Multi-source data collaboration method and device based on federal control, electronic equipment and medium

Abstract

The invention relates to the field of computer systems, in particular to a multi-source data collaboration method and device based on federated control, electronic equipment and a medium, and the multi-source data collaboration method based on federated control comprises the following steps: disassembling an overall target into a plurality of sub-targets through a consensus mechanism, and distributing the sub-targets to each node in a block chain network; conflicts among the nodes are eliminated through voting on the chain; game and cooperation among nodes are balanced through a decentralized game model, and nodes are distributed through an incentive mechanism based on decentralized bidding to obtain rewards; constructing an inter-node trust relationship through a cross-chain contract and a data circulation framework based on the cross-chain contract; and unifying an interaction rule between a supplier and a demander in cross-chain interaction by exciting a compatible data collaboration contract. The method has the advantages that the situation of inconsistent understanding between supply and demand is reduced, and cross-domain management and control of the heterogeneous space are facilitated.

Description

Technical Field

[0001] The present invention relates to the field of computer systems, and in particular to a multi-source data collaboration method, device, electronic device and medium based on federal control. Background Art

[0002] When studying the relationship between supply and demand, the two sides often face the severe challenge of "talking at cross purposes". This challenge mainly stems from the deviation in the understanding of technology demand and supply between the two sides, which makes it difficult for traditional data collaboration methods to effectively bridge this gap. Blockchain technology, as an important technology that has emerged in recent years, has significant advantages in achieving data decentralization, transparency and security, but it also cannot avoid communication barriers between the supply and demand sides.

[0003] In the process of data sharing and trading, although there are a large number of data demands and supplies, the proportion of direct matches is relatively low. Behind this, there are huge differences between the supply and demand sides in terms of data value recognition, application scenario understanding and technical requirements. According to statistics, there is a problem of inconsistent understanding between nearly 90% of data demands and supplies. This misalignment between supply and demand makes it difficult for both parties to reach an effective consensus during communication.

[0004] The Chinese patent with publication number CN115271603A discloses a supply chain data collaboration method based on blockchain, the steps are: establish a supply chain network system based on blockchain; establish a supply chain information collaborative sharing process; establish supply chain procurement and inventory management based on blockchain; establish supply chain sales management based on blockchain; establish supply chain logistics and distribution management based on blockchain. By sharing product procurement information on the chain, retailers can select distributors from a large number of distributors based on procurement information, and also select manufacturers accordingly, which is convenient for the selection of high-quality suppliers. The authenticity and reliability of procurement data information on the supply chain is guaranteed. The demand side can understand the supplier's product supply ability and identity information validity from the blockchain network as a whole, and choose the appropriate product supplier. On the other hand, as the buyer of the product, it can obtain dynamic information about the product provided by the supplier in real time.

[0005] Although writing the data of suppliers and demanders into the blockchain can alleviate the problem of difficulty in reaching a consensus between the two parties, in the face of more complex cross-chain interactions, the consensus mechanisms between suppliers and demanders in different blockchains are different, and each blockchain is a separate data island, making it still difficult for suppliers and demanders to reach an effective consensus. Summary of the invention

[0006] In order to solve the problem of inconsistent understanding between supply and demand, which leads to difficulties in cross-domain management and control of heterogeneous spaces, the present invention provides a multi-source data collaboration method, device, electronic device and medium based on federal control.

[0007] The present invention provides a multi-source data collaboration method based on federated control, which adopts the following technical solutions: A multi-source data collaboration method based on federated control includes the following steps: The overall goal is broken down into multiple sub-goals through a consensus mechanism and assigned to various nodes in the blockchain network, including supply and demand parties; Eliminate conflicts between the nodes through on-chain voting; The game and cooperation between the nodes are balanced through a decentralized game model, and rewards are allocated to the nodes through an incentive mechanism based on decentralized bidding; Build a trust relationship between nodes through cross-chain contracts and a data circulation framework based on the cross-chain contracts; Through incentive-compatible data collaboration contracts, the interaction rules between the supplier and the demander in cross-chain interactions are unified.

[0008] In a specific possible implementation, the decentralized game model adopts the PPLNS mechanism.

