Revenue distribution method and device, electronic equipment and medium

Abstract

The embodiment of the invention discloses a revenue distribution method and device, electronic equipment and a medium, and relates to the technical field of data transaction. A specific embodiment of the method comprises the steps of determining a total transaction income when at least one data supply node and a data demand node transact a target data set through a data operation node and a first transaction suggestion income of a first sub-data set in the target data set, wherein the first sub-data set is any one sub-data set in the target data set; determining a first distribution proportion of the data operation node and a second distribution proportion of a first data supply node in the at least one data supply node based on the market reference information and the first transaction suggestion income; and based on the total transaction revenue, the first distribution proportion and the second distribution proportion, determining the operation revenue of the data operation node and the supply revenue of the first data supply node. And fair and reasonable distribution of the total revenue between the data supply node and the data operation node is realized.

Description

Technical Field

[0001] This application relates to the field of data transaction technology, specifically to a revenue distribution method, apparatus, electronic device, and medium. Background Technology

[0002] In data circulation and trading scenarios, the core role of a data trading platform is to bridge the gap between data demanders and data providers by matching data needs. Since the target dataset for a single data demand often requires the joint supply of multiple data providers, how to achieve a reasonable distribution of revenue between the data platform and these multiple data providers, thereby effectively incentivizing data providers to output high-quality data and enabling the data platform to continuously optimize its service quality, has become a critical issue that urgently needs to be addressed in the current data trading field.

[0003] Currently, the distribution of revenue from data transactions only focuses on the revenue sharing among data providers. There is no revenue sharing and settlement scheme that simultaneously covers both the data platform and the data provider, which fails to adequately realize the value of both platform services and data resources. Summary of the Invention

[0004] This application provides a method, apparatus, electronic device, and medium for distributing revenue, in order to address the problem of insufficient revenue distribution in data transactions in related technologies.

[0005] In a first aspect, embodiments of this application provide a method for revenue distribution, the method comprising: determining the total transaction revenue of at least one data supply node and a data demand node when trading a target dataset through a data operation node, and a first transaction suggestion revenue of a first subset of the target dataset, wherein the first subset is any subset of the target dataset; determining a first allocation ratio of the data operation node and a second allocation ratio of the first data supply node among at least one data supply node based on market reference information and the first transaction suggestion revenue; and determining the operation revenue of the data operation node and the supply revenue of the first data supply node based on the total transaction revenue, the first allocation ratio, and the second allocation ratio.

[0006] In some embodiments, determining a first allocation ratio for data operation nodes and a second allocation ratio for a first data supply node among at least one data supply node, based on market reference information and transaction suggestion revenue from a subset of data, includes: determining a first allocation ratio for data operation nodes based on market reference information and a first adjustment factor, wherein the first adjustment factor is determined based on operational characteristic data of the data operation nodes; determining a Shapley value for a first data supply node based on the first transaction suggestion revenue; and determining a second allocation ratio for the first data supply node based on the Shapley value and a second adjustment factor, wherein the second adjustment factor is determined based on supply characteristic data of the first data supply node.

[0007] In some embodiments, the market reference information includes the reference transaction fee rate and the number of reference operating nodes. Determining the first allocation ratio of data operating nodes based on the market reference information and the first adjustment factor includes: determining the first basic allocation ratio of data operating nodes based on the reference transaction fee rate and the number of reference operating nodes; determining the first adjustment factor based on the operating characteristic data of data operating nodes; and adjusting the first basic allocation ratio according to the first adjustment factor to obtain the first allocation ratio of data operating nodes.

[0008] In some embodiments, the first adjustment factor includes at least one of a relationship adjustment factor, a size adjustment factor, and a transaction data quality factor. Determining the first adjustment factor based on the operational characteristic data of the data operation node includes: determining the relationship adjustment factor based on the relationship type between the data operation node and at least one data supply node in the operational characteristic data; determining the transaction data quality factor based on the data quality score of the target dataset in the operational characteristic data; and determining the size adjustment factor based on the transaction data of the target dataset in the operational characteristic data.

[0009] In some embodiments, determining the Shapley value of the first data supply node based on the revenue from the first transaction recommendation includes: determining a first contribution value of the first data supply node based on the revenue from the first transaction recommendation; and determining the Shapley value of the first data supply node based on the first contribution value and a preset weighting factor, wherein the preset weighting factor is determined based on the number of combinations of data supply combinations that do not include the first data supply node.

[0010] In some embodiments, determining the second allocation ratio of the first data supply node based on the Shapley value and the second adjustment factor includes: determining the second basic allocation ratio of the first data supply node based on the Shapley value; determining the second adjustment factor of the second basic allocation ratio based on the supply characteristic data of the first data supply node; adjusting the second basic allocation ratio based on the second adjustment factor to obtain the third allocation ratio of the first data supply node; and determining the second allocation ratio based on the third allocation ratio and the first allocation ratio of the data operation node.

[0011] In some embodiments, the second adjustment factor includes at least one of a supply data quality factor, a data importance adjustment factor, and a data subject characteristic adjustment factor. The second adjustment factor for determining the second basic allocation ratio based on the supply characteristic data of the first data supply node includes: determining a supply data quality factor based on the quality score of the first supply data corresponding to the first data supply node in the supply characteristic data; determining a data importance adjustment factor based on the combined data of the first data supply node in the supply characteristic data; and determining a data subject characteristic adjustment factor based on the credit data and supply participation data of the first data supply node in the supply characteristic data.

