CN112862486A - Multi-party chain crossing method and system based on mirror image chain crossing - Google Patents
Multi-party chain crossing method and system based on mirror image chain crossing Download PDFInfo
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Abstract
The invention discloses a multi-party chain-crossing method and a system based on mirror image chain crossing, which adopt a distributed prophetic machine to carry out multi-party asset value mapping to form a corresponding mirror image asset to be stored in an intelligent contract, respond to a chain-crossing request to call the intelligent contract to carry out circulation transaction of the mirror image asset, and realize intercommunication on a universal chain.
Description
Technical Field
The invention belongs to the technical field of block chain crossing, and particularly relates to a multi-party chain crossing method and system based on mirror image chain crossing.
Background
The block chain is used as a distributed account book technology, can be applied to the fields of finance, health care, supply chains, asset management and the like, but is limited by factors such as throughput, network isolation, supervision, flexibility and the like, the current block chain project cannot serve business application well, and particularly cannot perform multi-party asset transaction on the chain all around aiming at various entity assets under the chain. Among the problems faced by blockchains, network isolation prevents cooperative operation between different blockchains, and greatly limits the play space of blockchains. The chain crossing technology is the key for realizing the value internet, and most of assets are realized by the ways of centralized gateway chain crossing, multi-sign hosting chain crossing, light node chain crossing, mirror image chain crossing and the like at present.
Centralized gateway chaining: the chain crossing method is input by an authority external language machine Oracle mode, namely, the chain crossing method is input from two ends of a chain by a centralized organization, and external force is introduced to access the chain crossing, so that the chain crossing action is finished by means of arrangement of a third party, the chain crossing method is consistent with the transfer by means of a bank, and the centralized operation has high efficiency but potential safety hazard;
light node chain spanning: the interaction of data is completely managed by code, no middle man is needed, a universal interlinkage protocol interface and a universal light node protocol of a plurality of chains are customized, and a Header adaptor interlinkage, a transaction interaction interlinkage and a consensus interlinkage are established. At present, the universality technology of the light node cross-link protocol is not finished, and the applicability is weaker.
Multi-sign hosting cross-chain: a group of authority mechanisms are added to a single centralized root to vote for the interaction of cross-link data, the negotiation criterion is the same as the negotiation criterion, and multiple parties hold private keys to control one account, so that the effect of safe escrow of multiple parties is achieved, but due to the inconsistency of the authority mechanisms, the operation efficiency is greatly reduced;
mirror image cross-chaining: synthesizing other small assets through the maximum asset mirror image to finish the virtual chain crossing in economics, and utilizing the concept of synthesizing assets in economics; for example, the Synthetix project uses its digital assets SNX to make up small amounts of other finances in excess of exchange value SNX (the largest asset), and in the etherhouse system, the project provides a series of solutions, the market value of which has grown ten times in 2020, indirectly reflecting the urgent needs and concerns of the market. However, the problems of high cost, instability, low efficiency and the like of Gas exist in the Etherns.
Recently, the blockchain ecosphere introduced the concept of a predictive engine, transferring data down-chain to intelligent contracts on-chain. However, centralized predictive machines can present problems and thus compromise the security and reliability of the intelligent contracts on the chain. A prediction machine is introduced into the existing Synthetix mirror image cross-chain, and in a block chain, the prediction machine is a one-way digital agent and can search and verify real world data and submit information to an intelligent contract in an encrypted mode. When a certain intelligent contract on the block chain has a data interaction demand, the prediction machine helps the intelligent contract to collect external data outside the chain after receiving the demand, and feeds back the acquired data to the intelligent contract on the chain after verification. Patent-a cross-chain exchange method and system (CN111145023A) based on a credible prediction machine carries out cross-chain of assets or information by taking the prediction machine as a notary; a design mode (CN112150266A) of an intelligent contract prediction machine carries out reliability scoring and chaining by collecting data of the prediction machine; therefore, the prediction machine can realize the integrated uplink of the data resources under the chain, so that the development of a chain-crossing mode of adopting the prediction machine to carry out mirror image chain-crossing can effectively reduce the cost and realize the transaction of digital currency and traditional asset all-kind investment.
