A method and device for identifying a data rights subject, an electronic device and a medium

Abstract

The present disclosure relates to a method, device, electronic device and medium for identifying the subject of data rights. The method includes: judging the node where the data state is converted based on the practicality of the original data or the value of the data resource, confirming the current data state and the data element composition corresponding to the current data state; determining the original data owner, data resource owner, data product or data asset owner based on the data element composition corresponding to the original data state, data resource state and data product or data asset state; determining the data product or data asset operator using the operator identification model; calculating the closeness between each data owner and the data product or data asset operator based on the data element composition, and determining each data beneficiary according to the closeness ranking. The intelligent identification of the subject of data rights of original data, data resources, data products or data assets is realized in different circulation links of data generation, storage, collection and processing.

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 identifying a data rights subject. Background Art

[0002] my country has clear requirements for the confirmation of data-based rights holders, namely, to protect the legitimate rights and interests of all participants and to ensure that remuneration is determined according to contribution. The basic participants include at least: data sources, data processors such as data collectors and processors, and other factors. Each participant plays a different role and makes different contributions in the generation, storage, collection and processing of raw data, data resources, data products or data assets in the process from raw data to the data market, but needs to participate in the distribution under the market mechanism. Therefore, it is particularly important to confirm the rights holder in each link.

[0003] The data circulation process involves data generation, storage, collection and processing. Due to the complexity of the circulation links and the difficulty in distinguishing the data status of each link, it is also difficult to determine the right holders of each link.

[0004] Therefore, there is a problem in the existing technology that it is difficult to identify the various rights holders in the data circulation link. Summary of the invention

[0005] In order to solve the above technical problems, the present disclosure provides a method and device for identifying a data rights subject, an electronic device and a medium.

[0006] In a first aspect, the present disclosure provides a method for identifying a data rights subject, the method comprising:

[0007] Based on the practicality of the original data or the value of the data resources, determine the node where the data state changes, confirm the current data state and the data element composition corresponding to the current data state, where the data state includes the original data, data resources, and data products or data assets;

[0008] Determine the owner of the original data based on the data element composition corresponding to the original data status; determine the owner of the data resource based on the data element composition corresponding to the data resource status; determine the owner of the data product or data asset based on the data element composition corresponding to the data product or data asset status; determine the operator of the data product or data asset using the operator identification model; determine the data user based on sensor data using CNN (Convolutional Neural Networks) technology;

[0009] Based on the composition of data elements, the closeness between the original data owner and the data product or data asset operator is calculated, and the original data beneficiary is determined based on the closeness ranking; based on the composition of data elements, the closeness between the data resource owner and the data product or data asset operator is calculated, and the data resource beneficiary is determined based on the closeness ranking; based on the composition of data elements, the closeness between the data product or data asset owner and the data product or data asset operator is calculated, and the data product or data asset beneficiary is determined based on the closeness ranking.

[0010] Preferably, based on the practicality of the original data or the value of the data resource, before determining the node where the data state is converted and confirming the current data state and the data element composition corresponding to the current data state, the method further includes:

[0011] Based on database comparison, data mining is used to determine the data element composition of original data, data resources, data products and data assets.

[0012] Preferably, the step of determining the original data owner based on the data element composition corresponding to the original data state includes:

[0013] Based on the data elements corresponding to the original data, the original data owner is determined using the blockchain-based data forensics traceability PROV model, where the original data owner is the collection of the original data source and the original data collector;

[0014] The blockchain-based data forensics and traceability PROV model includes a light node module and a full node module. The light node module is used for identity authentication and data collection; the full node module is used for identity authentication, traceability verification and data storage.

[0015] Preferably, based on the practicality of the original data or the value of the data resource, the step of determining the node at which the data state changes, and confirming the current data state and the data elements corresponding to the current data state includes:

[0016] Based on the practicality judgment index, it is judged whether the practicality of the processed original data has changed. The practicality judgment index includes whether it violates the laws of nature, whether it can be actually put into use, and whether it can produce positive effects.

[0017] If the practicality changes, privacy-enhancing computing technology is used to determine whether the processed original data is compliant. Privacy-enhancing computing technology includes secure matching, joint analysis, joint modeling, and joint prediction.

[0018] If the data is in compliance with the regulations, the current data status is changed from raw data to data resources, and the data element composition of the data resources is determined; the data of historical processing permissions and historical processing behaviors in the historical raw data database are extracted to confirm the original data processor;

[0019] If it is not compliant, the process returns to the step of judging whether the practicality of the processed original data has changed based on the practicality judgment index.

[0020] Preferably, the step of determining the owner of the data resource based on the data element composition corresponding to the data resource state includes:

[0021] Based on the data elements corresponding to the data resources, the data resource owner is determined using the blockchain-based data forensics traceability PROV model, where the data resource owner is the collection of the original data source, the original data collector, the original data processor, and the data resource collector;

[0022] The blockchain-based data forensics and traceability PROV model includes a light node module and a full node module. The light node module is used for identity authentication and data collection; the full node module is used for identity authentication, traceability verification and data storage.

[0023] Preferably, based on the practicality of the original data or the value of the data resource, the step of determining the node at which the data state changes, and confirming the current data state and the data elements corresponding to the current data state includes:

[0024] Use the Shannon information entropy model to determine whether the value of processed data resources has changed;

[0025] If the value changes, privacy-enhancing computing technology is used to determine whether the processed data resources are compliant. Privacy-enhancing computing technology includes secure matching, joint analysis, joint modeling, and joint prediction.

