A multi-level comprehensive adjustment dynamic data transaction charging method, system and computer readable storage medium

Abstract

The application discloses a kind of multilevel comprehensive regulation dynamic data transaction billing method, system and computer readable storage medium.The method comprises the following steps: receiving the data demand party submits data query request;For payment field, obtain the corresponding field value density base price, according to the data configuration field level time limit value coefficient of payment field, and obtain the real-time supply and demand information related to payment field or its belonging data set, to determine supply and demand adjustment factor;Calculate the dynamic unit price of single record;Combined with transaction data volume and contract cycle, based on nonlinear batch discount function, the dynamic unit price of single record is corrected, the transaction total price of this data transaction is calculated, and the field level billing parameter details corresponding to transaction total price is output.The application realizes the fine, interpretable and adaptive billing of high-value data in the whole process of circulation, effectively solves the problems of coarse granularity, static value and market response lag in traditional data transaction pricing.

Description

Technical Field

[0001] This invention belongs to the field of data transaction technology, specifically relating to a multi-level integrated adjustment dynamic data transaction billing method, system, and computer-readable storage medium. Background Technology

[0002] In the current data trading market, mainstream billing models face deep-seated structural challenges. Pricing mechanisms lack granularity, generally using a uniform pricing based on the entire data package (such as GB units) or a fixed number of records, failing to identify the intrinsic value differences between different data fields.

[0003] For example, the survival period field in medical and health data has high value density due to its criticality in clinical research, while the value contribution of the gender field is relatively limited. However, the existing system uses the same pricing standard for both, resulting in an imbalance in resource allocation and a lack of fairness in transactions.

[0004] Meanwhile, the objective law of data value dynamically decaying over time is completely ignored, and the system adopts a static pricing strategy that fails to reflect the impact of timeliness. Real-time blood glucose monitoring values ​​may have urgent decision-making value at present, but the value of historical medical data increases significantly over time in real-world research and other scenarios. However, the existing billing method does not incorporate time decay or value-added mechanisms, making it difficult to accurately classify and evaluate the value of fields, thus causing value assessment distortion.

[0005] Furthermore, the regulatory effect of market supply and demand fluctuations on data value has not been effectively captured. The system lacks real-time perception capabilities and cannot automatically implement premium strategies when data is scarce or dynamically reduce prices when supply is excessive, resulting in delayed market response and inefficient resource allocation.

[0006] Traditional billing processes also suffer from serious transparency deficiencies. Buyers cannot trace the details of the cost composition, such as the inability to distinguish the value contribution and adjustment parameters of each field. This not only weakens the foundation of trust in transactions but also causes frequent disputes and frictions, hindering the standardized development of the data element market.

[0007] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0008] In view of the above-mentioned deficiencies of the prior art, the first aspect of the present invention provides a multi-level integrated adjustment dynamic data transaction billing method, characterized by comprising the following steps: Receive data query requests submitted by data requesters, wherein the data query requests specify at least one payment field for billing and include the corresponding transaction data volume and contract period information; For the paid field, obtain the corresponding field value density base price, configure the field-level timeliness value coefficient according to the data of the paid field, and obtain real-time supply and demand information related to the paid field or its dataset to determine the supply and demand adjustment factor; Based on the field value density base price, the field-level timeliness value coefficient, and the supply and demand adjustment factor, calculate the dynamic unit price of a single record; Based on the transaction data volume and the contract period, the dynamic unit price of each record is corrected using a nonlinear batch discount function to calculate the total transaction price of this data transaction, and the field-level billing parameter details corresponding to the total transaction price are output.

[0009] In the multi-level integrated adjustment dynamic data transaction billing method described above, optionally, the set of fields to be retrieved indicated in the data query request includes paid fields and free fields; wherein, only the set of paid fields is considered. During billing calculations, the free field is not included in the billing summation. The dynamic unit price for a single record is calculated using the following formula: ; Where i represents the unique identifier of the i-th record, i.e., the data record; t represents the time of inquiry or transaction in this transaction; Indicates the paid field Value density base price; Indicates the paid field Weighting coefficients; Indicates the paid field For the field-level timeliness value coefficient at transaction time t, one of the following three types of functions is configured according to the field semantic type: For attenuation type fields ; This refers to the time-specific decay rate of the field. For stable fields, ; For growth fields Or, in the form of a sigmoid with a saturation upper limit: ; in, or or or This is a field-specific growth parameter; The supply and demand adjustment factor, used to reflect real-time scarcity, is calculated using the following formula: ; in, This represents the query volume for this dataset over the past N days. This represents the platform's average daily query volume. To adjust the sensitivity.

[0010] In the multi-level integrated adjustment dynamic data transaction billing method described above, optionally, data query requests are divided into per-transaction, batch, or subscription transaction modes, wherein: The per-transaction or batch transaction mode, combined with the transaction data volume in the request, applies a non-linear batch discount function to correct the total cost calculated based on the dynamic unit price of the single record, and calculates the total price of the per-transaction transaction; The subscription transaction model calculates the subscription period price based on the contract period in the request, the predicted future period unit price, the data delivery volume, and a combination of risk discounts and cash discounts.

[0011] In the multi-level integrated adjustment dynamic data transaction billing method described above, optionally, the total price of the per-transaction transaction in the per-transaction or batch transaction mode is calculated using the following formula: ; N represents the transaction data volume, i.e., the number of records returned; Nonlinear batch discount function, i.e., batch-period joint discount function Include: Usage discount And long-term contract premium; If the contract period Additional discounts will be offered. .

[0012] In the multi-level integrated adjustment dynamic data transaction billing method described above, optionally, the subscription period price of the subscription transaction mode is calculated using the following formula: ; K represents the number of billing cycles during the subscription period; This represents the expected future unit price based on historical decline trends. The expected number of deliveries in period k; risk discount factor. ; This refers to the financial discount rate.

[0013] In the multi-level integrated adjustment dynamic data transaction billing method described above, optionally, before the steps of obtaining the field value density base price and determining the field-level timeliness value coefficient, the method further includes: reading configuration parameters related to the paid field from the field metadata management module; wherein, the configuration parameters include at least one of the following: field value density base price, field-level timeliness value coefficient, and field weight coefficient; the field metadata management module also stores the field's free marking information to distinguish between paid fields and free fields.

[0014] Optionally, the dynamic data transaction billing method with multi-level integrated adjustment as described above may also include a parameter optimization step: Collect historical transaction pricing data and buyer feedback ratings; Based on price data and feedback ratings, the field-level timeliness value coefficient and / or the field weight coefficient are optimized and updated in reverse using a machine learning model.

