Data product sharing method and device based on block chain, equipment and medium

Abstract

The invention provides a data product sharing method and device based on a block chain, equipment and a medium, and can be applied to the technical field of block chains. The method comprises the following steps: in response to a received product request sent by a target user, obtaining contract data and product demand information from the product request, the contract data comprising a product sharing agreed rule; determining a target data product to be acquired by the target user based on the product demand information; generating a smart contract based on the contract data, and uploading the smart contract; generating a product voucher according to the target data product and the intelligent contract, and uploading the product voucher; sending the target data product to the target user; and sending the product voucher to the supervision user, so that the supervision user obtains the smart contract from the chain, and analyzes the product voucher based on the smart contract to determine the use permission of the target data product.

Description

Technical Field

[0001] The present disclosure relates to the field of blockchain, and more specifically to a blockchain-based data product sharing method, device, equipment, medium and program product. Background Art

[0002] With the in-depth development of a new round of scientific and technological revolution and industrial transformation, the value of data as a key production factor has become increasingly prominent. In existing data products, data products are often shared directly through the network or media after the intention to share is reached. There is a lack of a method to share data products while protecting data security. Summary of the invention

[0003] In view of the above problems, the present disclosure provides a blockchain-based data product sharing method, device, equipment, medium and program product.

[0004] According to the first aspect of the present disclosure, a data product sharing method based on blockchain is provided, and the method is applied to a sharing platform, comprising: in response to receiving a product request sent by a target user, obtaining contract data and product demand information from the product request, wherein the contract data includes product sharing agreement rules; determining a target data product to be obtained by the target user based on the product demand information; generating a smart contract based on the contract data, and putting the smart contract on the chain; generating a product certificate based on the target data product and the smart contract, and putting the product certificate on the chain; sending the target data product to the target user; and sending the product certificate to a supervisory user, so that the supervisory user obtains the smart contract from the chain and parses the product certificate based on the smart contract to determine the use rights of the target data product.

[0005] According to an embodiment of the present disclosure, the above-mentioned usage rights include the usage period of the above-mentioned target data product, and the above-mentioned method also includes: when the above-mentioned usage period expires, receiving a termination request sent by the above-mentioned supervisory user, and obtaining the target user identifier and the target data product identifier in the above-mentioned usage request; sending a termination instruction to the target terminal corresponding to the above-mentioned target user identifier, so that the above-mentioned target terminal terminates the use of the above-mentioned target data product; receiving a termination confirmation message returned by the above-mentioned terminal, and updating the product certificate corresponding to the above-mentioned target data product identifier in the above-mentioned blockchain.

[0006] According to an embodiment of the present disclosure, the above-mentioned sharing platform stores multiple data products, and the above-mentioned determination of the target data product that the above-mentioned target user intends to obtain based on the above-mentioned product demand information includes: preprocessing the above-mentioned demand data to obtain preprocessed demand data; inputting the above-mentioned preprocessed demand data into a clustering model to obtain a clustering result, and the above-mentioned clustering model is obtained by training based on the above-mentioned multiple data products using a preset clustering algorithm. The above-mentioned clustering result includes the target product category corresponding to the above-mentioned demand data, the candidate data products of the above-mentioned target product category, and the matching degree of the candidate data products; the candidate data product with the highest matching degree with the candidate product is determined as the target data product.

[0007] According to an embodiment of the present disclosure, the above-mentioned clustering model is obtained by the following method: using a preset clustering algorithm to process the above-mentioned multiple data products to obtain the category to which each data product belongs and the cluster center corresponding to each category; determining the target distance between each data product and the cluster center of the category to which each data product belongs; using a probability density estimation algorithm to obtain the distance threshold of each of the above-mentioned categories based on the above-mentioned target distance; and obtaining the above-mentioned clustering model based on the above-mentioned distance threshold and the above-mentioned cluster center.

[0008] According to an embodiment of the present disclosure, the above-mentioned data product also includes a program product, the above-mentioned sharing platform includes multiple multi-party secure computing nodes, and the above-mentioned method also includes: determining multiple multi-party secure computing nodes based on the product identification of the target data product, wherein each multi-party secure computing node stores target data product slices related to the program product obtained by dividing the above-mentioned target data product through a secret sharing protocol; sending a common computing instruction to the above-mentioned multiple multi-party secure computing nodes, so that the above-mentioned multiple multi-party secure computing nodes calculate multiple target data product calculation results based on the above-mentioned target data product; in response to receiving the multiple target data product calculation results sent by the above-mentioned multiple target multi-party secure computing nodes, using a preset secret merging algorithm to obtain the above-mentioned target program product based on the above-mentioned multiple target data product calculation results.

[0009] According to an embodiment of the present disclosure, the above-mentioned data product includes an artificial intelligence model, the above-mentioned sharing platform includes multiple distributed server terminals, each of the above-mentioned distributed server terminals stores a set of training sample data, and the above-mentioned method also includes: sending an initial model to the above-mentioned multiple distributed server terminals, so that the multiple server terminals are respectively trained based on their respective training sample data to obtain multiple groups of sub-update parameters; in response to receiving the multiple groups of sub-update parameters sent by the above-mentioned multiple distributed server terminals, obtaining update parameters based on the aggregation of the above-mentioned multiple groups of sub-update parameters; adjusting the above-mentioned initial model based on the above-mentioned update parameters to obtain the above-mentioned artificial intelligence model.

[0010] According to an embodiment of the present disclosure, the above-mentioned data product also includes shared data, and the above-mentioned sending of the target data product to the above-mentioned target user includes: sending the encrypted shared data to the trusted execution environment in the terminal of the above-mentioned target user, and the above-mentioned trusted execution environment is built based on the trusted hardware in the above-mentioned terminal.