[0026] If the data is in compliance with the regulations, the current data status is changed from data resources to data products or data assets, and the data elements of data products or data assets are determined; the data of historical processing permissions and historical processing behaviors in the historical data resource database are extracted to confirm the data resource processor;

[0027] If it is not compliant, then return to the step of using the Shannon information entropy model to determine whether the value of the processed data resource has changed.

[0028] Preferably, the step of determining the owner of a data product or data asset based on the data element composition corresponding to the state of the data product or data asset includes:

[0029] Based on the data elements corresponding to the data product or data asset, the owner of the data product or data asset is determined using the blockchain-based data forensics traceability PROV model, where the owner of the data product or data asset is the collection of the original data source, the original data collector, the original data processor, the data resource collector, and the data resource processor;

[0030] The blockchain-based data forensics and traceability PROV model includes a light node module and a full node module. The light node module is used for identity authentication and data collection; the full node module is used for identity authentication, traceability verification and data storage.

[0031] Preferably, the step of determining the operator of a data product or data asset using the operator identification model includes:

[0032] According to the operator identification index and the subjects involved in the identification, the operator identification model is established by using the approximate ideal solution sorting algorithm;

[0033] According to the composition of data elements, the operator identification model is used to obtain the closeness ranking of each entity involved in the identification and the optimal value, and the operator of the data product or data asset is determined based on the closeness ranking.

[0034] Preferably, based on the composition of data elements, the closeness between the original data owner and the data product or data asset operator is calculated, and the original data beneficiary is determined according to the closeness ranking; based on the composition of data elements, the closeness between the data resource owner and the data product or data asset operator is calculated, and the data resource beneficiary is determined according to the closeness ranking; based on the composition of data elements, the closeness between the data product or data asset owner and the data product or data asset operator is calculated, and the data product or data asset beneficiary is determined according to the closeness ranking, including:

[0035] Based on the composition of data elements, the closeness between the original data owner and the data product or data asset operator is calculated using an approximate ideal solution sorting algorithm, and the original data beneficiaries are determined based on the closeness ranking;

[0036] Based on the composition of data elements, the closeness between the data resource owner and the data product or data asset operator is calculated using an approximate ideal solution sorting algorithm, and the data resource beneficiaries are determined based on the closeness ranking;

[0037] Based on the composition of data elements, the closeness between the owner of the data product or data asset and the operator of the data product or data asset is calculated using an approximate ideal solution sorting algorithm, and the beneficiaries of the data product or data asset are determined based on the closeness ranking.

[0038] In a second aspect, the present invention further provides a device for identifying a data rights subject, comprising:

[0039] Data status judgment module: used to judge the node where the data status changes based on the practicality of the original data or the value of the data resources, and confirm the current data status and the data element composition corresponding to the current data status, where the data status includes the original data, data resources, and data products or data assets;

[0040] Right subject determination module: used to determine the original data owner based on the data element composition corresponding to the original data status; determine the data resource owner based on the data element composition corresponding to the data resource status; determine the data product or data asset owner based on the data element composition corresponding to the data product or data asset status; determine the data product or data asset operator using the operator identification model;

[0041] Beneficiary determination module: used to calculate the closeness between the original data owner and the data product or data asset operator based on the composition of data elements, and determine the original data beneficiary based on the closeness ranking; calculate the closeness between the data resource owner and the data product or data asset operator based on the composition of data elements, and determine the data resource beneficiary based on the closeness ranking; calculate the closeness between the data product or data asset owner and the data product or data asset operator based on the composition of data elements, and determine the data product or data asset beneficiary based on the closeness ranking.

[0042] In a third aspect, the present disclosure further provides an electronic device, including: one or more processors;

[0043] A memory for storing one or more programs or instructions;

[0044] The processor is used to execute the steps of the method provided in the first aspect by calling the program or instructions stored in the memory.

[0045] 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.

[0046] Compared with the prior art, the technical solution provided by the embodiments of the present disclosure has the following advantages:

[0047] The present disclosure provides a method for identifying a data right subject, the method comprising: judging a node where a data state is converted based on the practicality of original data or the value of data resources, confirming a current data state and a data element composition corresponding to the current data state, wherein the data state includes original data, data resources, and data products or data assets; determining an original data owner based on a data element composition corresponding to the original data state; determining a data resource owner based on a data element composition corresponding to a data resource state; determining a data product or data asset owner based on a data element composition corresponding to a data product or data asset state; determining a data product or data asset operator using an operator identification model; determining a data user using CNN technology based on sensor data; calculating a closeness between an original data owner and a data product or data asset operator based on a data element composition, and determining an original data beneficiary based on a closeness ranking; calculating a closeness between a data resource owner and a data product or data asset operator based on a data element composition, and determining a data resource beneficiary based on a closeness ranking; calculating a closeness between a data product or data asset owner and a data product or data asset operator based on a data element composition, and determining a data product or data asset beneficiary based on a closeness ranking.