[0012] Secondly, embodiments of this application provide a revenue distribution device, the device comprising: The first determining unit is used to determine the total transaction revenue when at least one data supply node and data demand node trade the target dataset through the data operation node, as well as the first transaction recommendation revenue of the first subset of the target dataset, wherein the first subset is any subset of the target dataset. The second determining unit is used to determine, based on market reference information and the returns of the first transaction suggestion, a first allocation ratio of the data operation node and a second allocation ratio of the first data supply node among at least one data supply node. The allocation unit is used to determine the operating revenue of the data operation node and the supply revenue of the first data supply node based on the total transaction revenue, the first allocation ratio, and the second allocation ratio.

[0013] Thirdly, embodiments of this application provide an electronic device, including: a processor and a memory for storing a computer program capable of running on the processor, wherein, when the processor runs the computer program, it performs the method described in any embodiment of the first aspect.

[0014] Fourthly, embodiments of this application provide a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to perform the methods described in any embodiment of the first aspect.

[0015] Fifthly, embodiments of this application provide a computer program product including a computer program that, when executed by a processor, performs the method described in any embodiment of the first aspect.

[0016] This application provides a revenue distribution method that determines the total transaction revenue when at least one data supply node and a data demand node trade a target dataset through a data operation node, as well as the first suggested transaction revenue for a first subset of the target dataset, where the first subset can be any subset of the target dataset. Based on market reference information and the first suggested transaction revenue, a first allocation ratio for the data operation node and a second allocation ratio for the first data supply node among at least one data supply node are determined. Based on the total transaction revenue, the first allocation ratio, and the second allocation ratio, the operating revenue of the data operation node and the supply revenue of the first data supply node are determined. This application constructs a dynamic dual-role allocation mechanism in data transactions that simultaneously covers both data operators and data suppliers, achieving a fair and reasonable distribution of total revenue between data supply nodes and data operation nodes.

[0017] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are merely embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort, and this application can be applied to other similar scenarios based on the provided drawings.

[0019] Figure 1 A flowchart illustrating a revenue distribution method provided in this application embodiment; Figure 2 A flowchart illustrating the second revenue distribution method provided in this application embodiment; Figure 3 A schematic diagram illustrating an embodiment of this application for obtaining a first allocation ratio; Figure 4 A schematic diagram illustrating an embodiment of this application for obtaining a second allocation ratio; Figure 5 A schematic diagram of a comparison table of improvements to the PageRank method provided in an embodiment of this application; Figure 6 A schematic diagram illustrating a specific revenue distribution method provided in an embodiment of this application; Figure 7 A functional block diagram of a specific revenue distribution system provided in this application embodiment; Figure 8 A schematic diagram of a specific revenue distribution system provided in this application embodiment; Figure 9 A schematic diagram of the structure of a revenue distribution device 900 provided in an embodiment of this application; Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0020] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. The described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0021] It should be noted that the terms "system," "device," "unit," and / or "module" used in this application are methods of distinguishing different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they can be replaced by other expressions.

[0022] Hereinafter, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first," "second," or "third" may explicitly or implicitly include one or more of that feature.

[0023] In the process of data circulation and trading, the core function of a data trading platform is to establish a connection channel between data demanders and data providers by matching data needs. However, the target dataset corresponding to a single data demand often requires the joint supply of multiple data providers. Therefore, how to achieve a reasonable distribution of data transaction revenue among the data platform and multiple data providers, thereby effectively incentivizing data providers to output high-quality data and enabling the data platform to continuously optimize service quality, has become a core issue that urgently needs to be addressed.

[0024] Currently, research on revenue-sharing and settlement methods for data platforms is scarce. Only a few patents focus on revenue distribution designs among data providers, but no revenue-sharing and settlement scheme covering both data platforms and data providers has yet been developed. Furthermore, these related technologies employ static allocation logic, ignoring the dynamic changes in data value due to factors such as scenario and timeliness, resulting in a lack of scientific allocation methods in practice. Simultaneously, these technologies do not incorporate blockchain technology to control the data acquisition and transfer process, making it difficult to effectively guarantee the authenticity and security of the data. The automation level of the evaluation and settlement process is also relatively low, hindering the efficiency and credibility of data transactions.

[0025] To address the problems in existing technologies, this application proposes a revenue distribution method. By utilizing market reference information, a basic distribution ratio and adjustment factor for the data operation phase are determined. The basic distribution ratio and dynamic adjustment factor for each data supply node are determined using the Shapley value and dynamic correction coefficients (data importance coefficient, data quality coefficient, data provider characteristic coefficient, etc.) based on the transaction suggested revenue for each subset of data. Based on this, and combined with the total transaction revenue, the revenue sharing and settlement ratio (first distribution ratio and second distribution ratio) and settlement amount (operational revenue and supply revenue) for the data operation phase and each data supply node are determined. Furthermore, this application utilizes blockchain technology to construct a dynamic revenue sharing and settlement system (i.e., a revenue distribution system) for the data platform, ensuring the authenticity and confidentiality of data acquisition and the traceability of the entire revenue sharing and settlement process. This helps to scientifically and rationally determine the contributions of each participant, thereby reasonably determining the revenue sharing and settlement ratio and amount.

[0026] The following section provides a detailed description of a revenue distribution method provided in this application, with reference to the accompanying drawings.

[0027] Figure 1 A flowchart illustrating a revenue distribution method provided in an embodiment of this application is shown. Figure 1 As shown, the profit distribution method includes steps 101-103.

[0028] Step 101: Determine the total transaction revenue of at least one data supply node and data demand node when trading the target dataset through the data operation node, as well as the first transaction recommendation revenue of the first subset of the target dataset, where the first subset is any subset of the target dataset.