[0009] In a specific possible implementation scheme, the incentive mechanism adopts the GSP auction mechanism.

[0010] In a specific implementation scheme, the incentive-compatible data collaboration contract is embedded with a VCG mechanism, an MP mechanism and a TSOS mechanism.

[0011] In a specific implementation scheme, the method in which the VCG mechanism constrains the demander and the supplier is: The demand parties quote for the required data, and the demand parties that meet the screening rules are stored in the winner set. The demand parties in the winner set can obtain the required data, and the payment price is the negative value of the marginal contribution it brings to other demand parties when joining the winner set, plus the highest bid of all the demand parties except the demand party.

[0012] In a specific implementation scheme, the method in which the MP mechanism constrains the demand side and the supply side is: The demander submits a data demand with an accompanying quotation; Sorting the quotations of all the demanders in descending order; After receiving the quotation, the supplier determines the monopoly price and supply scale.

[0013] In a specific implementation scheme, the method in which the TSOS mechanism constrains the demander and the supplier is: The demander's demand and estimated value for writing data on the blockchain; The supplier issues a quotation based on the demand and its own data. The supplier whose quotation is greater than the estimated value is saved in the potential supply set. If the potential supply set is an empty set, the demander rewrites the estimated value. The supplier in the potential supply set submits a data sample to the blockchain; The demander obtains the data sample in the blockchain, evaluates the data sample, and determines the supplier that meets the demand based on the evaluation result.

[0014] The present invention provides a multi-source data collaboration device based on federated control, which adopts the following technical solution: A multi-source data collaboration device based on federated control, comprising: one or more processors; memory; and one or more computer programs, wherein one or more of the computer programs are stored in the memory, and one or more of the computer programs include instructions, which, when executed by the processor, enable the multi-source data collaboration device based on federal control to perform the above-mentioned multi-source data collaboration method based on federal control.

[0015] The present invention provides an electronic device, which adopts the following technical solution: An electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by at least one of the processors, and the instructions are executed by at least one of the processors so that at least one of the processors can execute the above-mentioned multi-source data collaboration method based on federal control.

[0016] The present invention provides a multi-source data collaboration method based on federated control, which adopts the following technical solutions: A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute the above-mentioned multi-source data collaboration method based on federal control.

[0017] In summary, the present invention has the following beneficial effects: 1. Through pre-target alignment, in-process rule alignment, and post-trust alignment, the semantic alignment of suppliers and demanders in heterogeneous space cross-domain scenarios is carried out to reduce the inconsistency between supply and demand and facilitate the cross-domain management and control of heterogeneous space.

[0018] 2. When the rules are aligned in the process, the PPLNS mechanism and the GSP auction mechanism are combined to suppress the selfish behavior of nodes in pursuit of individual interests, balance the cooperation and game between nodes, and evenly distribute the rewards obtained by the nodes. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flowchart of a multi-source data collaboration method based on federated control.

[0020] Figure 2 It is a flowchart of ex ante goal alignment.

[0021] Figure 3 It is a flowchart of the alignment of rules in the process.

[0022] Figure 4 is a flow chart of post hoc trust alignment.

[0023] Figure 5 This is an architectural diagram of a multi-source data collaboration method based on federated control. DETAILED DESCRIPTION

[0024] The following combination Figure 1-Figure 5 The present invention is described in further detail.

[0025] The multi-source data collaboration method based on federated control provided in the embodiment of the present invention can be applied on a server or a terminal. The server can be a physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms. The terminal can be a user equipment (User Equipment, UE) such as a mobile phone, smart phone, laptop computer, digital broadcast receiver, personal digital assistant (PDA), tablet computer (PAD) with strong computing power, handheld device, vehicle-mounted device, wearable device, computing device or other processing device connected to a wireless modem, mobile station (Mobile Station, MS), mobile terminal (Mobile Terminal), etc., which is not limited in the present invention.

[0026] To facilitate understanding, some professional terms are explained: The PPLNS mechanism is a payment method used in cryptocurrency mining pools. Its main feature is that rewards are calculated based on the valid shares submitted by miners in the recent period, rather than based on the miners' actual mining success rate.

