A data asset whole life cycle measurement and value evaluation method for data element circulation

Abstract

The application discloses a data asset whole life cycle measurement and value evaluation method for data element circulation, and belongs to the technical field of data asset management. The method comprises the following steps: constructing a data asset object and associating multiple source data; performing cost penetration allocation according to five dimensions of the whole life cycle of the data asset to obtain a dynamic cost data set; counting the usage of the data asset to obtain a usage data set; measuring the income of the data asset to obtain an income data set; constructing a cost-usage-income three-dimensional cross evaluation model to calculate a value index; performing ladder grading pricing driven by the value index; performing circulation measurement and multi-element settlement; constructing a four-quadrant matrix based on the value index and the usage growth rate to perform four-quadrant hierarchical operation of the data asset value. The evaluation method realizes unified valuation of multiple source heterogeneous data and whole life cycle closed loop management, and effectively improves the accuracy of data asset measurement and value evaluation and the efficiency of subsequent operation management.

Description

Technical Field

[0001] This invention relates to the field of data asset management technology, specifically to a method for measuring and assessing the value of data assets throughout their entire lifecycle, oriented towards the circulation of data elements. Background Technology

[0002] As data becomes a key production factor, government and enterprise units have accumulated massive amounts of data during their digital transformation, urgently requiring the management, pricing, circulation, and operation of this data as an asset. However, the intangible, heterogeneous, and volatile nature of data assets presents numerous technical challenges to their measurement, valuation, and continuous operation. Currently, there are some exploratory solutions in the field of data asset measurement and valuation, mainly categorized as follows: 1. Traditional valuation models based on the cost approach, income approach, or market approach. These methods borrow from intangible asset valuation frameworks, calculating the cost value, expected revenue, or market comparison value of data assets by setting fixed formulas or static parameters. Their drawbacks include: the valuation process relies on manual estimation and expert scoring, failing to automatically aggregate actual resource costs; particularly in scenarios with multi-source heterogeneous data from both automatically collected IoT devices and manually entered data, there is a lack of technical means to unify the two data sources into the valuation system, resulting in incomplete valuation coverage, lagging results, and a disconnect from real production and consumption activities.

[0003] 2. Asset grading methods based on data quality or data lineage. Existing technologies assign static grades (e.g., high, medium, low) to data assets by analyzing quality dimensions such as data integrity, accuracy, and consistency, or by allocating costs through data lineage tracing technology. However, this method has a single evaluation dimension, considering only quality or cost, and does not incorporate the actual usage and business revenue generated by the data asset into the quantitative model. It cannot reflect the true contribution of the data asset in the circulation process; moreover, the evaluation result is only used as a label and does not form a closed loop with subsequent pricing, settlement, and operational decisions, thus failing to guide the market circulation of data assets.

[0004] 3. Static pricing and single settlement model in data transactions. Currently, data trading platforms or internal data sharing systems generally adopt static pricing strategies such as fixed prices and pay-per-use, and settlement methods are limited to monetary payments. However, this pricing method does not dynamically link the actual value of data assets or market supply and demand fluctuations, resulting in excessively low pricing for high-value data and difficulty in naturally eliminating inefficient data. At the same time, the single settlement method cannot meet the diverse circulation needs within the data ecosystem, such as points redemption and data exchange, thus limiting the liquidity and transaction efficiency of data elements.

[0005] 4. Inefficient data asset operation and management methods. Existing management methods are mostly reactive, relying on manual inspections or simple threshold alarms to identify inefficient assets, lacking a proactive, tiered operation mechanism based on quantitative indicators. However, this approach cannot differentiate operations based on asset value contribution and growth trends, resulting in high-value assets not being promoted, low-value or dormant assets occupying storage and computing resources for extended periods, and overall low vitality of the asset catalog.

[0006] Therefore, existing technologies lack a solution capable of integrating multi-source heterogeneous data, dynamically cross-evaluating costs, usage, and benefits, and directly driving tiered pricing, diversified settlement, and four-quadrant hierarchical operation based on the evaluation results. This prevents the realization of quantitative management and value maximization throughout the entire lifecycle of data assets. There is an urgent need to design a data asset measurement and value assessment method oriented towards data circulation to address the shortcomings and deficiencies of existing technologies. Summary of the Invention

[0007] The purpose of this invention is to overcome the defects and shortcomings of the prior art and provide a data asset measurement and value assessment method for data circulation. By constructing a three-dimensional cross-evaluation model of cost-usage-revenue, it connects multi-source heterogeneous data, realizes accurate measurement of data assets, and drives tiered pricing, diversified settlement and four-quadrant hierarchical operation to form a closed-loop management system covering the entire life cycle of data assets.