[0029] In the embodiments of this application, a data supply node refers to an entity (such as an enterprise, institution, or individual) that provides the data resources required for the target dataset. It can also be understood as a data provider. The data provided by all data supply nodes constitutes the target dataset.

[0030] Data demand nodes refer to entities (such as enterprises and research institutions) that propose data usage needs and pay transaction fees. They can also be understood as data demanders and are the payers of the total transaction revenue.

[0031] Data operation nodes refer to the platform entities that build data trading scenarios, provide demand matching, facilitate transactions, and ensure transaction security. They can also be understood as data platforms, whose core function is to connect supply and demand sides and provide transaction services.

[0032] Target dataset It refers to a complete dataset proposed by a data demand node that meets its specific needs, which is formed by a combination of data provided by one or more data supply nodes (for example, a demander needs consumption data of a first-tier city in a certain year, which is composed of consumption data A provided by node A, consumption data B provided by node B, etc.).

[0033] Total transaction revenue This refers to the total cost paid by data demand nodes to acquire the target dataset. It is the core basis for revenue distribution and can be determined through data operation node matching pricing or automatic system pricing.

[0034] First Subset This refers to any subset of the target dataset, which can be provided independently by a single data supply node or jointly by multiple data supply nodes (for example, if the target dataset is consumption data for East China in 2024, the first subset could be consumption data for a certain region provided independently by node A, or consumption data for a certain region jointly provided by nodes A and B, or consumption data for a certain region jointly provided by nodes C and B). Its core function is to serve as the basic unit for quantifying the contribution of data supply nodes. Whether supplied independently by a single node or jointly by multiple nodes, the contribution value of the data supply node is derived from the transaction suggested revenue of this subset, providing the core basis for subsequent allocation ratio calculations.

[0035] First trading suggestion: profit. It refers to the market reference value of the first subset of data, used to quantify the contribution of each subset to the target dataset, and to provide a basis for the allocation ratio of subsequent data supply nodes.

[0036] Step 102: Based on market reference information and the first transaction recommendation profit, determine the first allocation ratio of the data operation node and the second allocation ratio of the first data supply node among at least one data supply node.

[0037] In the embodiments of this application, the application may determine the first allocation ratio of data operation nodes based on market reference information, and the application may also determine the second allocation ratio of each data supply node based on the revenue from the first transaction suggestion.

[0038] The first data supply node is any one of at least one data supply nodes.

[0039] Market reference information refers to the market-based basis used to calculate the allocation ratio of data operation nodes, including the number of similar data trading platforms (market reference operation nodes) (number of reference operation nodes) and transaction rates (reference transaction rates).

[0040] First allocation ratio This refers to the proportion of revenue that a data operation node should be allocated to in the total transaction revenue, used to reflect the value of the transaction services provided by the data operation node (such as demand matching, transaction security, platform maintenance, etc.).

[0041] Second allocation ratio It refers to the proportion of the revenue that the first data supply node should be allocated to in the total transaction revenue. It is used to reflect the resource value of the subset of data provided by the node and is a proportion determined after adjustment based on the quantitative results of its contribution.

[0042] Step 103: Based on the total transaction revenue, the first allocation ratio, and the second allocation ratio, determine the operating revenue of the data operation node and the supply revenue of the first data supply node.

[0043] In the embodiments of this application, after the total transaction revenue is determined, the platform takes away the corresponding share (operating revenue) according to the first allocation ratio, and each supply node takes away the corresponding share (supply revenue) according to its own second allocation ratio. The sum of the revenue of all roles equals the total transaction revenue, ensuring a closed-loop allocation logic without omissions or duplicate allocations.

[0044] Operating revenue This refers to the final revenue amount that the data operation node receives based on the first allocation ratio.

[0045] Supply revenue This refers to the revenue that the first data supply node ultimately receives based on the second allocation ratio.

[0046] In summary, based on the revenue distribution method proposed in this application, a revenue sharing and settlement framework with two roles is constructed by clearly defining the core inputs of total transaction revenue and the revenue from suggested transactions of subset datasets, accurately calculating the allocation ratio of data operation nodes (matching their matching services, transaction guarantees, and other values) in conjunction with market reference information, and determining their allocation ratio based on the contribution quantification results of data supply nodes (matching the actual value of their data resources). This not only fills the gap in related technologies regarding dual-role revenue distribution methods, but also adapts to the fluctuating characteristics of data value through market-based references and dynamic contribution quantification logic, ensuring fair distribution, effectively stimulating the enthusiasm of all parties to participate in data circulation and trading, and providing technical support for the standardized and efficient development of the data trading market.

[0047] based on Figure 1 The embodiment shown, Figure 2 A flowchart of the second revenue distribution method is further shown. Figure 2 based on Figure 1 The illustrated embodiment further defines step 102. Figure 2 In the illustrated embodiment, step 102 includes steps 202 and 203, and steps 207 and 208. For example... Figure 2As shown, the method includes the following steps: Step 201: Determine the total transaction revenue when at least one data supply node and data demand node trade the target dataset through the data operation node, and the first transaction recommendation revenue of the first subset of the target dataset, wherein the first subset is any subset of the target dataset.

[0048] In this embodiment of the application, the target dataset can be formed through system pricing and platform-matched transactions. The final transaction price (i.e., the total transaction proceeds) Total transaction revenue As the starting point for revenue sharing and settlement, participate in the revenue sharing and settlement process.

[0049] This application can also automatically generate a target dataset through system pricing. Each subset The system's suggested price (i.e., the first transaction suggested profit of the first subset of the dataset) This will serve as the basis for subsequent contribution allocation.

