Data rights allocation and revenue clearing method, device, electronic equipment and medium

Abstract

The present disclosure relates to a method, device, electronic device and medium for data rights distribution and revenue clearing. The method includes: establishing a rights distribution network including multiple rights nodes based on the record tracing of the data element circulation network; determining at least one right subject to which each right node belongs, establishing a rights distribution model for the right subject in the right node according to the operation evaluation index of the right subject, and obtaining the rights proportion of different right subjects in the right node; establishing a data value distribution model between right nodes according to the value evaluation index of the data form of the right node, and obtaining the data value conversion contribution rate of the right node; establishing a revenue clearing model according to the conversion contribution evaluation index of the right subject, and obtaining the revenue data of each right subject under each right node, which solves the problem of difficulty in confirming rights of each participant in data circulation in the prior art and ensures the reasonable distribution of data operation revenue.

Description

Technical Field

[0001] The present disclosure relates to the technical field of data rights confirmation, and in particular to a method, device, electronic device and medium for data rights distribution and revenue clearing. Background Art

[0002] The legitimate rights and interests of all participants must be protected during the confirmation process, but according to the current status of data confirmation, not all rights holders have made contributions in the terminal market. Each participant plays a different role in each link of the process from raw data to the data market, and makes different contributions, but needs to participate in the distribution under the market mechanism, so it is necessary to confirm the rights in each link. At present, data confirmation technologies and products are all staged confirmations, and there is no confirmation technology that unifies the data from raw data to data products to data assets. Due to the lack of system continuity, it is difficult for each participant to obtain actual rights and interests in the terminal market. In addition, there is currently no confirmation technology that clarifies the distribution of rights and interests, especially the confirmation of data operation rights and interests distribution cannot be realized, which often leads to low enthusiasm for participation by all participants and the inability to fully release the data dividend. Summary of the invention

[0003] In order to solve the above technical problems, the present disclosure provides a data rights distribution and revenue clearing method, device, electronic device and medium.

[0004] In a first aspect, the present disclosure provides a method for allocating data rights and clearing revenue, the method comprising:

[0005] Based on the record tracing of the data element circulation network, a rights distribution network containing multiple rights nodes is established. The rights nodes are nodes where rights change in the data element circulation link;

[0006] Determine at least one right holder to which each right node belongs, establish a rights allocation model for the right holders within the right node based on the job evaluation indicators of the right holders, and obtain the rights proportions of different right holders within the right node;

[0007] According to the value evaluation index of the data form of the right node, a data value distribution model between the right nodes is established to obtain the data value conversion contribution rate of the right node. Among them, different right nodes correspond to different data forms in the circulation link of data elements, and different data forms have different data values;

[0008] Based on the data value conversion contribution rate of the rights node and the conversion contribution evaluation indicators of the right holder, a revenue distribution model is established to obtain the revenue data of each right holder under each right node.

[0009] Preferably, the rights nodes include original data ownership, original data collection rights, practical processing rights, data resource ownership, data resource collection rights, value processing rights, data product or data asset intellectual property rights, data product or data asset management rights, income rights and use rights;

[0010] After the step of establishing a rights distribution network including multiple rights nodes based on the record tracing of the data element circulation network, the method further includes:

[0011] By comparing the database, the data unit with the original attribute is determined to be the data element unit of the original data, and each data element of the original data is identified and encoded;

[0012] By comparing the database, the data units with value attributes are determined as the data element units of the data resources, and each data element of the data resources is identified and coded;

[0013] The identification of the original data and data elements of the data resources is stored using blockchain;

[0014] Perform hash operations on the data elements of the original data and the data resources respectively to obtain hash values ​​of the data elements of the original data and the data resources;

[0015] The data element hash values ​​of the original data and the data element hash values ​​of the data resources are stored on the blockchain to form an association between the data elements of the original data and the data resources.

[0016] Preferably, the step of determining at least one right holder to which each right node belongs includes:

[0017] According to the preset data management mode, determine at least one patent subject to which each right node belongs;

[0018] Determine the set of right subjects of the right node, and by constructing a rights allocation matrix and a judgment function, verify whether the three rights separation is satisfied between the original data ownership, the practical processing rights and the data product operating rights, as well as between the data resource ownership, the value processing rights and the data product operating rights.

[0019] Preferably, after the step of establishing a rights allocation model for the rights holders in the rights node according to the job evaluation index of the rights holders and obtaining the rights proportions of different rights holders in the rights node, the step further includes:

[0020] Based on the correlation of data elements, a correlation model between right nodes and their corresponding right holders is established to obtain the correlation coefficients of the corresponding right holders between adjacent right nodes and the correlation degree between adjacent right nodes;

[0021] The correlation coefficient and correlation degree are tracked and recorded through the blockchain.

[0022] Preferably, the step of establishing a rights allocation model for the rights holders in the rights node according to the job evaluation index of the rights holders and obtaining the rights proportions of different rights holders in the rights node includes:

[0023] Based on the right holders of original data ownership and data resource ownership, the first right allocation model is established through the data quality operation evaluation index to obtain the rights proportion of different right holders in the right nodes of original data ownership and data resource ownership;

[0024] Based on the right holders of the original data collection rights, practical processing rights, data resource collection rights, value processing rights, data product or data asset intellectual property rights and data product or data asset operating rights, a second rights distribution model is established through the operation evaluation indicators of numerical value-added work elements to obtain the rights proportions of different right holders within the right nodes of the original data collection rights, practical processing rights, data resource collection rights, value processing rights, data product or data asset intellectual property rights and data product or data asset operating rights.