[0015] In the multi-level integrated adjustment dynamic data transaction billing method described above, optionally, the output field-level billing parameter details specifically include generating and storing a structured transaction audit log; wherein, the audit log records all key parameters and intermediate calculation results used in this billing, including at least one of them: supply and demand adjustment factors. Field-level timeliness value coefficients for each paid field The relevant parameters or result values ​​of the pricing correction function; when the transaction mode is per transaction or batch transaction, the relevant parameters of the pricing correction function recorded in the audit log are the values ​​of the nonlinear batch discount function. .

[0016] To achieve the above objectives, a second aspect of the present invention provides a multi-level integrated adjustment dynamic data transaction billing system, wherein the method for implementing the multi-level integrated adjustment dynamic data transaction billing as described in any one of the first aspects includes: The field metadata management module is used to store and manage metadata related to data fields. The metadata includes at least: field value density base price, field-level timeliness value coefficient, field weight parameters, and field free tag information. The real-time market heat monitor is used to continuously monitor query activities on the data trading platform. Based on the dynamic relationship between the query volume of the target data field or dataset and the platform's benchmark query volume within a preset time window, it calculates and outputs the real-time supply and demand adjustment factor. The dynamic billing engine, which communicates with both the field metadata management module and the real-time market popularity monitor, performs the following operations: Receive a data query request, wherein the request specifies at least one payment field and includes the corresponding transaction data volume and contract period information; For the paid field, the corresponding field value density base price is obtained from the field metadata management module, the field-level timeliness value coefficient is configured according to the data of the paid field, and the real-time supply and demand adjustment factor is obtained from the real-time market heat monitor. Based on the field value density base price, the field-level timeliness value coefficient, and the real-time supply and demand adjustment factor, calculate the dynamic unit price of a single record; Based on the transaction data volume and the contract period, the dynamic unit price of each record is corrected using a nonlinear batch discount function to calculate the total transaction price of this data transaction. A field-level timeliness value coefficient learner is connected to the field metadata management module and the dynamic billing engine. It is used to collect historical transaction data and buyer feedback ratings, perform reverse optimization on the field-level timeliness value coefficient and / or field weight coefficient through a machine learning model, and update the optimized parameters to the field metadata management module. An API or smart contract interface is connected to the dynamic billing engine to receive data query requests submitted by external systems and forward the requests to the dynamic billing engine. It also outputs the total transaction price and corresponding field-level billing parameter details calculated by the dynamic billing engine in a structured data format and supports automatic execution of transaction settlement through smart contracts.

[0017] To achieve the above objectives, a third aspect of the present invention provides a computer-readable storage medium storing computer-executable instructions or a computer program, which, when executed by a processor, implements a dynamic data transaction billing method with multi-level integrated adjustment as described in any of the first aspects above.

[0018] This invention provides a multi-level integrated adjustment dynamic data transaction billing method, system, and computer-readable storage medium, which can dynamically evaluate the value of data fields and generate differentiated quotes based on the semantic attributes and time evolution patterns of the data fields themselves. It is particularly suitable for scenarios such as healthcare, financial credit, and the Internet of Things, and includes composite datasets containing heterogeneous fields such as rapidly decaying fields (e.g., real-time location), long-term stable fields (e.g., genotype), and value-increasing fields (e.g., follow-up duration, long-term efficacy records).

[0019] In summary, this application addresses the issues of lack of granularity in pricing, neglect of timeliness and supply-demand fluctuations in existing technologies, and achieves granular billing based on field level, taking into account data timeliness and dynamic changes in market supply and demand, thereby improving the fairness and transparency of billing. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the concept, specific structure and technical effects of the present invention will be further explained below in conjunction with the accompanying drawings, so as to fully understand the purpose, features and effects of the present invention.

[0021] Figure 1 This is a flowchart illustrating an embodiment of a multi-level integrated adjustment dynamic data transaction billing method provided by the present invention; Figure 2 This is a schematic diagram of the field-level timeliness value coefficient according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the supply and demand adjustment factor S(t) fluctuating with the query volume in one embodiment of the present invention. Detailed Implementation

[0022] In this document, to make the technical means, inventive features, achieved objectives and effects of the invention readily understandable, the invention is further described below with reference to specific illustrations. However, the invention is not limited to the embodiments described below.

[0023] It should be noted that the structures, proportions, sizes, etc., illustrated in the accompanying drawings of this specification are only used to complement the content disclosed in the specification for those skilled in the art to understand and read, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0024] Terms such as “comprising” and “including” indicate that, in addition to the components that are directly and explicitly stated in the specification and claims, the technical solution of the present invention does not exclude the presence of other components that are not directly or explicitly stated.

[0025] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0026] Traditional data trading systems suffer from structural flaws in their pricing mechanisms. Specifically, the granularity of billing objects cannot be refined to the field level, making it impossible to distinguish the intrinsic value differences between different fields; data value assessment does not incorporate time-related variables, causing a disconnect between assessment results and data timeliness; dynamic changes in market supply and demand are not captured in real time, resulting in price adjustments lagging behind market conditions; and the lack of parameterized traceability mechanisms in the billing process creates trust barriers between transacting parties. These problems directly lead to reduced billing accuracy, decreased market resource allocation efficiency, and increased transaction friction, thereby affecting the activity and sustainability of data circulation.

[0027] For example, in a healthcare data transaction scenario, a data requester needs to obtain patients' historical medical records for training a disease prediction model. Their query request only requires the "real-time blood glucose monitoring value" field, but the existing system forces pricing based on the entire dataset (including low-value fields such as "gender" and "date of birth"). Because the high time-sensitivity of "real-time blood glucose monitoring values" is not considered, its value does not dynamically adjust as the data generation time decreases or increases. Furthermore, when a public health emergency causes a surge in demand for this field, the system cannot automatically increase the unit price based on real-time supply and demand, while low-value fields retain their original price. In addition, after paying, the buyer cannot know the specific billing breakdown of each field; only the overall data package price is displayed, resulting in opaque cost attribution.

[0028] If the above problems are not addressed, a value mismatch will occur in the data trading market. High-time-sensitivity fields will be over-consumed due to low pricing, exacerbating their scarcity, while low-value fields will be subject to reduced trading willingness due to high pricing. The continuous accumulation of market supply and demand imbalance will further weaken the regulatory role of the price mechanism on data resources. The lack of traceability in the billing process will amplify the information asymmetry between the trading parties, ultimately leading to the collapse of market trust and hindering the market-based allocation of data elements.

[0029] In response, this application provides a multi-level integrated adjustment method for dynamic data transaction billing, such as... Figure 1 As shown, the specific steps may include the following: The system receives data query requests submitted by data requesters. Each data query request specifies at least one payable field for billing purposes and includes the corresponding transaction data volume and contract period information. The data requester refers to an entity or user submitting a data query request to obtain specific data within the data trading platform. This entity or user can be an individual, enterprise, or other organization. In an optional embodiment, datasets can be customized for sale based on specified fields.