[0050] Optionally, this application can accurately determine the total transaction revenue through market-based and multi-dimensional pricing logic. and the first trading recommendation profit This provides an objective and quantifiable basis for subsequent revenue distribution. It avoids unfair distribution caused by subjective pricing decisions and ensures that the price truly reflects the actual value of the dataset.

[0051] Total transaction revenue The determining logic is to adopt a dual model of system pricing and platform matching, which combines customer objectivity and flexibility.

[0052] The system's pricing is based on market reference transaction cases. It automatically generates an objective benchmark price by calculating a weighted average of the reference price multiplied by multiple adjustment coefficients.

[0053] The specific formula is as follows: ; Where i represents the market data available for reference; m represents the number of market data available for reference. The transaction price of data transaction i is available for reference; The data feature adjustment coefficients for transaction i are available for reference. That is, the ratio of the number of features in the pricing dataset N to the number of features in the reference transaction dataset i; This is a data size adjustment factor for data transaction i that can be used as a reference. That is, the ratio of the size of the pricing dataset N to the size of the reference transaction dataset i; This serves as a data quality adjustment factor for data transactions i, which can be used as reference data. , which is the ratio of the data quality score of the priced dataset N to the data quality score of the reference transaction dataset i; This is a transaction time adjustment factor for transaction i, which can be used as reference data. That is, considering the pricing time point The time value factor between the reference transaction time and the reference transaction time.

[0054] Platform matching refers to the process by which supply and demand parties can negotiate and adjust the system's pricing if they disagree, ultimately reaching a mutually agreed-upon transaction price. This ensures that the price reflects the actual transaction intention.

[0055] Understandably, the first transaction recommendation in this application yields profits. The determination logic and The pricing logic is consistent and is tailored to the target dataset. Each first subset of the data after splitting Each system individually matches a market reference trading case and calculates its suggested price using the aforementioned multi-dimensional adjustment coefficients. .For example: Composed of 3 Composition, calculate each separately of ,all of sum and The differences are fine-tuned through platform matching to ensure that the value of sub-data is consistent with the overall value logic. Simply put, this step is equivalent to giving the complete dataset... and each sub-dataset Value assessment: By referencing market cases, we then adjust the differences between the two in terms of characteristics, scale, quality, and timeliness, and finally obtain a price that reflects the true value, laying the core foundation for subsequent profit sharing based on value.

[0056] Step 202: Based on market reference information and the first adjustment factor, determine the first allocation ratio of the data operation nodes. The first adjustment factor is determined based on the operational characteristic data of the data operation nodes.

[0057] In this embodiment, the data operation node can specifically be a secure and reliable venue provided by the data platform construction and operation party for data transactions, thereby improving transaction efficiency. For example... Figure 3 The diagram shown illustrates how this application obtains the first allocation ratio. (Refer to...) Figure 3 The first allocation ratio (i.e., platform revenue sharing ratio) of the data operation node in this application can be calculated by multiplying the first basic allocation ratio (i.e., basic ratio) and the first adjustment factor (adjustment coefficient).

[0058] This application can first determine the initial basic allocation ratio of data operation nodes based on market reference information. In this application, market reference information refers to the market-based basis used to calculate the basic allocation ratio of data operation nodes, and its core includes the reference transaction fee rate and the number of reference operation nodes. Reference operation nodes refer to similar data trading platforms (such as government data trading platforms or enterprise data sharing platforms in the same industry) that have the same type of data operation nodes as the data operation nodes in this application and similar service scenarios.

[0059] Specifically, this application can calculate the first basic allocation ratio using the following formula. : , in, The first basic allocation ratio for data operation nodes (i.e., the basic contribution ratio of the data platform). is the reference transaction fee rate for the i-th market reference operation node (i.e., the transaction fee rate of the i-th referenced data trading platform); N is the number of reference operation nodes (i.e., the number of referenced data trading platforms).

[0060] Based on the initial allocation ratio, this application can further determine a first adjustment factor according to the relationship between the data supply node (data provider) and the data operation node (data platform), the size of the transaction dataset, and the quality of the transaction data, and adjust the initial allocation ratio according to the first adjustment factor to determine the final initial allocation ratio (i.e., the data platform contribution ratio). The specific formula for determining the initial allocation ratio is as follows: , in, 1 is the first allocation ratio (i.e., the basic contribution ratio of the data platform); r is the relationship adjustment factor (i.e., the relationship adjustment coefficient between the data provider and the data platform); s is the size adjustment factor (i.e., the size adjustment coefficient of the transaction dataset); q is the transaction data quality factor (i.e., the transaction data quality score (out of 100)).

[0061] Specifically, this application can determine the relationship adjustment factor based on the relationship type between data operation nodes and at least one data supply node in the operational characteristic data, namely: ; This application can also determine the scale adjustment factor based on the transaction data of the target dataset in the operational characteristic data, that is: ; This application can also determine the transaction data quality factor based on the data quality score of the target dataset in the operational characteristic data, that is: .

[0062] Step 203: Based on the first transaction recommendation return, determine the Shapley value of the first data supply node, and based on the Shapley value and the second adjustment factor, determine the second allocation ratio of the first data supply node. The second adjustment factor is determined based on the supply characteristic data of the first data supply node.

[0063] In this embodiment, the application can accurately capture the actual value of the first data supply node in a collaborative scenario using the marginal contribution method. The value of data is often reflected in combined applications. Data from a single node may not meet the needs, but if its addition to a combination significantly improves the usability and completeness of the combined dataset, then that node should receive higher recognition for its contribution. The specific logic is as follows: The data operation node first matches the needs of the data demand node with its own data reserves, and then integrates the data provided by the N data supply nodes into the target dataset through methods such as splitting and combining. Therefore, the value of the target dataset is essentially the sum of the collaborative value of all participating nodes.

