WO2020177298A1 - Procédé et appareil de gestion et d'exploitation de valeurs d'actifs numériques, support et dispositif informatique - Google Patents

Procédé et appareil de gestion et d'exploitation de valeurs d'actifs numériques, support et dispositif informatique Download PDF

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Publication number
WO2020177298A1
WO2020177298A1 PCT/CN2019/106872 CN2019106872W WO2020177298A1 WO 2020177298 A1 WO2020177298 A1 WO 2020177298A1 CN 2019106872 W CN2019106872 W CN 2019106872W WO 2020177298 A1 WO2020177298 A1 WO 2020177298A1
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user
transaction
digital
digital assets
data
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PCT/CN2019/106872
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English (en)
Chinese (zh)
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钟山
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钟山
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Priority to PCT/CN2019/106872 priority Critical patent/WO2020177298A1/fr
Priority to CN201980006029.1A priority patent/CN111566686A/zh
Publication of WO2020177298A1 publication Critical patent/WO2020177298A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0202—Market predictions or forecasting for commercial activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0207—Discounts or incentives, e.g. coupons or rebates
    • G06Q30/0222—During e-commerce, i.e. online transactions
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
    • H04L9/50—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols using hash chains, e.g. blockchains or hash trees

Definitions

  • the embodiments of the present invention relate to the field of computer technology. More specifically, the embodiments of the present invention relate to a digital asset value management and operation method, device, medium, and computing equipment.
  • Blockchain is called distributed ledger technology, which is an Internet database technology, which is characterized by decentralization, openness and transparency, and allows everyone to participate in database records.
  • Blockchain technology uses block chain data structures to verify and store data, uses distributed node consensus algorithms to generate and update data, uses cryptography to ensure the security of data transmission and access, and uses intelligence composed of automated script codes.
  • a new distributed infrastructure and computing paradigm that uses contracts to program and manipulate data.
  • Blockchain technology itself has many advantages. On the one hand, since the blockchain network can achieve self-restraint through algorithms, any malicious deception system behavior will be rejected and inhibited by other nodes in the network. Therefore, the blockchain system does not need to rely on With the support of the central authority and credit endorsement, secure online transactions can be realized.
  • the embodiments of the present invention are expected to provide a digital asset value management and operation method, device, medium and computing device.
  • a digital asset value management and operation method including:
  • the method further includes:
  • a digital identity is constructed for each user, and transactions are performed based on the digital identity of the user, wherein when the transaction is completed, the ownership of digital assets and rewards is recorded in the distributed ledger based on the digital identity of the user.
  • the transaction requirement is uploaded by the second user.
  • the transaction requirement is obtained based on data analysis uploaded by the second user.
  • the transaction requirement is obtained based on data analysis uploaded by the first user.
  • the method further includes:
  • the transaction demand is obtained by analyzing the data uploaded by the user based on a preset demand analysis model, wherein the demand analysis model is established based on a deep neural network.
  • the preset demand analysis model is also configured to be automatically updated iteratively based on expert correction.
  • the digital assets involved in the data uploaded by the first user are analyzed through a preset data model, and a basic reward is set for the digital assets.
  • analyzing the digital assets involved in the data uploaded by the first user, and setting basic rewards for the digital assets including:
  • the data type of the digital asset of the first user includes at least one of image, text, video, and personal information.
  • the digital assets of the personal information type of the first user include behavior habits in different time periods.
  • the behavior data of the first user in different time dimensions is analyzed and acquired from the behavior data uploaded by the first user.
  • the authorization of the first user to the digital asset includes at least an authorization scope and an authorization period.
  • the reward settlement further includes:
  • the final reward is determined according to the reward settlement weight and the basic reward of the digital asset for reward settlement.
  • the method further includes:
  • the authorization period of the digital asset expires, the authorization of the digital asset is recovered and recorded in the distributed ledger.
  • the user's digital assets and rewards are processed through a distributed ledger, a decentralized empowering community is established, and the user's digital assets are tokenized.
  • multilateral transaction matching is performed in the community based on the digital assets recorded in the distributed ledger.
  • multilateral transaction matching is performed in the community.
  • a preset smart contract automatically executes the transaction on the distributed ledger to complete the right change of the digital asset and reward settlement to ensure mutual trust between the two parties.
  • the user's digital assets and rewards are processed through a distributed ledger preset with smart contracts to ensure the pass operation of digital assets.
  • a user involves at least one node in the distributed ledger.
  • the smart contract is jointly voted by all or part of the nodes in the distributed ledger to decide whether to record in the distributed ledger to achieve community autonomy.
  • the voting decisions related to the smart contract are made by the master node in the distributed ledger.