[0048] The data rights subject identification method provided by the present invention is adopted to determine the node where the data state changes, thereby clarifying the current data state and the current data circulation link, thereby obtaining the data element unit composition of the data under different data states based on the division of the data element units under different data states, and tracing the data owner based on the data element unit composition, thereby determining the data owner under different data states; and determining the data product or data asset operator through the operator identification model; determining the data user through the CNN technology; and determining the beneficiary of the data under each data state by calculating the progress ranking between the data owner and the data product or data asset operator. Based on the judgment of the data state conversion node, the intelligent identification of the data rights subject of the original data, data resources, data products or data assets in the different circulation links of data generation, storage, collection and processing is realized, thereby effectively solving the problem of difficulty in confirming the rights subjects in the data circulation link, and protecting the legitimate rights and interests of all participants in the data circulation link. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] 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.

[0050] 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.

[0051] Figure 1 A flowchart of a method for identifying a data rights subject provided by an embodiment of the present disclosure;

[0052] Figure 2 This is a structural diagram of the blockchain-based data forensics and traceability PROV model in the embodiment of the present disclosure;

[0053] Figure 3 for Figure 1 A detailed flow chart of step S101 in the method for identifying the data rights subject is shown;

[0054] Figure 4 for Figure 1 Another detailed flow chart of step S101 in the method for identifying the data rights subject is shown;

[0055] Figure 5 for Figure 1 Another detailed flow chart of step S101 in the method for identifying the data rights subject is shown;

[0056] Figure 6 for Figure 1 Another detailed flow chart of step S101 in the method for identifying the data rights subject is shown;

[0057] Figure 7 for Figure 1 A detailed flow chart showing step S102 in the method for identifying a data rights subject;

[0058] Figure 8 for Figure 1 A detailed flow chart showing step S103 in the method for identifying the data rights subject;

[0059] Fig. 9 A flowchart of another method for identifying a data rights subject provided by an embodiment of the present disclosure;

[0060] Fig.10 A structural block diagram of a device for identifying a data rights subject provided by an embodiment of the present disclosure;

[0061] Fig.11 A schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0062] 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.

[0063] 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.

[0064] like Figure 1 As shown, the embodiment of the present disclosure provides a method for identifying a data rights subject, the method comprising S101 to S103:

[0065] S101: Based on the practicality of the original data or the value of the data resources, determine the node where the data state changes, confirm the current data state and the data element composition corresponding to the current data state, where the data state includes the original data, data resources, and data products or data assets.

[0066] S102: Determine the owner of the original data based on the data element composition corresponding to the original data status; determine the owner of the data resource based on the data element composition corresponding to the data resource status; determine the owner of the data product or data asset based on the data element composition corresponding to the data product or data asset status; determine the operator of the data product or data asset using the operator identification model; determine the data user based on the sensor data using CNN technology.

[0067] S103: Based on the composition of data elements, calculate the closeness between the original data owner and the data product or data asset operator, and determine the original data beneficiary based on the closeness ranking; based on the composition of data elements, calculate the closeness between the data resource owner and the data product or data asset operator, and determine the data resource beneficiary based on the closeness ranking; based on the composition of data elements, calculate the closeness between the data product or data asset owner and the data product or data asset operator, and determine the data product or data asset beneficiary based on the closeness ranking.

[0068] Specifically, by determining the node where the data state is converted, the current data state and the current data circulation link are clarified, that is, by determining the node where the original data is converted into data resources, and the node where the data resources are converted into data products and data assets, the current data state is confirmed. Based on the division of data element units under different data states, the data element unit composition of data under different data states is obtained. Based on the data element unit composition, the data owner can be traced to determine the data owner under different data states; and the operator of data products or data assets is determined through the operator identification model. By calculating the progress ranking between data owners and data product or data asset operators under different data states, the beneficiaries of data under each data state are determined.

[0069] Through the above method, the embodiment of the present disclosure realizes the intelligent identification of data rights holders of original data, data resources, data products or data assets in different circulation links of data generation, storage, collection and processing based on the judgment of data state transition nodes, thereby effectively solving the problem of difficulty in confirming the rights holders in the data circulation link and protecting the legitimate rights and interests of all participants in the data circulation link.

[0070] In one implementation of the embodiment of the present disclosure, before step S101, the method further includes: based on database comparison, using data mining to determine the data element composition of original data, data resources, data products and data assets.

[0071] Optionally, generate and store raw data, data resources, data products, and data assets, compare historical raw data, data resources, data products, and data asset databases, mine the data elements of raw data, data resources, data products, and data assets through data mining methods, and determine their data element composition.

[0072] Specifically, take the data element mining of raw data as an example:

[0073] (1) Data cleaning: Fill in missing values, smooth noisy data, identify or remove outliers, and resolve inconsistencies to "clean the data." (2) Data integration: Use multiple databases, data cubes, or files.

[0074] (3) Data reduction: replacing metadata with alternative, smaller data representations to minimize the loss of information content. Methods include dimensionality reduction, number reduction, and data compression.

[0075] (4) Data transformation: transforming data into a form that can be used for mining.

[0076] (5) Use the K-Nearest Neighbors (KNN) algorithm to divide the data element units of the original data: determine the original data units through database comparison and division, and register the new data element labels.

[0077] (6) Convert unstructured data into structured data. Use OCR (optical character recognition) technology to convert unstructured data.