[0064] Iterate through all possible combinations of non-empty data supply nodes S, and assign a corresponding contribution value C(S) to each combination S (i.e., the combination forms a subset of the dataset). First trading recommendation profit This ensures that the value of each collaborative scenario can be quantified.

[0065] For the first data supply node (node ​​i), calculate its value increment in each combination S. That is, the difference between the contribution value C(S∪{i}) of the new combination (S∪{i}) after node i joins combination S and the contribution value C(S) of the original combination S before node i joins. This difference is the value increment of node i in that combination. The formula for the first contribution value in this scenario is: Where C(S∪{i}) is the overall contribution value of the data provider combination S after the first data provider node i is added; C(S) is the contribution value of the data provider combination S excluding the first data provider node i, and C(S) is determined based on the revenue from the first transaction suggestion. That is, the contribution value of the data provider combination is determined through the system pricing of the data provider combination dataset, i.e., C(N) = C(S) = The specific system pricing method can be found in the market adjustment method in step 201, and will not be elaborated here.

[0066] After obtaining the contribution value of a single data provider (i.e., the first contribution value of the first data supply node), this application can determine the Shapley value of each data supply node (i.e., the Shapley value of the first data supply node) based on the contribution value of each data supply node and a preset weighting factor.

[0067] The formula for calculating the Shapley value of each data supply node i is as follows: , in, Provide the Shapley value for the first data supply node i; S is the data provider combination that does not include the first data provider node i (i.e., the data provider combination); The non-empty subset of; w(S) is the preset weight factor.

[0068] The formula for calculating the preset weighting factor is: , in, The number of data supply combinations that do not include the first data supply node i. for The number of non-empty subsets.

[0069] In this application, as Figure 4 The diagram shown illustrates how this application obtains the second allocation ratio. (Refer to...) Figure 4 The basis for determining the revenue sharing ratio among data providers is the Shapley value calculation result (i.e., the Shapley value of the first data supply node). The allocation ratio among data providers is determined by combining dynamic second adjustment factors such as data importance, data quality, and supplier characteristics (i.e., the second allocation ratio). The remaining ratio after the platform's share is dynamically allocated based on this.

[0070] Specifically, this application can determine the contribution base ratio (i.e., the second base allocation ratio) of the first data supply node based on the Shapley value of the first data supply node determined above, as shown in the following formula: , in, The contribution base ratio for the first data supply node i (i.e., the second base allocation ratio); M represents the Shapley value of the data supply node; M is the number of data supply nodes involved in the transaction.

[0071] Furthermore, based on the second basic allocation ratio, this application can also dynamically adjust the second basic allocation ratio according to the data importance assessment results, the data provider's data quality assessment results, and the data provider's characteristics (including the data provider's reputation and platform participation), etc., to determine the final adjusted data provider contribution ratio (i.e., the third allocation ratio). The specific formula is as follows: , in, The second basic allocation ratio for the first data supply section; This is the data importance assessment result based on the improved PageRank (i.e., the data importance adjustment factor). Provide data quality scores (i.e., supply data quality factors) for data supply nodes. The data supply node subject characteristic adjustment coefficient (i.e., data subject characteristic adjustment factor).

[0072] Optionally, in this application, the data importance adjustment factor can be calculated based on an improved PageRank, going beyond the scope of the current transaction data combination. From the overall perspective of the data platform, a method for dynamically evaluating the data importance of a specific data provider is constructed based on historical data combinations. The traditional PageRank formula is as follows: , in, For nodes The weights are: N = number of summary points; d = damping coefficient. Pointing to a node The set of nodes; For nodes The number of outgoing chains.

[0073] Compared to the traditional PageRank method, the improvements in this application are mainly reflected in four aspects: node definition, edge weight, decay factor, and value propagation direction. For example... Figure 5 The image shows a diagram illustrating the comparison of improvements to the PageRank method.

[0074] Based on the traditional PageRank formula, the improved formula for calculating the data importance adjustment factor is as follows: , in, The importance assessment adjustment factor for data i (i.e., the data importance adjustment factor for data i). The historical number of times data i was combined with other datasets; The x-th combination is the number of days from the current time; the first The total number of data sets; The damping coefficient; The first set consists of data that have been combined with data i; the second set consists of data that have been combined with data i. The historical number of times data j has been combined with other datasets; The importance assessment adjustment coefficient for data j (i.e., the data importance adjustment factor for data j).

[0075] Optionally, the data subject characteristic adjustment factor in this application It can be calculated using the following formula: , in, The credit adjustment factor for data providers is set with reference to corporate credit ratings and international credit rating classifications: ; The data provider engagement adjustment factor is calculated using the following formula: , in, T represents the number of transactions in which the data provider participated; T represents the total number of transactions completed by the platform.

[0076] Based on the aforementioned dynamically adjusted data provider contribution ratio (i.e., the third allocation ratio) and the final data platform contribution ratio (i.e., the first allocation ratio) determines the final contribution ratio of each data provider (i.e., the second allocation ratio of the first data supply node).

[0077] , Where N is the total number of data providers participating in the transaction.

[0078] Step 204: Based on the total transaction revenue, the first allocation ratio, and the second allocation ratio, determine the operating revenue of the data operation node and the supply revenue of the first data supply node.

[0079] In this embodiment of the application, the application can be based on the total transaction revenue. The final contribution ratio of the data platform And the final contribution ratio of each data provider. Calculate and confirm the dynamic revenue sharing settlement result (i.e., operating revenue) for this transaction. and supply revenue ): Data platform division Data provider revenue sharing .