[0011] The second aspect of the present disclosure provides a data product sharing device based on blockchain, including: an acquisition module, used to respond to a product request sent by a target user, and acquire contract data and product demand information from the above product request, wherein the above contract data includes product sharing agreement rules; a determination module, used to determine the target data product that the above target user intends to acquire based on the above product demand information; a smart contract generation module, used to generate a smart contract based on the above contract data, and put the above smart contract on the chain; a product certificate generation module, used to generate a product certificate based on the above target data product and the smart contract, and put the above product certificate on the chain; a sending module, used to send the above target data product to the above target user; and a product certificate sending module, used to send the above product certificate to a supervisory user, so that the above supervisory user obtains the above smart contract from the chain, parses the above product certificate based on the above smart contract, and determines the use rights of the above target data product.

[0012] According to an embodiment of the present disclosure, the above-mentioned usage rights include the usage period of the above-mentioned target data product, and the above-mentioned device also includes: a termination request receiving module, which is used to receive the termination request sent by the above-mentioned supervisory user when the above-mentioned usage period expires, and obtain the target user identifier and target data product identifier in the above-mentioned usage request; a termination instruction sending module, which is used to send a termination instruction to the target terminal corresponding to the above-mentioned target user identifier, so that the above-mentioned target terminal terminates the use of the above-mentioned target data product; a termination confirmation message receiving module, which is used to receive the termination confirmation message returned by the above-mentioned terminal, and update the product certificate corresponding to the above-mentioned target data product identifier in the above-mentioned blockchain.

[0013] According to an embodiment of the present disclosure, the above-mentioned sharing platform stores multiple data products, and the above-mentioned determination module includes: a preprocessing submodule, which is used to preprocess the above-mentioned demand data to obtain preprocessed demand data; an input submodule, which is used to input the above-mentioned preprocessed demand data into a clustering model to obtain a clustering result. The above-mentioned clustering model is obtained by training based on the above-mentioned multiple data products using a preset clustering algorithm. The above-mentioned clustering result includes the target product category corresponding to the above-mentioned demand data, the candidate data products of the above-mentioned target product category and the matching degree of the candidate data products; the target data product determination submodule is used to determine the candidate data product with the highest matching degree with the candidate product as the target data product.

[0014] According to an embodiment of the present disclosure, the above-mentioned device also includes: a cluster center determination module, which is used to process the above-mentioned multiple data products using a preset clustering algorithm to obtain the category to which each data product belongs and the cluster center corresponding to each category; a target distance determination module, which is used to determine the target distance between each data product and the cluster center of the category to which each data product belongs; a distance threshold determination module, which is used to obtain the distance threshold of each of the above-mentioned categories based on the above-mentioned target distance using a probability density estimation algorithm; and a cluster model determination module, which is used to obtain the above-mentioned cluster model based on the above-mentioned distance threshold and the above-mentioned cluster center.

[0015] According to an embodiment of the present disclosure, the above-mentioned data product also includes a program product, the above-mentioned sharing platform includes multiple multi-party secure computing nodes, and the above-mentioned device also includes: a multi-party secure computing node determination module, which is used to determine multiple multi-party secure computing nodes based on the product identification of the target data product, wherein each multi-party secure computing node stores target data product slices related to the program product obtained by dividing the above-mentioned target data product through a secret sharing protocol; a common computing instruction sending module, which is used to send a common computing instruction to the above-mentioned multiple multi-party secure computing nodes, so that the above-mentioned multiple multi-party secure computing nodes calculate multiple target data product calculation results based on the above-mentioned target data product; a secret merging module, which is used to respond to receiving multiple target data product calculation results sent by the above-mentioned multiple target multi-party secure computing nodes, and use a preset secret merging algorithm to obtain the above-mentioned target program product based on the above-mentioned multiple target data product calculation results.

[0016] According to an embodiment of the present disclosure, the above-mentioned data product includes an artificial intelligence model, the above-mentioned sharing platform includes multiple distributed server terminals, each of the above-mentioned distributed server terminals stores a set of training sample data, and the above-mentioned device also includes: an initial model sending module, which is used to send the initial model to the above-mentioned multiple distributed server terminals, so that the multiple server terminals are respectively trained based on their respective training sample data to obtain multiple groups of sub-update parameters; an aggregation module, which is used to respond to receiving multiple groups of sub-update parameters sent by the above-mentioned multiple distributed server terminals, and obtain update parameters based on the above-mentioned multiple groups of sub-update parameters; an update module, which is used to adjust the above-mentioned initial model based on the above-mentioned update parameters to obtain the above-mentioned artificial intelligence model.

[0017] According to an embodiment of the present disclosure, the above-mentioned data product also includes shared data, and the above-mentioned sending module includes: a trusted sending sub-module, used to send the encrypted shared data to the trusted execution environment in the terminal of the above-mentioned target user, and the above-mentioned trusted execution environment is built based on the trusted hardware in the above-mentioned terminal.

[0018] A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors execute the blockchain-based data product sharing method.

[0019] The fourth aspect of the present disclosure also provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, causes the processor to execute the above-mentioned blockchain-based data product sharing method.

[0020] The fifth aspect of the present disclosure also provides a computer program product, including a computer program, which implements the above-mentioned blockchain-based data product sharing method when executed by a processor.