[0078] In one implementation of the disclosed embodiment, in step S102, the original data owner is determined based on the data element composition corresponding to the original data state, and specifically also includes: based on the data element composition corresponding to the original data, using the blockchain-based data forensics traceability PROV model to determine the original data owner, wherein the original data owner is a collection of the original data source and the original data collector. The blockchain-based data forensics traceability PROV model includes a light node module and a full node module, wherein the light node module is used for identity authentication and data collection; the full node module is used for identity authentication, traceability verification and data storage.

[0079] Optionally, a chain data structure is used to store the original data, wherein the blockchain-based data forensics traceability PROV model is a blockchain-based electronic data forensics traceability PROV model, which ensures that the data is not tampered with during transmission, realizes reliable data traceability, and standardizes the traceability and evidence collection process. On this basis, the original data source and the original data collector corresponding to the data elements of the original data are confirmed according to the data element composition of the original data. Among them, the functions of the blockchain-based data forensics traceability PROV model include: ensuring the reliability of the original data, providing data verification function, and enabling each node to send the data to be queried to the blockchain and verify the data. Figure 2 As shown in Figure 1, these functions are mainly implemented through light nodes and full nodes. The light node module includes identity authentication and data collection. The full node module includes identity authentication, data collection, traceability verification, and data storage.

[0080] like Figure 3 As shown, in one implementation of the embodiment of the present disclosure, step S101 specifically further includes steps S301 to S302:

[0081] S301: Based on the practicality judgment index, determine whether the practicality of the processed original data has changed; the practicality judgment index includes whether it violates the laws of nature, whether it can be actually put into use, and whether it can produce positive effects.

[0082] If the practicality has changed, execute step S302; if the practicality has not changed, return to step S301.

[0083] S302: The current data state is transformed from raw data to data resources, and the data element composition of the data resources is determined.

[0084] Specifically, determine whether the practical status of the data has changed, that is, determine whether there is a practical difference before and after the data is processed. Among them, practical judgment indicators include whether it violates the laws of nature, whether it can be put into practical use, and whether it can produce positive effects. On the premise that there is a practical difference and the processed original data meets the compliance requirements, it is determined that the status of the original data has changed and has been transformed into a data resource.

[0085] like Figure 4 As shown, in one implementation of the embodiment of the present disclosure, step S101 specifically further includes steps S401 to S403:

[0086] S401: Based on the practicality judgment index, determine whether the practicality of the processed original data has changed; the practicality judgment index includes whether it violates the laws of nature, whether it can be actually put into use, and whether it can produce positive effects.

[0087] If the practicality has changed, execute step S402; if the practicality has not changed, return to step S401.

[0088] S402: Use privacy-enhancing computing technology to determine whether the processed original data is compliant, where privacy-enhancing computing technology includes secure matching, joint analysis, joint modeling, and joint prediction.

[0089] If it is compliant, execute step S403 and step S404; if it is not compliant, return to step S401.

[0090] S403: The current data state is transformed from raw data to data resources, and the data element composition of the data resources is determined.

[0091] S404: Extract the data of historical processing authority and historical processing behavior from the historical original data database, and confirm the original data processor.

[0092] Specifically, determine whether the processed data is compliant, follow the requirements of "original data does not leave the domain, data is available but not visible", protect the legitimate rights and interests of all participants, and use privacy-enhanced computing technology to determine whether the data is compliant. The technology can perform calculations in the following ways: secure matching, joint analysis, joint modeling, and joint prediction. At the same time, the processing rights of the original data are determined based on whether it is compliant. If compliance cannot be guaranteed, the original data cannot confirm the data processing rights, that is, the original data processor cannot be confirmed.

[0093] Optionally, by extracting data on historical processing permissions and historical processing behaviors from the historical original data database, based on the data element composition of the processed original data, the original data processor is confirmed using the blockchain-based data forensics traceability PROV model.

[0094] In one implementation of the disclosed embodiment, in step S102, based on the data element composition corresponding to the data resource state, the data resource owner is determined, specifically including: based on the data element composition corresponding to the data resource, using the blockchain-based data forensics traceability PROV model to determine the data resource owner, wherein the data resource owner is a collection of the original data source, the original data collector, the original data processor, and the data resource collector. The blockchain-based data forensics traceability PROV model includes a light node module and a full node module, wherein the light node module is used for identity authentication and data collection; the full node module is used for identity authentication, traceability verification, and data storage.

[0095] Specifically, after the "confirmation of the original data processor", it is determined that there is a data processor, and the owner of the data resource is the set of the original data source, the original data collector, the original data processor, and the data resource collector. The K-Nearest Neighbors (KNN) algorithm is used to collect and divide the data element units of the data resource: the data units of the data resource are divided by comparing the database, and the new data element labels are registered. It should be noted that the data resource is formed on the basis of the original data through the processing of the original data processor, so the owner of the data resource must include the set of original data owners and the set of data resource collectors.

[0096] like Figure 5 As shown, in one implementation of the embodiment of the present disclosure, S101 specifically further includes steps S501 to S502:

[0097] S501: Using the Shannon information entropy model, determine whether the value of the processed data resources has changed.

[0098] If the value has changed, execute step S502; if the value has not changed, return to step S501.

[0099] S502: The current data status is transformed from a data resource to a data product or a data asset, and the data element composition of the data product or the data asset is determined.

[0100] Optionally, determine whether the value of the data resource has changed, that is, determine whether there is a value difference between the data resource before and after processing. The Shannon information entropy model can be used to determine the value change of the processed data resource. The specific steps include:

[0101] (1) Construct a confirmation influence network of the de-rights subject, determine the nodes of the influence network, and form a feature vector based on this.