[0080] In summary, the revenue distribution method proposed in this application fully considers the service value of data operation nodes (data platforms) in data transactions and the data resource value of data supply nodes (data providers). Based on the collaborative revenue logic of the two roles, a market adjustment method is used to achieve a scientific division of transaction revenue between the two: First, referring to market reference information and combining the first adjustment factor of data supply nodes and data operation nodes, the basic contribution ratio and final adjustment ratio of data operation nodes are determined, and "1 - data operation node contribution ratio" is used as the total revenue base of data supply nodes; then, for individual data supply nodes, their Shapley value is calculated based on the system pricing of the data supply combination dataset, and combined with the second adjustment factor, such as the data importance coefficient, data quality coefficient, and data provider subject characteristic coefficient determined by the PageRank algorithm (which incorporates a dynamic decay mechanism and time factor, breaking through the traditional isolated calculation mode and capturing the value of data combination structure), the basic contribution ratio and dynamic adjustment ratio of each data supply node are determined; finally, the final revenue settlement ratio of an individual data supply node is determined by the calculation method of "(1 - data operation node contribution ratio) × adjusted data supply node contribution ratio", achieving a precise match between the value and revenue of the two roles.

[0081] For ease of understanding, such as Figure 6 As shown, this application provides a schematic diagram of a specific method for distributing profits. (Refer to...) Figure 6 The main processes of the revenue distribution method in this application include five stages: inputting the matching transaction price / segment system pricing price, determining and adjusting the basic contribution ratio of the data platform, evaluating the contribution of data providers, determining and dynamically adjusting the contribution ratio of each data provider, and outputting the dynamic revenue sharing settlement results of the data platform. The specific implementation steps for each stage can be found in [reference needed]. Figures 1 to 2 The embodiments shown will not be described in detail here.

[0082] based on Figures 1 to 2 The illustrated embodiments, such as Figure 7 As shown, this application provides a specific functional module diagram of a revenue distribution system.

[0083] This application can establish a data platform dynamic revenue sharing and settlement system (i.e., revenue distribution system) based on blockchain technology, based on the above-mentioned dynamic revenue sharing and settlement method of data operation nodes based on improved Shapley value. It includes four modules: data provider (data supply node), data platform (data operation node), data demander (data demand node), and underlying blockchain and other technical support.

[0084] Reference Figure 7 , Figure 7Specifically, this is a dynamic data platform revenue sharing and settlement system module based on blockchain technology. This module encompasses the entire process of establishing, applying, and outputting the data platform's revenue sharing and settlement algorithm. It provides a shared and universal blockchain underlying application consortium blockchain environment. From the underlying technical architecture, it deploys a unified CA, unified key algorithm, and smart contracts for blockchain applications. Identity authentication is performed through a unified gateway, enabling user permission authentication for the revenue sharing and settlement chain, as well as the reading, retrieval, and sharing of revenue sharing and settlement results.

[0085] like Figure 8 The diagram shown is a specific profit distribution system provided in this application.

[0086] Reference Figure 8 , Figure 8 Specifically, it is a data platform dynamic revenue sharing and settlement system based on blockchain technology. Figure 8 The functions of each module are as follows: 1) Build a distributed blockchain application underlying environment based on a consortium blockchain structure, where each participating entity joins the consortium blockchain after obtaining authorization. To facilitate the sharing and use of evaluation results among node areas, establish various unified basic capability modules, including a unified CA module, a unified key algorithm, and unified SDK and API interfaces, to achieve low-cost and high-efficiency cross-chain data interoperability and sharing between blockchains.

[0087] 2) Public Cloud Resource Pool: Provides cloud resources for the entire blockchain architecture. Deploys blockchain node software on the public cloud resource pool. Adopts a multi-channel chain building method under a single virtual machine to achieve independent process management of each channel application without mutual interference. It is used to support the establishment of multiple evaluation chains based on consortium blockchains.

[0088] 3) Smart Contracts: By signing and executing smart contracts on the blockchain regarding the dynamic revenue sharing and settlement method of the data platform, revenue sharing and settlement rules are set, smart contract code is written, and the empowerment results are corrected.

[0089] 4) Privacy-preserving computation: Modern cryptography and information security technologies, such as secure multi-party computation, homomorphic encryption, federated learning, and trusted execution environments, perform data computation and analysis while ensuring the privacy and security of the original data, achieving the goal of making the data "usable but not visible".

[0090] 5) Data Input Module: This module is divided into public data input and system data input. Data such as enterprise credit ratings, market transaction volume, and market transaction rates are input through data mining; data quality scores and data system pricing are input through the internal system.

[0091] 6) On-chain storage module: Stores the indicator data received from the data input module in the system, adds source information, and ensures the traceability of on-chain data.

[0092] 7) Dynamic parameter adjustment module: Based on information such as supplier characteristics and historical data combinations, dynamically adjust the revenue sharing and settlement ratio to ensure a scientific and fair revenue sharing ratio.

[0093] 8) Algorithm creation module: The data supply platform dynamic revenue sharing and settlement method based on the improved Shapley value in Method 1 above is transformed into an algorithm creation method through a five-step process: data input - determination and adjustment of the basic contribution ratio of the data platform - evaluation of the contribution of the data provider - determination and adjustment of the contribution ratio of each data provider - output of the dynamic revenue sharing and settlement result of the data platform.

[0094] 9) Revenue Sharing and Settlement Calculation Module: Based on the revenue sharing and settlement method, retrieve the processed and cleaned data, and input it to calculate the final revenue sharing ratio and amount.