[0030] For paid fields, obtain the corresponding field value density base price, configure the field-level timeliness value coefficient based on the data of the paid fields, and obtain real-time supply and demand information related to the paid fields or their respective datasets to determine the supply and demand adjustment factors.

[0031] Among them, the field value density base price refers to a quantitative indicator that measures the intrinsic value of a specific data field. This parameter reflects the differences in information content, scarcity, and application value among different fields. The supply and demand adjustment factor refers to an adjustment coefficient determined based on the real-time supply and demand situation of a data field or its dataset in the market. When the demand for specific data is strong and the supply is relatively scarce, this factor may cause the price to rise; conversely, when the supply is excessive, this factor may cause the price to fall, thereby achieving a dynamic balance between market supply and demand.

[0032] The dynamic unit price of a single record is calculated based on the field value density base price, the field-level timeliness value coefficient, and the supply and demand adjustment factor.

[0033] In optional embodiments, the set of fields to be retrieved indicated in the data query request specifically includes paid fields and free fields. This distinction can be achieved in several ways. For example, in the structured data (such as JSON or XML) of the data query request, a boolean flag (e.g., `"is_paid": true` or `"is_paid": false`) can be added to each field to directly indicate its paid status. Alternatively, the data trading platform can maintain a field metadata management module that pre-stores the paid / free attributes of all available fields. When a query request is received, the system automatically queries this module based on the field name in the request to determine its paid status. This explicit field classification is the basis for subsequent accurate billing, ensuring a clear definition between billed and non-billed objects.

[0034] Specifically, billing calculations are performed only on paid fields; free fields are excluded from the billing summation. This means that when executing the unit price calculation logic, the system filters out all fields marked as free, incorporating only the value contribution of paid fields into the unit price calculation. For example, within the billing engine, a processing flow can be designed to first parse the data query request and identify the set of paid fields. The paid fields are then passed as input to the unit price calculation module, while the free fields are excluded from the calculation. Alternatively, in the implementation of the unit price calculation formula, a zero weight can be set for the free fields, or their value can be skipped directly through conditional judgment. This mechanism ensures the accuracy and reasonableness of billing, avoiding unnecessary fees paid by the buyer due to the presence of free fields.

[0035] In this embodiment, the dynamic unit price of a single record is calculated using the following formula: .

[0036] Where i represents the unique identifier of the i-th record, i.e., the data record; t represents the time of inquiry or transaction in this transaction; Indicates the paid field The value density base price quantifies the inherent information content, scarcity, or commercial value of a specific paid field. This parameter can be preset through expert experience assessment, historical transaction data analysis, or based on field type (such as sensitive data or core business indicators). It can also be dynamically adjusted through machine learning models based on the frequency of field usage and its impact on decision-making in different business scenarios. Indicates the paid field The weighting coefficients can be manually configured or dynamically adjusted through online learning (such as reinforcement learning) to further adjust the relative importance of the paid fields in the overall unit price calculation. For example, in a specific business scenario, some paid fields may be more valuable for decision-making than other fields, so they can be given higher weights.

[0037] Indicates the paid field For the field-level timeliness value coefficient at transaction time t, one of the following three types of functions is configured according to the field semantic type: For decay-type fields, such as real-time blood glucose and location information, ; This refers to the time-specific decay rate of the field. For stable fields, such as gender and genotype, ; For growth-related fields, such as follow-up duration, overall survival, and long-term medication adherence, Or, in the form of a sigmoid with a saturation upper limit: ; in, or or or This is a field-specific growth parameter; The supply and demand adjustment factor, used to reflect real-time scarcity, is calculated using the following formula: ; in, This represents the query volume for this dataset over the past N days. This represents the platform's average daily query volume. To adjust the sensitivity.

[0038] Taking a decay-type field as an example, its field-level timeliness value coefficient is: Right now This is used to describe how the value of data changes over time. Among them, The field-specific decay rate is a key parameter in the field-level time-related value coefficient. It characterizes the rate at which the value of data in a specific field decays. Different types of data (such as real-time sensor data and historical archive data) can have different decay rates. Figure 3 As shown, As a supply and demand adjustment factor, it reflects the impact of market supply and demand on data prices and real-time scarcity. When data is scarce or demand is high, this factor will cause prices to rise, and vice versa.

[0039] In this embodiment, the calculation formula is as follows: ; in, This represents the query volume for this dataset over the past N days. The platform's average daily query volume. Optionally, N can be set to 7, indicating that the factor can be calculated based on the ratio of the query volume of the set of fields to be retrieved in the past 7 days to the platform's average daily query volume. The query volume of the set of fields to be retrieved in the past 7 days is obtained by monitoring query activities on the data platform in real time, while the platform's average daily query volume represents the market's benchmark demand.

[0040] To adjust sensitivity, This is used to adjust the sensitivity of the supply and demand adjustment factor to changes in market supply and demand. Higher sensitivity leads to greater price fluctuations, while lower sensitivity results in more stable prices.

[0041] The combined application of these parameters enables the dynamic unit price of a single record to comprehensively and dynamically reflect the inherent value, timeliness, and market supply and demand changes of the data itself. This embodiment's algorithm logic achieves, for the first time, coupled calculation of field-level decay rate and real-time market heat feedback. In particular, through the analysis of… Based on the semantic type of the field, three types of functions are configured in a refined manner to uniformly handle the three value evolution modes of decay, stability and growth. Free fields are automatically excluded from summation to ensure billing fairness and interpretability.

[0042] Through the aforementioned technical solution, this application can clearly distinguish between paid and free fields in data query requests and ensure that billing calculations are performed only on paid fields, thereby avoiding the problem of free fields being incorrectly billed. This significantly improves the accuracy and transparency of data transaction billing, enabling buyers to clearly understand the composition of their payments and enhancing their trust in the billing mechanism. Simultaneously, by strictly applying a refined dynamic unit price calculation formula to paid fields, this application effectively reduces buyers' transaction costs and minimizes transaction friction caused by unclear billing, while simultaneously achieving dynamic responsiveness to data value, timeliness, and market supply and demand. This optimizes the overall data transaction experience.

[0043] Based on the transaction data volume and contract period, the dynamic unit price of a single record is adjusted using a non-linear batch discount function. For example, as the transaction data volume increases, the price per unit of data may decrease at a decreasing rate, or long-term contracts may offer additional discounts. This allows the calculation of the total transaction price for this data transaction and the output of field-level billing parameter details corresponding to the total transaction price.

[0044] In this context, "transaction data volume" refers to the number of data records or the total amount of data that the data requester expects to obtain in a single data transaction. "Contract period" refers to the duration of the data transaction agreed upon by both parties, such as a per-transaction, monthly, or annual subscription.

