approval department, and the right to revenue is associated with the business profit-generating unit. A unique identifier is assigned to each type of data asset, establishing ownership files and a full lifecycle traceability chain.

[0035] In the dynamic evaluation model selection and parameter configuration phase, based on the value characteristics of financial customer data assets, the income approach is selected as the basic evaluation model from the dynamic evaluation model library. Combined with a multi-dimensional value weight optimization module, it integrates the analytic hierarchy process (AHP), entropy weight method, and fuzzy comprehensive evaluation method. Factors such as risk management priority in corporate strategic objectives, the proportion of credit allocation in business priorities, and customer churn rate in the market competition environment are incorporated into the weight influencing factor indicator system. Cost weights, revenue weights, risk weights, and strategic weights are adjusted. The rationality of the weight configuration is verified through consistency checks and sensitivity analysis to generate the optimal weight configuration scheme. For time-sensitive data assets such as customer transaction data, a value decay factor calibration module is activated, using a value decay calculation formula... Calibrate the initial evaluation results and set the initial evaluation value. The amount is 1 million yuan, and the time decay coefficient is... The value is 0.02, and the data duration is... The data quality impact weight is calculated over a 12-month period. The data quality compliance rate is 0.3. The value is 0.95. Substituting this into the formula, we can obtain... =100×e^(-0.02×12)×(1-0.3×(1-0.95))=100×e^(-0.24)×(1-0.3×0.05)=100×0.7866×0.985≈774,800 yuan, accurately quantifying the value fluctuations of data assets as time and quality change.

[0036] In the data asset value calculation and analysis phase, based on the selected income approach model, configuration parameters, and calibration factors, the current value, historical value changes, and value components of the data assets are calculated. The impact mechanism and correlation strength of factors such as customer credit rating and transaction frequency on value are analyzed. Historical assessment data is compared with industry benchmark data to identify value fluctuation trends, core driving factors, and potential risks, generating a detailed value analysis report. After the full-process intelligent monitoring is activated, the intelligent monitoring and early warning module monitors the quality status, circulation and usage, security risk level, and value fluctuation range of data assets in real time. Multiple early warning thresholds and trigger conditions are set. When the monthly fluctuation range of data value exceeds 10%, early warning information is pushed through multiple channels such as system pop-ups, emails, SMS, and enterprise collaboration platform messages, recording the trajectory and scope of abnormal events. The data asset operation analysis module statistically analyzes the frequency of data asset calls, reuse rate, contribution to efficiency, and circulation efficiency. It analyzes the value transformation path, upstream and downstream related value, and cross-departmental collaborative benefits of data assets in business processes such as credit approval and risk control. It identifies idle customer survey data and outputs multi-dimensional operational analysis conclusions and optimization configuration solutions. In the monitoring results processing and system optimization phase, targeted solutions are developed based on early warning information and operational analysis conclusions. Rectification measures are implemented and their effectiveness tracked. Data asset operation strategies and system monitoring rules are optimized. The system's adaptive upgrade module iterates model parameters, updates the evaluation indicator system and weight configuration, forming a closed-loop optimization. The access control and security protection module uses a role-based access control mechanism, allocating data viewing permissions to ordinary business personnel, editing permissions to data analysts, and export permissions to department heads. It employs security technologies such as encrypted transmission, encrypted storage, access log auditing, data anonymization, and operational behavior tracing to build a multi-layered security protection system. The visualization and reporting output module presents evaluation results and monitoring data intuitively through dashboards, trend charts, heatmaps, and relationship graphs, automatically generating monthly evaluation reports and customer risk analysis reports to meet internal management and external regulatory needs.

[0037] Table 1. Comparison of the accuracy of data asset valuation in Example 1 Table 1 clearly demonstrates the significant advantage of this system in the accuracy of data asset valuation within the financial industry. Traditional static valuation methods fail to consider the impact of data timeliness and quality fluctuations on value, relying solely on historical data for one-time calculations. This results in a deviation rate generally exceeding 18% between the valuation results and the actual value, with customer complaint data showing a deviation rate as high as 25%, failing to provide a reliable basis for financial enterprises' customer risk management and business decisions. This system, through dynamic valuation model selection, multi-dimensional weight optimization, and value decay factor calibration, achieves dynamic quantification of data asset value, controlling the valuation deviation rate to within 4%, and the customer credit data deviation rate to only 2%. This effect stems from the deep adaptation of the valuation model to the characteristics of financial industry data, accurately reflecting the value changes of data assets throughout their entire lifecycle, providing scientific support for the refined management and value mining of financial enterprises' data assets.