[0102] (2) Measure the amount of information of each element in the feature vector. The Shannon entropy model is used to calculate the information entropy of each element. The greater the information entropy, the greater the contribution of the element to the uncertainty of value. The Shannon entropy calculation model is:

[0103]

[0104] In the above formula, p(x i ) represents a random event x i probability;

[0105] (3) Normalize the information entropy to obtain the information contribution of each factor. The information contribution can be regarded as the weight of each factor in confirming the right holder, which is used to reflect its value.

[0106] (4) Weighted sum of the information contribution of each factor to obtain the total amount of information confirmed by the right holder.

[0107] (5) By decomposing the information contribution of each factor, we can obtain the absolute contribution of each factor to the confirmation of the right holder. The absolute contribution represents the influence of each factor on the confirmation of the right holder, and is used to analyze the specific mechanism of value formation of different factors.

[0108] (6) By iteratively expanding the calculation scope, the comprehensive impact of each factor can be finally obtained.

[0109] The Shannon information entropy model is used to determine the value change of processed data resources, realizing the quantitative decomposition and calculation of the value confirmation of the right holder at different levels. The Shannon information entropy model is used to determine whether the value status of the data resource has changed, that is, to determine whether there is a value difference before and after data processing. If there is a value difference, the current data status is transformed from a data resource to a data product or data asset.

[0110] like Figure 6 As shown, in one implementation of the embodiment of the present disclosure, S101 specifically further includes steps S601 to S603:

[0111] S601: Using the Shannon information entropy model, determine whether the value of the processed data resource has changed.

[0112] If the value has changed, execute step S602; if the value has not changed, return to step S601.

[0113] S602: Use privacy-enhancing computing technology to determine whether the processed data resources are compliant.

[0114] If it is compliant, execute steps S603 and S604; if it is not compliant, return to step S601.

[0115] S603: The current data status is transformed from data resources to data products or data assets, and the data element composition of the data products or data assets is determined.

[0116] S604: Extract the data of historical processing authority and historical processing behavior in the historical data resource database, and confirm the data resource processor.

[0117] Specifically, since the value of the processed data resources has changed, it is necessary to determine whether the processed data resources are compliant, that is, whether the data converted into data products or data assets after processing complies with regulations. Privacy-enhancing computing technology can be used to determine whether the data is compliant, thereby protecting the legitimate rights and interests of all parties involved. If it is compliant, the processing rights of the data resources can be determined. If compliance cannot be guaranteed, the processing rights of the data resources cannot be determined, that is, the data resource processor cannot be determined.

[0118] Optionally, by extracting data on historical processing permissions and historical processing behaviors from the historical data resource database, based on the data element composition of the processed data resource, the data resource processor is confirmed using the blockchain-based data forensics traceability PROV model.

[0119] In one implementation of the disclosed embodiment, in step S102, based on the data element composition corresponding to the data product or data asset status, the owner of the data product or data asset is determined, specifically including: based on the data element composition corresponding to the data product or data asset, using the blockchain-based data forensics traceability PROV model to determine the owner of the data product or data asset, wherein the owner of the data product or data asset is a collection of the original data source, the original data collector, the original data processor, the data resource collector, and the data resource processor. The blockchain-based data forensics traceability PROV model includes a light node module and a full node module, wherein the light node module is used for identity authentication and data collection; the full node module is used for identity authentication, traceability verification, and data storage.

[0120] Specifically, after "data resource processor confirmation", it is determined that there is a data processor, and the owner of the data product or data asset is the original data source, the original data collector, the original data processor, the data resource collector, and the collection of data resource processors. The K-Nearest Neighbors (KNN) algorithm is used to collect and divide the data element units of the data product or data asset: the data units of the data product or data asset are divided through database comparison, and the new data element labels are registered. It should be noted that data products or data assets are formed on the basis of data resources through the processing of data resource processors. Therefore, the owners of data products or data assets must include the collection of data resource owners and the collection of data product or data asset collectors.

[0121] like Figure 7 As shown, in one implementation of the embodiment of the present disclosure, in step S102, the operator identification model is used to determine the operator of the data product or data asset, which specifically includes steps S701 to S702:

[0122] S701: Based on the operator identification index and the subjects involved in the identification, an operator identification model is established by using an approximate ideal solution sorting algorithm.

[0123] S702: Based on the composition of data elements, the operator identification model is used to obtain the closeness ranking of each entity involved in the identification and the optimal value, and the operator of the data product or data asset is determined based on the closeness ranking.

[0124] Optionally, if the original data is public data and meets the compliance requirements after processing, the data resources can be operated, and the data resources meet the compliance requirements based on privacy-enhanced computing technology after processing, and the data assets and data products can be operated, the operator identification model can be used to determine the operator of the data product or data asset.