[0025] Preferably, the step of establishing a data value distribution model among right nodes based on the value evaluation index of the data form of the right node and obtaining the data value conversion contribution rate of the right node includes:

[0026] Based on the rights nodes whose data form is original data or data resources, a first value allocation model is established through the value evaluation index of data quality to obtain the data value of the rights nodes to which the original data or data resources belong; wherein the rights nodes whose data form is original data or data resources include: original data ownership, original data collection right, practical processing right, data resource ownership, data resource collection right and value processing right;

[0027] Based on the rights nodes of data products or data assets, a second value allocation model is established through the value evaluation index of the numerical value-added work element to obtain the data value of the rights nodes to which the data products or data assets belong; wherein the rights nodes of data products or data assets include: intellectual property rights of data products or data assets and operating rights of data products or data assets;

[0028] According to the data value of each right node, the data value is used as the income distribution weight of each right node to obtain the data value conversion contribution rate of each right node.

[0029] Preferably, the step of establishing a revenue allocation model based on the data value conversion contribution rate of the rights node and the conversion contribution evaluation index of the rights holder to obtain the revenue data of each rights holder under each rights node includes:

[0030] Based on the right holders of original data ownership and data resource ownership, through the transformation contribution evaluation index of data quality and the association of data elements between original data and data resources, the first income clearing model is established to obtain the income data of each right holder under the right node of original data ownership and data resource ownership;

[0031] Based on the rights holders of original data collection rights, practical processing rights, data resource collection rights, value processing rights, intellectual property rights of data products or data assets, and operating rights of data products or data assets, a second income clearing model is established through the transformation contribution evaluation indicators of numerical value-added work elements to obtain the income data of each right holder under the right nodes of original data collection rights, practical processing rights, data resource collection rights, value processing rights, intellectual property rights of data products or data assets, and operating rights of data products or data assets.

[0032] In a second aspect, the present invention further provides a data rights allocation and revenue clearing device, comprising:

[0033] Modeling network module: used to establish a rights distribution network containing multiple rights nodes based on the record tracing of the data element circulation network. The rights nodes are the nodes where the rights change in the data element circulation link;

[0034] Rights division module: used to determine at least one right subject to which each right node belongs, establish a rights allocation model for the right subject within the right node according to the job evaluation index of the right subject, and obtain the rights proportion of different right subjects within the right node;

[0035] Data value module: used to establish a data value distribution model between right nodes based on the value evaluation index of the data form of the right node, and obtain the data value conversion contribution rate of the right node. Different right nodes correspond to different data forms in the circulation link of data elements, and different data forms have different data values;

[0036] Revenue allocation module: It is used to establish a revenue allocation model based on the data value conversion contribution rate of the rights node and the conversion contribution evaluation information indicators of the rights holder, so as to obtain the revenue data of each right holder under each right node.

[0037] In a third aspect, the present disclosure further provides an electronic device, comprising: one or more processors; a memory for storing one or more programs or instructions; the processor executes the steps of the method provided in the first aspect by calling the programs or instructions stored in the memory.

[0038] In a fourth aspect, the present disclosure further provides a computer-readable storage medium, which stores a program or instruction, and the program or instruction enables a computer to execute the steps of the method provided in the first aspect.

[0039] Compared with the prior art, the technical solution provided by the embodiments of the present disclosure has the following advantages:

[0040] The present disclosure provides a method for data rights distribution and revenue clearing, the method comprising: based on the record tracing of the data element circulation network, establishing a rights distribution network including multiple rights nodes, the rights node is the node where the rights change in the data element circulation link; by establishing the rights distribution network, the rights nodes to which the data in different links of the data element circulation network belongs, and the participating subjects under different rights nodes are clarified. Determine at least one right subject to which each right node belongs, establish a rights distribution model for the right subject in the right node according to the operation evaluation index of the right subject, and obtain the rights proportion of different right subjects in the right node; the right subject to which each right node belongs can be one or more, that is, multiple right subjects can have rights to the same right node, and by establishing the rights distribution model, the rights proportion of each right subject in the right node is obtained, that is, the rights proportion of each participant under each right node is clarified. According to the value evaluation index of the data form of the right node, a data value distribution model between the right nodes is established to obtain the data value conversion contribution rate of the right node, wherein different right nodes correspond to different data forms in the data element circulation link, and different data forms have different data values; by constructing a data value distribution model, the data value of different data forms corresponding to different right nodes can be calculated, and the data value of the right node is used as the weight to obtain the data value conversion contribution rate of each right node, which also clarifies the data value conversion contribution rate of each right node to the final income. Based on the data value conversion contribution rate of the right node, according to the conversion contribution evaluation index of the right subject, a revenue clearing model is established to obtain the income data of each right subject under each right node; by establishing a revenue clearing model, the data value conversion contribution rate of each right subject to the right node to which it belongs can be clarified, thereby combining the data value conversion contribution rate of the right node to obtain the income data of each right subject under the right node. The data rights distribution and revenue clearing method provided by the present invention is adopted, by establishing a rights distribution model of the right subject in the right node and a data value distribution model between the right nodes, not only the rights distribution of each participant in the data element circulation process is clarified, but also the clearing ratio of each participant based on the final income is clarified. Rights are distributed at all stages of the entire process, ensuring that all participants can achieve fair distribution of benefits according to their contributions, which is conducive to the full release of data dividends. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0043] Figure 1 A flowchart of a data rights allocation and revenue sharing method provided in an embodiment of the present disclosure;

[0044] Figure 2 A schematic diagram of the structure of a rights allocation network in an embodiment of the present disclosure;

[0045] Figure 3 for Figure 1 A detailed flow chart after S101 in the data rights allocation and revenue clearing method shown;