[0095] 10) Settlement and Distribution Module: Performs specific settlement and distribution operations based on the calculation results of the profit-sharing settlement calculation module.

[0096] It should be noted that the specific implementation process of each module in the profit distribution system can be found by referring to... Figures 1 to 2 The embodiments shown will not be described in detail here.

[0097] To implement the above embodiments, this application also provides a revenue distribution device. Figure 9 This is a schematic diagram of the structure of a revenue distribution device 900 provided in an embodiment of this application. Figure 9 As shown, the device includes: The first determining unit 910 is used to determine the total transaction revenue when at least one data supply node and data demand node trade the target dataset through the data operation node, and the first transaction recommendation revenue of the first subset of the target dataset, wherein the first subset is any subset of the target dataset. The second determining unit 920 is used to determine, based on market reference information and the first transaction suggestion profit, a first allocation ratio of the data operation node and a second allocation ratio of the first data supply node among at least one data supply node. Allocation unit 930 is used to determine the operating revenue of the data operation node and the supply revenue of the first data supply node based on the total transaction revenue, the first allocation ratio and the second allocation ratio.

[0098] In some embodiments, the second determining unit 920 is configured to: determine a first allocation ratio for data operation nodes based on market reference information and a first adjustment factor, wherein the first adjustment factor is determined based on the operational characteristic data of the data operation nodes; determine the Shapley value of a first data supply node based on the first transaction recommendation revenue; and determine a second allocation ratio for the first data supply node based on the Shapley value and a second adjustment factor, wherein the second adjustment factor is determined based on the supply characteristic data of the first data supply node.

[0099] In some embodiments, the market reference information includes the reference transaction rate of the market reference operating node and the number of reference operating nodes. The second determining unit 920 is used to: determine the first basic allocation ratio of the data operating node based on the reference transaction rate and the number of reference operating nodes; determine the first adjustment factor based on the operating characteristic data of the data operating node; and adjust the first basic allocation ratio according to the first adjustment factor to obtain the first allocation ratio of the data operating node.

[0100] In some embodiments, the first adjustment factor includes at least one of a relationship adjustment factor, a size adjustment factor, and a transaction data quality factor. The second determining unit 920 is configured to: determine a relationship adjustment factor based on the relationship type between data operation nodes and at least one data supply node in the operational feature data; determine a transaction data quality factor based on the data quality score of the target dataset in the operational feature data; and determine a size adjustment factor based on the transaction data of the target dataset in the operational feature data.

[0101] In some embodiments, the second determining unit 920 is configured to: determine a first contribution value of the first data supply node based on the first transaction recommendation revenue; and determine the Shapley value of the first data supply node based on the first contribution value and a preset weighting factor, wherein the preset weighting factor is determined based on the number of combinations of data supply combinations that do not include the first data supply node.

[0102] In some embodiments, the second determining unit 920 is configured to: determine a second basic allocation ratio for the first data supply node based on the Shapley value; determine a second adjustment factor for the second basic allocation ratio based on the supply characteristic data of the first data supply node; adjust the second basic allocation ratio based on the second adjustment factor to obtain a third allocation ratio for the first data supply node; and determine a second allocation ratio based on the third allocation ratio and the first allocation ratio for the data operation node.

[0103] In some embodiments, the second adjustment factor includes at least one of a supply data quality factor, a data importance adjustment factor, and a data subject characteristic adjustment factor. The second determining unit 920 is configured to: determine a supply data quality factor based on the quality score of the first supply data corresponding to the first data supply node in the supply feature data; determine a data importance adjustment factor based on the combined data of the first data supply node in the supply feature data; and determine a data subject characteristic adjustment factor based on the credit data and supply participation data of the first data supply node in the supply feature data.

[0104] The methods and apparatus provided in the embodiments of this application have been described above. To implement the functions of the methods provided in the embodiments of this application, the electronic device may include a hardware structure and software modules, and may implement the above functions in the form of a hardware structure, software modules, or a hardware structure plus software modules. One of the above functions may be executed in the form of a hardware structure, software modules, or a hardware structure plus software modules.

[0105] Figure 10 This is a block diagram illustrating an electronic device 1000 for implementing the above-described revenue distribution method, according to an exemplary embodiment. For example, the electronic device 1000 may be a mobile phone, computer, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc.

[0106] Reference Figure 10 The electronic device 1000 may include a communication interface 1001, capable of interacting with other devices; a processor 1002, connected to the communication interface 1001 to interact with other devices, used to execute the methods provided by one or more of the above-described technical solutions when running a computer program; and a memory 1003, on which the computer program is stored. Specifically, the specific processing procedure of the processor 1002 can refer to the revenue distribution method described in the above embodiments of this disclosure.

[0107] Of course, in practical applications, the various components in electronic device 1000 are coupled together through bus system 1004. It can be understood that bus system 1004 is used to realize the connection and communication between these components. In addition to a data bus, bus system 1004 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, in... Figure 10 The general labeled all buses as Bus System 1004.

[0108] The memory 1003 in this embodiment is used to store various types of data to support the operation of the electronic device 1000. Examples of such data include any computer program used to operate on the electronic device 1000.

[0109] The methods disclosed in the embodiments of this application can be applied to processor 1002, or implemented by processor 1002. Processor 1002 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in processor 1002 or by instructions in the form of software. The processor 1002 may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 1002 can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. A general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the methods disclosed in the embodiments of this application can be directly manifested as being executed by a hardware decoding processor, or being executed by a combination of hardware and software modules in the decoding processor. The software modules may be located in a storage medium, which is located in memory 1003. Processor 1002 reads the information in memory 1003 and completes the steps of the aforementioned method in combination with its hardware.