[0045] In an optional embodiment, data query requests are categorized into one-off, batch, or subscription transaction modes, wherein the one-off or batch transaction mode is combined with the amount of transaction data in the request.

[0046] For pay-as-you-go or bulk transaction models, the aim is to provide flexible and attractive billing schemes for short-term, one-off, or large-volume data purchases, encouraging users to conduct bulk transactions through discount mechanisms. The billing system first calculates the dynamic unit price per record using the aforementioned method, then multiplies this unit price by the amount of transaction data requested to obtain a base total cost. Next, the system calls a preset non-linear bulk discount function, which takes the transaction data volume as input and outputs a discount factor or a directly adjusted price. For example, the discount function can be designed so that the discount rate increases non-linearly with the increase in transaction data volume, resulting in a lower average unit price for larger purchase volumes. Furthermore, the platform can maintain a discount rule table, which defines the discount rate or discount function corresponding to different transaction data volume ranges. When a pay-as-you-go or bulk transaction request is received, the system looks up the corresponding discount rule based on the requested transaction data volume and applies it to the base total cost to calculate the final pay-as-you-go total price. The non-linearity is reflected in the fact that the increase in the discount rate is not directly proportional to the data volume, but may accelerate or decelerate after a certain threshold.

[0047] For subscription-based transaction models, suitable for long-term, continuous data needs, this model provides users with stable and reasonable long-term data service prices by considering future uncertainties (price fluctuations, changes in delivery volume) and the time value of money. When processing subscription requests, the billing system first predicts the expected dynamic unit price and projected data delivery volume for each billing cycle (e.g., monthly or weekly) within the contract period, based on historical data trends and market forecasting models. Then, the system introduces a risk discount factor, which can be set based on the volatility of data fields and the uncertainty of market supply and demand forecasts, to compensate for potential future price or delivery volume risks. Simultaneously, considering the time value of money, the system applies a financial discount rate to discount the fees for each future cycle, converting future payments into present value, and finally summing them to obtain the cycle price for the subscription model. Alternatively, the platform can utilize machine learning models to analyze historical transaction data and predict the average unit price and projected delivery volume for specific data fields in different future time periods. For risk discounts, different risk levels and corresponding discount factors can be set based on factors such as the scarcity of data fields, update frequency, and market competition. The discount rate can be set using an industry-standard discount rate or based on the platform's own cost of capital. All these parameters are combined and processed through a predefined composite calculation model to derive the periodic price under the subscription model.

[0048] Through the aforementioned technical solutions, this application can provide more refined and business-intelligent billing strategies based on different transaction models for data query requests. For pay-as-you-go or bulk transactions, the nonlinear bulk discount function can effectively incentivize large-scale data purchases while ensuring the dynamism and rationality of prices. For subscription transactions, by predicting future prices and data delivery volumes and comprehensively considering risk discounts and cash discounts, it can provide stable, fair, and forward-looking pricing for long-term data services, thereby effectively solving the problem of the lack of consideration for future price fluctuations, risk management, and the time value of money in the subscription model. Overall, this application significantly improves the flexibility and adaptability of the data transaction billing system, enabling it to better meet diverse market demands and enhance users' trust in billing transparency and fairness.

[0049] In an optional embodiment, the total price of a single transaction in the single-transaction or batch transaction mode is calculated using the following formula: ; It organically combines dynamic unit price, transaction data volume, and nonlinear batch discount function to provide a structured and quantifiable billing method.

[0050] Here, N represents the transaction data volume, i.e., the number of records returned. It signifies the actual number of records delivered to the data requester in this data transaction, directly reflecting the scale of the transaction. Specifying N as the number of returned records ensures consistency between billing and the actual data delivery volume, avoiding billing disputes caused by ambiguous data volume definitions. For example, in the data query request processing module, the value of N can be determined by counting the query result set; or in the data delivery module, the actual number of records transmitted can be recorded as the value of N.

[0051] Nonlinear batch discount function, i.e., batch-period joint discount function This function aims to non-linearly adjust the base unit price to reflect the scale effect of bulk transactions and the value of long-term cooperation. It is not merely a simple linear discount, but rather a comprehensive consideration of both transaction volume and contract duration. The function includes a usage discount. And long-term contract premiums. Among them, the usage discount is designed to encourage data demanders to make larger-scale data purchases. The larger the transaction volume, the higher the discount rate per unit of data. However, the growth of the discount rate is not linear, but shows a marginal decreasing trend.

[0052] Long-term contract premiums aim to incentivize data demanders to sign long-term data transaction contracts, providing more stable revenue expectations and deeper partnerships. Under long-term contracts, in addition to usage discounts, additional price benefits or better service terms are also available. In this embodiment, if the contract period... Additional discounts will be offered. .

[0053] By combining the considerations of the intrinsic value, timeliness, and market supply and demand of data in the basic solution, this solution enables a more comprehensive and refined adjustment of data transaction prices, providing a fairer, more reasonable, and mutually beneficial pricing basis for both parties in data transactions.

[0054] In an optional embodiment, the subscription period price of the subscription transaction model is calculated using the following formula: ; It integrates multiple factors that affect data value and transaction risk into a unified calculation model, ensuring the standardization, transparency, and enforceability of the billing process.

[0055] Where K represents the number of billing cycles within the subscription period. For example, if the subscription period is one year and billed monthly, then K is 12; if billed quarterly, then K is 4. This parameter allows billing to flexibly adapt to subscription contracts of different lengths and billing frequencies, ensuring that the billing cycle aligns with the actual contract terms and avoiding the rigid approach of simply treating long-term transactions as one-off transactions. Its determination can be based on the contract terms negotiated between the data requester and the data provider; for example, billing can be monthly, quarterly, or annual. If a daily cycle is used, then K=T.

[0056] The expected future unit price, predicted based on historical decay trends, represents the anticipated price of a single data record within the k-th billing cycle. Its core lies in predicting the data value at different future points in time by analyzing the decay patterns of historical data value. For example, time series analysis models (such as ARIMA models and exponential smoothing) or machine learning models (such as recurrent neural networks (RNNs) and long short-term memory networks (LSTMs)) can be used to train historical unit price data to capture the trends and periodicity of data value changes over time, thereby predicting the expected unit price for each future cycle. This dynamic prediction mechanism solves the problem of static, fixed data value, enabling pricing to reflect real-time changes in value due to data timeliness.

[0057] The expected number of data items to be delivered in the k-th period can be determined by statistical analysis of historical data delivery volumes, combined with historical query patterns of data requesters, business growth expectations, and market trends. For example, the average or median of historical delivery volumes can be used, or regression analysis and machine learning models (such as user behavior prediction or seasonal adjustment models) can be employed to predict future data delivery volumes. By predicting the delivery volume for each period, billing can more accurately reflect actual data usage expectations, rather than being based on a fixed and potentially inaccurate preset value.