[0021] According to the embodiments of the present disclosure, by obtaining product demand information and contract data in a product request and determining the target data product that the target user intends to obtain based on the product demand information, the target data product that matches the product demand information can be quickly determined, thereby improving the matching efficiency; a product certificate is generated based on the target data product and the smart contract, and the smart contract and the product certificate are uploaded to the chain and sent to the supervising user, so that the supervising user obtains the smart contract from the chain, parses the product certificate, and determines the use rights of the target data product, thereby ensuring the security of the data product sharing process and improving the efficiency of data product sharing. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The above contents and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0023] Figure 1 The following schematically shows an application scenario diagram of a data product sharing method and device based on blockchain according to an embodiment of the present disclosure;

[0024] Figure 2 A flowchart of a data product sharing method based on blockchain according to an embodiment of the present disclosure is schematically shown;

[0025] Figure 3 A schematic diagram schematically shows a data product sharing system architecture according to an embodiment of the present disclosure;

[0026] Figure 4 The following schematically shows a schematic diagram of data product sharing implemented through a data product sharing system architecture according to an embodiment of the present disclosure;

[0027] Figure 5 The structure block diagram of the data product sharing device based on blockchain according to an embodiment of the present disclosure is schematically shown;

[0028] Figure 6 A block diagram of an electronic device suitable for implementing a blockchain-based data product sharing method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0029] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0030] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0031] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0032] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0033] In the technical solution of the present invention, the user information (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0034] In the scenario of using personal information for automated decision-making, the methods, devices, and systems provided by the embodiments of the present invention provide users with corresponding operation portals for users to choose to agree or reject the automated decision-making results; if the user chooses to reject, the expert decision-making process will be entered. The expression "automated decision-making" here refers to the activity of automatically analyzing and evaluating an individual's behavioral habits, interests and hobbies, or economic, health, credit status, etc. through computer programs, and making decisions. The expression "expert decision-making" here refers to the activity of making decisions by people who specialize in a certain field, have specialized experience, knowledge and skills, and have reached a certain level of professionalism.

[0035] With the continuous advancement of scientific and technological revolution and industrial transformation, data has become a vital production factor, and its value and influence are constantly increasing. After the existing data products reach the intention of sharing, they are shared directly through the network or media. There is a lack of a method to share data products while protecting data security.

[0036] In view of this, an embodiment of the present disclosure provides a data product sharing method based on blockchain, and the method is applied to a sharing platform, and the method includes: in response to receiving a product request sent by a target user, obtaining contract data and product demand information from the product request, wherein the contract data includes product sharing agreement rules; determining the target data product that the target user intends to obtain based on the product demand information; generating a smart contract based on the contract data, and putting the smart contract on the chain; generating a product certificate based on the target data product and the smart contract, and putting the product certificate on the chain; sending the target data product to the target user; and sending the product certificate to the supervising user, so that the supervising user obtains the smart contract from the chain and parses the product certificate based on the smart contract to determine the use rights of the target data product.

[0037] Figure 1 The application scenario diagram of the blockchain-based data product sharing method and device according to an embodiment of the present disclosure is schematically shown.

[0038] like Figure 1 As shown, the application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used to provide a medium for a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or optical fiber cables, etc.

[0039] The user can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only for example).

[0040] The first terminal device 101, the second terminal device 102, and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0041] The server 105 may be a server that provides various services, such as a background management server (only as an example) that provides support for websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0042] It should be noted that the data product sharing method based on blockchain provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the data product sharing device based on blockchain provided in the embodiment of the present disclosure can generally be set in the server 105. The data product sharing method based on blockchain provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Accordingly, the data product sharing device based on blockchain provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105.

[0043] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0044] The following will be based on Figure 1 The scene described by Figure 2~Figure 4 The data product sharing method based on blockchain in the disclosed embodiment is described in detail.

[0045] Figure 2The flowchart of the data product sharing method based on blockchain according to an embodiment of the present disclosure is schematically shown.

[0046] like Figure 2 As shown, the blockchain-based data product sharing method of this embodiment includes operations S210 to S260.

[0047] In operation S210, in response to receiving a product request sent by a target user, contract data and product requirement information are acquired from the product request.

[0048] Among them, contract data includes product sharing agreement rules.

[0049] According to an embodiment of the present disclosure, the above-mentioned product demand information may include information such as data security, product demand business field, business line, product target users, and common product problems.

[0050] According to an embodiment of the present disclosure, the above-mentioned contract data may include, for example, the use period of the product, and the contract agreed upon between the target user and the supplier of the product, etc.

[0051] In operation S220, a target data product that the target user intends to obtain is determined based on the product demand information.

[0052] According to an embodiment of the present disclosure, the sharing platform can determine a target data product to send to a target user based on the demand business field in the product demand information, or it can determine multiple candidate data products to send to the target user for the target user to select and obtain the target data product. The present disclosure does not impose any restrictions on this.

[0053] In operation S230, a smart contract is generated based on the contract data, and the smart contract is put on the chain.

[0054] According to an embodiment of the present disclosure, a smart contract is a digital contract that defines contract terms through computer code.

[0055] In operation S240, a product certificate is generated according to the target data product and the smart contract, and the product certificate is uploaded to the chain.

[0056] According to the embodiment of the present disclosure, the above-mentioned smart contract and product certificate are put on the chain, which means that the information of the smart contract and product certificate is recorded on the blockchain to ensure its non-tamperability and transparency.

[0057] In operation S250, the target data product is sent to the target user.

[0058] In operation S260, the product certificate is sent to the supervisory user, so that the supervisory user obtains the smart contract from the chain and parses the product certificate based on the smart contract to determine the use rights of the target data product.