Disclosure of Invention
Based on the background and the problems in the prior art, the invention aims to design a multi-party chain crossing method and system based on mirror image chain crossing.
A multi-party chain-crossing method based on mirror image chain crossing adopts a distributed prediction machine to carry out multi-party asset value mapping, forms a corresponding mirror image asset to be stored in an intelligent contract, responds to a chain-crossing request to call the intelligent contract to carry out circulation transaction of the mirror image asset, and realizes intercommunication on a universal chain, wherein the multi-party chain-crossing method comprises the following steps:
creating a transaction circulation chain, deploying an intelligent contract on the chain, associating the intelligent contract with a distributed type prediction machine, forming a decentralized prediction machine node network by using a plurality of prediction machines as nodes, and synthesizing required assets into corresponding mirror image assets through the distributed type prediction machine and transmitting the mirror image assets to the intelligent contract on the chain;
the transaction flow chain is used as a relay chain of multi-party cross chains, intelligent contracts required by the cross chains and distributed prediction machines associated with the intelligent contracts are borne on the chain, and multi-party cross chain users can come from other chains or under the chain; compared with the traditional language prediction machine, a trusted third party is required to be used as a verifier or a self-verification clearance of a checking organization, and the distributed language prediction machine performs mutual verification among a plurality of speakers (Oracles), so that a distributed oracle is provided, and the safety problem of the centralized language prediction machine is solved;
the intelligent contract monitors the nodes of the prediction machines, records the image assets formed each time in real time, creates a multi-party image asset list and updates in real time; the multi-party mirror image assets are sequentially compared with inherent assets on the chain to form asset exchange rates, and dynamic asset exchange rates are generated in the multi-party mirror image asset list;
the smart contract has non-tamper-proofability and verifiability, and is automatically executed when a set condition is satisfied using code logic of IF/THEN. The intelligent contract can be a single intelligent contract or an intelligent contract library consisting of a plurality of intelligent contracts, namely the intelligent contract collects the image assets formed each time and presents the image assets in a multi-party image asset list, the asset keywords, the image asset value constant and the asset exchange rate are shown in the list, and the list content is updated in real time according to the output state of the prediction machine;
and responding to any cross-chain request, calling the mirror image assets of the intelligent contract associated demand assets, and generating circulation values according to the exchange rate of the mirror image assets to perform cross-chain asset conversion, so that multi-party asset mirror image cross-chain is realized.
That is, no matter which asset cross-chain request relates to, the intelligent contract is used to associate with a multi-party mirror image asset list or a distributed prediction machine to select the mirror image asset of the required asset, and cross-chain transaction is realized according to the interactive asset exchange rate; the multi-party mirror image asset cross-chain is that cross-chain transaction can be realized by multi-party users and various assets.
Further, the process of synthesizing the multi-party assets under the chain into the corresponding mirror image assets and transmitting the mirror image assets to the intelligent contract on the chain through the distributed prediction machine is concretely implemented as follows:
setting a request protocol on a transaction flow chain, responding to a cross-chain request, calling an intelligent contract on the chain, and sending a cross-chain requirement;
the request protocol registers cross-chain requirements as events and then creates a plurality of sub-contracts corresponding to the events on the chain, including verification contracts, matching contracts and aggregation contracts;
the verification dating looks up the historical service level of the talker server, verifies the authenticity and the historical performance of the talker server, and eliminates talker nodes with poor reputation or low reliability;
the distributed prediction machine is additionally provided with a multiple signature mechanism and an external adapter, and other prediction machine nodes are enabled to check whether a speaker providing services has improper behaviors and whether the transaction is reasonable by using a verification contract and adopting a threshold (Schnorr) signature technology, and at least more than half of available nodes are required to implement signature under a chain to implement the transaction;
the matching contract sends the cross-chain requirement in the intelligent contract to the nodes of the prediction machines, accepts the bidding of the nodes of the prediction machines (in this case, the intelligent contract is requested not to select the nodes by itself), and then the matching contract selects the prediction machines with proper quantity and type to copy the market value of the target assets;
in the process, the setting of the external adapter is utilized to enable the language predicting machine to split the complex data calling requirement into a plurality of subtasks, and a plurality of speakers participate in the service process in a division and cooperation mode.