  • the preset smart contract is obtained based on various business analysis.
  • a corresponding set of smart contracts is analyzed.
  • each type of service is used to process at least one of rewards, digital assets, and user identity information.
  • each node in the distributed ledger adopts a Byzantine fault-tolerant algorithm to reach a consensus.
  • the circulation mode of the data/digital assets to other users is determined.
  • the matching between the user's digital assets and needs is performed based on a preset transaction matching model.
  • the transaction matching model is constructed based on artificial intelligence.
  • the transaction matching model includes at least one of a Bayesian network, a reinforcement learning model, a deep learning model, and a generative confrontation network.
  • a digital asset value management and operation device including:
  • the digital asset analysis module is configured to analyze the digital assets involved in the data uploaded by at least one first user, wherein the digital assets are recorded in a distributed ledger;
  • the transaction matching module is configured to match digital assets of at least one second user and at least one of the first users based on transaction requirements;
  • the transaction push module is configured to initiate a transaction invitation to the matched parties
  • the transaction settlement module is configured to complete the right change of the digital asset and reward settlement in response to the authorization of the matching parties for the transaction;
  • the device further includes:
  • the digital identity building module is configured to construct a digital identity for each user and conduct transactions based on the user’s digital identity, wherein when the transaction is completed, the digital assets and rewards are recorded in the distributed ledger based on the user’s digital identity Attribution.
  • the device further includes an enabling module configured to enable the user's data by analyzing the digital assets involved in the user's data.
  • the transaction requirement is uploaded by the second user.
  • the transaction requirement is obtained based on data analysis uploaded by the second user.
  • the transaction requirement is obtained based on data analysis uploaded by the first user.
  • the device further includes:
  • the license authorization obtaining module is configured to obtain the user's permission to use the data when receiving the data uploaded by the user.
  • the transaction demand is obtained by analyzing the data uploaded by the user based on a preset demand analysis model, wherein the demand analysis model is established based on a deep neural network.
  • the preset demand analysis model is also configured to be automatically updated iteratively based on expert correction.
  • the digital asset analysis module is further configured to analyze digital assets involved in the data uploaded by the first user through a preset data model, and set basic rewards for the digital assets.
  • the digital asset analysis module includes:
  • the digital asset analysis unit is configured to analyze all digital assets involved in the data uploaded by the first user;
  • the digital asset classification unit is configured to classify all digital assets of the first user
  • the basic reward distribution unit is configured to give corresponding basic rewards based on the type of digital asset.
  • the data type of the digital asset of the first user includes at least one of image, text, video, and personal information.
  • the digital assets of the personal information type of the first user include behavior habits in different time periods.
  • the behavior data of the first user in different time dimensions is analyzed and acquired from the behavior data uploaded by the first user.
  • the authorization of the first user to the digital asset includes at least an authorization scope and an authorization period.
  • the transaction settlement module includes:
  • the reward settlement weight determination unit is configured to determine the reward settlement weight based on the authorization range and the authorization period of the first user for the digital asset;
  • the reward settlement unit is configured to determine a final reward based on the reward settlement weight and the basic reward of the digital asset for reward settlement.
  • the device further includes an authorization monitoring module configured to monitor the authorization period of the digital asset authorized by the first user after the transaction is completed; and
  • the authorization period of the digital asset expires, the authorization of the digital asset is recovered and recorded in the distributed ledger.
  • the device processes the user's digital assets and rewards through a distributed ledger, establishes a decentralized empowerment community, and certifies the users' digital assets.
  • the device further includes:
  • the promotion contribution recording module is configured to record the contribution made by the third user to the promotion operation of the digital assets in the community in the distributed ledger;
  • the promotion reward settlement module is configured to allocate corresponding promotion rewards to the third user based on the third user's contribution when the digital asset transaction is completed.
  • the transaction matching module is further configured to perform multilateral transaction matching in the community based on the digital assets recorded in the distributed ledger in response to the user's selection or authorization.
  • the transaction matching module is further configured to perform multilateral transaction matching in the community for specific types of digital assets recorded in the distributed ledger.
  • a preset smart contract automatically executes the transaction on the distributed ledger to complete the rights change of the digital asset and the reward settlement, and ensure the mutual trust between the parties to the transaction.
  • the user's digital assets and rewards are processed through a distributed ledger preset with smart contracts to ensure the pass operation of digital assets.
  • a user involves at least one node in the distributed ledger.
  • the smart contract is jointly voted by all or part of the nodes in the distributed ledger to decide whether to record in the distributed ledger to achieve community autonomy.
  • the voting decisions related to the smart contract are made by the master node in the distributed ledger.