[0110] In an exemplary embodiment, the electronic device 1000 may be implemented by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers (MCUs), microprocessors, or other electronic components to perform the aforementioned method.

[0111] Embodiments of this disclosure also propose a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute the revenue distribution method described in the above embodiments of this disclosure.

[0112] Embodiments of this disclosure also provide a computer program product, including a computer program that is executed by a processor using the revenue distribution method described in the above embodiments of this disclosure.

[0113] Embodiments of this disclosure also propose a chip including one or more interface circuits and one or more processors; the interface circuits are used to receive signals from the memory of an electronic device and send signals to the processors, the signals including computer instructions stored in the memory, which, when executed by the processor, cause the electronic device to perform the revenue distribution method described in the above embodiments of this disclosure.

[0114] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0115] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0116] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0117] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processing module, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (control method), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0118] It should be understood that various parts of the embodiments of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0119] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0120] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc.

[0121] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for distributing profits, characterized in that, include: Determine the total transaction revenue when at least one data supply node and data demand node trade the target dataset through the data operation node, and the first transaction recommendation revenue of a first subset of the target dataset, wherein the first subset is any subset of the target dataset; Based on market reference information and the benefits of the first transaction suggestion, a first allocation ratio for the data operation node and a second allocation ratio for the first data supply node among the at least one data supply node are determined. Based on the total transaction revenue, the first allocation ratio, and the second allocation ratio, the operating revenue of the data operation node and the supply revenue of the first data supply node are determined.

2. The method according to claim 1, characterized in that, The determination of the first allocation ratio of the data operation node and the second allocation ratio of the first data supply node among the at least one data supply node, based on market reference information and the transaction suggestion revenue of the subset of data, includes: Based on the market reference information and the first adjustment factor, a first allocation ratio for the data operation node is determined, wherein the first adjustment factor is determined based on the operational characteristic data of the data operation node; Based on the revenue from the first transaction recommendation, the Shapley value of the first data supply node is determined, and based on the Shapley value and a second adjustment factor, a second allocation ratio for the first data supply node is determined, wherein the second adjustment factor is determined based on the supply characteristic data of the first data supply node.

3. The method according to claim 2, characterized in that, The market reference information includes the reference transaction fee rate and the number of reference operating nodes for the market reference operating nodes. The determination of the first allocation ratio of the data operation node based on the market reference information and the first adjustment factor includes: Based on the reference transaction fee rate and the reference number of operating nodes, determine the first basic allocation ratio of the data operating nodes; The first adjustment factor is determined based on the operational characteristic data of the data operation node; Based on the first adjustment factor, the first basic allocation ratio is adjusted to obtain the first allocation ratio of the data operation node.

4. The method according to claim 3, characterized in that, The first adjustment factor includes at least one of the following: a relationship adjustment factor, a size adjustment factor, and a transaction data quality factor. The determination of the first adjustment factor based on the operational characteristic data of the data operation node includes: The relationship adjustment factor is determined based on the relationship type between the data operation node and the at least one data supply node in the operational characteristic data; The transaction data quality factor is determined based on the data quality score of the target dataset in the operational feature data; The scale adjustment factor is determined based on the transaction data of the target dataset in the operational characteristic data.

5. The method according to claim 2, characterized in that, The determination of the Shapley value of the first data supply node based on the return from the first transaction suggestion includes: Based on the revenue from the first transaction suggestion, determine the first contribution value of the first data supply node; Based on the first contribution value and a preset weighting factor, the Shapley value of the first data supply node is determined. The preset weighting factor is determined based on the number of combinations of data supply combinations that do not include the first data supply node.

6. The method according to claim 2, characterized in that, The determination of the second allocation ratio of the first data supply node based on the Shapley value and the second adjustment factor includes: Based on the Shapley value, determine the second basic allocation ratio of the first data supply node; Based on the supply characteristic data of the first data supply node, a second adjustment factor is determined for the second basic allocation ratio; Based on the second adjustment factor, the second basic allocation ratio is adjusted to obtain the third allocation ratio of the first data supply node; The second allocation ratio is determined based on the third allocation ratio and the first allocation ratio of the data operation node.

7. The method according to claim 6, characterized in that, The second adjustment factor includes at least one of the following: supply data quality factor, data importance adjustment factor, and data subject characteristic adjustment factor. Based on the supply characteristic data of the first data supply node, the second adjustment factor for determining the second basic allocation ratio includes: Based on the quality score of the first supply data corresponding to the first data supply node in the supply feature data, the supply data quality factor is determined; Based on the combined data of the first data supply node in the supply characteristic data, the data importance adjustment factor is determined; Based on the credit data and supply participation data of the first data supply node in the supply characteristic data, the data subject characteristic adjustment factor is determined.

8. A profit distribution device, characterized in that, The device includes: The first determining unit is used to determine the total transaction revenue when at least one data supply node and data demand node trade the target dataset through the data operation node, as well as the first transaction recommendation revenue of a first subset of the target dataset, wherein the first subset is any subset of the target dataset. The second determining unit is used to determine, based on market reference information and the revenue from the first transaction suggestion, a first allocation ratio of the data operation node and a second allocation ratio of the first data supply node among the at least one data supply node; The allocation unit is used to determine the operating revenue of the data operation node and the supply revenue of the first data supply node based on the total transaction revenue, the first allocation ratio, and the second allocation ratio.

9. An electronic device, characterized in that, include: The processor and the memory used to store computer programs that can run on the processor. When the processor is used to run the computer program, it performs the method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1 to 7.

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