[0046] Figure 4 for Figure 1 A detailed flow chart of S102 in the data rights allocation and revenue clearing method shown;

[0047] Figure 5 It is a schematic diagram of the data element circulation network structure under the full-delegation data management mode in the embodiment of the present disclosure;

[0048] Figure 6 This is a schematic diagram of the data element circulation network structure under the processing business data management mode in the embodiment of the present disclosure;

[0049] Figure 7 for Figure 1 A detailed flow chart after S102 in the data rights allocation and revenue clearing method shown;

[0050] Figure 8 for Figure 1 Another detailed flow chart of S102 in the data rights allocation and revenue clearing method shown;

[0051] Fig. 9 for Figure 1 A detailed flow chart of S103 in the data rights allocation and revenue clearing method shown;

[0052] Fig.10 for Figure 1 A detailed flow chart of S104 in the data rights allocation and revenue clearing method shown;

[0053] Fig.11 A structural block diagram of a data rights allocation and revenue clearing device provided in an embodiment of the present disclosure;

[0054] Fig.12 A schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0055] In order to more clearly understand the above-mentioned objectives, features and advantages of the present disclosure, the scheme of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features in the embodiments can be combined with each other without conflict.

[0056] In the following description, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments.

[0057] In conjunction with the accompanying drawings, the following is an illustrative description of a data rights allocation and revenue clearing method, device, electronic device and medium provided in this embodiment.

[0058] like Figure 1 As shown, the embodiment of the present disclosure provides a method for allocating data rights and clearing benefits:

[0059] S101: Based on the record tracing of the data element circulation network, a rights distribution network including multiple rights nodes is established. The rights nodes are the nodes where the rights change in the data element circulation link.

[0060] S102: Determine at least one right holder to which each right node belongs, establish a rights allocation model for the right holders in the right node according to the job evaluation index of the right holders, and obtain the rights proportions of different right holders in the right node.

[0061] Among them, each rights node can cover multiple rights holders, or the right can be enjoyed by a single subject.

[0062] S103: Based on the value evaluation index of the data form of the right node, a data value distribution model between the right nodes is established to obtain the data value conversion contribution rate of the right node, wherein different right nodes correspond to different data forms in the data element circulation link, and different data forms have different data values.

[0063] S104: Based on the data value conversion contribution rate of the rights node and the conversion contribution evaluation index of the rights holder, a revenue clearing model is established to obtain the revenue data of each rights holder under each rights node.

[0064] Optionally, Merkle trees can be established to record and trace the data element circulation network. Merkle trees can achieve efficient verification of transactions and the immutability of the entire chain by aggregating data in a compact and tamper-proof manner. Merkle trees are established using this feature, with the original data of each original data owner as the root of the Merkle trees. The data form changes and data authorization process are tracked and recorded through the branching process of Merkle trees. After the data assets or data products are formed, the records are traced, and the authorization process is obtained through the data acquisition records. Based on this, a data rights distribution network framework in the data element circulation network is constructed. Among them, each right node in the rights distribution network represents a certain data right, and the lines between the right nodes represent the rights transfer process. By giving new values ​​to data elements through different processing, the rights change.

[0065] Specifically, by establishing a rights distribution network, the rights nodes to which data in different links of the data element circulation network belongs, as well as the participating entities under different rights nodes are clarified. The rights subject to which each right node belongs can be one or more, that is, multiple rights subjects can have rights to the same right node. By establishing a rights distribution model, the rights ratio of each right subject in the right node is obtained, that is, the rights ratio of each participant under each right node is clarified. By constructing a data value distribution model, the data value of different data forms corresponding to different rights nodes can be calculated, and the data value of the right node is used as the weight to obtain the data value conversion contribution rate of each right node, which also clarifies the data value conversion contribution rate of each right node to the final revenue. By establishing a revenue clearing model, the data value conversion contribution rate of each right subject to the right node to which it belongs can be clarified, and the revenue data of each right subject under the right node can be obtained by combining the data value conversion contribution rate of the right node.

[0066] Through the above method, the disclosed embodiment not only clarifies the rights distribution of each participant in the data element circulation process, but also clarifies the clearing ratio of each participant based on the final income by establishing the rights distribution model of the right subject within the right node and the data value distribution model between the right nodes. The rights are distributed from all aspects of the whole process, ensuring that each participant can achieve fair income distribution according to their contribution, which is conducive to the full release of data dividends.

[0067] In one possible implementation, the rights nodes of the rights distribution network include original data ownership, original data collection rights, practical processing rights, data resource ownership, data resource collection rights, value processing rights, data product or data asset intellectual property rights, data product or data asset operation rights, income rights and use rights.

[0068] Alternatively, if Figure 2 The rights distribution network shown is based on the original data ownership node as the starting point of the network, and then links downward to form the rights transfer chain of original data ownership, original data collection rights, practical processing rights, data resource ownership, data resource collection rights, value processing rights, data product or data asset intellectual property rights, data product or data asset management rights, income rights and use rights. The data element circulation network contains a total of 10 data rights nodes. The distribution of rights within the nodes will depend on the value of the data provided by each subject or the value of the data value-added services provided; the lines between the nodes represent the rights transfer process, and the rights change by giving new values ​​to the data elements through different processing; the link relationship between the nodes depends on the data management model adopted.

[0069] Optionally, each node pair can include multiple rights holders, representing the distribution process of rights, or a single subject can enjoy the right. Let the rights holder under the i-th right be .

[0070] Among them, the links of data circulation corresponding to each right node include: original data generation and storage, collection, practical processing, data resource generation and storage, collection, value processing, and data product or data asset generation, operation, and use.