[0059] According to the embodiments of the present disclosure, the target user can be ensured to comply with the product sharing agreement rules in the smart contract by issuing product certificates to the supervising user, or the target user can be automatically supervised to comply with the product sharing agreement rules in the smart contract through smart certificates. This application does not impose any restrictions on this.

[0060] According to the embodiments of the present disclosure, by obtaining product demand information and contract data in a product request and determining the target data product that the target user intends to obtain based on the product demand information, the target data product that matches the product demand information can be quickly determined, thereby improving the matching efficiency; a product certificate is generated based on the target data product and the smart contract, and the smart contract and the product certificate are uploaded to the chain and sent to the supervising user, so that the supervising user obtains the smart contract from the chain, parses the product certificate, and determines the use rights of the target data product, thereby ensuring the security of the data product sharing process and improving the efficiency of data product sharing.

[0061] According to an embodiment of the present disclosure, the above-mentioned usage rights include the usage period of the target data product, and the method also includes: when the usage period expires, receiving a termination request sent by a supervising user, obtaining the target user identifier and the target data product identifier in the usage request; sending a termination instruction to the target terminal corresponding to the target user identifier, so that the target terminal terminates the use of the target data product; receiving a termination confirmation message returned by the terminal, and updating the product certificate corresponding to the target data product identifier in the blockchain.

[0062] According to the disclosed embodiment, the above-mentioned supervisory user can receive a prompt for determining the usage authority of the target data product sent by the sharing platform at regular intervals. After the supervisory user parses the product certificate according to the smart contract and determines that the product's usage period has expired, the supervisory user sends a request to terminate the use of the target data product to the sharing platform, so that the sharing platform sends a termination instruction to the target user, thereby stopping the use of the target data product, and updates the connected product certificate after receiving the termination confirmation message returned by the terminal.

[0063] According to the embodiments of the present disclosure, by supervising the use rights of users to monitor the target data products, the timely termination of the use rights of the data products can be ensured, thereby preventing data abuse. Further, after the sharing platform receives the termination message, the product certificate on the blockchain is updated, ensuring that the data status on the blockchain is consistent with the actual situation, thereby improving data transparency.

[0064] According to an embodiment of the present disclosure, the above-mentioned sharing platform stores multiple data products, and determines the target data product that the target user intends to obtain based on the product demand information, including: preprocessing the demand data to obtain preprocessed demand data; inputting the preprocessed demand data into a clustering model to obtain a clustering result, the clustering model is obtained by training based on multiple data products using a preset clustering algorithm, and the clustering result includes the target product category corresponding to the demand data, the candidate data products of the target product category, and the matching degree of the candidate data products; the candidate data product with the highest matching degree with the candidate product is determined as the target data product.

[0065] According to an embodiment of the present disclosure, the above-mentioned clustering model is obtained by the following method: using a preset clustering algorithm to process multiple data products to obtain the category to which each data product belongs and the cluster center corresponding to each category; determining the target distance between each data product and the cluster center of the category to which each data product belongs; using a probability density estimation algorithm to obtain the distance threshold of each category based on the target distance; and obtaining a clustering model based on the distance threshold and the cluster center.

[0066] Exemplarily, for a demand data, after it is preprocessed and input into the clustering model, the corresponding target product category A can be obtained, the candidate data is all data products with the target product category A, and the matching degree between each candidate data product and the candidate data product of the demand data, the candidate data products can be sorted according to their matching degree, and the candidate data product with the highest matching degree is selected as the target data product.

[0067] According to the embodiment of the present disclosure, the above-mentioned preset clustering algorithm may be, for example, a density-based clustering algorithm, a hierarchy-based clustering algorithm, etc. The clustering algorithm divides the data into different categories according to the characteristics of the data, and each category has a corresponding cluster center.

[0068] According to an embodiment of the present disclosure, the target distance may be determined by calculating the Euclidean distance, Manhattan distance, or other distances between each data product and the assigned cluster center.

[0069] According to the embodiments of the present disclosure, by constructing a clustering model, after receiving product demand information sent by the target user, the demand data can be directly input into the clustering model to quickly obtain the target data product. Through clustering model analysis, the characteristics and classification of the data product can be more clearly understood, thereby improving the availability and pertinence of the data product.

[0070] According to an embodiment of the present disclosure, the above-mentioned data product also includes a program product, the sharing platform includes multiple multi-party secure computing nodes, and the method also includes: determining multiple multi-party secure computing nodes based on the product identification of the target data product, wherein each multi-party secure computing node stores a target data product slice related to the program product obtained by splitting the target data product through a secret sharing protocol; sending a common computing instruction to the multiple multi-party secure computing nodes, so that the multiple multi-party secure computing nodes obtain multiple target data product calculation results based on the target data product; in response to receiving the multiple target data product calculation results sent by the multiple target multi-party secure computing nodes, using a preset secret merging algorithm to obtain the target program product based on the multiple target data product calculation results.

[0071] According to the embodiments of the present disclosure, after the above-mentioned target data product is uploaded to the sharing platform, the target data product can be sliced ​​and stored in several fixed multi-party secure computing nodes, or it can be stored in all multi-party secure computing nodes. The present disclosure does not impose any restrictions on this.

[0072] According to the embodiments of the present disclosure, the target data product may also be encrypted by a private key, and then the private key may be sliced ​​and sent to multiple multi-party secure computing nodes. When the target data product is needed, the private key slices may be obtained from the multiple multi-party secure computing nodes, and the private keys may be merged before unlocking the target data product.