  • the preset smart contract is obtained based on various business analysis.
  • a corresponding set of smart contracts is analyzed.
  • each type of service is used to process at least one of rewards, digital assets, and user identity information.
  • each node in the distributed ledger adopts a Byzantine fault-tolerant algorithm to reach a consensus.
  • the circulation mode of the data/digital assets to other users is determined.
  • the matching between the user's digital assets and needs is performed based on a preset transaction matching model.
  • the transaction matching model is constructed based on artificial intelligence.
  • the transaction matching model includes at least one of a Bayesian network, a reinforcement learning model, a deep learning model, and a generative confrontation network.
  • a computer-readable storage medium storing program code, which, when executed by a processor, implements the method according to any one of the embodiments of the first aspect .
  • a computing device including a processor and a storage medium storing program code, the program code when being executed by the processor implements any of the embodiments of the first aspect The method described.
  • the user’s data is empowered by analyzing the digital assets involved in the user’s data; and the user’s digital assets are processed through a distributed ledger And rewards, establish a decentralized empowering community, and certify users’ digital assets, so that users’ data rights are well protected.
  • Figure 1 shows a schematic flow diagram of a digital asset value management and operation method according to an embodiment of the present invention
  • FIG. 2 shows a schematic diagram of modules of a digital asset value management and operation device according to an embodiment of the present invention
  • Fig. 3 shows a schematic diagram of a computing device according to an embodiment of the present invention
  • the digital asset value management and operation method includes:
  • Step S110 Analyze the involved digital assets from the data uploaded by at least one first user, where the digital assets are recorded in a distributed ledger;
  • users may also produce some digital works when using digital electronic products, such as photos taken, audio/video produced by recording and editing, and pictures and texts created and edited. These digital works will also be shared and shared by users on the Internet. Public, but public digital works shared on the Internet will face a great risk of being stolen.
  • the user's data can be empowered by analyzing the digital assets involved in the user's data, and further, the user's digital assets and rewards can be processed through the distributed ledger to establish a decentralized empowered community , To certify the user’s digital assets.
  • the data uploaded by the (first) user is first analyzed to determine the digital assets involved, and then recorded in the distributed ledger
  • the user's permission to use the data will also be obtained, that is, the method proposed by the present invention will confirm whether the data uploaded by the user is authorized for analysis and transaction, only Only the data authorized by the user will be analyzed, and then transactions will be conducted on the corresponding platform.
  • the digital assets involved in the data uploaded by the first user are analyzed through a preset data model, and a basic reward is set for the digital assets.
  • the data uploaded by the first user is analyzed. All digital assets involved in the data; classify all digital assets of the first user; assign corresponding basic rewards based on the type of digital assets; then record in the distributed ledger, for example, user A uploads a large amount of data at once.
  • the data includes user A’s personal information (such as user A’s identity information and recent behavioral data), as well as recent photos and clips.
  • the data related to user behavior is divided into one category, and the data related to digital works is divided into one category, and then based on preset rules, corresponding basic rewards are given to them according to the type of digital assets, and finally recorded in the distributed ledger .
  • classification of users’ digital assets is not limited to the ones listed in the above embodiments, and can also be more detailed classifications, such as dividing users’ digital assets into personal attribute information (which can include name , Age, gender and other information), behavior information (can include behavior habits in different time periods), works (can include images, videos, audio, text, etc.), etc. It should be noted that each category is under All of them can be further refined and classified, which will not be described by examples here. In addition, in other examples of this embodiment, other classifications can also be made according to actual conditions, which is not limited in the present invention.
  • different valuation models can be used for pricing, and for the same digital asset, multiple different valuation models can also be used for multiple pricing, and then aggregated based on the multiple pricing obtained
  • different valuation models may be applicable to different scenarios, that is, in different application scenarios, the value of the same digital asset is also different. Therefore, it can be based on the specific transaction.
  • the scenario uses a corresponding valuation model to determine the value of digital assets.
  • the transaction method selected by the user when receiving data or digital assets uploaded by the user, the transaction method selected by the user is obtained.
  • the transaction method includes transfer, license use, pledge lending, etc.
  • the above transaction methods are also acceptable Including more specific restrictions on trading conditions, which are not specifically limited in this embodiment.
  • the corresponding valuation model is determined according to the corresponding trading method, and then the Describe the basic rewards of digital assets.
  • the user's choice of the transaction method for the digital assets he owns can be done when the user uploads data or digital assets for the first time, or after analyzing the digital assets involved in the user's data.
  • the user It is also possible to select some or all of the digital assets owned by them. That is, in an embodiment of this embodiment, some default transaction methods of digital assets can be preset, and the user can adjust based on the default selection, or change The default choice is the trading method of the digital assets it owns. That is, in this embodiment, the user is provided with a valuation model that meets the current transaction (purpose) scenario for the user to choose.