[0071] like Figure 3 As shown, in one implementation of the embodiment of the present disclosure, after S101, specifically S301 to S303 are also included:

[0072] S301: Determine, through database comparison, that a data unit with original attributes is a data element unit of original data, and identify and encode each data element of the original data.

[0073] S302: By comparing the database, determine that the data unit with value attributes is the data element unit of the data resource, and identify and encode each data element of the data resource.

[0074] S303: The identifiers of the original data and the data elements of the data resources are stored using the blockchain.

[0075] Optionally, the original data ownership entities are , defining the data set it provides as , determine the data units with original attributes through database comparison and division, and register the new data elements according to their sources. , defining the data set it provides as , through database comparison and division, determine the data units with value, and register the new data elements according to the value division of data production factors. A subset of , defined as Each data resource element unit is A subset of , defined as , assigning a unique identifier to each data element, which can be tracked on the blockchain. Taking advantage of the tamper-proof nature of the blockchain, when a data element changes, the time, content, reason and other information of the change are recorded on the blockchain, thereby ensuring the authenticity and credibility of the data element during the circulation process.

[0076] like Figure 3 As shown, in one implementation of the embodiment of the present disclosure, after S303, specifically S304 to S305 are also included:

[0077] S304: Perform hash operations on the data elements of the original data and the data resources respectively to obtain hash values ​​of the data elements of the original data and the data resources.

[0078] S305: Store the data element hash value of the original data and the data element hash value of the data resource on the blockchain to form an association between the data elements of the original data and the data resource.

[0079] Optionally, it can be seen from the above embodiment that the original data element unit is , the data resource element unit is , use the Spooky Hash function to hash the identified and encoded data elements Perform hash operation to generate the corresponding hash value , associate the generated hash value with the data elements of the original data and data resources to ensure the unique correspondence between them, and realize arrive These hash values ​​are stored on the blockchain and their security and immutability are ensured. In this way, the data elements of the original data and data resources can be associated on the blockchain through hash values, thereby ensuring the integrity and credibility of the data.

[0080] like Figure 4 As shown, in one implementation of the embodiment of the present disclosure, at least one right subject to which each right node belongs is determined in S102, and specifically further includes S401 to S402:

[0081] S401: According to a preset data management mode, determine at least one patent entity to which each right node belongs.

[0082] S402: Determine the set of right subjects of the right node, and verify whether the three rights separation is satisfied between the original data ownership, practical processing rights and data product operating rights, as well as between the data resource ownership, value processing rights and data product operating rights by constructing a rights allocation matrix and a judgment function.

[0083] Optionally, a collection is created to cover the subjects to be allocated under various rights. The collection contains 10 collection elements in total, each of which represents a subject to be allocated of a right. The data rights subject allocation corresponding to each link in the collection is created as follows:

[0084] ① Original data generation and storage - original data ownership (allocation by original data owner);

[0085] ② Original data collection - original data collection rights (allocated by the original data collector);

[0086] ③ Practical processing of original data - right of practical processing (allocated by the original data processor);

[0087] ④Data resource generation and storage - data resource ownership (allocated by the original data owner or jointly allocated by the original data owner, collector, and processor);

[0088] ⑤Data resource collection - data resource collection rights (allocated by data resource collectors);

[0089] ⑥ Data resource value processing - value processing rights (allocated to data resource processors);

[0090] ⑦ Generation of data products or data assets - intellectual property rights of data products or data assets (allocated by the original data owner or jointly allocated by the data resource owner, collector, and processor);

[0091] ⑧ Data product or data asset management - data product or data asset management rights (allocated by data resource operators);

[0092] ⑨Use of data products or data assets - the right to operate and use data products or data assets (allocated to data resource users);

[0093] ⑩Income from data products or data assets - the right to income from the operation of data products or data assets (allocated by the original data owner or jointly distributed by the data resource owner, collector, and processor);

[0094] Each element of the set represents a subject to be assigned a right. For example, the set of subjects to be assigned the original data ownership is Based on this, we sort out the link relationship between each right holder and the rights connection process between nodes in the data element circulation network node according to different data management modes, and use data access rights control technology to assign corresponding permissions to different right holders. Each subject has different access and use permissions for data elements to ensure the legality and compliance of data use. At the same time, the inheritance and transfer of relevant permissions are set to achieve smooth transfer of rights between different nodes. Introduce data element audit tracking technology to record and supervise the use of data elements by different subjects, promptly discover and deal with violations, and ensure the legality and compliance of data element circulation.

[0095] Optionally, the preset data management mode is divided into a full entrusted data management mode and a processing operation management mode. Figure 5 As shown, Figure 5 This is a schematic diagram of the data element circulation network structure under the fully entrusted data management model. Figure 6 As shown, Figure 6 This is a diagram of the data element circulation network structure under the processing and management model. The same right holder has different sets of rights distribution subjects under different data management models. The biggest difference is that under the full entrusted management model, the data operator directly obtains the income, but the income rights are jointly cleared by all parties who provide data value. The right of use is authorized by the operator to the data user. Under the processing and management model, the right of income belongs to the original data owner, but the original data owner needs to pay the data collection fee and data processing fee to the data processor, and pay the operating expenses to the data operator, and the right of use is authorized by the original data owner to the data user.