[0073] According to an embodiment of the present disclosure, the above-mentioned secret sharing protocol can be, for example, any one of a secret sharing protocol based on an interactive protocol, an additive secret sharing protocol, and a replicated secret sharing protocol. The above-mentioned preset secret merging algorithm is a secret merging algorithm corresponding to the secret sharing protocol. The present disclosure does not impose any restrictions on the secret sharing protocol algorithm.

[0074] According to the embodiments of the present disclosure, by storing the target data product shards in multiple multi-party secure computing nodes, collaborative computing is performed based on the target data product after receiving a common computing instruction to obtain multiple target data product calculation results, and the final target data product is obtained by merging the multiple target data product calculation results, thereby obtaining the target data product without exposing the original data, thereby enhancing data security and improving computing efficiency and flexibility.

[0075] According to an embodiment of the present disclosure, the above-mentioned data product includes an artificial intelligence model, the sharing platform includes multiple distributed servers, each distributed server stores a set of training sample data, and the method also includes: sending an initial model to multiple distributed servers, so that the multiple servers are trained based on their respective training sample data to obtain multiple groups of sub-update parameters; in response to receiving multiple groups of sub-update parameters sent by multiple distributed servers, obtaining update parameters based on the aggregation of the multiple groups of sub-update parameters; adjusting the initial model based on the update parameters to obtain an artificial intelligence model.

[0076] According to the embodiments of the present disclosure, the above-mentioned multiple distributed servers train the initial model locally for multiple rounds until a threshold is reached, thereby obtaining model parameters and sending them to the sharing platform. The sharing platform receives the updated model parameters uploaded by the multiple distributed servers, aggregates the model parameters, and changes the initial model to obtain an artificial intelligence model; it is also possible that the distributed server uploads the updated parameters to the sharing platform after each round of training, and the sharing platform calculates whether the iteration threshold is reached after aggregation. If the iteration threshold is not reached, the initial model after the parameter update will be sent to the multiple distributed servers to continue training.

[0077] According to the embodiments of the present disclosure, by training the initial model through multiple distributed servers that store training sample data, the model generalization ability can be improved. In the process of obtaining the trained artificial intelligence model, each distributed server only needs to upload the model update parameters, which protects data security while reducing the amount of data transmission.

[0078] According to an embodiment of the present disclosure, the above-mentioned data product also includes shared data, and sending the target data product to the target user includes: sending the encrypted shared data to the trusted execution environment in the terminal of the target user, and the trusted execution environment is built based on the trusted hardware in the terminal.

[0079] According to the embodiments of the present disclosure, the trusted execution environment can be composed of hardware and software, and the trusted execution environment can provide an isolated operating environment outside the terminal operating system to protect the internal running programs and data from being affected and attacked by the external environment. The trusted hardware can be, for example, a security chip, which can create a trusted root and provide basic support for implementing various trusted computing functions and various data encryption and decryption operations.

[0080] According to the embodiment of the present disclosure, the above-mentioned sharing platform can also be implemented using the following architecture: Figure 3 As shown, it is a schematic diagram of a data product sharing system architecture according to an embodiment of the present disclosure, and the architecture may include: a data product registration module 310, a data product sharing module 320, a data product operation module 330, a data product management module 340, a basic service module 350, and a message service module 360.

[0081] The data product registration module 310 includes an account management submodule 311, a data product registration and filing submodule 312, and a data product listing service submodule 313. The account management submodule 311 is used to manage user account registration, maintenance, and deletion; the data product registration and filing submodule 312 is used to complete the data product registration function, which may include data product information registration, data product information review, data product information release, and data product information maintenance, etc. The data product information review needs to review the data product positioning, target users, usage scenarios, data sources, data security, and compliance with relevant regulations, etc.; the data product listing service submodule 313 is responsible for the data product listing and delisting process, including the gray-scale listing and delisting of data products and the formal listing and delisting of data products.

[0082] The data product sharing module 320 includes a product sharing submodule 321 , a demand registration submodule 322 , a supply and demand matching submodule 323 and a knowledge sharing submodule 324 . The product sharing submodule 321 is used to visualize the data product types and can provide services such as browsing, searching, viewing and experiencing data products; the demand registration submodule 322 is used to register user needs, which may include demand registration, demand review, demand release and demand maintenance, etc. It can use the current demand as a guide to ensure that the demand meets the internal standards, analyze the priorities in multiple dimensions through business areas, business lines, target users, business performance, and common problems, and determine whether to use the data products currently stored on the sharing platform to meet the demand or develop a new data product; the supply and demand matching submodule 323 is used to recommend data products based on the intelligent recommendation model, which is composed of modules such as feature engineering, recall algorithm, and sorting model. It can realize automatic learning through feature preprocessing, automatic feature screening, automatic collection of positive and negative sample data, and automatic model training according to different user needs; the knowledge sharing submodule 324 is used to summarize the experience of using data products, that is, to determine the data product listing standards, data product design technical standards, product manuals and other related report documents and product documents by analyzing the review process and data products.

[0083] The data product operation module 330 includes a product operation analysis submodule 331, a user operation analysis submodule 332, a channel operation analysis submodule 333, and a content operation analysis submodule 334. The product operation analysis submodule 331 analyzes the value of data products through a preset formula and introduces it into the above-mentioned supply and demand matching submodule 323 for product recommendation; the user operation analysis submodule 332 is used to manage the user life cycle, that is, to calculate the user retention rate, so as to form a customer system and level; the channel operation analysis submodule 333 is used to register the cooperative channels for supplying users, determine possible cooperation points, and conduct operation analysis such as channel contribution and channel cost; the content operation analysis submodule 334 is used to determine the value of data product content and other operation analysis.