[0008] The technical solution provided by this invention is as follows: A method for measuring and assessing the value of data assets throughout their entire lifecycle, oriented towards the circulation of data elements, includes the following steps: Step 1: Create a data asset object and assign it a globally unique asset identifier. Then, use a metadata mapping mechanism to uniformly associate multiple heterogeneous data sources with the data asset object. Step 2: Collect resource costs according to various dimensions of the entire lifecycle of data asset objects. For different heterogeneous data sources, adopt differentiated cost allocation strategies to penetrate the total platform cost to individual assets and form a dynamic cost dataset. Step 3: In the circulation process of data assets, data assets are statistically analyzed by tracking points, logs of each call are recorded, and call indicators are aggregated and calculated according to a preset time window to form the usage dataset of the data assets in that period. Step 4: Calculate the total economic value generated by the data assets, and then weight and merge the values ​​of each part of the total economic value according to the time window to form a revenue dataset; Step 5: Construct a three-dimensional cross-evaluation model of cost-usage-benefit, and calculate the value index based on the dynamic cost dataset, usage dataset, and benefit dataset; Step 6: Obtain the data quality level of the data asset. When the data quality level meets the preset minimum threshold, the benchmark price of the corresponding tier is automatically activated according to the value index range into which the value index falls. The supply and demand heat factor is introduced on the basis of the benchmark price for floating adjustment to obtain the circulation price.

[0009] Furthermore, in step 1, the associated heterogeneous data sources include at least: data tables automatically collected by the equipment, data fields from manually filled-out forms, and source data extracted from the production system; During the association process, a corresponding metadata mapping record is generated for each data source, and the data lineage is marked to clarify the field-level source and access method of any data asset item.

[0010] Furthermore, in step 2, the various dimensions of the entire lifecycle of the data asset object include: data collection, storage, computation, processing, and operation and maintenance. The cost-sharing strategy includes: Automatically collect data from devices and share the costs of server resources, network transmission, and IoT platform access. For manually entered data, in addition to resource costs, the labor costs of the reviewers will be measured separately. Extract data from the production system and distribute the computational resource consumption for data extraction from the source database, the load cost of the ETL / CDC engine, and the network overhead for data transmission.

[0011] Furthermore, in step 3, the statistical dimensions include at least: API call count, data service request count, and data product download count.

[0012] Furthermore, in step 4, the total economic value includes: Direct transaction revenue refers to the amount of orders generated through data trading platforms or internal settlement systems; Indirect benefits of business empowerment are the quantifiable business increments generated after downstream business systems call the data, and their discounted value after the contribution ratio is broken down according to the preset attribution model. The equivalent value of data exchange is the monetary equivalent calculated based on the appraised value or market price of the exchanged assets when exchanging data assets for other data products or services.

[0013] Furthermore, in step 5, the specific method for calculating the value index is as follows: Normalize the cost dataset C, usage dataset U, and revenue dataset R respectively; Dynamic weighting coefficients α, β, and γ are introduced, which correspond to the weighting coefficients of the cost dataset, usage dataset, and revenue dataset, respectively. The data asset value index V is calculated using the formula V = α·f(C) + β·g(U) + γ·h(R), where f(C), g(U), and h(R) are the normalized score functions of cost, usage, and revenue, respectively.

[0014] Furthermore, in step 6, the supply and demand heat factor is calculated by weighting demand-side indicators and supply-side indicators, and the calculation formula is: circulation price = benchmark price × supply and demand heat factor; Demand-side metrics include: API application growth rate over the past 30 days and asset detail page views. Supply-side indicators include: the quantity of assets supplied under the same theme and whether it is an exclusive data source.

[0015] Furthermore, the evaluation method also includes step 7: performing circulation measurement and multi-level settlement. When a data consumer initiates a data service request, the actual usage is recorded in real time and the corresponding price level is matched according to the consumer's identity, and a bill is automatically generated. In the settlement process, the multi-level settlement module is called to support any one or a combination of monetary payment, points deduction and data exchange for settlement.