[0096] Optionally, in a set, a certain right is jointly allocated by multiple subjects, and a subject may also participate in the allocation of multiple rights. In order to achieve the separation of the three rights between the original data ownership, the original data processing and use rights, and the data product operation rights, as well as the data resource ownership, data resource processing and use rights, and data product operation rights. Based on the traceability records of Merkle trees, the subject set to be allocated under the rights is sorted out, so as to construct a rights allocation matrix C ij , each column of the matrix represents a right, and each row represents a set of right owners. The value of 1 indicates that the right owner set participates in a certain right allocation, and the value of 0 indicates that the right owner set has no right to participate in a certain right allocation. The right allocation matrix under the full delegation model is:

[0097]

[0098] The rights distribution matrix under the OEM business management model is as follows:

[0099]

[0100] Through matrix calculation, we can obtain the available set of rights for each type of right holder and construct the judgment function:

[0101] ,and ;

[0102] If this judgment condition is established, it means that the data element circulation network has realized the "three rights separation" between the original data ownership, the original data processing and use rights, and the data product operation rights, as well as the data resource ownership, data resource processing and use rights, and data product operation rights. It can be verified that both the full entrusted management model and the processing and management model are in line with the principle of "three rights separation". Realizing "three rights separation" is helpful to encourage data development and utilization, guide data product transactions, and release the value of data elements.

[0103] like Figure 7 As shown, in one implementation of the embodiment of the present disclosure, S102 specifically includes S701 to S702.

[0104] S701: Based on the correlation of data elements, establish a correlation model between right nodes and their corresponding right subjects, obtain the correlation coefficient of the corresponding right subjects between adjacent right nodes, and the correlation degree between adjacent right nodes.

[0105] S702: Track and record the correlation coefficient and correlation degree through the blockchain.

[0106] Optionally, the data elements, that is, the original data generation, is used as the starting point of the circulation elements, and the Apriori, K-means clustering, grey correlation analysis and other methods are used to establish the correlation between the data elements of the circulation network nodes, and the blockchain technology is used to track and record the correlation to improve the data circulation line.

[0107] Specifically, taking grey relational analysis as an example, the right distribution ratio of the previous right node is taken as the parent sequence:

[0108] The current rights node's rights distribution ratio is a subsequence:

[0109] After dimensionless processing, the correlation coefficient between the parent sequence and the ath right holder of the child sequence is calculated as follows:

[0110] in, , called the resolution coefficient. The smaller ρ is, the greater the resolution is. It is usually taken as . Thus we get i The correlation between a right node and the previous right node is:

[0111] The correlation coefficient between the right holders and the correlation between the right nodes are tracked and recorded by blockchain technology to improve the data circulation line. The right holders are clarified in each link, the data circulation network nodes are located through the division of rights and responsibilities of the subjects, the circulation network lines are linked through data tracking technology, and the conversion authorization process between different right nodes is clarified. The higher the correlation, the more one-to-one authorization relationships can be established as much as possible in the authorization process between the two adjacent right nodes, that is, the right holder of the previous node corresponds to the single right holder of the next node, reducing the redundancy of the authorization process. When the correlation is low, some one-to-many authorization relationships are required. At this time, the correlation coefficient between the right holder of the previous right node and the right holder of the next right node is determined, and the authorization relationship is established between the subjects with high correlation coefficients. Therefore, the calculation of correlation coefficient and correlation provides quantitative reference and technical support for the establishment of authorization relationship. The rights allocation matrix clarifies whether a subject participates in the allocation of a certain right, and the calculation of correlation and correlation coefficient clarifies the authorization relationship between different right holders after the rights allocation. Based on this, a data rights allocation network based on data value is constructed and improved, thereby clarifying the authorization relationship between different right holders.

[0112] like Figure 8 As shown, in one implementation of the disclosed embodiment, in S102, a rights distribution model of the right holder in the right node is established according to the job evaluation index of the right holder, and the rights proportions of different right holders in the right node are obtained, which specifically also includes S801 to S802.

[0113] S801: Based on the right holders of original data ownership and data resource ownership, a first rights allocation model is established through the operation evaluation index of data quality to obtain the rights proportions of different right holders in the right nodes of original data ownership and data resource ownership.

[0114] Optionally, the operational evaluation indicators of data quality include accuracy, completeness, consistency, standardization, timeliness and accessibility. The original data or data resources provided by different entities are evaluated for their data value by using the entropy method or the CRITIC weight analysis method through indicators such as accuracy, completeness, consistency, standardization, timeliness, accessibility, etc., and the ownership of the original data or data resources is allocated according to the evaluation results.

[0115] Specifically, taking the entropy method as an example, the right holder under the original data ownership or data resource ownership is , assuming that the original data matrix under n rights holders and 6 indicators is:

[0116] Each column of the original data is dimensionless to make it comparable.

[0117] For positive indicators:

[0118] For negative indicators:

[0119] Then calculate the weight matrix of the indicator:

[0120] The entropy value of each indicator is calculated as follows:

[0121] Calculate the coefficient of variance:

[0122] The weights of each indicator are calculated:

[0123] Finally, the comprehensive score of the original data or data resources provided by each right holder is calculated:

[0124] Then the ownership ratio of original data or data resources of the jth right holder is:

[0125] S802: Based on the right holders of original data collection rights, practical processing rights, data resource collection rights, value processing rights, intellectual property rights of data products or data assets, and operating rights of data products or data assets, a second rights allocation model is established through the job evaluation indicators of numerical value-added work elements to obtain the rights proportions of different right holders within the right nodes of original data collection rights, practical processing rights, data resource collection rights, value processing rights, intellectual property rights of data products or data assets, and operating rights of data products or data assets.

[0126] Optionally, for entities that contribute to the formation of data products and data assets, that is, the right holders of the original data collection rights, practical processing rights, data resource collection rights, value processing rights, data product or data asset intellectual property rights, and data product or data asset operating rights, the entropy method and CRITIC weight analysis method are used to evaluate the numerical value-added work elements to determine their contributions, and the data value created by the entity to realize the rights is obtained, thereby allocating the original data collection rights, practical processing rights, data resource collection rights, value processing rights, data product or data asset intellectual property rights, and data product or data asset operating rights.