And the aggregation contract acquires market value data of all assets from the selected prediction machine, verifies and aggregates the data, and finally synthesizes a mirror image asset value constant and transmits the mirror image asset value constant to the intelligent contract for storage.
In the verification process, a market value threshold is set, the market value data meeting the threshold are aggregated, the aggregated market value is equivalent to the value constant of the corresponding mirror image asset, and the process of synthesizing the mirror image asset by the prediction machine is concretely shown below.
Particularly, the distributed prediction machine counts the market value of the target asset in real time, adjusts the corresponding composite asset value constant in real time according to the fluctuation of the market value and outputs the composite asset value constant to the intelligent contract;
the process of synthesizing the multi-party assets under the chain into the corresponding mirror image assets and transmitting the mirror image assets to the intelligent contract on the chain through the distributed prediction machine further comprises the following steps:
setting a function for computing the market value of multiple assets in each prediction machine node, performing value search statistics on the mainstream circulation website related to the assets according to specific required asset keywords of cross-chain requirements, copying the market value within a threshold range, performing aggregation operation by using the computing function to form a mirror image asset value constant, and storing the mirror image asset value constant to an intelligent contract. Wherein, the multi-party mirror image assets have uniform constant units.
In addition to the value search in related mainstream websites by using the prediction machine, a plurality of related third-party search websites (Google, Baidu and the like) can be set for the prediction machine in advance, and the related third-party websites are directly searched in response to the cross-chain requirement;
the threshold value is set to prevent the occurrence of the statistical result deviating from the normal market value of the asset, reduce the synthetic error of the mirror image asset and improve the accuracy.
Furthermore, the intelligent contract monitors the nodes of the prediction machine, records the image assets formed each time in real time, creates a multi-party image asset list and updates in real time; the multi-party mirror image assets are sequentially compared with inherent assets on the chain to form asset exchange rates, and the dynamic asset exchange rates generated in the multi-party mirror image asset list are as follows:
the nodes of the prediction machines count the market values of the required assets in real time, adjust the corresponding composite asset value constants in real time according to the fluctuation of the market values and output the values to the intelligent contracts;
based on all the received composite asset value constants, correspondingly storing the composite asset value constants in a multi-party mirror image asset list according to target asset key words, and updating the composite asset value constants in real time, wherein the target asset key words are key words of assets required by chain crossing;
and based on the received composite asset value constant, obtaining an asset exchange rate through the ratio of the composite asset value constant to the inherent assets on the chain, and adding the asset exchange rate into the multi-party mirror image asset list, wherein the asset exchange rate is dynamically changed along with the real-time adjustment of the composite asset value constant to form a dynamic asset exchange rate.
And when receiving each composite asset value constant, the intelligent contract calculates the ratio of the composite asset value constant to the inherent assets on the chain, namely the asset exchange rate, and presents the asset exchange rate in the multi-party mirror image asset list, wherein the asset exchange rate is dynamically changed along with the real-time adjustment of the composite asset value constant to form the dynamic asset exchange rate.
Because the actual market environment changes, the market value of the required assets (assets involved in cross-chain trading) is changed in a real-time fluctuation mode, so that the participation prediction machine needs to count the market value of the required assets in real time and calculate and adjust the corresponding mirror image asset value constant; wherein, the intrinsic asset value is regulated and controlled according to the mirror image asset type and the real-time value.
The output port of the predictive speaker node is associated with an associated intelligent contract on a transaction circulation chain, the intelligent contract collects the value constant of the mirror image assets output by the predictive speaker in real time and presents the value constant in a multi-party mirror image asset list, namely, a plurality of dynamic mirror image assets are presented in the multi-party mirror image asset list, the value constant, the conversion rate and the asset key words of the dynamic mirror image assets comprise transaction indexes of cross-chain assets, and in the list, the assets with different attributes generate interactive relevance through the asset conversion rate.