[0038] Example 2: Application of Collaborative Data Asset Valuation and Monitoring in the Manufacturing Supply Chain This embodiment targets cross-enterprise data assets such as supplier data, production data, and logistics data in a collaborative manufacturing supply chain scenario. It adopts a dynamic assessment and intelligent monitoring system and method for enterprise data asset value to realize the value assessment, benefit distribution, and full-process monitoring of joint data assets of multiple enterprises. The system adopts a cloud deployment mode and connects to multiple data sources such as the production management system of the core enterprise in the supply chain, the supply system of the supplier, and the transportation tracking system of the logistics enterprise.

[0039] After system startup, the data acquisition and preprocessing module accesses multi-source data, including core enterprise production plan data, supplier raw material quality inspection data, and logistics enterprise transportation trajectory data, through multi-source data interfaces. The cleaning and transformation unit performs deduplication, completion, format standardization, outlier detection and correction. Based on data integrity, accuracy, and consistency verification rules, it performs quality scoring, filters low-quality data, generates standardized evaluation data sources, and establishes a data quality issue ledger and rectification tracking mechanism. The data asset classification and ownership module uses machine learning classification algorithms to classify data into four levels based on business scenarios, data types, value density, and sensitivity levels, constructing a data asset classification spectrum. The classification results are refined through manual review by enterprises across the industry chain, clarifying that production data ownership belongs to the core enterprise, supplier data ownership belongs to each supplier, and the revenue rights of jointly operated data are allocated according to contribution. A unique identifier is assigned to each type of data asset, establishing ownership files and a full lifecycle traceability chain.

[0040] In the dynamic evaluation model selection and parameter configuration phase, based on the characteristics of industry chain collaborative data assets, the market approach is selected as the basic evaluation model from the dynamic evaluation model library. This is combined with a multi-dimensional value weight optimization module to adjust various weights and generate the optimal weight configuration scheme. For time-sensitive data assets such as production data, a value decay factor calibration module is activated, using a value decay calculation formula... Calibrate the initial evaluation results and set the initial evaluation value. The amount is 2 million yuan, and the time decay coefficient is... The value is 0.015, and the data duration is... The data quality impact weight is calculated over an 18-month period. The data quality compliance rate is 0.25. The value is 0.98. Substituting this into the formula, we can obtain... =200×e^(-0.015×18)×(1-0.25×(1-0.98))=200×e^(-0.27)×(1-0.25×0.02)=200×0.7634×0.995≈1,518,200 yuan, accurately quantifying the fluctuation of data asset value. The cross-enterprise data asset collaborative assessment module adopts privacy computing technologies such as federated learning and secure multi-party computation. Under the premise of protecting the data privacy and trade secrets of each enterprise, it realizes the sharing of assessment data and collaborative computation, constructs a cross-enterprise data asset association graph, clarifies the contribution assessment standards of joint data assets, establishes a fair and reasonable value distribution model, and distributes revenue according to the contribution of core enterprises, suppliers, and logistics enterprises.

[0041] In the data asset value calculation and analysis phase, based on the selected market approach model, configuration parameters, and calibration factors, the current value, historical value changes, and value components of the joint data assets are calculated. The impact of factors such as production efficiency and supply timeliness on value is analyzed, and historical assessment data is compared with industry benchmark data to generate a detailed value analysis report. After the full-process intelligent monitoring is activated, the intelligent monitoring and early warning module monitors the quality status, circulation and usage, security risk level, and value fluctuation range of data assets in real time. Multiple early warning thresholds and trigger conditions are set. When the data quality compliance rate falls below 95%, an early warning is triggered, and warning information is pushed through multiple channels. The abnormal behavior intelligent identification module uses recurrent neural networks and graph neural networks to analyze data access and transmission operations, constructing user behavior profiles and normal behavior baselines. It identifies risky behaviors such as unauthorized access and high-frequency downloads, quantifies security risk values ​​and their impact range, establishes a risk behavior graded response mechanism, automatically blocks high-risk operations, and triggers manual review. The data asset operation and analysis module statistically analyzes the frequency of data asset usage, reuse rate, contribution to efficiency, and circulation efficiency. It analyzes the value transformation path of data assets in supply chain collaboration, identifies inefficiently used logistics trajectory data, and outputs multi-dimensional operational analysis conclusions and optimization configuration schemes. In the monitoring result processing and system optimization phase, targeted processing solutions are developed for early warning information and operational analysis conclusions. The system adaptive upgrade module iterates model parameters, updates the evaluation indicator system and weight configuration, and forms a closed-loop optimization. The access control and security protection module allocates operation permissions based on a role-based access control mechanism and employs multi-layered security technologies to prevent data leakage risks. The visualization and report output module intuitively presents evaluation results and monitoring data, automatically generating quarterly evaluation reports and supply chain collaboration-specific analysis reports to meet the collaborative management needs of multiple enterprises. The data asset value prediction step constructs a multi-variable value prediction model based on historical value data, business development plans, and other factors, and uses predictive calculation formulas... Predict future value and set current assessed value. The business growth correlation coefficient was 1.5182 million yuan. The business growth rate is 0.8. The value is 0.12, and the prediction period duration is... The period is 24 months, and the market risk impact coefficient is... The market risk incidence rate is 0.4. The value is 0.05. Substituting this into the formula, we can obtain... =151.82×(1+0.8×0.12)^24×(1-0.4×0.05)=151.82×(1.096)^24×0.98≈151.82×8.02×0.98≈11.905 million yuan, providing forward-looking support for the long-term planning of collaborative data assets in the industrial chain.