[0084] The data product management module 340 includes a user management submodule 341, a directory management submodule 342, a grayscale management submodule 343, and a content management submodule 344. The user management submodule 341 can be responsible for the registration management and user rights management of the supply users, including user registration, login management, user activity records and logout, user rights authorization, adjustment, and control, etc. The directory management submodule 342 is responsible for the addition and maintenance of the data product directory and the addition and maintenance of data product labels; the grayscale management submodule 343 is responsible for the grayscale update and formal update of data products; the content management submodule 344 is used to store data product promotion content and product Q&A and other related content life cycle management, including release, review, and offline, etc.

[0085] The basic service module 350 includes a product development service submodule 351, a privacy computing service submodule 352, and a data security service submodule 353. The product development service submodule 351 is used to provide a fast packaging tool for data product supply users, which can communicate data with the big data workstation development platform, the applet development tool, and the artificial intelligence development tool, so as to put the research and development products of the aforementioned development platform on the shelves as data products; the privacy computing service submodule 352 is used to provide blockchain services and privacy computing services (i.e., for implementing operations S210 to S260 in the disclosed embodiment), and the blockchain service implements the chain storage verification of the original data, and the chain storage backtracking of the key data and links of the calculation process. The privacy computing service provides three data methods: federated learning, multi-party secure computing, and trusted computing. Federated learning uses cryptographic algorithms such as secret sharing, homomorphic encryption, and differential privacy to achieve "data does not move, model moves", and only the intermediate calculation results of the model are exchanged. Multi-party secure computing uses cryptographic algorithms such as secret sharing, homomorphic encryption, obfuscated circuits, oblivious transmission, homomorphic commitment, zero-knowledge proof, and differential privacy to realize the exchange of encrypted original data. Trusted computing implements encryption of original data based on dedicated hardware and executes it in a "trusted environment"; the data security service submodule 353 is used to provide data security technical services, including hierarchical management of data products, data authority control, monitoring and auditing, etc.

[0086] The message service module 360 ​​includes a consultation service submodule 361, a complaint service submodule 362, and a notification message submodule 363. The consultation service submodule 361 is used for consultation acceptance, consultation allocation, consultation processing, and consultation feedback; the complaint service submodule 362 is used for complaint acceptance, complaint allocation, complaint processing, and complaint feedback; the notification message submodule 363 is used for sharing the platform to configure and send terminal messages.

[0087] Figure 4 The following schematically shows a schematic diagram of data product sharing implemented through a data product sharing system architecture according to an embodiment of the present disclosure.

[0088] like Figure 4 As shown, the target user and the supply user connect the data product to the product development service submodule, and can choose to connect to the security service submodule. If the data product is connected to the big data workstation development platform, the data development pipeline is used to complete the data development through sandbox data extraction, production data exploration, data development and data testing services. If the mini program development tool is used, the mini program SDK (Software Development Kit) can be used to achieve low-code rapid front-end development. If the data security technology platform is connected, the data security classification service and data permission management service are applied to achieve basic security management. Each data product can be registered, including data product relationship information, business information and technical information. The management information includes the data product name, the company name, the data product form, version information, registration time, etc. The business information includes the data product target users, target user scale, business field, product label, application scenario, etc. The technical information includes the data product timeliness, data product core technology, data product interface information, etc. The data product is connected to the privacy computing service submodule, and the data product target users, target user scale, business field, product label, application scenario, etc. Technical information includes data product timeliness, data product core technology, data product interface information, etc. After receiving the product request initiated by the target user, obtain the target user, usage scenario and related technical requirements of the demand product described in the demand request, and review and register the demand according to the relevant regulations of the demand registration submodule, and determine the matching data product after the review is passed.

[0089] Based on the above-mentioned blockchain-based data product sharing method, the present disclosure also provides a blockchain data product sharing device. Figure 5 The device is described in detail.

[0090] Figure 5 The structural block diagram of a data product sharing device based on blockchain according to an embodiment of the present disclosure is schematically shown.

[0091] like Figure 5 As shown, the blockchain-based data product sharing device 500 of this embodiment includes an acquisition module 510, a determination module 520, a smart contract generation module 530, a product certificate generation module 540, a sending module 550, and a product certificate sending module 560.

[0092] The acquisition module 510 is used to obtain contract data and product demand information from the product request in response to receiving the product request sent by the target user, wherein the contract data includes product sharing agreement rules. In one embodiment, the acquisition module 510 can be used to perform the operation S210 described above, which will not be repeated here.

[0093] The determination module 520 is used to determine the target data product that the target user intends to obtain based on the product demand information. In one embodiment, the determination module 520 can be used to perform the operation S220 described above, which will not be repeated here.

[0094] The smart contract generation module 530 is used to generate a smart contract based on the contract data and put the smart contract on the chain. In one embodiment, the smart contract generation module 530 can be used to perform the operation S230 described above, which will not be repeated here.

[0095] The product certificate generation module 540 is used to generate a product certificate according to the target data product and the smart contract. In one embodiment, the product certificate generation module 540 can be used to perform the operation S240 described above, which will not be repeated here.

[0096] The sending module 550 is used to send the target data product to the target user. In one embodiment, the sending module 550 can be used to perform the operation S250 described above, which will not be described in detail here.

[0097] The product certificate sending module 560 is used to send the product certificate to the supervising user, so that the supervising user obtains the smart contract from the chain and parses the product certificate based on the smart contract to determine the use rights of the target data product. In one embodiment, the product certificate sending module 560 can be used to perform the operation S260 described above, which will not be repeated here.