  • some corresponding application fields suitable for the digital assets are preset in the above-mentioned transaction methods.
  • portrait digital assets uploaded by users can be used in the traditional clothing field.
  • the user’s digital assets can maximize the value and fully explore various possibilities.
  • the user’s digital assets can also be used for exploratory applications in multiple fields based on the user’s authorization. For example, after confirming the user’s digital assets, he can actively ask the user whether he wants to maximize the value of his digital assets to obtain better returns. If the user is authorized to allow his digital assets to be explored in multiple directions, he will be trading When matching, it will not only be limited to the traditional application fields of the digital assets, but will also be matched with some non-traditional application fields.
  • the behavior data uploaded by users may not be handled well, that is, the behavior data uploaded by users may simply indicate what behavior the user has performed when and where, and does not have a regular summary. For example, the user What will happen every day, every week, every month, or every year at a certain fixed time. Therefore, in an example of this embodiment, the user’s behavior data can be analyzed to obtain the user’s behavior habit information, and then form Corresponding digital assets, for example, can classify users' behavior habits according to time periods, and then set corresponding basic rewards for users' behavior habits in different time periods. In external transactions, users can choose to sell behaviors in different time periods separately. Habitual digital assets can also choose to package and sell some or all time periods of behavioral habit digital assets.
  • the behavior data uploaded by the user can be analyzed based on the deep convolutional neural network including the residual neural network, so as to obtain the behavioral habit-type digital assets of the first user in different time dimensions (periods).
  • the The deep convolutional neural network includes multiple convolutional layers, each level of convolutional layer is only connected to the next level of convolutional layer, and each level of convolutional layer is also connected to a residual neural network and output the current
  • the user’s behavior habit data obtained by the layered convolutional layer has a longer time period, that is, if the deep convolutional neural network includes five layers, the most The outer convolutional layer obtains the user’s daily behavior habit data, the second layer’s result is the user’s weekly behavior habit data, the third layer’s result is the monthly behavior habit data, the fourth layer is obtained The result of is the user's quarterly behavior data, and the result of the fifth layer is the user's annual behavior data.
  • step S120 can be executed to match the digital assets of at least one second user and at least one of the first users based on transaction requirements;
  • the transaction demand can be uploaded by the second user, or can be obtained based on the analysis of data uploaded by the second user.
  • the transaction demand can be obtained by analyzing the data uploaded by the user based on a preset demand analysis model.
  • the transaction requirements where the demand analysis model is established based on a deep neural network, for example, the second user uploads the project that he wants to implement, the demand analysis model can automatically determine the required project according to the project’s implementation goal Material requirements are then matched based on the requirements with the digital assets stored in the distributed ledger (the transaction requirements can be based on a preset transaction matching model, which is constructed based on artificial intelligence and includes at least Bayesian Network, reinforcement learning model, deep learning model and generative confrontation network), determine whether there is a digital asset (data) that meets the requirements, and the data uploaded by the second user may not be specific to be implemented
  • the project is only the business status or R&D direction of the enterprise itself.
  • the demand analysis model can give the corresponding project direction and the material requirements for implementing the project based
  • the demand analysis model also needs to be iteratively updated to adapt to the current social environment.
  • the preset demand analysis model is also configured to automatically update iteratively based on expert correction, which is similar to supervised learning, that is, the demand analysis model can be automatically updated and iteratively trained under expert correction To adapt to the current application scenarios.
  • step S130 can be executed to initiate a transaction invitation to the matched parties;
  • all transaction parties that meet the requirements can be invited to initiate a transaction at the same time.
  • a requirement is obtained by analyzing the data of the second user a, and then matching is performed based on the requirement, and it is found that the requirement is met.
  • the transaction invitation for which the second user has clear transaction requirements can directly display the digital assets that meet the transaction requirements
  • Step S140 in response to the authorization of the matching parties for the transaction, complete the right change of the digital asset and the reward settlement;
  • the transaction can be completed to complete the change of digital asset rights and reward settlement.
  • the first user A owns the digital asset a recorded in the distributed ledger
  • the second user B has a transaction requirement that matches the digital asset a
  • the first user A and the second user B will respectively initiate a transaction invitation to trade the digital asset a
  • the transaction request based on the first user A and the second user B
  • the response determines whether to conduct the transaction. If one of the two does not agree to the transaction, the transaction fails.
  • the reward settlement includes:
  • the transaction reward of the digital asset is determined based on the basic reward of the digital asset and the authorization range and authorization period of the first user to the digital asset.