Further, the multi-party asset mirroring cross-chain comprises a casting, transaction and destruction protocol, and the specific flow is as follows:
casting: responding to a cross-chain request, wherein the cross-chain request comprises a user owned asset keyword, a target asset keyword and a specific transaction event, and calling an intelligent contract to select mirror image assets of the owned asset and the target asset and corresponding asset exchange rates in a multi-party mirror image asset list according to the user owned asset keyword and the target asset keyword;
trading: performing value transaction according to the mirror image asset exchange rate of the held asset and the target asset and a specific cross-chain transaction, generating a cross-chain transaction certificate after the transaction is successful, storing the cross-chain transaction certificate on a chain and sending the cross-chain transaction certificate to a user account for storage, wherein the transaction certificate is used as a user cross-chain asset transaction evidence;
destroying: and after a new cross-chain transaction voucher is generated, replacing the historical transaction voucher with the new cross-chain transaction voucher, destroying the historical transaction voucher, and enabling the user to always hold the latest cross-chain transaction voucher as a cross-chain asset transaction evidence.
In particular, the casting process also comprises the following steps:
when the image assets of the assets or the target assets held by the user do not exist in the existing multi-party image asset list, the intelligent contract association distributed prediction machine is called to synthesize the image assets of the required assets and present the image assets in the multi-party image asset list.
The multi-party asset mirror image cross-chain is a complete flow of cross-chain transaction, whether a required mirror image asset exists in a multi-party mirror image asset list or not is checked in a casting protocol, if the required mirror image asset exists, a transaction protocol is directly called to carry out cross-chain transaction, if the required mirror image asset does not exist, a prediction machine is required to be associated to carry out synthesis of the mirror image asset of the required asset, and after the mirror image asset is synthesized, the cross-chain transaction is carried out;
after the transaction is completed, a cross-chain transaction certificate is generated, the certificate is generated only when the transaction is successful, the latest cross-chain transaction certificate is always held to be effective, the certificate can be used as a certificate converted by entity assets under the chain, and the certificate is stored on the chain, traceable and cannot be tampered.
Further, the present invention provides a multi-party chain crossing system based on mirror image chain crossing, which includes: the system comprises a creating module, an association module, an updating module and a conversion module; through the modular design, the multi-party chain crossing method forms an effective operating multi-party chain crossing system;
the creating module is used for creating a transaction flow chain and deploying an intelligent contract;
the association module is used for associating the intelligent contract with the distributed prediction machine, synthesizing the required assets into corresponding mirror image assets through the distributed prediction machine and transmitting the mirror image assets to the chain intelligent contract;
the updating module monitors distributed prediction machine nodes through the intelligent contract, records image assets in real time, creates and updates a multi-party image asset list, forms asset exchange rates based on the multi-party image assets and inherent assets on the chain and forms dynamic asset exchange rates in the multi-party image asset list;
the conversion module is used for responding to the cross-chain request, calling the mirror image assets of the intelligent contract associated demand assets, generating circulation values according to the dynamic asset exchange rate of the intelligent contract to convert the cross-chain assets, and further realizing multi-party asset mirror image cross-chain.