[0016] Furthermore, the evaluation method also includes step 8: performing data asset value four-quadrant hierarchical operation. For data assets in each evaluation period, a four-quadrant matrix is ​​constructed with the value index V as the horizontal axis and the data usage growth rate ΔU as the vertical axis, including: star assets, cash cow assets, problem assets and dog assets. By comparing the value index V and the data usage growth rate ΔU of the data assets with the preset value threshold and growth rate threshold, the data assets are divided into four quadrants, and differentiated operation tasks are triggered for each quadrant. If the value index V is greater than or equal to the value threshold, and the data usage growth rate ΔU is greater than or equal to the growth rate threshold, then the data asset is classified into the star asset quadrant, and promotion tasks and development tasks are automatically generated and assigned to the data product manager. If the value index V is greater than or equal to the value threshold, and the data usage growth rate ΔU is less than the growth rate threshold, then the data asset is classified into the Golden Bull Asset quadrant, and stability monitoring tasks and cost optimization tasks are generated and assigned to the operation and maintenance personnel. If the value index V is less than the value threshold and the data usage growth rate ΔU is greater than or equal to the growth rate threshold, the data assets are classified into the problem asset quadrant, and quality rectification tasks and value mining tasks are generated and assigned to the data governance responsible person. If the value index V is less than the value threshold and the data usage growth rate ΔU is less than the growth rate threshold, the data asset will be classified into the dog asset quadrant, triggering an alert and initiating a countdown to delisting assessment.

[0017] Compared with the prior art, the present invention discloses the following technical effects: 1. Achieve unified valuation of multi-source heterogeneous data, with comprehensive coverage and accurate aggregation.

[0018] Existing technologies rely on manual estimation or a single data source, failing to integrate heterogeneous data—from automated equipment collection, manual reporting, and extraction from production systems—into a unified valuation system. This invention, through metadata mapping and lineage association mechanisms, seamlessly binds these three data sources to the same asset object, achieving full coverage of all data assets. Simultaneously, it employs differentiated cost allocation strategies tailored to the characteristics of different data sources (e.g., additional measurement and auditing of manual reporting labor costs, and allocation of ETL engine load for production system extraction), transforming cost aggregation from a coarse total allocation to a refined approach based on resource consumption duration and usage, penetrating down to individual assets. Ultimately, this solves the problems of incomplete coverage and unrealistic costs inherent in traditional valuation methods, significantly improving the completeness and accuracy of valuations.

[0019] 2. Integrating three-dimensional cross-evaluation and dynamic weighting, it truly reflects the comprehensive value of assets.

[0020] Traditional evaluation methods often rely on a single dimension such as cost or quality, failing to reflect the true contribution of data assets in market circulation. This invention constructs a three-dimensional cross-evaluation model of cost, usage, and revenue, incorporating actual resource costs, market usage, and direct and indirect economic benefits into the calculation. Dynamic weighting coefficients α, β, and γ are adjusted according to the asset's lifecycle stage (e.g., increasing usage weight during the growth stage to encourage promotion, and increasing revenue weight during the maturity stage to reflect monetization ability), enabling the value index V to more scientifically reflect the comprehensive value of the asset at different stages. This overcomes the shortcomings of existing technologies' single and static evaluation dimensions, providing a more reliable quantitative basis for subsequent pricing and operation.

[0021] 3. Pricing is driven by both value index and supply and demand factors, resulting in fair pricing and flexible settlement.

[0022] Existing data transactions often employ static strategies such as fixed prices, resulting in pricing that is disconnected from the actual value of assets and market supply and demand, with settlement methods limited to currency. This invention automatically matches tiered benchmark prices through a value index V and introduces a supply and demand heat factor (weighted by demand-side application growth rate and supply-side scarcity) for real-time fluctuation, ensuring that the circulation price reflects both the intrinsic value of the asset and responds to external market changes, preventing high-value assets from being undervalued or inefficient assets from being overpriced. Furthermore, this invention supports multiple settlement methods, including currency, points, and data exchange, breaking through the limitations of single-currency settlement and lowering the transaction threshold for data circulation. This improvement directly addresses the problems of pricing being detached from reality and the limited settlement methods, enhancing the market circulation efficiency of data elements.

[0023] 4. Four-quadrant hierarchical operation and automatic task assignment enable refined asset management and resource optimization.

[0024] Existing technologies lack proactive operational mechanisms based on quantitative indicators, leading to inefficient assets occupying resources for extended periods. This invention utilizes a value index V and usage growth rate ΔU to construct a four-quadrant matrix, automatically classifying assets into four categories: Stars, Cash Cows, Problems, and Dogs. Differentiated operational tasks (such as promotion, optimization, rectification, and elimination) are triggered for each asset category, coupled with a full lifecycle state machine to achieve automatic state transitions. This transforms the past extensive model of manual inspection and passive response into a refined closed-loop operation of automatic system identification and proactive task assignment, driving resources towards high-value assets, promptly cleaning up zombie assets, effectively reducing storage and computing costs, and enhancing the overall vitality and return on investment of the data asset catalog.

[0025] 5. Full lifecycle closed-loop automation reduces reliance on manual labor and improves management efficiency.