[0127] Among them, the right holders of the original data collection rights, data resource collection rights, practical processing rights, value processing rights, data products or data asset intellectual property rights shall use the work ability, foundation and actual workload, and work quality evaluation indicators to determine their contributions. The right holders of the data product or data asset management rights shall use the business scope, foundation and actual workload, and work quality evaluation indicators to determine their contributions.

[0128] Specifically, taking the CRITIC weight analysis method as an example, the rights holders of the original data collection rights, practical processing rights, data resource collection rights, value processing rights, data product or data asset intellectual property rights, and data product or data asset management rights are , assuming that the original data matrix under n rights holders and 3 job evaluation indicators is:

[0129] Each column of the original data is dimensionless to make it comparable.

[0130] For positive indicators:

[0131] For negative indicators:

[0132] Then calculate the standard deviation of each indicator. The standard deviation of indicator a is, the standard deviation of indicator b is, and the correlation coefficient between indicator a and indicator b is used to calculate the conflict of each indicator:

[0133] Get the information amount of the bth indicator:

[0134] Thus, the weight of each indicator is obtained:

[0135] Finally, the comprehensive score of the data value-added services provided by each right holder is calculated:

[0136] Then the rights proportion of the jth right holder is:

[0137] According to the above method, the rights proportion of each right holder in each node is obtained as follows:

[0138] Through this embodiment, rights are allocated to right subjects under different rights nodes based on the principle of equal rights and responsibilities.

[0139] like Fig. 9 As shown, in one implementation of the embodiment of the present disclosure, S103 specifically further includes S901 to S903:

[0140] S901: Based on the rights nodes whose data form is original data or data resources, a first value distribution model is established through the value evaluation index of data quality to obtain the data value of the rights nodes to which the original data or data resources belong, wherein the rights nodes whose data form is original data or data resources include: original data ownership, original data collection rights, practicality processing rights, data resource ownership, data resource collection rights and value processing rights.

[0141] Optionally, the value of the original data or data resources is evaluated through accuracy, completeness, consistency, standardization, timeliness, and accessibility indicators, and the weight is determined according to the data value score using the entropy method and CRITIC weight analysis method, and the value of the original data or data resources is determined based on the weight.

[0142] S902: Based on the rights nodes in the form of data products or data assets, a second value allocation model is established through the value evaluation indicators of numerical value-added work elements to obtain the data value of the rights nodes to which the data products or data assets belong, wherein the rights nodes in the form of data products or data assets include: intellectual property rights of data products or data assets and operating rights of data products or data assets.

[0143] Optionally, the entropy method and CRITIC weight analysis method are used to determine the weights of data products or data assets by evaluating the numerical value-added work elements including the data processing capabilities of each subject, the data processing foundation, the actual workload, and the work quality, and the value of the data products or data assets is determined based on the weights.

[0144] S903: According to the data value of each right node, the data value is used as the income distribution weight of each right node to obtain the data value conversion contribution rate of each right node.

[0145] Specifically, the terminal node of the data element circulation network is the income right. After obtaining the income, it is necessary to distribute the income to each right holder in the network according to the contribution. To this end, it is necessary to clarify the contribution rate of data value conversion of each subject. By evaluating the data value of different data forms of each right node, the data value of each right node is obtained as follows:

[0146] Taking data value as the weight of interest distribution of rights nodes, the data value conversion contribution rate of each node is obtained:

[0147] like Fig.10 As shown, in one implementation of the embodiment of the present disclosure, S104 specifically further includes S1001 to S1002:

[0148] S1001: Based on the right holders of original data ownership and data resource ownership, through the data quality conversion contribution evaluation index and utilizing the association between data elements between original data and data resources, a first revenue clearing model is established to obtain the revenue data of each right holder under the right nodes of original data ownership and data resource ownership.

[0149] Optionally, the original data or data resources provided by different entities can be evaluated for their data value using the entropy method and CRITIC weight analysis method through the indicators of accuracy, completeness, consistency, standardization, timeliness, and accessibility. At the same time, the usage portion of the data assets or data products is determined based on the associated tracking records of the data element units of the original data or data resources, and the data value of the used portion of the original data or data resources is used as the data value conversion contribution rate of the right holder at the right node.

[0150] S1002: Based on the rights holders of the intellectual property rights of data products or data assets, a second revenue clearing model is established through the transformation contribution evaluation indicators of the numerical value-added work elements to obtain the revenue data of each rights holder under the rights node of the intellectual property rights of data products or data assets.

[0151] Optionally, the entity that contributes to the formation of data products or data assets can use the entropy method and CRITIC weight analysis method to determine its contribution by evaluating the numerical value-added work elements, and obtain the data value created by the entity to realize the rights, which is used as the entity's data value conversion contribution rate at the rights node.

[0152] Specifically, the data value of each node in different data forms is:

[0153] The profit distribution ratio of each entity in the node is:

[0154] Determine the data value conversion contribution rate of each entity to the final revenue as follows:

[0155] If the total income is Inc, the income of the j-th entity under the i-th node participating in the income right distribution is:

[0156] The above profit clearing model based on the data value conversion contribution rate is entered into the clearing system as the clearing rule layer to achieve reasonable clearing of the final profit to various entities.

[0157] The technical solution provided by the embodiment of the present disclosure establishes a rights distribution network to distribute rights in all links of the whole process. Based on data value calculation and the principle of equal rights and responsibilities, the rights distribution of all participants in the data flow process is clarified through a reasonable evaluation method. At the same time, the income clearing rules are clarified, and an income clearing model based on the contribution rate of data value conversion is established to ensure that all entities can distribute income fairly according to their contributions.