[0058] Risk discount factor This is used to quantify and adjust for various risks that may exist in subscription transactions, thereby correcting prices. These risks may include fluctuations in data quality, decreased data value due to increased market competition, default risk from data requesters, and the risk of data providers failing to deliver data on time. Risk discount factors can be comprehensively assessed and quantified based on default rates in historical transaction data, data quality assessment reports, market volatility indicators, and risk clauses in contract terms. For example, a baseline risk factor can be set and adjusted based on the risk assessment results of a specific transaction; a decline in data quality may lead to a lower risk discount factor (i.e., a larger price discount), while a long-term, stable partnership may lead to a higher risk discount factor (i.e., a smaller discount).

[0059] A financial discount rate (e.g., 3% / year) is used to discount future cash flows to their present value to reflect the time value of money. Since subscription transactions involve payments over multiple future periods, future revenues have a lower value in the present. The discount rate increases with the duration of the subscription (reflecting data obsolescence and increased compliance risks). The financial discount rate can be determined by referencing current market risk-free interest rates (e.g., government bond yields), benchmark bank lending rates, or based on the data provider's cost of capital. For example, the weighted average cost of capital (WACC) can be used as the discount rate, or the industry average rate of return on investment can be referenced. By introducing a financial discount rate, this solution ensures that the subscription price not only reflects the value of the data itself but also considers the differences in the value of money at different points in time, making the pricing of long-term contracts more reasonable and fair.

[0060] Through the aforementioned technical solution, this application can significantly improve the accuracy, reliability, and interpretability of data transaction billing under the subscription transaction model. This not only solves the problems of inaccurate long-term transaction pricing, unquantified risk, and neglect of the time value of money in the traditional model, but also constructs a more complete and adaptable multi-level comprehensive adjustment billing system, thereby significantly improving the efficiency and trustworthiness of data transactions.

[0061] Furthermore, before obtaining the field value density base price and determining the field-level timeliness value coefficient, the process includes reading configuration parameters related to the paid field from the field metadata management module. These configuration parameters may include the field value density base price, the field-level timeliness value coefficient, and the field weight coefficient. The field metadata management module also stores the field's free tag information to distinguish between paid and free fields. This is not an isolated step, but a crucial link in engineering and systematizing the core pricing model of this invention (field value, timeliness decay, free / paid distinction). It ensures that the entire dynamic billing system, while possessing high complexity and flexibility, remains reliable, manageable, and evolvable, thus strongly supporting the practicality, innovation, and commercial value of the invention from a technical implementation perspective.

[0062] In an optional embodiment, the method further includes a parameter optimization step: Collecting historical transaction pricing data and buyer feedback ratings is crucial for enabling the billing model to learn and improve from real-world operation, avoiding billing deviations caused by fixed parameters. This step can be achieved through periodic manual review and adjustment, with expert teams making empirical corrections to parameters based on market feedback and business needs; or through automated algorithms, such as rule-based engines or statistical analysis methods, automatically adjusting parameters according to preset optimization goals and thresholds. Collecting historical transaction pricing data and buyer feedback ratings provides accurate and reliable input data for parameter optimization. Pricing data reflects actual transaction costs and market acceptance, while buyer feedback ratings directly reflect user satisfaction or dissatisfaction with the billing results, serving as a key indicator for evaluating the billing model's performance. The data collection process can automatically record information such as the final billing price, transaction volume, and fields involved in each data transaction through the transaction log system within the data platform. Meanwhile, buyer feedback ratings can be collected through user evaluation systems, questionnaires, or customer service feedback after the transaction is completed; or by integrating external data analysis tools, transaction data and user feedback can be aggregated from multiple data sources (such as CRM systems and user behavior analysis platforms) to form a unified dataset.

[0063] Based on price data and feedback ratings, a machine learning model is used to perform reverse optimization and updates on field-level timeliness value coefficients and / or field weight coefficients. Reverse optimization means that the model adjusts parameters based on actual performance (price data and feedback ratings) to ensure future billing results better align with market expectations and user satisfaction. This machine learning model can employ supervised learning models, such as linear regression, decision trees, random forests, or neural networks, using historical price data and feedback ratings as input to predict or optimize field-level timeliness value coefficients and / or field weight coefficients; or it can use reinforcement learning models, treating the billing process as a decision-making process. The model continuously learns and adapts through interaction with the environment (market transactions and user feedback) to find the optimal parameter adjustment strategy. The optimization process can be iterative, with the model running periodically to calculate new parameter values ​​based on the latest historical data and feedback ratings, and then updating these values ​​in the billing system.

[0064] As a specific implementation method, the parameter optimization process of this application can be implemented as follows: First, the system continuously collects historical transaction data from the transaction database of the data trading platform, including the final billing price of each transaction, the data volume of the transaction, the payment fields involved, and the transaction timestamp. Simultaneously, through a feedback module integrated into the transaction completion page or user center, it collects buyer satisfaction ratings (e.g., 1 to 5 stars). This data is periodically (e.g., weekly or monthly) aggregated into a data warehouse. Subsequently, a machine learning service deployed on a cloud server is triggered. This service internally runs a regression model based on a gradient boosting tree. The model uses historical billing price data and buyer feedback ratings as training objectives, and field-level timeliness value coefficients and field weight coefficients as variables to be optimized. For example, the model analyzes the average buyer feedback rating when the decay rate of a certain field is set to X; and whether the transaction price matches market expectations when the weight coefficient is set to Y. Through iterative training, the model learns how to adjust these parameters to maximize buyer satisfaction while ensuring platform revenue. Once the model has been trained and converged, it outputs a set of optimized field-level timeliness value coefficients and field weight coefficients. These new parameter values ​​are then automatically updated in the field metadata management module via an API interface. For example, if the model finds that a specific industry has higher data timeliness requirements and its decay rate should be faster, it will update the field-level timeliness value coefficient corresponding to that field. Similarly, if a field exhibits higher scarcity or importance in a specific market environment, its field weight coefficient will be adjusted accordingly. In this way, during the subsequent billing process, the dynamic billing engine will directly use these parameters optimized and updated by the machine learning model when obtaining the field value density base price and determining the field-level timeliness value coefficients, thereby ensuring the dynamic nature and accuracy of billing.

[0065] Through the aforementioned technical solution, this application effectively addresses the problem that traditional billing methods cannot adapt field-level time-sensitive value coefficients and field weight coefficients to historical transaction data and buyer feedback. During billing, the base price for field value density, field-level time-sensitive value coefficients, and supply-demand adjustment factors will be more accurate and reasonable, significantly improving billing accuracy, enhancing the market responsiveness of the billing system, and ultimately resolving the dynamic adjustment issues related to data value decay or appreciation over time and real-time supply-demand changes. This provides a fairer, more transparent, and more adaptable billing mechanism for data transactions. Furthermore, since parameter adjustments are based on traceable historical data and feedback, the transparency and interpretability of the billing process are also enhanced, effectively mitigating the trust deficiencies and transaction friction issues present in traditional billing models.