[0042] Table 2. Comparison of Efficiency of Cross-Enterprise Data Asset Collaborative Assessment in Example 2 Table 2 clearly demonstrates the system's significant advantages in improving the efficiency of collaborative data asset assessment within the manufacturing supply chain. Traditional manual assessment methods require coordinating data from multiple entities across enterprises, taking up to 15 hours; value calculation relies on manual calculation and negotiation, taking 20 hours; and profit distribution requires repeated communication due to inconsistent standards, taking 12 hours. The overall assessment cycle is long and inefficient, failing to meet the rapid decision-making needs of supply chain collaboration. This system automatically integrates cross-enterprise data through multi-source data interfaces, its intelligent assessment model quickly completes value calculation, and its contribution-based value distribution model automatically generates allocation schemes. The time required for each step is significantly reduced, resulting in an overall assessment efficiency improvement of over 10 times. This effect stems from the system's intelligent and collaborative design, breaking down enterprise data barriers, enabling efficient assessment and fair allocation of cross-enterprise data assets, and promoting the release of value from collaborative data assets across the supply chain.

[0043] Reference Figure 3 This diagram visually demonstrates the core advantage of this invention in terms of assessment accuracy. Traditional static assessment methods rely solely on a single model for one-time calculations, failing to consider dynamic factors such as data timeliness and quality fluctuations. As a result, the assessment deviation rate for various data assets exceeds 18%, with survey feedback data even reaching 25%, failing to reflect the true value of data assets. This invention, through dynamic assessment model library selection, multi-dimensional weight optimization, and value decay factor calibration, achieves dynamic quantification of data asset value, controlling the assessment deviation rate for various data assets to within 4%. This effect stems from the deep adaptation of the assessment model to the characteristics of different types of data assets, accurately capturing the value changes throughout the data's lifecycle, and providing a reliable value accounting basis for the refined management of enterprise data assets.

[0044] Reference Figure 4 This figure highlights the high efficiency of the intelligent monitoring module of this invention. Traditional monitoring methods rely on manual inspections and post-event verification, with anomaly response times generally exceeding 20 minutes. For high-risk anomalies such as batch data export, response times can even reach 35 minutes, easily leading to data leaks and value loss. This invention, through multi-dimensional real-time monitoring and multi-level early warning mechanisms, combined with intelligent abnormal behavior recognition algorithms, achieves automatic identification and rapid response to abnormal events. The response time for all types of anomalies is controlled within 2 minutes, and high-risk anomalies such as unauthorized access and batch data export require only 1 minute. This advantage significantly shortens the anomaly handling window, effectively curbing the escalation of risks and improving the security and stability of data asset operations.

[0045] Reference Figure 5This diagram visually demonstrates the advantages of this invention's dynamic adaptation capability to compliance risks. Traditional data asset compliance management lacks a dynamic update mechanism, failing to keep up with regulatory changes in a timely manner. Compliance risk levels consistently remain above level 7, even rising to level 9 in Q1 of the following year, easily leading to legal disputes. This invention, by tracking regulatory updates in real time, dynamically adjusts compliance assessment indicators and risk thresholds, and automates compliance verification throughout the entire process, keeping the compliance risk level stable at level 2 or below, and even dropping to level 1 in Q3 and Q4. This effect ensures that the entire process of data asset collection, storage, use, and transfer complies with legal regulations, effectively reducing compliance risks and providing continuous protection for the legal operation of enterprise data assets.