[0098] According to an embodiment of the present disclosure, the above-mentioned usage rights include the usage period of the above-mentioned target data product, and the above-mentioned device also includes: a termination request receiving module, which is used to receive the termination request sent by the above-mentioned supervisory user when the above-mentioned usage period expires, and obtain the target user identifier and target data product identifier in the above-mentioned usage request; a termination instruction sending module, which is used to send a termination instruction to the target terminal corresponding to the above-mentioned target user identifier, so that the above-mentioned target terminal terminates the use of the above-mentioned target data product; a termination confirmation message receiving module, which is used to receive the termination confirmation message returned by the above-mentioned terminal, and update the product certificate corresponding to the above-mentioned target data product identifier in the above-mentioned blockchain.

[0099] According to an embodiment of the present disclosure, the above-mentioned sharing platform stores multiple data products, and the above-mentioned determination module includes: a preprocessing submodule, which is used to preprocess the above-mentioned demand data to obtain preprocessed demand data; an input submodule, which is used to input the above-mentioned preprocessed demand data into a clustering model to obtain a clustering result. The above-mentioned clustering model is obtained by training based on the above-mentioned multiple data products using a preset clustering algorithm. The above-mentioned clustering result includes the target product category corresponding to the above-mentioned demand data, the candidate data products of the above-mentioned target product category and the matching degree of the candidate data products; the target data product determination submodule is used to determine the candidate data product with the highest matching degree with the candidate product as the target data product.

[0100] According to an embodiment of the present disclosure, the above-mentioned device also includes: a cluster center determination module, which is used to process the above-mentioned multiple data products using a preset clustering algorithm to obtain the category to which each data product belongs and the cluster center corresponding to each category; a target distance determination module, which is used to determine the target distance between each data product and the cluster center of the category to which each data product belongs; a distance threshold determination module, which is used to obtain the distance threshold of each of the above-mentioned categories based on the above-mentioned target distance using a probability density estimation algorithm; and a cluster model determination module, which is used to obtain the above-mentioned cluster model based on the above-mentioned distance threshold and the above-mentioned cluster center.

[0101] According to an embodiment of the present disclosure, the above-mentioned data product also includes a program product, the above-mentioned sharing platform includes multiple multi-party secure computing nodes, and the above-mentioned device also includes: a multi-party secure computing node determination module, which is used to determine multiple multi-party secure computing nodes based on the product identification of the target data product, wherein each multi-party secure computing node stores target data product slices related to the program product obtained by dividing the above-mentioned target data product through a secret sharing protocol; a common computing instruction sending module, which is used to send a common computing instruction to the above-mentioned multiple multi-party secure computing nodes, so that the above-mentioned multiple multi-party secure computing nodes calculate multiple target data product calculation results based on the above-mentioned target data product; a secret merging module, which is used to respond to receiving multiple target data product calculation results sent by the above-mentioned multiple target multi-party secure computing nodes, and use a preset secret merging algorithm to obtain the above-mentioned target program product based on the above-mentioned multiple target data product calculation results.

[0102] According to an embodiment of the present disclosure, the above-mentioned data product includes an artificial intelligence model, the above-mentioned sharing platform includes multiple distributed server terminals, each of the above-mentioned distributed server terminals stores a set of training sample data, and the above-mentioned device also includes: an initial model sending module, which is used to send the initial model to the above-mentioned multiple distributed server terminals, so that the multiple server terminals are respectively trained based on their respective training sample data to obtain multiple groups of sub-update parameters; an aggregation module, which is used to respond to receiving multiple groups of sub-update parameters sent by the above-mentioned multiple distributed server terminals, and obtain update parameters based on the above-mentioned multiple groups of sub-update parameters; an update module, which is used to adjust the above-mentioned initial model based on the above-mentioned update parameters to obtain the above-mentioned artificial intelligence model.

[0103] According to an embodiment of the present disclosure, the above-mentioned data product also includes shared data, and the above-mentioned sending module includes: a trusted sending submodule, which is used to send the encrypted shared data to the trusted execution environment in the terminal of the above-mentioned target user, and the above-mentioned trusted execution environment is built based on the trusted hardware in the above-mentioned terminal. According to an embodiment of the present disclosure, any multiple modules of the acquisition module 510, the determination module 520, the smart contract generation module 530, the product certificate generation module 540, the sending module 550, and the product certificate sending module 560 can be combined in one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the acquisition module 510, the determination module 520, the smart contract generation module 530, the product voucher generation module 540, the sending module 550, and the product voucher sending module 560 may be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or may be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware, and firmware or in an appropriate combination of any of them. Alternatively, at least one of the acquisition module 510, the determination module 520, the smart contract generation module 530, the product voucher generation module 540, the sending module 550, and the product voucher sending module 560 may be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding function may be executed.

[0104] Figure 6 A block diagram of an electronic device suitable for implementing a blockchain-based data product sharing method according to an embodiment of the present disclosure is schematically shown.

[0105] like Figure 6As shown, the electronic device 600 according to an embodiment of the present disclosure includes a processor 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage part 608 to a random access memory (RAM) 603. The processor 601 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a dedicated microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 601 may also include an onboard memory for caching purposes. The processor 601 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0106] In RAM 603, various programs and data required for the operation of electronic device 600 are stored. Processor 601, ROM 602 and RAM 603 are connected to each other via bus 604. Processor 601 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 602 and / or RAM 603. It should be noted that the program can also be stored in one or more memories other than ROM 602 and RAM 603. Processor 601 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in one or more memories.