[0158] Corresponding to the data rights allocation and revenue clearing method provided by the embodiment of the present disclosure, the embodiment of the present disclosure also provides a data rights allocation and revenue clearing device. Fig.11The structural block diagram of the data rights allocation and revenue clearing device provided in the embodiment of the present disclosure is as follows: Fig.11 As shown, the data rights allocation and revenue clearing device includes:

[0159] Modeling network module 1101: used to establish a rights distribution network including multiple rights nodes based on the record tracing of the data element circulation network, where the rights nodes are nodes where the rights change in the data element circulation link;

[0160] Rights division module 1102: used to determine at least one right subject to which each right node belongs, establish a rights allocation model for the right subject in the right node according to the job evaluation index of the right subject, and obtain the rights proportion of different right subjects in the right node;

[0161] Data value module 1103: used to establish a data value distribution model between right nodes according to the value evaluation index of the data form of the right node, and obtain the data value conversion contribution rate of the right node, wherein different right nodes correspond to different data forms in the data element circulation link, and different data forms have different data values;

[0162] Revenue allocation module 1104: used to establish a revenue allocation model based on the data value conversion contribution rate of the right node and the conversion contribution evaluation information of the right holder, and obtain the revenue data of each right holder under each right node.

[0163] The data rights allocation and revenue clearing device disclosed in the above embodiments can execute the data rights allocation and revenue clearing methods disclosed in the above embodiments, and has the same or corresponding beneficial effects. To avoid repetition, it will not be described here.

[0164] The embodiment of the present disclosure also provides an electronic device, including: one or more processors; a memory for storing one or more programs or instructions; the processor calls the programs or instructions stored in the memory to execute the steps of any of the above methods to achieve corresponding beneficial effects.

[0165] Fig.12 Schematic diagram of the hardware structure of the electronic device provided by the embodiment of the present disclosure. Fig.12 As shown, the electronic device includes one or more processors 1201 and a memory 1202 .

[0166] The processor 1201 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0167] The memory 1202 may include one or more computer program products, and the computer program product may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, a random access memory (RAM) and / or a cache memory (cache), etc. The non-volatile memory may include, for example, a read-only memory (ROM), a hard disk, a flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 1201 may run the program instructions to implement the stop line prediction method of the embodiment of the present disclosure described above, and / or other desired functions. Various contents such as input signals, signal components, noise components, etc. may also be stored in the computer-readable storage medium.

[0168] In one example, the electronic device may further include: an input device 1203 and an output device 1204 , and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0169] In addition, the input device 1203 may also include, for example, a keyboard, a mouse, and the like.

[0170] The output device 1204 can output various information to the outside, including the determined distance information, direction information, etc. The output device 1204 can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto, and the like.

[0171] Of course, to simplify, Fig.12 Only some of the components related to the present disclosure in the electronic device are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, according to specific application situations, the electronic device may further include any other appropriate components.

[0172] The embodiment of the present disclosure also provides a computer-readable storage medium, which stores a program or instruction, and the program or instruction enables a computer to execute the steps of any of the above methods.

[0173] Optionally, when executed by a computer processor, the computer executable instructions may also be used to execute the technical solution of any of the above-mentioned stop line prediction methods provided in the embodiments of the present disclosure, thereby achieving corresponding beneficial effects.

[0174] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the embodiments of the present disclosure can be implemented with the help of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the embodiments of the present disclosure is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment of the present disclosure.

[0175] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0176] The above are only specific embodiments of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to these embodiments herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for allocating data rights and clearing revenue, characterized in that: The method comprises: Based on the record tracing of the data element circulation network, a rights distribution network containing multiple rights nodes is established. The rights nodes are nodes where rights change in the data element circulation link; the rights nodes include original data ownership, original data collection rights, practical processing rights, data resource ownership, data resource collection rights, value processing rights, data product or data asset intellectual property rights, data product or data asset management rights, income rights and use rights; By comparing the database, the data unit with original attributes is determined to be the data element unit of the original data, and each data element of the original data is identified and encoded; by comparing the database, the data unit with value attributes is determined to be the data element unit of the data resource, and each data element of the data resource is identified and encoded; the identifiers of the data elements of the original data and the data resource are stored using the blockchain; hash operations are performed on the data elements of the original data and the data resource respectively to obtain the hash values ​​of the data elements of the original data and the data resource; the hash values ​​of the data elements of the original data and the data elements of the data resource are stored on the blockchain to form an association between the data elements of the original data and the data resource; Determine at least one right holder to which each right node belongs, establish a rights allocation model for the right holders within the right node based on the job evaluation indicators of the right holders, and obtain the rights proportions of different right holders within the right node; According to the value evaluation index of the data form of the right node, a data value distribution model between the right nodes is established to obtain the data value conversion contribution rate of the right node. The value evaluation index of the data form of the right node includes the value evaluation index of data quality and the value evaluation index of the value-added work element. Among them, different right nodes correspond to different data forms in the circulation link of data elements, and different data forms have different data values; Based on the data value conversion contribution rate of the rights node and according to the conversion contribution evaluation indicators of the rights holders, a revenue clearing model is established to obtain the revenue data of each right holder under each right node. The conversion contribution evaluation indicators of the rights holders include the conversion contribution evaluation indicators of data quality and the conversion contribution evaluation indicators of numerical value-added work elements.

2. The data rights allocation and revenue sharing method according to claim 1 is characterized in that: The step of determining at least one right subject to which each right node belongs includes: According to the preset data management mode, determine at least one patent subject to which each right node belongs; Determine the set of right subjects of the right node, and by constructing a rights allocation matrix and a judgment function, verify whether the three rights separation is satisfied between the original data ownership, the practical processing rights and the data product operating rights, as well as between the data resource ownership, the value processing rights and the data product operating rights.