[0046] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A dynamic assessment and intelligent monitoring system for enterprise data asset value, characterized in that, Includes the following modules: The data acquisition and preprocessing module integrates multi-source data interfaces and cleaning and transformation units, supports structured, semi-structured and unstructured data access, and generates evaluation data sources, sets data quality verification rules, and establishes a data quality scoring system through deduplication, completion, format standardization, outlier detection and correction. The data asset classification and ownership confirmation module uses a combination of machine learning classification algorithms and manual review to classify data assets into four levels based on business scenarios, data types, value density, and sensitivity levels. This constructs a data asset classification system and establishes unique identifiers, ownership files, and a full lifecycle traceability chain for data assets. The dynamic evaluation model library module includes a built-in basic evaluation model, expands to industry-customized models, combines enterprise business attributes and data application scenarios, optimizes model parameter weights, and provides indicator weight configuration tools and model effect verification functions. The intelligent monitoring and early warning module monitors the multi-dimensional status of data assets in real time and sets early warning thresholds and early warning triggering conditions; Display relevant trends and distributions through visual charts; Supports system pop-ups, emails, and SMS push notifications for alerts; The data asset operation and analysis module statistically analyzes the frequency of data asset calls, reuse rate, contribution to efficiency, and circulation efficiency; analyzes the value transformation path of data assets in business processes, upstream and downstream related value, and cross-departmental collaborative benefits; identifies data problems; and generates operation analysis reports. The access control and security protection module assigns data operation permissions based on a role-based access control mechanism and uses security technologies to build a security protection system. The visualization and report output module presents the assessment, monitoring and analysis results of data assets in a visual way through charts and graphs. It supports custom report templates, provides generation, export and sharing functions, and automatically generates regular and special analysis reports.

2. The enterprise data asset value dynamic assessment and intelligent monitoring system according to claim 1, characterized in that, It also includes a value decay factor calibration module, which analyzes the impact of data timeliness, completeness, accuracy, and relevance on value, and constructs a multi-dimensional value decay model. The value decay calculation formula is as follows: ,in for The value of real-time data assets For initial valuation, The time decay coefficient, For data storage duration, The weighting is influenced by data quality. To improve data quality compliance, the module has a built-in dynamic optimization engine for attenuation coefficients. Based on industry data characteristics and enterprise data operation practices, the engine automatically adjusts the coefficient values ​​to quantify the value fluctuations of data assets over time and quality changes, and outputs value attenuation trend analysis and intervention suggestions.

3. The enterprise data asset value dynamic assessment and intelligent monitoring system according to claim 1, characterized in that, It also includes a multi-dimensional value weight optimization module, which integrates the hierarchical analysis algorithm, entropy weight method and fuzzy comprehensive evaluation method to construct a weight influencing factor index system, dynamically adjust the cost weight, benefit weight, risk weight and strategic weight of data assets, verify the rationality of weight configuration through consistency test and sensitivity analysis, generate weight configuration scheme, support historical traceability of weight configuration, version comparison and one-click rollback, and provide weight configuration training and guidance tools to reduce the operation threshold.

4. The enterprise data asset value dynamic assessment and intelligent monitoring system according to claim 1, characterized in that, It also includes a data asset transaction value assessment module, which constructs a multi-factor transaction value assessment model to quantify the fair value of data assets in transaction scenarios, integrates an industry transaction case database covering historical cases of different industries, data types, and transaction models; provides transaction pricing range calculation and negotiation strategy suggestions; and has a built-in compliance review checklist to comprehensively verify the legality and compliance of data asset transactions.

5. The enterprise data asset value dynamic assessment and intelligent monitoring system according to claim 1, characterized in that, It also includes a cross-enterprise data asset collaborative assessment module, which supports joint assessment, value sharing and benefit distribution of data assets among multiple enterprises. It adopts privacy computing technology to achieve assessment data sharing and collaborative computing under the premise of protecting data privacy and trade secrets, constructs a cross-enterprise data asset association map, establishes a fair value distribution model, and provides collaborative assessment process management, result confirmation and dispute mediation tools.