[0066] Furthermore, the output field-level billing parameter details may specifically include generating and storing structured transaction audit logs. The audit logs record all key parameters and intermediate calculation results used in this billing process, and may include: supply and demand adjustment factors. The log includes field-level time-value coefficients for each payment field, and relevant parameters or result values ​​of the pricing correction function. This log can be implemented in various ways. For example, it can be stored in a relational database, creating a separate record for each transaction and setting corresponding fields for each parameter; alternatively, distributed ledger technology can be used to store the billing details of each transaction as blockchain data, providing greater security and transparency. When the transaction mode is per-transaction or batch transactions, the relevant parameters of the pricing correction function recorded in the audit log can be non-linear batch discount function values. .

[0067] Through the aforementioned technical solution, this application effectively addresses the problems of unexplainable billing, the inability of buyers to trace the details of cost composition, and the resulting lack of trust and transaction friction in traditional data transactions. By generating and storing structured transaction audit logs, the transparency of the billing process is significantly improved. Buyers can clearly view and verify every component of their payment, including dynamically changing supply and demand adjustment factors, field-level timeliness value coefficients reflecting data timeliness, and the specific application of various pricing adjustments (such as non-linear batch discounts). This comprehensive and traceable recording mechanism not only enhances buyers' trust in the billing results and reduces the risk of transaction disputes, but also provides data trading platforms with powerful auditing capabilities, ensuring the fairness and reliability of the billing system. Especially under the complex multi-level billing model constructed by the aforementioned dynamic data transaction billing method, the introduction of audit logs fully demonstrates the rationality of dynamic pricing.

[0068] This application proposes a multi-level, integrated, dynamic data transaction billing method. For the first time, it unifies the modeling of five dimensions—field value, timeliness curve, supply and demand elasticity, batch effect, and subscription risk—to form a computable, auditable, and learnable dynamic pricing engine. This aims to solve problems in traditional data transactions such as coarse pricing granularity, static value fixation, slow market response, and unexplainable billing. First, the method receives data query requests submitted by data requesters. These requests specify at least one payable field for billing and include the corresponding transaction data volume and contract period information. Data requesters can submit query requests in various ways, such as manually selecting the required fields and quantities through a graphical user interface (GUI) or sending requests in a structured format through an application programming interface (API). In one implementation, the system can provide a preset list of fields for users to select and allow users to manually input the desired transaction data volume and contract period. Second, for each payable field, the method obtains the corresponding field value density base price. The field value density base price can be obtained through a pre-configured static lookup table, where each field is assigned a fixed value density value. For example, during system initialization, the data administrator can manually set a basic value density parameter for each field based on factors such as sensitivity, scarcity, or industry importance. Further, a field-level time-sensitive value coefficient is configured based on the data of the paid fields. This function can be determined using a simplified linear decay model, where the older the data was generated, the greater the value decay. For example, a fixed daily decay rate can be set, and a linear decay coefficient can be calculated based on the difference between the data generation time and the current time. Simultaneously, this method obtains real-time supply and demand information related to the paid field or its dataset to determine the supply and demand adjustment factor. Real-time supply and demand information can be obtained by simply counting the number of queries to the paid field or its dataset within a specific time window (e.g., the past 24 hours). For example, if the number of queries for a certain field increases significantly in a short period, its demand can be considered to have increased, thus calculating a positive supply and demand adjustment factor. Based on this, the dynamic unit price of a single record is calculated based on the field value density base price, the field-level time-sensitive value coefficient, and the supply and demand adjustment factor. Subsequently, considering the transaction data volume and contract period, the dynamic unit price of each record is corrected based on a nonlinear batch discount function to calculate the total transaction price. Finally, this method outputs a detailed list of field-level billing parameters corresponding to the total transaction price. This list can be generated as a simple text log file, recording the total transaction price, as well as key information such as the field value density base price, field-level timeliness value coefficient, and supply and demand adjustment factor used in the calculation process.

[0069] The following embodiment will use non-small cell lung cancer (NSCLC) research data transactions as an example to illustrate the specific application of the multi-level integrated adjustment dynamic data transaction billing method proposed in this invention: Example of a biomedical scenario: A pharmaceutical company requested 10,000 data entries from a data platform for non-small cell lung cancer patients, generated five years prior, to evaluate the long-term efficacy and safety of targeted drugs in the real world. The dataset includes the following fields: Patient ID (system identifier, free), Age (free), Gender (free), Survival period (growing field, base value density 0.6 yuan, α=0.3 / year), Survival status (growing field, base value density 0.6 yuan, α=0.3 / year), EGFR mutation status (stable field, base value density 0.5 yuan), Medication history (stable field, base value density 0.8 yuan), Drug response (stable field, base value density 0.8 yuan), and Side effects (stable field, base value density 0.8 yuan).

[0070] The system calculates the time-value coefficient for each paid field: Lifespan and Life Status (Growth Type): EGFR mutation, medication records, drug response, and side effects (stable type): (Value does not change over time).

[0071] The basic unit price for a single record is: Since this dataset is for routine scientific research use, no supply and demand premium adjustment (S) will be applied. (t) = 1), the unit price for a single dynamic item remains at 5.200 yuan.

[0072] Applying a non-linear batch discount function : .

[0073] Final transaction price: P txn =10,000×5.200×(1-0.2398)=39,530.4 yuan.

[0074] The system synchronously outputs structured audit logs, details of which are shown in Table 1 below: Table 1 This embodiment uses dynamic adjustment of field-level timeliness value coefficients to accurately quantify the 91.6% value increase generated by the scarcity of long-term follow-up data (survival period / survival status), while maintaining a constant valuation for static clinical attributes (genotype, medication, etc.). Ultimately, under conditions of no market premium, a reasonable transaction price of RMB 39,530.4 is achieved after a 23.98% bulk discount. The audit log fully discloses the value contribution and adjustment factors of each field, ensuring a transparent and explainable pricing process, effectively addressing the core pain points of undervaluation and opaque pricing of long-term follow-up data in traditional data transactions.

[0075] Example of a financial credit reporting scenario: In a financial credit data transaction, a commercial bank requested 100,000 credit records of small and micro enterprises from a data platform for training its credit risk control model. This dataset includes the following fields: Unified Social Credit Code (free), registered capital (stable, Pbase=0.05 yuan, V=1.0), and repayment records for the past 24 months (growing, Pbase=0.8 yuan, α=0.6 / year, data generated 3 years ago). Number of overdue payments in the past 90 days (decaying type, Pbase=0.05, Vmin=0.2 yuan, λ=1.5 / year, data from 0.25 years ago). Due to the recent easing of credit policies for micro and small enterprises, the platform monitored that the query volume for this dataset in the past 7 days reached 3.5 times the daily average (β=0.4), indicating a supply and demand adjustment factor. .