3. The data rights allocation and revenue sharing method according to claim 1 is characterized in that: After the step of establishing a rights allocation model for the rights holders in the rights node according to the job evaluation index of the rights holders and obtaining the rights proportions of different rights holders in the rights node, the method further includes: Based on the correlation of data elements, a correlation model between right nodes and their corresponding right holders is established to obtain the correlation coefficients of the corresponding right holders between adjacent right nodes and the correlation degree between adjacent right nodes; The correlation coefficient and correlation degree are tracked and recorded through the blockchain.

4. The data rights allocation and revenue sharing method according to claim 1 is characterized in that: The step of establishing a rights allocation model for the rights holders in the rights node according to the job evaluation index of the rights holders and obtaining the rights proportions of different rights holders in the rights node includes: Based on the right holders of original data ownership and data resource ownership, the first right allocation model is established through the data quality operation evaluation index to obtain the rights proportion of different right holders in the right nodes of original data ownership and data resource ownership; Based on the right holders of the original data collection rights, practical processing rights, data resource collection rights, value processing rights, data product or data asset intellectual property rights and data product or data asset operating rights, a second rights distribution model is established through the operation evaluation indicators of numerical value-added work elements to obtain the rights proportions of different right holders within the right nodes of the original data collection rights, practical processing rights, data resource collection rights, value processing rights, data product or data asset intellectual property rights and data product or data asset operating rights.

5. The data rights allocation and revenue sharing method according to claim 1 is characterized in that: The step of establishing a data value distribution model among right nodes based on the value evaluation index of the data form of the right node and obtaining the data value conversion contribution rate of the right node includes: Based on the rights nodes whose data form is original data or data resources, a first value allocation model is established through the value evaluation index of data quality to obtain the data value of the rights nodes to which the original data or data resources belong; wherein the rights nodes whose data form is original data or data resources include: original data ownership, original data collection right, practical processing right, data resource ownership, data resource collection right and value processing right; Based on the rights nodes of data products or data assets, a second value allocation model is established through the value evaluation index of the numerical value-added work element to obtain the data value of the rights nodes to which the data products or data assets belong; wherein the rights nodes of data products or data assets include: intellectual property rights of data products or data assets and operating rights of data products or data assets; According to the data value of each right node, the data value is used as the income distribution weight of each right node to obtain the data value conversion contribution rate of each right node.

6. The data rights allocation and revenue sharing method according to claim 2 is characterized in that: The step of establishing a revenue clearing model based on the data value conversion contribution rate of the rights node and the conversion contribution evaluation index of the rights subject to obtain the revenue data of each rights subject under each rights node includes: Based on the right holders of original data ownership and data resource ownership, through the data quality conversion contribution evaluation index, using the association between data elements between original data and data resources, the first revenue clearing model is established to obtain the revenue data of each right holder under the right node of original data ownership and data resource ownership; Based on the rights holders of original data collection rights, practical processing rights, data resource collection rights, value processing rights, intellectual property rights of data products or data assets, and operating rights of data products or data assets, a second income clearing model is established through the transformation contribution evaluation indicators of numerical value-added work elements to obtain the income data of each right holder under the right nodes of original data collection rights, practical processing rights, data resource collection rights, value processing rights, intellectual property rights of data products or data assets, and operating rights of data products or data assets.

7. A data rights allocation and revenue clearing device, characterized in that: include: Modeling network module: used to establish a rights distribution network containing multiple rights nodes based on the record tracing of the data element circulation network, and the rights node is the node where the rights change in the data element circulation link; the rights nodes include original data ownership, original data collection rights, practical processing rights, data resource ownership, data resource collection rights, value processing rights, data product or data asset intellectual property rights, data product or data asset management rights, income rights and use rights; through database comparison, determine that the data unit with original attributes is the data element unit of the original data, and identify and encode each data element of the original data; through database comparison, determine that the data unit with value attributes is the data element unit of the data resource, and identify and encode each data element of the data resource; store the identifiers of the data elements of the original data and data resources using the blockchain; perform hash operations on the data elements of the original data and data resources respectively to obtain the hash values ​​of the data elements of the original data and data resources; store the hash values ​​of the data elements of the original data and the hash values ​​of the data elements of the data resources on the blockchain to form an association between the data elements of the original data and the data resources; Rights division module: used to determine at least one right subject to which each right node belongs, establish a rights allocation model for the right subject within the right node according to the job evaluation index of the right subject, and obtain the rights proportion of different right subjects within the right node; Data value module: used to establish a data value distribution model between right nodes according to the value evaluation index of the data form of the right node, and obtain the data value conversion contribution rate of the right node. The value evaluation index of the data form of the right node includes the value evaluation index of data quality and the value evaluation index of the value-added work element. Different right nodes correspond to different data forms in the circulation link of data elements, and different data forms have different data values. Revenue allocation module: It is used to establish a revenue clearing model based on the data value conversion contribution rate of the right node and the conversion contribution evaluation indicators of the right holder, so as to obtain the revenue data of each right holder under each right node. The conversion contribution evaluation indicators of the right holder include the conversion contribution evaluation indicators of data quality and the conversion contribution evaluation indicators of numerical value-added work elements.

8. An electronic device, characterized in that: include: one or more processors; A memory for storing one or more programs or instructions; The processor is used to execute the steps of the method according to any one of claims 1 to 6 by calling the program or instruction stored in the memory.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program or instruction, and the program or instruction enables a computer to execute the steps of the method according to any one of claims 1 to 6.

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