[0027] The GSP auction mechanism is a bidding auction method that determines rewards by combining bids and quality scores, and incentivizes nodes to make real bids by paying the second highest bid, thereby achieving more efficient reward distribution.

[0028] In a static and complete information game environment, all nodes have a complete understanding of the rules of the game, the strategies of other nodes, the profit functions, and other information, and this information does not change during the game.

[0029] In a dynamic game environment with incomplete information, during the game, information is incomplete and changes over time, and nodes need to adjust their strategies based on the observed behavior of other nodes.

[0030] Reference Figure 1 ,The multi-source data collaboration method based on federated control includes the following steps: S100, ex ante goal alignment.

[0031] Obtain the overall goals and requirements, as well as the consensus mechanism, and use the consensus mechanism to break down the overall goals into multiple sub-goals and assign them to each node in the network for processing. When conflicts or inconsistencies occur between nodes, the conflicts between nodes are eliminated through on-chain voting. The rules of on-chain voting are pre-written into the smart contract of the blockchain.

[0032] A heterogeneous space cross-domain scenario refers to a complex and diversified environment or field that contains different spaces, which may be physical space, digital space or other abstract space, as well as fields such as technology, economy, law, etc. There may be significant differences between these spaces and fields, including but not limited to rules, culture, and technical level. In a heterogeneous space cross-domain scenario, multiple independent but interrelated systems (hereinafter referred to as nodes for ease of description) need to work together to complete their own sub-goals and thus achieve an overall goal. Nodes include both supply and demand sides.

[0033] During the working process of these nodes, due to the lack of guidance from third-party organizations and unified central control, the nodes interact according to their own sub-goals and interests, which leads to disordered node decision-making, possible conflicts between decisions, and difficulty in interaction and coordination between nodes. To this end, the on-chain voting method provides a transparent, unchangeable, and decentralized coordination mechanism by writing the voting results into the blockchain, breaking down the overall goal into multiple sub-goals and assigning them to the corresponding nodes, eliminating possible conflicts in the node decision-making process and improving the orderliness of decision-making, so that the goals of multiple nodes are aligned and move towards the overall goal together.

[0034] S200, in-process rule alignment.

[0035] Obtain a decentralized game model, balance and alleviate information asymmetry in node games through peer-to-peer information disclosure on blockchain, and thus balance the game between nodes. Obtain an incentive mechanism based on decentralized bidding, and use the incentive mechanism to suppress the behavior of nodes pursuing individual interests.

[0036] There are two behaviors in node interaction: cooperation and game. When the sub-goals and values ​​between nodes reach a consensus, the nodes form cooperation; when there is resource competition and conflict of interest between nodes, it will lead to game between nodes. Cooperation and game between nodes will evolve dynamically with the change of incentive mechanism.

[0037] In this embodiment, the PPLNS mechanism is used to construct a centralized game model. The quality, completion time, task type and other completion information of the sub-goals completed by each node will be written into the blockchain. Through the transparent characteristics of the blockchain, the nodes can understand each other's completion of the sub-goals, alleviating the information asymmetry between the nodes. The number of node sub-goals completed is counted, and after multiple rounds, the nodes that meet the preset workload can be rewarded. The workload is the preset number of completed sub-goals, that is, after a certain number of rounds, the node can only be rewarded when the number of sub-goals completed by the node is greater than or equal to the workload, otherwise it cannot be rewarded. The reward is a mixed value that integrates tokens, reputation and social value. Through the decentralized game model, nodes are encouraged to continue to contribute, and nodes are more focused on the completion of the overall goal, pursuing overall interests, weakening the pursuit of the node's own interests, and reducing the unfairness of rewards caused by short-term fluctuations.

[0038] When distributing rewards to nodes, an incentive mechanism constructed by the GSP auction mechanism is used. The number and quality of sub-goals completed by nodes are used as the basis for ranking node contributions, and each node that can receive rewards is ranked, and rewards are distributed to nodes based on the ranking. The incentive mechanism can suppress the selfish behavior of nodes that simply pursue individual interests, and can operate stably and effectively in a game environment with static complete information and dynamic incomplete information.