[0125] Specifically, the operator identification problem is solved by using the method of approximating the ideal solution sorting. Common operator identification problems involve the following operator identification indicators, including operating ability, credit certificate, relationship with data source, data collector, data processor, data user, data product, data asset owner, data beneficiary (including owner, operator) and other rights subjects. In order to make the method more universal and have more subjects, we set the number of participating identification subjects (i.e., rights subjects) to be m, and the number of identification data to be evaluated (i.e., operator identification indicators) to be n. The method includes:

[0126] (1) Construct the eigenvalue matrix of evaluation indicators. Suppose there are n items of data to be evaluated and m subjects participating in the evaluation, then there is the eigenvalue matrix of the evaluation subject:

[0127] Aij =[a ij ] mn

[0128] Among them, a ij is the mth entity of the nth item of data;

[0129] (2) Standardize the eigenvalue matrix (homogenize heterogeneous indicators). Since the measurement units of various subjects are not uniform, they must be standardized before they are used for calculation, that is, the absolute value of the indicator is converted into a relative value, so as to solve the homogeneity problem of various heterogeneous subjects. The normalization method is shown in the following formula;

[0130]

[0131] (3) Determine the optimal value vector Z based on the Z matrix + and the worst value vector Z - ;

[0132] (4) According to Z + and Z - Calculate the optimal value of each right holder The distance to the worst value

[0133]

[0134] (5) Calculate the closeness Ci of each subject to the optimal value;

[0135]

[0136] (6) The larger the calculated value of the subject, the higher the ranking of the subject, which means it is more important.

[0137] The above method is used to construct an operator identification model, and finally the closeness ranking of each entity involved in the identification and the optimal value is obtained, and the operator of the data product or data asset is determined based on the closeness ranking.

[0138] like Figure 8 As shown, in one implementation of the embodiment of the present disclosure, step S103 specifically further includes steps S801 to S803:

[0139] S801: Based on the composition of data elements, the closeness between the original data owner and the data product or data asset operator is calculated using an approximate ideal solution sorting algorithm, and the original data beneficiary is determined based on the closeness ranking.

[0140] S802: Based on the composition of data elements, the closeness between the data resource owner and the data product or data asset operator is calculated using an approximate ideal solution sorting algorithm, and the data resource beneficiaries are determined based on the closeness ranking.

[0141] S803: Based on the composition of data elements, the closeness between the owner of the data product or data asset and the operator of the data product or data asset is calculated using an approximate ideal solution sorting algorithm, and the beneficiary of the data product or data asset is determined based on the closeness ranking.

[0142] Specifically, the beneficiaries of original data, data resource beneficiaries, and data product or data asset beneficiaries are determined on the premise that the data assets or data products have the right to operate.

[0143] Among them, the original data beneficiaries are determined by using the approximate ideal solution sorting algorithm. The specific steps refer to the method of constructing the operator identification model in the above embodiment. The data source of the original data, the closeness and ranking of the operator are determined according to the data element composition, and then the original data beneficiaries are determined. Similarly, the confirmation method of data resource beneficiaries, data product or data asset beneficiaries is the same as the confirmation method of original data beneficiaries. In order to avoid repetition, it will not be repeated here.

[0144] In one implementation of the disclosed embodiment, it also includes: under the premise of protecting public interests, data security, and the legitimate rights and interests of data sources, based on sensor data, data users are determined through CNN technology, thereby realizing the identification of data users during the data flow process.

[0145] like Fig. 9 As shown, in one implementation of the embodiment of the present disclosure, steps S101 to S103 specifically also include: generating and storing raw data and processing it, converting unstructured data using OCR (optical character recognition), mining raw data through data preprocessing and K-nearest neighbor method; using the blockchain-based PROV model to determine the data source and data collector of the raw data; judging the compliance of the processed data to determine the processor of the raw data, and then determining whether the data is a data resource based on whether the practicality has changed; judging the compliance of the processed data to determine the processor of the data resource, and using Shannon entropy to determine whether the value of the data resource has changed; and then determining whether the processed data is a data product or a data asset; then using the approximate ideal solution sorting method to determine the data beneficiary, and then using this method to determine the right holder; finally using CNN technology based on sensor data to identify the data user.

[0146] The technical solution provided by the disclosed embodiment realizes the intelligent identification of the data rights holders of raw data, data resources, data products or data assets in different circulation links of data generation, storage, collection and processing based on the judgment of data state transition nodes, and is applicable to various data types such as structured data, semi-structured data, and unstructured data. It effectively solves the problem of difficulty in identifying the rights holders in the data circulation link and protects the legitimate rights and interests of all participants in the data circulation link.

[0147] Corresponding to a method for identifying a data rights subject provided by an embodiment of the present disclosure, an embodiment of the present disclosure also provides a device for identifying a data rights subject. Fig.10 A structural block diagram of a device for identifying a data rights subject provided in an embodiment of the present disclosure, such as Fig.10 As shown, the identification device of the data rights subject includes:

[0148] Data status judgment module 1001: is used to judge the node where the data status changes based on the practicality of the original data or the value of the data resources, and to confirm the current data status and the data element composition corresponding to the current data status, wherein the data status includes the original data, data resources, and data products or data assets.

[0149] The right holder determination module 1002 is used to determine the original data owner based on the data element composition corresponding to the original data status; determine the data resource owner based on the data element composition corresponding to the data resource status; determine the data product or data asset owner based on the data element composition corresponding to the data product or data asset status; determine the data product or data asset operator using the operator identification model; determine the data user based on sensor data through CNN technology.

[0150] Beneficiary determination module 1003: used to calculate the closeness between the original data owner and the data product or data asset operator based on the composition of data elements, and determine the original data beneficiary according to the closeness ranking; calculate the closeness between the data resource owner and the data product or data asset operator based on the composition of data elements, and determine the data resource beneficiary according to the closeness ranking; calculate the closeness between the data product or data asset owner and the data product or data asset operator based on the composition of data elements, and determine the data product or data asset beneficiary according to the closeness ranking.