  • the digital asset is a work
  • the corresponding basic reward is 100.
  • the first user’s authorization scope for the digital asset is only to grant the use right and the authorization period is 1 year
  • the respective reward coefficients can be determined according to the authorization scope and the authorization period (the settlement coefficient of the work use right is 0.6, the authorization The reward coefficient for a period of 1 year is 1)
  • the reward settlement weight is determined according to the reward coefficients of the two, for example, the reward settlement weight of the two is directly multiplied to obtain the final reward settlement weight.
  • the reward coefficients determined according to the authorization scope and authorization period may be independent of each other or not, that is, when determining the reward coefficient of the authorization scope and the authorization period, the two can be mutually independent. Influence, there are multiple mapping relationships; or the two may not influence each other, and are only related to their own values, which is not limited in the present invention.
  • the final reward is determined according to the reward settlement weight and the basic reward of the digital asset for reward settlement.
  • step S150 the completed transaction is recorded on the distributed ledger.
  • a digital identity is constructed for each user, and transactions are performed based on the digital identity of the user.
  • the attribution of digital assets and rewards is recorded in the distributed ledger based on the user's digital identity.
  • the authorization period of the digital asset authorized by the first user is monitored
  • the authorization period of the digital asset expires, the authorization of the digital asset is recovered and recorded in the distributed ledger.
  • the transaction is automatically executed on the distributed ledger by a preset smart contract
  • the smart contract is preset on the distributed ledger and is derived based on various business analysis, that is, for each type of business (each type of business It can be used to process at least one of rewards, digital assets and user identity information), and can be analyzed to obtain a corresponding set of smart contracts. For example, the construction of a user’s digital identity can be based on a pre-set smart contract on the distributed ledger.
  • the analysis and pricing of digital assets can also be based on smart contracts preset on the distributed ledger.
  • the smart contract can run automatically when certain conditions are met, and the smart contract is set in the distributed ledger.
  • All or part of the nodes (each node in the distributed ledger adopts the Byzantine fault-tolerant algorithm to reach a consensus) jointly vote (a user involves at least one node in the distributed ledger) to decide whether to record in the distributed ledger, to Achieve community autonomy.
  • digital assets can also be managed on a family basis.
  • user a and user b are both members of family a, then both user a and user b can act as administrators to manage digital assets shared by all members of the family. For example, the consumption of water, electricity, and gas for user a and user b as a family as a whole.
  • the family administrator can also Some private digital assets that are set as family assets are managed.
  • a work-type digital asset is jointly created by multiple users, so when these multiple users create works,
  • the distributed ledger also synchronously records each person's contribution to the work, and when there are subsequent transactions, the corresponding reward settlement is further carried out according to each person's contribution.
  • each data/digital asset determines the circulation method of the said data/digital asset to other users. Specifically, if the user’s authorization for a piece of data owned by him is prohibited from being disclosed, no one except the user can view it. As far as the data is reached, as for the data that is authorized to be disclosed or partially disclosed by the user, corresponding display or disclosure is carried out according to the corresponding authorized scope, that is, the data is both circulated and closed.
  • the first user is an individual user
  • the second user is an enterprise user.
  • each user has inherent attributes (personal or enterprise), it can be in different stages or
  • the transaction center plays different roles, that is, both individual users and corporate users can be buyers and sellers of digital assets. Therefore, in an embodiment of this embodiment, the transaction requirements can also be uploaded or uploaded by the first user. It is obtained by analyzing the data uploaded by the first user, and the digital asset may also be obtained by analyzing the data uploaded by the second user.
  • the users in the present invention are not limited to individuals and enterprises, but can also be supply chains and financial institutions. That is, the present invention can provide value matching for cooperation between individual users, enterprises, supply chains and financial institutions. .
  • the user's digital assets and rewards are processed through a distributed ledger to establish a decentralized empowerment community, and the user's digital assets are tokenized to promote multilateral matching transactions of digital assets.
  • part or all of the user’s digital assets can be put into the digital asset pool in response to the user’s selection or authorization (by a large number of digital assets belonging to multiple organizations or individuals).
  • the operation and promotion of digital assets are carried out.
  • the user's digital assets can be published, shared, forwarded, and liked in a similar manner to Weibo.
  • corresponding rewards will be allocated based on the user’s promotion contribution to motivate the user.
  • user A publishes digital asset a in the digital asset pool
  • user B browses to digital asset a. It shares (contribution degree +10), forwards (contribution degree +5), collects (contribution degree +1), likes (contribution degree +1), then the digital asset a matches the corresponding transaction demand user C, and When the transaction is completed, the corresponding reward can be allocated to user B based on the promotion contribution made by user B.