[0026] This invention integrates data asset construction, cost collection, usage and revenue statistics, three-dimensional evaluation, tiered pricing, diversified settlement, and layered operation into an automated closed loop, and manages the entire lifecycle status of assets through a state machine. Compared with the fragmented nature of existing technologies that require extensive manual intervention, this invention achieves manageable, controllable, and traceable data asset lifecycles, significantly reducing manual intervention and improving management response speed and data asset operation efficiency. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 A flowchart illustrating the data asset lifecycle measurement and valuation method provided in this embodiment of the invention; Figure 2 A flowchart illustrating the data asset lifecycle measurement and valuation method provided in this embodiment of the invention; Figure 3 A structural diagram of the cost-usage-benefit three-dimensional cross-evaluation model provided in the embodiments of the present invention; Figure 4 A schematic diagram illustrating the process of automatic data collection from equipment, manual data entry, and extraction of unified valuation from multiple sources by the production system, provided for embodiments of the present invention; Figure 5 This is a schematic diagram of the tiered pricing process provided in an embodiment of the present invention; Figure 6 The four-quadrant operation matrix and task dispatch logic diagram provided for embodiments of the present invention. Detailed Implementation

[0029] 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.

[0030] This invention provides a method for measuring and assessing the value of data assets throughout their entire lifecycle, oriented towards the circulation of data elements. Figure 1-2 As shown, it includes: Step 1: Construct data asset objects and associate them with multi-source data.

[0031] First, create a data asset object in the data asset management platform and assign it a globally unique asset identifier. Then, through a metadata mapping mechanism, associate at least the following three heterogeneous data sources with this asset object: 1. Automatically collected data tables by the device, such as time-series data tables generated by IoT sensors; 2. Manually filled-in form data fields, such as business data items filled in by grassroots units through online forms; 3. Production system source data, which is data extracted from the business database using ETL or CDC tools.

[0032] During the association process, the system automatically generates corresponding metadata mapping records for each data source, marks the data lineage, and clarifies the field-level source and access method of any data asset item (such as the process of automatic collection from equipment, manual entry, and extraction from the production system for unified valuation of multiple sources). Figure 3 (As shown). This step ensures that all data assets, whether collected automatically by machines or manually, are included in the subsequent valuation scope without discrimination, solving the problem of incomplete asset coverage in traditional methods.

[0033] Step 2: Perform cost penetration allocation to form a dynamic cost dataset C.

[0034] Resource costs for data assets throughout their entire lifecycle are aggregated across five dimensions: data acquisition, storage, computation, processing, and operation and maintenance. Different cost allocation strategies are employed for different data sources. 1. Automatically collect data from devices and share the costs of server resources, network transmission, and IoT platform access; 2. For manually entered data, in addition to the above resource costs, the labor cost of the reviewers will be additionally measured; 3. Extract data from the production system and distribute the computational resource consumption for data extraction from the source database, the load cost of the ETL / CDC engine, and the network overhead for data transmission.

[0035] During the cost allocation process, the total platform cost is broken down to individual assets based on refined indicators such as the proportion of data volume occupied by data assets and the CPU / memory time consumed by computing tasks, ultimately forming a dynamic cost dataset C. This dataset is updated in real time as assets are used and updated, ensuring the timeliness of cost information.

[0036] Step 3: Calculate the usage and form a usage dataset U.

[0037] During the access, retrieval, and download of data assets, statistical data is collected and recorded through data gateways or API management modules, providing detailed logs for each call. Statistical dimensions include at least the number of API calls, data service requests, and data product downloads. These metrics are aggregated and calculated according to preset time windows (e.g., daily, weekly, monthly) to form a usage dataset U for the data asset object within that period.

[0038] Step 4: Calculate the revenue and generate the revenue dataset R.

[0039] Calculate the total economic value generated by data assets, including: 1. Direct transaction revenue, which refers to the amount of orders generated through data trading platforms or internal settlement systems; 2. Indirect benefits from business empowerment, namely the quantifiable business increment generated after downstream business systems call this data, and the discounted value after the contribution ratio is broken down according to the preset attribution model; 3. Equivalent value of data exchange, which is the monetary equivalent calculated based on the appraised value or market price of the exchanged assets when exchanging data assets for other data products or services.

[0040] The revenue from each of the above components is weighted and merged according to the time window to form the revenue dataset R.

[0041] Step 5: Construct a three-dimensional cross-evaluation model of cost-usage-benefit and calculate the value index V.

[0042] like Figure 3 As shown, the cost dataset C, usage dataset U, and revenue dataset R are first normalized. The normalization function is set according to the business characteristics and dimensional differences of each dimension, mapping the original data to a unified score range.

[0043] Then, dynamic weighting coefficients α (cost weight), β (usage weight), and γ (revenue weight) are introduced, all of which satisfy α + β + γ = 1. The weighting coefficients are dynamically adjusted according to the lifecycle stage of the data asset: in the introduction stage, the usage weight is increased to encourage promotion; in the growth stage, a balanced approach is taken; and in the maturity stage, the revenue weight is increased to reflect value realization.