[0039] The incentive mechanism is stable and not easily manipulated, which can improve the overall stability of the system. Since higher contributions can bring more rewards, the incentive mechanism can motivate nodes to improve the efficiency and quality of sub-goal completion, improve their own contributions to obtain more rewards, and thus improve system efficiency. Furthermore, the PPLNS mechanism can ensure that nodes that complete sub-goals for a long time can receive rewards. The incentive mechanism can distribute rewards to nodes based on their contributions, making the reward distribution more fair and realizing the rule alignment of cross-domain collaborative behaviors in heterogeneous spaces.

[0040] S300, ex post trust alignment.

[0041] Obtain cross-chain contracts, as well as data circulation frameworks based on cross-chain contracts, obtain incentive-compatible data collaboration contracts, and embed decentralized game models and incentive mechanisms into incentive-compatible data collaboration contracts. Incentive-compatible data collaboration contracts are a type of smart contract that uses algorithms to replace human trust through the characteristics of blockchain smart contracts without human interference and automatic execution, ensuring the establishment, maintenance and transmission of trust relationships in cross-domain transactions.

[0042] Different blockchain networks have different consensus mechanisms, encryption algorithms, etc. Through cross-chain contracts and data circulation frameworks, a secure, efficient, and scalable data exchange environment is built to ensure the consistency of data in cross-chain interactions between nodes, as well as the integrity, security, and traceability of cross-chain data. The incentive-compatible data collaboration contract stipulates the interaction rules such as the rights, obligations, and incentive mechanisms of nodes in the cross-chain interaction process, ensuring that while nodes pursue their own interests, they also promote the healthy operation of the entire system and the smooth flow of data.

[0043] The incentive-compatible data collaboration contract also embeds the VCG (Vickrey-Clarke-Groves) mechanism for processing small-scale data interactions, the MP (Monopolistic Price) mechanism and the TSOS (Two-Stage Obscure Selection) mechanism for large-scale data interactions.

[0044] Under the VCG mechanism, the demand side bids for the required data based on its own valuation, and the demand side that is the winner is screened out according to the screening rules written in the incentive-compatible data collaboration contract to build a winner set. These demand sides will obtain the data provided by the supplier, and the payment price of each demand side is the negative value of the marginal contribution it brings to other winners when joining the winner set, plus the highest bid of all winners except itself, that is, payment price = bid - marginal contribution. Marginal contribution refers to the value that the sum of all winners' valuations will decrease if a winner does not participate in the auction. Then the transaction between the demand side and the supplier is reached.

[0045] In the VCG mechanism, it is difficult to manipulate the results with false quotations, which allows the demand side to truthfully quote its own quotations, ensuring the fairness, transparency and efficiency of the transaction.

[0046] The MP mechanism is used in high-demand scenarios. Under the MP mechanism, the demander submits data requirements to the system, along with an estimate or quotation of the data. After the system collects the quotations from all demanders, it sorts them in descending order. After the supplier obtains the quotation output by the system, it selects the smallest acceptable quotation as the monopoly price. The monopoly price is usually less than the highest quotation in the quotation, so that the supplier can get payment from at least one demander. The supplier determines the supply scale based on the principle of maximizing the supplier's benefits. Then the transaction between the demander and the supplier is concluded.

[0047] The MP mechanism can facilitate rapid transaction completion and prevent the demand side from deliberately lowering the bid to obtain the required data at a lower price.

[0048] In the TSOS mechanism, the demander writes the demand for data and the estimated value of the data on the VChain-D2S blockchain. After the supplier obtains the demand, it evaluates its own data and issues a bid. Construct a potential supply set and save the suppliers whose bids are lower than the estimated value. If the potential supply set is an empty set, the demander needs to increase the estimated value. The suppliers in the potential supply set submit data samples to VChain-D2S, but the correspondence between the data samples and the data required by the demander is hidden from the demander to avoid dishonest data samples. After the demander obtains the data sample, it evaluates and scores the data sample by itself. Based on the evaluation results and scores, the final winner, that is, the supplier who meets the needs of the demander, is determined, and then the transaction is concluded.

[0049] The TSOS mechanism can encourage demanders to honestly evaluate and score suppliers' data samples and honestly output estimated values. It can also encourage suppliers to quote reasonably based on the market and costs, so that suppliers and demanders can obtain maximum benefits through honest quotations.