  • the contribution in the above example is only exemplary data, and the contribution calculation method of the present invention is not Limited to this, the corresponding contribution degree calculation weight can also be given based on the user's promotion attribute information (such as the number of fans or authority or exposure), that is, the user's promotion contribution to a certain digital asset is the basic contribution combined with the contribution of the promotion operation The calculated weight is calculated.
  • valuation model pricing can be directly recommended based on the user's transaction purpose and matched with the relevant demand side for transactions.
  • multi-party matching may be required. For example, it is necessary to match the five sides of the holder, the user, the supply chain, the investment institution, and the consumer. Therefore, for the more complicated transaction matching described above, in an embodiment of this embodiment, in the above embodiment It can be carried out directly in the community constructed in China, and for matching that only involves both parties to the transaction (more on traditional digital assets), it can be carried out directly, simplifying the transaction process and saving transaction costs.
  • users can be gathered to form a discussion based on their transaction purposes, that is, the community can be divided into sections based on the purpose of transactions, and different sections conduct different transaction matching. And communicate and discuss.
  • the above method of the present invention can help individual or family users to protect privacy and other data, and enable users to obtain the corresponding digital asset income.
  • some assets are placed without circulation, and their value cannot be realized, and it may even be possible. Depreciation, and investment can enable users to gain income or capital increase in the foreseeable period in the future. Therefore, in an embodiment of this embodiment, investment portfolio management can also be performed based on the user’s asset management needs. Specifically, Diversified management of assets in accordance with asset selection theory and investment portfolio theory to achieve the investment objectives of diversifying risks and improving efficiency.
  • the present invention also provides a digital asset value management and operation device, including:
  • the digital asset analysis module 210 is configured to analyze the digital assets involved in the data uploaded by at least one first user, wherein the digital assets are recorded in a distributed ledger;
  • the transaction matching module 220 is configured to match digital assets of at least one second user and at least one of the first users based on transaction requirements;
  • the transaction push module 230 is configured to initiate a transaction invitation to the matched parties
  • the transaction settlement module 240 is configured to complete the right change of the digital asset and the reward settlement in response to the authorization of the matching parties for the transaction;
  • the device further includes:
  • the digital identity building module is configured to construct a digital identity for each user and conduct transactions based on the user’s digital identity, wherein when the transaction is completed, the digital assets and rewards are recorded in the distributed ledger based on the user’s digital identity Attribution.
  • the device further includes an enabling module configured to enable the user's data by analyzing the digital assets involved in the user's data.
  • the device processes the user's digital assets and rewards through a distributed ledger, establishes a decentralized empowerment community, and certifies the users' digital assets.
  • the transaction requirement is uploaded by the second user.
  • the device further includes:
  • the license authorization obtaining module is configured to obtain the user's permission to use the data when receiving the data uploaded by the user.
  • the transaction requirement is obtained based on data analysis uploaded by the second user.
  • the transaction requirement is obtained based on data analysis uploaded by the first user.
  • the transaction demand is obtained by analyzing the data uploaded by the user based on a preset demand analysis model, wherein the demand analysis model is established based on a deep neural network.
  • the preset demand analysis model is also configured to be automatically updated iteratively based on expert correction.
  • the digital asset analysis module is further configured to analyze the digital assets involved in the data uploaded by the first user through a preset data model, and set basic rewards for the digital assets.
  • the digital asset analysis module includes:
  • the digital asset analysis unit is configured to analyze all digital assets involved in the data uploaded by the first user;
  • the digital asset classification unit is configured to classify all digital assets of the first user
  • the basic reward distribution unit is configured to give corresponding basic rewards based on the type of digital asset.
  • the data type of the digital asset of the first user includes at least one of image, text, video, and personal information.
  • the digital assets of the personal information type of the first user include behavior habits in different time periods.
  • the behavior data of the first user in different time dimensions is analyzed and acquired from the behavior data uploaded by the first user.
  • the authorization of the first user to the digital asset includes at least an authorization scope and an authorization period.
  • the transaction settlement module includes:
  • the reward settlement weight determination unit is configured to determine the reward settlement weight based on the authorization range and the authorization period of the first user for the digital asset;
  • the reward settlement unit is configured to determine a final reward based on the reward settlement weight and the basic reward of the digital asset for reward settlement.
  • the device further includes an authorization monitoring module configured to monitor the authorization period of the digital asset authorized by the first user after the transaction is completed; and
  • the authorization period of the digital asset expires, the authorization of the digital asset is recovered and recorded in the distributed ledger.
  • a preset smart contract automatically executes the transaction on the distributed ledger to complete the right change of the digital asset and reward settlement to ensure mutual trust between the two parties.