[0076] The dynamic unit price for a single record is calculated as follows: .

[0077] Apply non-linear batch discounts: , Final transaction price .

[0078] The system's audit logs clearly show: repayment records show a 103% value increase due to a complete 3-year history; overdue data retains 75% value due to time-related degradation; market scarcity triggers a 100% premium; bulk purchases enjoy a 36.5% scale discount. This accurately reflects the dual characteristics of historical behavior value accumulation and recent risk sensitivity degradation in credit data, while reasonable pricing parameters control the total price within an acceptable range. The system synchronously outputs structured audit logs, details of which are shown in Table 2 below. Table 2 Example of a connected vehicle scenario: In such Figure 2In the example shown, a national property insurance company submitted a data procurement request to a municipal transportation big data platform, aiming to obtain 300,000 instances of anonymized vehicle driving characteristic data to develop a regional commercial vehicle risk scoring model.

[0079] The dataset fields include: anonymized vehicle ID (free), vehicle type (stable, base price 0.1 yuan), and frequency of rapid acceleration and braking in the past 30 days (attenuated, base price 0.6 yuan). V min =0.2 yuan, λ=8.0 / year, data generated 15 days ago. In that year, the value coefficient was revised to The percentage of nighttime driving time in the past 90 days (decaying type, base price 0.5 yuan, λ=6.0 / year, data generated 30 days ago, i.e., Δt=0.0822 years), with the value coefficient corrected to... ), cumulative mileage (increasing type, base price 0.8 yuan, α=0.5 / year, 3-year complete cycle, value coefficient adjusted to Historical accident frequency (stable type, base price 1.2 yuan), average driving speed statistics by time period (growing type, base price 0.4 yuan, α=0.4 / year, 3-year cycle). (and the area of ​​permanent operation (stable type, base price 0.1 yuan)). It is worth noting that the rapid acceleration / emergency braking data only comes from commercial vehicles that are required to have ADAS equipment installed, and the proportion of nighttime driving is calculated based on the cross-calculation of GPS trajectory timestamps and sunset time. Both types of data have legal collection and processing basis. The query volume for this type of data in the past 7 days has reached 3.0 times the platform's daily average. Combined with the market sensitivity coefficient β=0.2, the supply and demand adjustment factor is calculated as follows: S(t) =1+0.2×(3.0-1)=1.400, which triggers a 40.0% rigid demand premium.

[0080] The dynamic unit price for a single record is: In response to the massive procurement of 300,000 vehicles, Applying a non-linear batch discount function , The final transaction price was Yuan.

[0081] The system synchronously outputs structured audit logs, details of which are shown in Table 3 below: Table 3 It should also be noted that the application of this invention is premised on the data provider having collected and processed the relevant data through legal and compliant means (such as having performed necessary personal privacy desensitization, corporate business information declassification, and obtained the corresponding data usage rights or authorization). The data transaction billing method and system described in this invention aim to provide refined and dynamic pricing technology support for the market circulation of such compliant data resources.

[0082] To achieve the above objectives, the present invention also provides a multi-level integrated adjustment dynamic data transaction billing system, wherein implementing the dynamic data transaction billing method as described in any of the embodiments of the first aspect may include: a field metadata management module, a real-time market heat monitor, a dynamic billing engine, a field-level time-value coefficient learner, and an API or smart contract interface.

[0083] The field metadata management module is used to store and manage metadata related to data fields. Metadata may include field value density base price, field-level timeliness value coefficient, field weight parameters, and field free tag information, etc.

[0084] The real-time market heat monitor is used to continuously monitor query activities on the data trading platform. Based on the dynamic relationship between the query volume of the target data field or dataset and the platform's benchmark query volume within a preset time window, it calculates and outputs the real-time supply and demand adjustment factor.

[0085] The dynamic billing engine communicates with both the field metadata management module and the real-time market popularity monitor to perform the following operations: Receive data query requests, which must specify at least one payment field and include the corresponding transaction data volume and contract period information; For paid fields, the corresponding field value density base price is obtained from the field metadata management module, the field-level timeliness value coefficient is configured according to the data of the paid fields, and the real-time supply and demand adjustment factor is obtained from the real-time market heat monitor. Calculate the dynamic unit price of a single record based on the field value density base price, the field-level timeliness value coefficient, and the real-time supply and demand adjustment factor; By combining the transaction data volume and contract period, the dynamic unit price of a single record is corrected based on a nonlinear batch discount function, and the total transaction price of this data transaction is calculated.

[0086] The field-level timeliness value coefficient learner is connected to the field metadata management module and the dynamic billing engine. It is used to collect historical transaction data and buyer feedback ratings. The learner uses a machine learning model to perform reverse optimization on the field-level timeliness value coefficient and / or field weight coefficient, and updates the optimized parameters to the field metadata management module.

[0087] The API or smart contract interface connects to the dynamic billing engine to receive data query requests submitted by external systems and forward the requests to the dynamic billing engine. It also outputs the total transaction price and corresponding field-level billing parameter details calculated by the dynamic billing engine in a structured data format and supports automatic execution of transaction settlement through smart contracts.

[0088] The specific implementation methods have been described in detail above and will not be repeated here.

[0089] This application achieves refined billing logic in the following five dimensions through the aforementioned multi-level dynamic pricing formula: Differentiated billing based on field granularity: pricing is based on the intrinsic value density of each field, achieving precise billing based on the field used; Value changes dynamically over time: Through field-level timeliness value coefficients, the objective law of how data value changes over time is reflected, enabling time-based pricing; Market supply and demand elasticity adjustment: The price coefficient is dynamically adjusted based on real-time query popularity, so that the data price responds to the market scarcity and realizes a market-based pricing mechanism; Incentives for scale and stability: By using a non-linear batch discount function, price discounts are offered for bulk purchases and long-term contracts to encourage large-scale and stable cooperation in data resources; Risk-aware subscription pricing: Under the subscription model, risk discounts and cash discounting mechanisms are introduced to compensate for the potential risks and capital costs of future data delivery, ensuring the fairness and sustainability of long-term transactions.

[0090] This billing logic system systematically integrates the inherent attributes of data, timeliness characteristics, market signals, transaction volume, and contract risks, forming a complete, calculable, and interpretable dynamic data pricing system.

[0091] To achieve the above objectives, the present invention also provides a computer-readable storage medium storing computer-executable instructions or computer programs, which, when processed and executed, implement the multi-level integrated adjustment dynamic data transaction billing method described above.