[0044] The asset value index V is calculated as follows: V = α·f(C) + β·g(U) + γ·h(R), where f(C), g(U), and h(R) are the normalized score functions of cost, usage, and revenue, respectively. The process for obtaining the asset value index V from multiple sources—automatic data collection from equipment, manual reporting, and unified valuation extracted from the production system—is as follows: Figure 4 As shown.

[0045] Step 6: Implement value index-driven tiered pricing.

[0046] like Figure 5 As shown, the system obtains the data asset value index V calculated for the current period and the data quality level Q (which includes at least the dimensions of completeness and accuracy) automatically assessed by the system. When the quality level Q meets the preset minimum threshold, the corresponding tier's benchmark price is automatically activated based on the value index range that V falls into. For example, four tiers are preset, L1 to L4, corresponding to the benchmark prices P1 to P4 for the Basic, Standard, Advanced, and Exclusive versions, respectively.

[0047] Based on the benchmark price, a supply and demand factor is introduced for fluctuation adjustment. The supply and demand factor is calculated by weighting demand-side indicators (such as the growth rate of API applications in the past 30 days and the number of views on the asset details page) and supply-side indicators (such as the supply quantity of similar assets and whether it is an exclusive data source), reflecting the real-time demand tightness of the market for the asset. The final price calculation formula is: Circulating Price = Benchmark Price × Supply and Demand Factor.

[0048] Furthermore, this invention generates two differentiated schemes—internal settlement price and external market price—based on the different users, and writes the complete price configuration information into the data asset catalog.

[0049] Step 7: Perform circulation measurement and multi-party settlement.

[0050] When a data consumer initiates a data service request, the system records the actual usage in real time and matches the corresponding price tier based on the consumer's identity, automatically generating a bill. The settlement process utilizes a multi-payment module, supporting any one or a combination of the following settlement methods: For monetary payments, the corresponding amount will be deducted from the pre-deposited account; Points deduction: Deduct user points according to a preset redemption ratio; In the data swap process, once both parties confirm the swap conditions, the system automatically triggers the swap process, freezing the right to use the data assets to be swapped until the transaction is completed.

[0051] Step 8: Implement a four-quadrant hierarchical operation of data asset value.

[0052] like Figure 6 As shown, for data assets within each evaluation period, a four-quadrant matrix is ​​constructed with the value index V as the horizontal axis and the usage growth rate ΔU as the vertical axis. By comparing the value index V and data usage growth rate ΔU of the data assets with preset value thresholds and growth rate thresholds, the data assets are automatically divided into four quadrants: If the value index V is greater than or equal to the value threshold, and the data usage growth rate ΔU is greater than or equal to the growth rate threshold, then the data asset is classified into the star asset quadrant, and promotion and development tasks are automatically generated and assigned to the data product manager. If the value index V is greater than or equal to the value threshold, and the data usage growth rate ΔU is less than the growth rate threshold, then the data asset is classified into the Golden Bull Asset quadrant, and stability monitoring tasks and cost optimization tasks are generated and assigned to the operation and maintenance personnel. If the value index V is less than the value threshold and the data usage growth rate ΔU is greater than or equal to the growth rate threshold, the data assets are classified into the problem asset quadrant, and quality rectification tasks and value mining tasks are generated and assigned to the data governance responsible person. If the value index V is less than the value threshold and the data usage growth rate ΔU is less than the growth rate threshold, the data asset will be classified into the dog asset quadrant, triggering an alert and initiating a countdown to delisting assessment.

[0053] If the indicators of a dog asset fail to improve within two consecutive evaluation periods, it will be automatically frozen or delisted, thereby completing the survival of the fittest among data assets.

[0054] The evaluation method of the present invention also includes a data asset lifecycle state machine, in which the states stored include at least: draft, under evaluation, priced, listed, in operation, frozen, and delisted; the transition between each state is automatically driven by the above steps or triggered by manual review, ensuring that the data asset is manageable, controllable, and traceable from creation to delisting.

[0055] This invention integrates multi-source data unified valuation, three-dimensional dynamic evaluation, tiered pricing, diversified settlement, and four-quadrant operation into a complete closed loop, making up for the shortcomings of existing technologies such as single evaluation dimensions, pricing that is out of touch with reality, and extensive operation and management, and realizing quantitative management and value maximization of data assets throughout their entire life cycle.

[0056] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to specific embodiments.