[0151] The data rights subject identification device disclosed in the above embodiments can execute the data rights subject identification method disclosed in the above embodiments, and has the same or corresponding beneficial effects. To avoid repetition, it will not be described again here.

[0152] 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.

[0153] Fig.11 Schematic diagram of the hardware structure of the electronic device provided by the embodiment of the present disclosure. Fig.11 As shown, the electronic device includes one or more processors 1101 and a memory 1102 .

[0154] The processor 1101 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.

[0155] The memory 1102 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 1101 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.

[0156] In one example, the electronic device may further include: an input device 1103 and an output device 1104 , and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0157] In addition, the input device 1103 may also include, for example, a keyboard, a mouse, and the like.

[0158] The output device 1104 can output various information to the outside, including the determined distance information, direction information, etc. The output device 1104 can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto, and the like.

[0159] Of course, to simplify, Fig.11 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] It should be noted that, in this article, the terms "include", "comprises" or any other variations 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, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0164] The above description is only a specific embodiment 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 may 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 the embodiments described herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for identifying a data rights subject, characterized in that: The method comprises: Based on database comparison, data mining is used to determine the data element composition of original data, data resources, data products and data assets; based on the practicality of original data or the value of data resources, the node where the data state changes is determined, and the current data state and the data element composition corresponding to the current data state are confirmed, where the data state includes original data, data resources, and data products or data assets; The step of judging the node where the data state is converted based on the practicality of the original data or the value of the data resource, and confirming the current data state and the data element composition corresponding to the current data state, includes: judging whether the practicality of the processed original data has changed based on the practicality judgment index, wherein the practicality judgment index includes whether it violates the laws of nature, whether it can be actually put into use, and whether it can produce positive effects; if the practicality has changed, using privacy-enhancing computing technology to judge whether the processed original data is compliant, wherein the privacy-enhancing computing technology includes security matching, joint analysis, joint modeling, and joint prediction; if it is compliant, the current data state is converted from original data to data resources, and the data element composition of the data resources is determined; extracting the data of historical processing permissions and historical processing behaviors in the historical original data database, and confirming the original data processor; if it is not compliant, returning to the step of judging whether the practicality of the processed original data has changed based on the practicality judgment index; Determine the owner of the original data based on the data element composition corresponding to the original data status; determine the owner of the data resource based on the data element composition corresponding to the data resource status; determine the owner of the data product or data asset based on the data element composition corresponding to the data product or data asset status; determine the operator of the data product or data asset using the operator identification model; determine the data user based on the sensor data through CNN technology; The step of determining the owner of the original data based on the data element composition corresponding to the original data state includes: determining the owner of the original data based on the data element composition corresponding to the original data, using the blockchain-based data forensics traceability PROV model, wherein the original data owner is a collection of the original data source and the original data collector; the blockchain-based data forensics traceability PROV model includes a light node module and a full node module, wherein the light node module is used for identity authentication and data collection; the full node module is used for identity authentication, traceability verification and data storage; The step of determining the owner of the data resource based on the data element composition corresponding to the data resource status includes: determining the owner of the data resource based on the data element composition corresponding to the data resource, using the blockchain-based data forensics traceability PROV model, wherein the data resource owner is a collection of the original data source, the original data collector, the original data processor, and the data resource collector; the blockchain-based data forensics traceability PROV model includes a light node module and a full node module, wherein the light node module is used for identity authentication and data collection; the full node module is used for identity authentication, traceability verification, and data storage; The step of determining the owner of a data product or data asset based on the data element composition corresponding to the state of the data product or data asset includes: determining the owner of the data product or data asset based on the data element composition corresponding to the data product or data asset using a blockchain-based data forensics traceability PROV model, wherein the owner of the data product or data asset is a collection of the original data source, the original data collector, the original data processor, the data resource collector, and the data resource processor; the blockchain-based data forensics traceability PROV model includes a light node module and a full node module, wherein the light node module is used for identity authentication and data collection; the full node module is used for identity authentication, traceability verification, and data storage; The step of using the operator identification model to determine the operator of the data product or data asset includes: establishing the operator identification model based on the operator identification index and the subjects participating in the identification by using the approximate ideal solution sorting algorithm; obtaining the closeness ranking of each subject participating in the identification and the optimal value based on the data element composition by using the operator identification model, and determining the operator of the data product or data asset based on the closeness ranking; Based on the composition of data elements, the closeness between the original data owner and the data product or data asset operator is calculated, and the original data beneficiary is determined based on the closeness ranking; based on the composition of data elements, the closeness between the data resource owner and the data product or data asset operator is calculated, and the data resource beneficiary is determined based on the closeness ranking; based on the composition of data elements, the closeness between the data product or data asset owner and the data product or data asset operator is calculated, and the data product or data asset beneficiary is determined based on the closeness ranking.