[0050] The incentive-compatible data collaboration contract uses the VCG mechanism, MP mechanism and TSOS mechanism to enable suppliers and demanders in different scenarios to remain as honest as possible during cross-domain data interaction, thereby improving the corresponding ratio of supply and demand. Through the automatic supervision and control of the incentive-compatible data collaboration contract, it facilitates cross-domain control of heterogeneous spaces.

[0051] The present invention also discloses a multi-source data collaboration device based on federated control, comprising: one or more processors; a memory; And one or more computer programs, wherein the one or more computer programs are stored in a memory, and the one or more computer programs include instructions, which, when executed by a processor, enable the multi-source data collaboration device based on federal control to perform the above-mentioned multi-source data collaboration method based on federal control.

[0052] The present invention also discloses an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the above-mentioned multi-source data collaboration method based on federal control.

[0053] The present invention also discloses a non-transitory computer-readable storage medium storing computer instructions, where the computer instructions are used to enable a computer to execute the above-mentioned multi-source data collaboration method based on federal control.

[0054] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-source data collaboration method based on federated control, characterized by: The steps include: The overall goal is broken down into multiple sub-goals through a consensus mechanism and assigned to various nodes in the blockchain network, including supply and demand parties; Eliminate conflicts between the nodes through on-chain voting; The game and cooperation between the nodes are balanced through a decentralized game model, and rewards are allocated to the nodes through an incentive mechanism based on decentralized bidding; Build a trust relationship between nodes through cross-chain contracts and a data circulation framework based on the cross-chain contracts; Through incentive-compatible data collaboration contracts, the interaction rules between the supplier and the demander in cross-chain interactions are unified.

2. The multi-source data collaboration method based on federated control according to claim 1 is characterized in that: The decentralized game model adopts the PPLNS mechanism.

3. The multi-source data collaboration method based on federated control according to claim 1 is characterized in that: The incentive mechanism adopts the GSP auction mechanism.

4. The multi-source data collaboration method based on federated control according to claim 1 is characterized in that: The incentive-compatible data collaboration contract is embedded with the VCG mechanism, the MP mechanism and the TSOS mechanism.

5. The multi-source data collaboration method based on federated control according to claim 4 is characterized in that: The method by which the VCG mechanism constrains the demander and the supplier is as follows: The demand parties quote for the required data, and the demand parties that meet the screening rules are stored in the winner set. The demand parties in the winner set can obtain the required data, and the payment price is the negative value of the marginal contribution it brings to other demand parties when joining the winner set, plus the highest bid of all the demand parties except the demand party.

6. The multi-source data collaboration method based on federated control according to claim 4 is characterized in that: The method by which the MP mechanism constrains the demand side and the supply side is: The demander submits a data demand with an accompanying quotation; Sorting the quotations of all the demanders in descending order; After receiving the quotation, the supplier determines the monopoly price and supply scale.

7. The multi-source data collaboration method based on federated control according to claim 4 is characterized in that: The method by which the TSOS mechanism constrains the demander and the supplier is: The demander's demand and estimated value for writing data on the blockchain; The supplier issues a quotation based on the demand and its own data. The supplier whose quotation is greater than the estimated value is saved in the potential supply set. If the potential supply set is an empty set, the demander rewrites the estimated value. The supplier in the potential supply set submits a data sample to the blockchain; The demander obtains the data sample in the blockchain, evaluates the data sample, and determines the supplier that meets the demand based on the evaluation result.

8. A multi-source data collaboration device based on federated control, characterized in that: include: one or more processors; Memory; and one or more computer programs, wherein one or more of the computer programs are stored in the memory, and one or more of the computer programs include instructions, which, when executed by the processor, enable the multi-source data collaboration device based on federal control to execute the multi-source data collaboration method based on federal control as described in any one of claims 1-7.

9. An electronic device, characterized in that: include: at least one processor; And a memory communicatively connected to at least one processor; wherein the memory stores instructions executable by at least one of the processors, and the instructions are executed by at least one of the processors so that at least one of the processors can execute the multi-source data collaboration method based on federal control as described in any one of claims 1-7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to enable a computer to execute the multi-source data collaboration method based on federal control as described in any one of claims 1-7.

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