6. The enterprise data asset value dynamic assessment and intelligent monitoring system according to claim 1, characterized in that, It also includes an intelligent abnormal behavior identification module, which uses deep learning algorithms to analyze the entire process of data operation behavior, build user behavior profiles and normal behavior baselines, combine data asset sensitivity levels and operational risk weights, identify risky behaviors, quantify security risk values ​​and impact ranges, establish a risk behavior graded response mechanism, and generate risk behavior analysis reports and security optimization suggestions.

7. The enterprise data asset value dynamic assessment and intelligent monitoring system according to claim 1, characterized in that, It also includes a system adaptive upgrade module that monitors changes in industry policies, updates to evaluation standards, technological development trends, and business adjustments in real time. It constructs an adaptability evaluation index system, automatically identifies the compatibility deviations between system functions, evaluation models, monitoring rules, index systems, and external environments and enterprise needs, and predicts industry development trends and changes in enterprise needs through big data analysis. It supports online iterative optimization and version management of evaluation models, monitoring rules, and index systems, and also supports seamless integration and data synchronization with other enterprise management systems.

8. A method for applying the enterprise data asset dynamic assessment and intelligent monitoring system according to any one of claims 1-7, characterized in that, Includes the following steps: Multi-source data acquisition and preprocessing: Access enterprise internal multi-channel data through the system's data interface, start the cleaning and transformation unit to perform deduplication, completion, format standardization, outlier detection and correction, perform quality scoring based on data integrity, accuracy and consistency verification rules, filter low-quality data, generate standardized evaluation data sources, and establish a data quality problem ledger and rectification tracking mechanism; Data asset classification and ownership confirmation employs machine learning classification algorithms to classify preprocessed data into four levels, combined with manual review to improve the classification results, clarify data asset ownership, define the relationship between data assets and business processes, assign a unique identifier to each type of data asset, and establish ownership files and a full lifecycle traceability chain. Dynamic evaluation model selection and parameter configuration: Select appropriate models based on data asset type, evaluation purpose and application scenario; adjust indicator weights in combination with corporate strategy and industry characteristics; conduct consistency test and sensitivity analysis; calibrate results using value decay model for time-sensitive data assets; verify and optimize model parameters. Data asset value calculation and analysis: Based on the selected assessment model, configuration parameters and calibration factors, calculate the current value, historical value changes and value components of data assets, analyze the mechanism and correlation strength of value influencing factors, compare historical assessment data with industry benchmark data, identify value fluctuation trends, core driving factors and potential risks, and generate a value analysis report. The entire process of intelligent monitoring is activated, which monitors the quality status, circulation and use, security risk level and value fluctuation range of data assets in real time. It compares the data with the preset monitoring thresholds and triggering conditions. If the data quality is substandard, the value fluctuation exceeds the range, or there are risky behaviors or abnormal events, a graded early warning mechanism is triggered. Early warning information is pushed through multiple channels, and the trajectory and impact range of abnormal events are recorded. Data asset operation efficiency assessment: Statistical data on the frequency of data asset calls, reuse rate, contribution to efficiency, and circulation efficiency; analysis of the value transformation path of data assets in business processes, upstream and downstream related value, and cross-departmental collaborative benefits; output operational analysis conclusions and optimization configuration solutions. For monitoring result processing and system optimization, targeted processing solutions are developed based on early warning information and operational analysis conclusions. If the adaptability of the evaluation model is involved, the model parameters are iterated through the adaptive upgrade module to update the evaluation indicator system and weight configuration, thus forming a closed-loop optimization.

9. The method for dynamic assessment and intelligent monitoring of enterprise data asset value according to claim 8, characterized in that, It also includes a data asset value prediction step, constructing a multivariate value prediction model to predict the trend of data asset value changes within a specific future period. The prediction calculation formula is as follows: ,in To predict the value of data assets during the forecast period, For the current assessment value, The correlation coefficient for business growth For the company's business growth rate, To predict the duration of the cycle, This represents the market risk impact coefficient. We generate value forecast reports based on market risk incidence rates.

10. The method for dynamic assessment and intelligent monitoring of enterprise data asset value according to claim 8, characterized in that, It also includes dynamic adaptation steps for compliance risks, establishing a compliance assessment indicator system, tracking updates to relevant laws and regulations and revisions to industry standards in real time, dynamically updating compliance assessment indicators, risk thresholds and verification rules, automating compliance verification of the entire process of data asset collection, storage, use, transfer and destruction, quantifying compliance risk levels, generating optimization suggestions and rectification plans for high-risk links, establishing a compliance risk ledger and rectification tracking mechanism, and regularly generating compliance assessment reports.

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