2. The method for identifying a data rights subject according to claim 1, characterized in that: The step of determining the node where the data state is converted based on the practicality of the original data or the value of the data resource, and confirming the current data state and the data elements corresponding to the current data state comprises: Use the Shannon information entropy model to determine whether the value of processed data resources has changed; If the value changes, privacy-enhancing computing technology is used to determine whether the processed data resources are compliant. Privacy-enhancing computing technology includes secure matching, joint analysis, joint modeling, and joint prediction. If the data is in compliance with the regulations, the current data status is changed from data resources to data products or data assets, and the data elements of data products or data assets are determined; the data of historical processing permissions and historical processing behaviors in the historical data resource database are extracted to confirm the data resource processor; If it is not compliant, then return to the step of using the Shannon information entropy model to determine whether the value of the processed data resource has changed.

3. The method for identifying a data rights subject according to claim 1, characterized in that: Based on the composition of data elements, the closeness between the original data owner and the data product or data asset operator is calculated, and the original data beneficiary is determined according to the closeness ranking; based on the composition of data elements, the closeness between the data resource owner and the data product or data asset operator is calculated, and the data resource beneficiary is determined according to the closeness ranking; based on the composition of data elements, the closeness between the data product or data asset owner and the data product or data asset operator is calculated, and the data product or data asset beneficiary is determined according to the closeness ranking, including: Based on the composition of data elements, the closeness between the original data owner and the data product or data asset operator is calculated using an approximate ideal solution sorting algorithm, and the original data beneficiaries are determined based on the closeness ranking; Based on the composition of data elements, the closeness between the data resource owner and the data product or data asset operator is calculated using an approximate ideal solution sorting algorithm, and the data resource beneficiaries are determined based on the closeness ranking; Based on the composition of data elements, the closeness between the owner of the data product or data asset and the operator of the data product or data asset is calculated using an approximate ideal solution sorting algorithm, and the beneficiaries of the data product or data asset are determined based on the closeness ranking.

4. A device for identifying a data rights subject, characterized in that: include: Data status judgment module: used to determine the data element composition of original data, data resources, data products and data assets by using data mining based on database comparison; based on the practicality of the original data or the value of the data resources, judge the node where the data status is converted, confirm the current data status and the data element composition corresponding to the current data status, wherein the data status includes original data, data resources and data products or data assets; the step of judging the node where the data status is converted based on the practicality of the original data or the value of the data resources, confirming the current data status and the data element composition corresponding to the current data status, includes: judging the practicality of the processed original data based on the practicality judgment index Whether it has changed, where practicality judgment indicators include whether it violates the laws of nature, whether it can be actually put into use, and whether it can produce positive effects; if practicality has changed, privacy-enhancing computing technology is used to judge whether the processed original data is compliant, where privacy-enhancing computing technology includes security matching, joint analysis, joint modeling, and joint prediction; if compliant, the current data state is transformed from original data to data resources, and the data element composition of the data resources is determined; the data of historical processing permissions and historical processing behaviors in the historical original data database are extracted to confirm the original data processor; if not compliant, return to the step of judging whether the practicality of the processed original data has changed based on the practicality judgment indicators; The right subject determination module is used to determine the original data owner based on the data element composition corresponding to the original data state; determine the data resource owner based on the data element composition corresponding to the data resource state; determine the data product or data asset owner based on the data element composition corresponding to the data product or data asset state; determine the data product or data asset operator using the operator identification model; determine the data user through CNN technology based on sensor data; the step of determining the original data owner based on the data element composition corresponding to the original data state includes: determining the original data owner based on the data element composition corresponding to the original data using the blockchain-based data forensics traceability PROV model The data owner is a set of the original data source and the original data collector; the blockchain-based data forensics traceability PROV model includes a light node module and a full node module, wherein the light node module is used for identity authentication and data collection; the full node module is used for identity authentication, traceability verification and data storage; the step of determining the data resource owner based on the data element composition corresponding to the data resource state includes: determining the data resource owner based on the data element composition corresponding to the data resource using the blockchain-based data forensics traceability PROV model, wherein the data resource owner is the original data source, the original data collector, the original data processor, and and a collection of data resource collectors; the blockchain-based data forensics and traceability PROV model includes a light node module and a full node module, wherein the light node module is used for identity authentication and data collection; the full node module is used for identity authentication, traceability verification and data storage; the step of determining the owner of a data product or data asset based on the data element composition corresponding to the data product or data asset status includes: based on the data element composition corresponding to the data product or data asset, using the blockchain-based data forensics and traceability PROV model to determine the owner of the data product or data asset, wherein the owner of the data product or data asset is the original data source, the original data collector, the original data processor, The data forensics and traceability PROV model based on blockchain includes a light node module and a full node module, wherein the light node module is used for identity authentication and data collection; the full node module is used for identity authentication, traceability verification and data storage; the step of using the operator identification model to determine the operator of a data product or data asset includes: according to the operator identification index and the subject involved in the identification, using the approximate ideal solution sorting algorithm to establish the operator identification model; according to the composition of the data elements, using the operator identification model to obtain the closeness ranking of each subject involved in the identification and the optimal value, and determining the operator of the data product or data asset according to the closeness ranking; Beneficiary determination module: used to calculate the closeness between the original data owner and the data product or data asset operator based on the composition of data elements, and determine the original data beneficiary based on the closeness ranking; calculate the closeness between the data resource owner and the data product or data asset operator based on the composition of data elements, and determine the data resource beneficiary based on the closeness ranking; calculate the closeness between the data product or data asset owner and the data product or data asset operator based on the composition of data elements, and determine the data product or data asset beneficiary based on the closeness ranking.

5. 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 3 by calling the program or instruction stored in the memory.

6. 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 3.

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