[0057] This embodiment uses a municipal-level big data management platform as an application scenario. This platform accesses real-time operational data from 30,000 IoT devices (automatically collected by the devices), monthly energy reporting data from 200 subordinate enterprises (manually submitted), and basic corporate information data extracted periodically from the municipal government data sharing and exchange platform (extracted from the production system). The platform needs to manage, value, price, and operate this multi-source data as data assets in a unified manner.

[0058] I. Asset object construction and multi-source data association.

[0059] First, create a new data asset object named "Regional Integrated Energy Consumption Analysis Data Product" in the data asset management platform. The system will automatically assign a unique asset identifier "Asset_202601001". Then, using the metadata management tool, establish mapping relationships between the following data sources and this asset: The "Total Active Power" and "Total Electricity Consumption" fields in the "iot_energy_hour" device data table of the IoT platform are bound to this asset item through metadata mapping, and the access method is marked as "Automatic Device Data Collection"; Bind the fields “Total Electricity Consumption”, “Total Water Consumption”, and “Standard Coal Equivalent” from the form template “Energy Monthly Report_2026” in the online reporting system to this asset, and mark the access method as “Manual Reporting”; Bind the "Company Name", "Industry Classification", and "Registered Capital" fields from the "Basic Information Table of Legal Entities" extracted daily from the municipal government data sharing and exchange platform to this asset, and mark the access method as "Extraction from Production System".

[0060] The system automatically generates a complete data lineage diagram, clearly showing the source system, data table, and access method of each field, ensuring that the three types of heterogeneous data sources are uniformly included in the valuation scope of the data asset object.

[0061] II. Cost penetration and allocation.

[0062] The platform aggregates the total cost of the asset across five dimensions: data collection, storage, computation, processing, and operation and maintenance. For example, in January 2026: Data acquisition costs: The average monthly amortized cost of the IoT platform access server is 1200 yuan (calculated based on the number of devices and the proportion of transmission traffic to this asset); the average monthly computing resource consumption of the ETL extraction task of the data sharing and exchange platform is equivalent to 800 yuan.

[0063] Storage cost: The data tables associated with this asset occupy a total of 0.5TB of object storage space, with an average monthly storage cost of 500 yuan.

[0064] Computing costs: Data cleaning, aggregation, model calculation and other tasks consume a total of 120 CPU cores per hour and 240 GB of memory per hour in the container cluster, with an average monthly computing cost of 1,600 yuan.

[0065] Processing costs: The computing resources consumed in data quality auditing, standardization, and other processing steps amount to RMB 600; for manual data entry, the monthly data review for 200 companies involves approximately 40 person-hours, which translates to RMB 2,000 in labor costs.

[0066] Operation and maintenance costs: The platform's operation and maintenance personnel allocate approximately 300 yuan to the daily inspection and alarm handling costs of this asset.

[0067] The total cost of the above five dimensions is 7,000 yuan, which the system collects into the dynamic cost dataset C for January 2026.

[0068] III. Usage Statistics.

[0069] The usage of this asset in January 2026 was statistically analyzed using the API gateway and data service module: The city's economic analysis system called its data query API a total of 560,000 times. The city's statistics bureau downloaded the data reports 120 times. The data push service was subscribed to by a certain energy research institute, with 80,000 requests per month.

[0070] The system aggregates the above metrics into a usage dataset U for January 2026.

[0071] IV. Revenue Calculation (corresponding to step S4) The income generated by this asset in January 2026 includes: Direct transaction revenue: 8,000 yuan was generated from the sale of data products to external parties; Indirect benefits from business empowerment: After the city's economic analysis system used the data from this asset to predict energy consumption trends, it optimized the regional energy dispatch plan. According to the attribution model, the indirect economic benefits contributed by this data are 12,000 yuan. Equivalent value of data exchange: A data exchange was conducted with a research team from a university, exchanging a portion of the historical data of this asset for the other party's meteorological dataset, which was valued at 3,000 yuan based on the market valuation of the other party's data.

[0072] The above three items are weighted and combined to form the revenue dataset R for January 2026, with a total amount of 23,000 yuan.

[0073] V. Three-dimensional cross-evaluation and value index calculation.

[0074] See Figure 3(Structure diagram of the three-dimensional cross-evaluation model of cost-usage-benefit), the system normalizes the cost set C (7000 yuan), usage set U (560,000 API calls, etc.), and benefit set R (23000 yuan) for January 2026 respectively: Cost normalization: By comparing the cost with the average of similar assets, the cost control of this asset is good, and the cost score f(C) = 75 is obtained; Usage normalization: The 560,000 calls ranked in the top 10% of assets on the same platform, resulting in a usage score g(U) = 90; Normalized revenue: The revenue of 23,000 yuan is relatively efficient relative to the cost of 7,000 yuan, resulting in a revenue score of h(R) = 88.