  • the user's digital assets and rewards are processed through a distributed ledger preset with smart contracts to ensure the pass operation of digital assets.
  • a user involves at least one node in the distributed ledger.
  • the smart contract is jointly voted by all or part of the nodes in the distributed ledger to decide whether to record in the distributed ledger to achieve community autonomy.
  • the voting decisions related to the smart contract are made by the master node in the distributed ledger.
  • the preset smart contract is obtained based on various business analysis.
  • a corresponding set of smart contracts is analyzed.
  • each type of service is used to process at least one of rewards, digital assets, and user identity information.
  • each node in the distributed ledger adopts a Byzantine fault-tolerant algorithm to reach a consensus.
  • the circulation mode of the data/digital assets to other users is determined.
  • the matching between the user's digital assets and needs is performed based on a preset transaction matching model.
  • the transaction matching model is constructed based on artificial intelligence.
  • the transaction matching model includes at least one of a Bayesian network, a reinforcement learning model, a deep learning model, and a generative confrontation network.
  • the device further includes an investment module configured to perform diversified management of assets based on asset selection theory and investment portfolio theory;
  • the investment module can realize the investment purpose of diversifying risks and improving efficiency.
  • each component in the above system can be configured by software, firmware, hardware or a combination thereof.
  • the specific means or methods that can be used for the configuration are well known to those skilled in the art, and will not be repeated here.
  • the program constituting the software is installed from a storage medium or network to a computer with a dedicated hardware structure (for example, the general-purpose computer 300 shown in FIG. 3).
  • a dedicated hardware structure for example, the general-purpose computer 300 shown in FIG. 3.
  • Fig. 3 shows a schematic block diagram of a computer that can be used to implement the method and system according to the embodiments of the present invention.
  • a central processing unit (CPU) 301 performs various processing in accordance with a program stored in a read only memory (ROM) 302 or a program loaded from a storage section 308 to a random access memory (RAM) 303.
  • the RAM 303 also stores data required when the CPU 301 executes various processes and the like as necessary.
  • the CPU 301, ROM 302, and RAM 303 are connected to each other via a bus 304.
  • the input/output interface 305 is also connected to the bus 304.
  • the following components are connected to the input/output interface 305: input part 306 (including keyboard, mouse, etc.), output part 307 (including display, such as cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.), Storage part 308 (including hard disk, etc.), communication part 309 (including network interface card such as LAN card, modem, etc.).
  • the communication section 309 performs communication processing via a network such as the Internet.
  • the driver 310 can also be connected to the input/output interface 305 as required.
  • a removable medium 311 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. can be installed on the drive 310 as needed, so that the computer program read out therefrom is installed in the storage part 308 as needed.
  • a program constituting the software is installed from a network such as the Internet or a storage medium such as a removable medium 311.
  • this storage medium is not limited to the detachable medium 311 shown in FIG. 3 which stores the program and is distributed separately from the device to provide the program to the user.
  • removable media 311 include magnetic disks (including floppy disks (registered trademarks)), optical disks (including compact disk read-only memory (CD-ROM) and digital versatile disks (DVD)), magneto-optical disks (including mini disks (MD) (registered trademarks) )) and semiconductor memory.
  • the storage medium may be a ROM 302, a hard disk included in the storage section 308, etc., in which programs are stored and distributed to users together with the devices containing them.
  • the present invention also proposes a program product storing machine-readable instruction codes.
  • the instruction code is read and executed by a machine, the above method according to the embodiment of the present invention can be executed.
  • a storage medium for carrying the above-mentioned program product storing machine-readable instruction codes is also included in the scope of the present invention.
  • the storage medium includes, but is not limited to, a floppy disk, an optical disk, a magneto-optical disk, a memory card, a memory stick, etc.
  • the method of the present invention is not limited to be executed according to the time sequence described in the specification, and may also be executed sequentially, in parallel or independently in other orders. Therefore, the execution order of the methods described in this specification does not limit the technical scope of the present invention.
  • the present invention provides the following technical solutions:
  • a method of digital asset value management and operation including:
  • a digital identity is constructed for each user, and transactions are performed based on the user's digital identity, wherein when the transaction is completed, the attribution of digital assets and rewards is recorded in the distributed ledger based on the user's digital identity.
  • the data type of the digital asset of the first user includes at least one of image, text, video, and personal information.
  • the final reward is determined according to the reward settlement weight and the basic reward of the digital asset for reward settlement.
  • the authorization period of the digital asset expires, the authorization of the digital asset is recovered and recorded in the distributed ledger.
  • the transaction matching model includes at least one of a Bayesian network, a reinforcement learning model, a deep learning model, and a generative confrontation network.