[0107] According to an embodiment of the present disclosure, the electronic device 600 may further include an input / output (I / O) interface 605, which is also connected to the bus 604. The electronic device 600 may further include one or more of the following components connected to the I / O interface 605: an input portion 606 including a keyboard, a mouse, etc.; an output portion 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 608 including a hard disk, etc.; and a communication portion 609 including a network interface card such as a LAN card, a modem, etc. The communication portion 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed, so that a computer program read therefrom is installed into the storage portion 608 as needed.

[0108] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0109] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, it may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the ROM 602 and / or RAM 603 described above and / or one or more memories other than ROM 602 and RAM 603.

[0110] The embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the blockchain-based data product sharing method provided by the embodiment of the present disclosure.

[0111] The above functions defined in the system / device of the embodiment of the present disclosure are performed when the computer program is executed by the processor 601. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0112] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 609, and / or installed from a removable medium 611. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0113] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 609, and / or installed from the removable medium 611. When the computer program is executed by the processor 601, the above functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, apparatus, module, unit, etc. described above can be implemented by a computer program module.

[0114] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0115] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0116] It will be appreciated by those skilled in the art that the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways, even if such combinations and / or combinations are not explicitly described in the present disclosure. In particular, the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways without departing from the spirit and teachings of the present disclosure. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0117] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. The scope of the present disclosure is defined by the attached claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A data product sharing method based on blockchain, characterized in that: The method is applied to a sharing platform, and the method comprises: In response to receiving a product request sent by a target user, acquiring contract data and product demand information from the product request, wherein the contract data includes product sharing agreement rules; Determining the target data product that the target user intends to obtain based on the product demand information; Generate a smart contract based on the contract data, and put the smart contract on the chain; Generate a product certificate based on the target data product and the smart contract, and upload the product certificate to the blockchain; sending the target data product to the target user; and The product certificate is sent to the supervising user, so that the supervising user obtains the smart contract from the chain, parses the product certificate based on the smart contract, and determines the use rights of the target data product.

2. The method according to claim 1, characterized in that: The usage rights include the usage period of the target data product, and the method further includes: When the usage period expires, receiving a termination request sent by the supervising user, and obtaining a target user identifier and a target data product identifier in the usage request; Sending a termination instruction to a target terminal corresponding to the target user identifier, so that the target terminal terminates the use of the target data product; Receive a termination confirmation message returned by the terminal, and update the product certificate corresponding to the target data product identifier in the blockchain.

3. The method according to claim 1, characterized in that The sharing platform stores a plurality of data products, and determining the target data product that the target user intends to obtain based on the product demand information includes: Preprocessing the demand data to obtain preprocessed demand data; Inputting the preprocessed demand data into a clustering model to obtain a clustering result, wherein the clustering model is obtained by training based on the multiple data products using a preset clustering algorithm, and the clustering result includes a target product category corresponding to the demand data, candidate data products of the target product category, and a matching degree of the candidate data products; The candidate data product with the highest matching degree with the candidate product is determined as the target data product.

4. The method according to claim 3, characterized in that The clustering model is obtained by the following method: Processing the multiple data products using a preset clustering algorithm to obtain the category to which each data product belongs and the cluster center corresponding to each category; Determine the target distance between each data product and the cluster center of the classification to which each data product belongs; Using a probability density estimation algorithm, the distance thresholds of the respective classifications are obtained based on the target distance; The clustering model is obtained based on the distance threshold and the cluster center.

5. The method according to claim 1, characterized in that: The data product also includes a program product, the sharing platform includes a plurality of multi-party secure computing nodes, and the method further includes: Determine a plurality of multi-party secure computing nodes based on a product identifier of the target data product, wherein each multi-party secure computing node stores a target data product slice related to the program product obtained by dividing the target data product through a secret sharing protocol; Sending a common computing instruction to the multiple multi-party secure computing nodes, so that the multiple multi-party secure computing nodes calculate multiple target data product calculation results based on the target data product; In response to receiving multiple target data product calculation results sent by the multiple target multi-party secure computing nodes, the target program product is obtained based on the multiple target data product calculation results using a preset secret merging algorithm.

6. The method according to claim 1, characterized in that The data product includes an artificial intelligence model, the sharing platform includes a plurality of distributed servers, each of the distributed servers stores a set of training sample data, and the method further includes: Sending the initial model to the multiple distributed servers, so that the multiple servers are trained based on their respective training sample data to obtain multiple groups of sub-update parameters; In response to receiving multiple groups of sub-update parameters sent by the multiple distributed servers, aggregating update parameters based on the multiple groups of sub-update parameters; The initial model is adjusted based on the update parameters to obtain the artificial intelligence model.

7. The method according to claim 1, characterized in that The data product further includes shared data, and sending the target data product to the target user includes: The encrypted shared data is sent to a trusted execution environment in a terminal of the target user, where the trusted execution environment is built based on trusted hardware in the terminal.

8. A data product sharing device based on blockchain, characterized in that: The device comprises: an acquisition module, configured to, in response to receiving a product request sent by a target user, acquire contract data and product demand information from the product request, wherein the contract data includes product sharing agreement rules; A determination module, configured to determine the target data product that the target user intends to obtain based on the product demand information; A smart contract generation module, used to generate a smart contract based on the contract data and put the smart contract on the chain; A product certificate generation module, used to generate a product certificate according to the target data product and the smart contract, and upload the product certificate to the chain; a sending module, configured to send the target data product to the target user; and The product certificate sending module is used to send the product certificate to the supervising user, so that the supervising user obtains the smart contract from the chain and parses the product certificate based on the smart contract to determine the use rights of the target data product.

9. An electronic device, comprising: one or more processors; a storage device for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

11. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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