[0075] Since the asset has been listed and operated for more than six months, the system determines that it is in the mature stage and automatically uses balanced weights α=0.33, β=0.33, γ=0.34 to calculate the value index: V=0.33×75+0.33×90+0.34×88=84.3.

[0076] VI. Tiered pricing.

[0077] The system first obtains the data quality level Q of the asset. After automatic evaluation, the integrity score is 98% and the accuracy score is 96%, meeting the minimum pricing threshold requirements. The value index V=84.3 falls into the preset L3 range (80≤V<90), activating the "Advanced Version" benchmark price P3=1000 yuan / 10,000 API calls.

[0078] Subsequently, the system calculated the supply and demand heat factor: the API application volume for this asset increased by 25% in the past 30 days, and the number of views on the asset details page increased by 18% month-on-month; at the same time, there are only 2 assets with the same theme of "energy consumption analysis" on the platform, indicating a high degree of scarcity. After weighted calculation, the supply and demand heat factor f=1.15.

[0079] The final circulation price = 1000 × 1.15 = 1150 yuan / 10,000 API calls. The system generates an internal settlement price (800 yuan / 10,000 calls for government departments) and an external market price (1150 yuan / 10,000 calls for enterprises) and writes the price information into the asset catalog.

[0080] VII. Circulation Measurement and Diversified Settlement.

[0081] A local financial institution applied to access this asset through a data trading portal, using the API 200,000 times. The system measures usage in real time, matches it with external market price tiers, and automatically generates a bill of 2,300 yuan. The institution chooses to use platform points for deduction, deducting 230,000 points at a conversion rate of 100 points = 1 yuan to complete the settlement. The system records the complete consumption transaction history and updates the usage dataset U and revenue dataset R.

[0082] 8. Four-quadrant hierarchical operation.

[0083] During the operational evaluation period at the end of January 2026, the value index V of this data asset was 84.3 (above the value threshold of 70), and the usage growth rate ΔU was 15% (above the growth rate threshold of 10%), and it was automatically classified into the star asset quadrant by the system.

[0084] The system then performs the following automatic dispatch: Assign the task to the data product manager: "Apply for featured placement on the homepage of the data zone, and update asset descriptions and use cases." Assign the following task to the platform operations and maintenance team: "Ensure that the timely refresh rate of the data associated with this asset is ≥99.9% and the API response latency is ≤200ms."

[0085] Meanwhile, another asset on the platform, named "Historical Email Archive Data," had a value index of V=32 (below the value threshold) and a usage growth rate of ΔU=-5% (below the growth rate threshold), falling into the "dog" asset quadrant for two consecutive periods. After the system triggered an alert notification, the asset still showed no improvement, and the state machine automatically migrated it from "operating" to "frozen" status and removed it from the catalog.

[0086] Through the aforementioned closed-loop process, the city-level platform completed the quantitative valuation and tiered operation of all 537 data assets within one operating cycle, automatically eliminating 12 inefficient assets, and increasing the overall transaction settlement amount of data assets by 35% month-on-month.

[0087] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for measuring and assessing the value of data assets throughout their entire lifecycle, oriented towards the circulation of data elements, characterized in that: Includes the following steps: Step 1: Create a data asset object and assign it a globally unique asset identifier. Then, use a metadata mapping mechanism to uniformly associate multiple heterogeneous data sources with the data asset object. Step 2: Collect resource costs according to various dimensions of the entire lifecycle of data asset objects. For different heterogeneous data sources, adopt differentiated cost allocation strategies to penetrate the total platform cost to individual assets and form a dynamic cost dataset. Step 3: In the circulation process of data assets, data assets are statistically analyzed by tracking points, logs of each call are recorded, and call indicators are aggregated and calculated according to a preset time window to form the usage dataset of the data assets in that period. Step 4: Calculate the total economic value generated by the data assets, and then weight and merge the values ​​of each part of the total economic value according to the time window to form a revenue dataset; Step 5: Construct a three-dimensional cross-evaluation model of cost-usage-benefit, and calculate the value index based on the dynamic cost dataset, usage dataset, and benefit dataset; Step 6: Obtain the data quality level of the data asset. When the data quality level meets the preset minimum threshold, the benchmark price of the corresponding tier is automatically activated according to the value index range that the value index falls into. The supply and demand heat factor is introduced on the basis of the benchmark price for floating adjustment to obtain the circulation price.

2. The method for measuring and assessing the value of data assets throughout their entire lifecycle according to claim 1, characterized in that, In step 1, the associated heterogeneous data sources include at least: data tables automatically collected by the equipment, data fields from manually filled-out forms, and source data extracted from the production system. During the association process, a corresponding metadata mapping record is generated for each data source, and the data lineage is marked to clarify the field-level source and access method of any data asset item.