  • a digital asset value management and operation device including:
  • the digital asset analysis module is configured to analyze the digital assets involved in the data uploaded by at least one first user, wherein the digital assets are recorded in a distributed ledger;
  • the transaction matching module is configured to match digital assets of at least one second user and at least one of the first users based on transaction requirements;
  • the transaction push module is configured to initiate a transaction invitation to the matched parties
  • the transaction settlement module is configured to complete the right change of the digital asset and reward settlement in response to the authorization of the matching parties for the transaction;
  • the digital identity building module is configured to construct a digital identity for each user and conduct transactions based on the user’s digital identity, wherein when the transaction is completed, the digital assets and rewards are recorded in the distributed ledger based on the user’s digital identity Attribution.
  • the device further includes an enabling module configured to enable the user's data by analyzing the digital assets involved in the user's data.
  • the license authorization obtaining module is configured to obtain the user's permission to use the data when receiving the data uploaded by the user.
  • the digital asset analysis module is further configured to analyze the digital assets involved in the data uploaded by the first user through a preset data model, and provide all The basic rewards for the digital assets are set.
  • the digital asset analysis unit is configured to analyze all digital assets involved in the data uploaded by the first user;
  • the digital asset classification unit is configured to classify all digital assets of the first user
  • the basic reward distribution unit is configured to give corresponding basic rewards based on the type of digital asset.
  • transaction settlement module includes:
  • the reward settlement weight determination unit is configured to determine the reward settlement weight based on the authorization range and the authorization period of the first user for the digital asset;
  • the reward settlement unit is configured to determine a final reward based on the reward settlement weight and the basic reward of the digital asset for reward settlement.
  • the device further comprises an authorization monitoring module configured to monitor the authorization period of the digital asset authorized by the first user after the transaction is completed;
  • the authorization period of the digital asset expires, the authorization of the digital asset is recovered and recorded in the distributed ledger.
  • the promotion contribution recording module is configured to record the contribution made by the third user to the promotion operation of the digital assets in the community in the distributed ledger;
  • the promotion reward settlement module is configured to allocate corresponding promotion rewards to the third user based on the third user's contribution when the digital asset transaction is completed.
  • transaction matching module is further configured to perform multilateral transaction matching in the community based on the digital assets recorded in the distributed ledger in response to a user's selection or authorization .
  • transaction matching module is further configured to perform multilateral transaction matching in the community for specific types of digital assets recorded in the distributed ledger.
  • each type of service is used to process at least one of rewards, digital assets, and user identity information.
  • each node in the distributed ledger adopts a Byzantine fault-tolerant algorithm to reach a consensus.
  • the transaction matching model includes at least one of a Bayesian network, a reinforcement learning model, a deep learning model, and a generative confrontation network.
  • a computer-readable storage medium storing program code, which when executed by a processor, implements the method of any one of claims 1-34.
  • a computing device comprising a processor and a storage medium storing program code, the program code, when executed by the processor, implements the method according to any one of claims 1-34.

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Abstract

L'invention concerne un procédé et un appareil de gestion et d'exploitation de valeurs d'actifs numériques, un support et un dispositif informatique. Le procédé consiste à : analyser des actifs numériques dans des données téléchargées par au moins un premier utilisateur, les actifs numériques étant enregistrés dans un registre distribué (S110) ; mettre en correspondance, sur la base d'exigences de transaction, des actifs numériques d'au moins un second utilisateur et dudit premier utilisateur (S120) ; lancer une invitation pour une transaction à des parties mises en correspondance (S130) ; réaliser, en réponse à des autorisations des parties mises en correspondance pour la transaction, une modification de droits des actifs numériques et un règlement de récompense (S140) ; enregistrer la transaction réalisée dans le registre distribué (S150). Le procédé analyse des actifs numériques dans des données d'utilisateurs afin d'obtenir une habilitation sur des données d'utilisateur, traite les actifs numériques et les récompenses des utilisateurs au moyen d'un registre distribué, établit une communauté d'habilitation décentralisée et segmente les actifs numériques des utilisateurs.
PCT/CN2019/106872 2019-09-20 2019-09-20 Procédé et appareil de gestion et d'exploitation de valeurs d'actifs numériques, support et dispositif informatique WO2020177298A1 (fr)

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PCT/CN2019/106872 WO2020177298A1 (fr) 2019-09-20 2019-09-20 Procédé et appareil de gestion et d'exploitation de valeurs d'actifs numériques, support et dispositif informatique
CN201980006029.1A CN111566686A (zh) 2019-09-20 2019-09-20 数字资产价值管理及运营方法、装置、介质和计算设备

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