[0092] The computer-readable storage medium is, for example, memory. Memory can be volatile or non-volatile, or it can include both volatile and non-volatile memory. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), which serves as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct rambus RAM (DRRAM).

[0093] If the integrated units in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in the aforementioned computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause one or more computer devices (which may be personal computers, servers, or network devices, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0094] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A multi-level integrated adjustment dynamic data transaction billing method, characterized in that, Includes the following steps: Receive data query requests submitted by data requesters, wherein the data query requests specify at least one payment field for billing and include the corresponding transaction data volume and contract period information; For the paid field, obtain the corresponding field value density base price, configure the field-level timeliness value coefficient according to the data of the paid field, and obtain real-time supply and demand information related to the paid field or its dataset to determine the supply and demand adjustment factor; Based on the field value density base price, the field-level timeliness value coefficient, and the supply and demand adjustment factor, calculate the dynamic unit price of a single record; Based on the transaction data volume and the contract period, the dynamic unit price of each record is corrected using a nonlinear batch discount function to calculate the total transaction price of this data transaction, and the field-level billing parameter details corresponding to the total transaction price are output.

2. The multi-level integrated regulation dynamic data transaction billing method according to claim 1, characterized in that, The set of fields to be retrieved, as indicated in the data query request, includes both paid and free fields; however, the request only pertains to the set of paid fields. During billing calculations, the free field is not included in the billing summation. The dynamic unit price for a single record is calculated using the following formula: ； Where i represents the unique identifier of the i-th record, i.e., the data record; t represents the time of inquiry or transaction in this transaction; Indicates the paid field Value density base price; Indicates the paid field Weighting coefficients; Indicates the paid field For the field-level timeliness value coefficient at transaction time t, one of the following three types of functions is configured according to the field semantic type: For attenuation type fields ; For the time-specific decay rate of the field, V min Set the minimum value coefficient for this field as a percentage of the base price; For stable fields, ; For growth fields Or, in the form of a sigmoid with a saturation upper limit: ; in, or or or This is a field-specific growth parameter; The supply and demand adjustment factor, used to reflect real-time scarcity, is calculated using the following formula: ； in, This represents the query volume for this dataset over the past N days. This represents the platform's average daily query volume. To adjust sensitivity.

3. The dynamic data transaction billing method according to claim 2, characterized in that, Data query requests are categorized into per-transaction, batch, or subscription transaction modes, where: The per-transaction or batch transaction mode, combined with the transaction data volume in the request, applies a non-linear batch discount function to correct the total cost calculated based on the dynamic unit price of the single record, and calculates the total price of the per-transaction transaction; The subscription transaction model calculates the subscription period price based on the contract period in the request, the predicted future period unit price, the data delivery volume, and a combination of risk discounts and cash discounts.

4. The multi-level integrated adjustment dynamic data transaction billing method according to claim 3, wherein the total transaction price per transaction in the per-transaction or batch transaction mode is calculated using the following formula: ； N represents the amount of transaction data, i.e., the number of records returned. Nonlinear batch discount function, i.e., batch-period joint discount function Include: Usage discount And long-term contract premium; If the contract period Additional discounts will be offered. .

5. The dynamic data transaction billing method according to claim 3, characterized in that, The subscription period price for the aforementioned subscription transaction model is calculated using the following formula: ； K represents the number of billing cycles during the subscription period; This represents the expected future unit price based on historical decline trends. The expected number of deliveries in period k; risk discount factor. ; This refers to the financial discount rate.

6. The multi-level integrated regulation dynamic data transaction billing method according to claim 1, characterized in that, Before obtaining the field value density base price and determining the field-level timeliness value coefficient, the method further includes: reading configuration parameters related to the paid field from the field metadata management module; wherein, the configuration parameters include at least one of the following: field value density base price, field-level timeliness value coefficient, and field weight coefficient; the field metadata management module also stores the field's free tag information to distinguish between paid fields and free fields.

7. The dynamic data transaction billing method according to claim 1 or 6, characterized in that, It also includes parameter optimization steps: Collect historical transaction pricing data and buyer feedback ratings; Based on price data and feedback ratings, the field-level timeliness value coefficient and / or the field weight coefficient are optimized and updated in reverse using a machine learning model.

8. The dynamic data transaction billing method according to claim 1, characterized in that, The output field-level billing parameter details specifically include generating and storing structured transaction audit logs; wherein, the audit logs record all key parameters and intermediate calculation results used in this billing, including at least one of them: supply and demand adjustment factors. Field-level timeliness value coefficients for each paid field The relevant parameters or result values ​​of the pricing correction function; when the transaction mode is per transaction or batch transaction, the relevant parameters of the pricing correction function recorded in the audit log are the values ​​of the nonlinear batch discount function. .

9. A multi-level integrated adjustment dynamic data transaction billing system, characterized in that, Implementing the dynamic data transaction billing method as described in any one of claims 1 to 8, comprising: The field metadata management module is used to store and manage metadata related to data fields. The metadata includes at least: field value density base price, field-level timeliness value coefficient, field weight parameters, and field free tag information. The real-time market heat monitor is used to continuously monitor query activities on the data trading platform. Based on the dynamic relationship between the query volume of the target data field or dataset and the platform's benchmark query volume within a preset time window, it calculates and outputs the real-time supply and demand adjustment factor. The dynamic billing engine, which communicates with both the field metadata management module and the real-time market popularity monitor, performs the following operations: Receive a data query request, wherein the request specifies at least one payment field and includes the corresponding transaction data volume and contract period information; For the paid field, the corresponding field value density base price is obtained from the field metadata management module, the field-level timeliness value coefficient is configured according to the data of the paid field, and the real-time supply and demand adjustment factor is obtained from the real-time market heat monitor. Based on the field value density base price, the field-level timeliness value coefficient, and the real-time supply and demand adjustment factor, calculate the dynamic unit price of a single record; Based on the transaction data volume and the contract period, the dynamic unit price of the single record is corrected using a nonlinear batch discount function to calculate the total transaction price of this data transaction; A field-level timeliness value coefficient learner is connected to the field metadata management module and the dynamic billing engine. It is used to collect historical transaction data and buyer feedback ratings, perform reverse optimization on the field-level timeliness value coefficient and / or field weight coefficient through a machine learning model, and update the optimized parameters to the field metadata management module. An API or smart contract interface is connected to the dynamic billing engine to receive data query requests submitted by external systems and forward the requests to the dynamic billing engine. It also outputs the total transaction price and corresponding field-level billing parameter details calculated by the dynamic billing engine in a structured data format and supports automatic execution of transaction settlement through smart contracts.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions or computer programs, which, when processed and executed, implement the dynamic data transaction billing method of multi-level integrated adjustment as described in any one of claims 1 to 8.

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