3. The method for measuring and assessing the value of data assets throughout their entire lifecycle according to claim 1, characterized in that, In step 2, the various dimensions of the entire lifecycle of the data asset object include: data collection, storage, computation, processing, and operation and maintenance. The cost-sharing strategy includes: Automatically collect data from devices and share the costs of server resources, network transmission, and IoT platform access. For manually entered data, in addition to resource costs, the labor costs of the reviewers will be measured separately. Extract data from the production system and distribute the computational resource consumption for data extraction from the source database, the load cost of the ETL / CDC engine, and the network overhead for data transmission.

4. The method for measuring and assessing the value of data assets throughout their entire lifecycle according to claim 1, characterized in that, In step 3, the statistical dimensions include at least: API call count, data service request count, and data product download count.

5. The method for measuring and assessing the value of data assets throughout their entire lifecycle according to claim 1, characterized in that, In step 4, the total economic value includes: Direct transaction revenue refers to the amount of orders generated through data trading platforms or internal settlement systems; Indirect benefits of business empowerment are the quantifiable business increments generated after downstream business systems call the data, and their discounted value after the contribution ratio is broken down according to the preset attribution model. The equivalent value of data exchange is the monetary equivalent calculated based on the appraised value or market price of the exchanged assets when exchanging data assets for other data products or services.

6. The method for measuring and assessing the value of data assets throughout their entire lifecycle according to claim 1, characterized in that, In step 5, the specific method for calculating the value index is as follows: Normalize the cost dataset C, usage dataset U, and revenue dataset R respectively; Dynamic weighting coefficients α, β, and γ are introduced, which correspond to the weighting coefficients of the cost dataset, usage dataset, and revenue dataset, respectively. The data asset value index V is calculated using the formula V = α·f(C) + β·g(U) + γ·h(R), where f(C), g(U), and h(R) are the normalized score functions of cost, usage, and revenue, respectively.

7. The method for measuring and assessing the value of data assets throughout their entire lifecycle according to claim 1, characterized in that, In step 6, the supply and demand heat factor is calculated by weighting demand-side indicators and supply-side indicators. The calculation formula is: circulation price = benchmark price × supply and demand heat factor. Demand-side metrics include: API application growth rate over the past 30 days and asset detail page views. Supply-side indicators include: the quantity of assets supplied under the same theme and whether it is an exclusive data source.

8. The method for measuring and assessing the value of data assets throughout their entire lifecycle according to claim 1, characterized in that, The evaluation method also includes step 7: performing circulation measurement and multi-level settlement. When a data consumer initiates a data service request, the actual usage is recorded in real time and the corresponding price level is matched according to the consumer's identity, and a bill is automatically generated. In the settlement process, the multi-level settlement module is called to support any one or a combination of monetary payment, points deduction and data exchange for settlement.

9. The method for measuring and assessing the value of data assets throughout their entire lifecycle according to claim 8, characterized in that, The evaluation method also includes step 8: performing four-quadrant hierarchical operation of data asset value. For data assets in each evaluation period, a four-quadrant matrix is ​​constructed with the value index V as the horizontal axis and the data usage growth rate ΔU as the vertical axis, including: star assets, cash cow assets, problem assets and dog assets. By comparing the value index V and the data usage growth rate ΔU of the data assets with the preset value threshold and growth rate threshold, the data assets are divided into four quadrants, and differentiated operation tasks are triggered for each quadrant. If the value index V is greater than or equal to the value threshold, and the data usage growth rate ΔU is greater than or equal to the growth rate threshold, then the data asset is classified into the star asset quadrant, and promotion tasks and development tasks are automatically generated and assigned to the data product manager. If the value index V is greater than or equal to the value threshold, and the data usage growth rate ΔU is less than the growth rate threshold, then the data asset is classified into the Golden Bull Asset quadrant, and stability monitoring tasks and cost optimization tasks are generated and assigned to the operation and maintenance personnel. If the value index V is less than the value threshold and the data usage growth rate ΔU is greater than or equal to the growth rate threshold, the data assets are classified into the problem asset quadrant, and quality rectification tasks and value mining tasks are generated and assigned to the data governance responsible person. If the value index V is less than the value threshold and the data usage growth rate ΔU is less than the growth rate threshold, the data asset will be classified into the dog asset quadrant, triggering an alert and initiating a countdown to delisting assessment.

10. The method for measuring and assessing the value of data assets throughout their entire lifecycle according to claim 9, characterized in that, The evaluation method also includes a data asset lifecycle state machine, in which the states stored include at least: draft, under evaluation, priced, listed, in operation, frozen, and delisted; the transition between each state is automatically driven or manually reviewed and triggered by steps 1-8.

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