Particularly, the system also comprises an account module, a mirror image asset module, a prediction machine module, a list management module and a browser module;
wherein the account module: the system is used for registering and logging in a transaction chain by a user, checking a response historical list, asset details and storing a cross-chain transaction certificate, wherein the asset details comprise the held asset exchange rate and multi-party mirror image asset list details; the account module corresponds to an access port layer of a multi-party user, the user registers an account, and the system can distribute a user account address and generate a private key password of the user;
a mirror asset module: the system comprises a casting module, a transaction module and a destruction module, wherein the casting module is used for selecting or synthesizing mirror image assets according to a cross-chain request; the transaction module is used for performing cross-chain transaction by utilizing the mirror image asset exchange rate of each asset; the destroying module is used for updating the cross-chain transaction voucher and destroying the historical transaction voucher after transaction; the module corresponds to a casting, transaction and destruction protocol, and realizes a mirror image cross-chain flow by calling a mirror image asset module;
the prediction machine module: the associated casting module copies the market values of the multi-party assets, synthesizes a mirror image asset value constant, and carries out real-time statistical updating according to market value fluctuation; calling a predicting machine module to start a distributed predicting machine node network to synthesize mirror image assets;
a list management module: the association prediction machine module is used for managing a multi-party mirror image asset list and calculating and updating the mirror image assets and the exchange rate of the mirror image assets in real time; the list management module provides front and back interfaces for managing the multi-party mirror image asset list and updates the list content in real time according to the output state of the prediction machine;
a browser module: the cross-chain transaction overview is checked, cross-chain transactions are searched, the cross-chain transactions comprise cross-chain transaction details, casting details, destroying details and cross-chain request address details, the module serves as a display browsing module of the multi-party cross-chain system, other modules are associated, and a user can conveniently meet the requirements through one key.
In particular, it also comprises:
the casting module is configured to: responding to the cross-link request, and calling an intelligent contract to select the mirror image assets of the held assets and the target assets and corresponding asset exchange rates in a multi-party mirror image asset list according to the asset key words held by the user and the target asset key words; when the existing multi-party mirror image asset list does not have mirror image assets of user held assets or target assets, calling an intelligent contract associated distributed prediction machine to synthesize the mirror image assets of the required assets and displaying the mirror image assets in the multi-party mirror image asset list, wherein the cross-chain request comprises user held asset keywords, target asset keywords and specific transaction events;
the transaction module is configured to: performing value transaction according to the mirror image asset exchange rate of the held asset and the target asset and a specific cross-chain transaction, generating a cross-chain transaction certificate after the transaction is successful, storing the cross-chain transaction certificate on a chain and sending the cross-chain transaction certificate to a user account for storage, wherein the transaction certificate is used as a user cross-chain asset transaction evidence;
the destruction module is configured to: and after a new cross-chain transaction voucher is generated, replacing the historical transaction voucher with the new cross-chain transaction voucher, destroying the historical transaction voucher, and enabling the user to always hold the latest cross-chain transaction voucher as a cross-chain asset transaction evidence.
The invention designs a multi-party chain-crossing method and a multi-party chain-crossing system based on mirror image chain crossing, which utilize a distributed propler to synthesize real-time mirror image assets, ensure the accuracy of the mirror image asset synthesis, utilize an intelligent contract to monitor a propler node network in real time, collect the mirror image assets of multi-party assets, increase a dynamic multi-party mirror image asset list, realize the efficient and rapid chain-crossing transaction of the multi-party assets, and accelerate the economy of a block chain-crossing technology ground entity.
Drawings
FIG. 1 is a schematic view of the overall structure of the present invention;
FIG. 2 is a multi-party cross-chaining flow diagram of the present invention;
FIG. 3 is a schematic flow diagram of a distributed predictive engine of the present invention;
FIG. 4 is a cross-chain flow diagram of a multi-party asset mirroring in accordance with the present invention;
FIG. 5 is a block diagram of a multi-party cross-linking system according to the present invention.
Detailed Description
In order to clearly illustrate the present invention and make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, so that those skilled in the art can implement the technical solutions by referring to the description text, and the interactive processes of the specific implementations can be described by using the common asset transaction process as an example, so as to facilitate the understanding of those skilled in the art.
Specific example 1:
a multi-party chain crossing method based on mirror image chain crossing adopts a distributed prophetic machine to map multi-party asset values to form corresponding mirror image assets to be stored in an intelligent contract, responds to a chain crossing request to call the intelligent contract to carry out circulation transaction of the mirror image assets, and realizes intercommunication on all things chains, as shown in figure 1, the multi-party assets are an integral structure diagram of the multi-party chain crossing method based on the mirror image chain crossing, the multi-party assets comprise digital currency Token, U.S. dollar USD, Gold, educational assets, medical assets, real estate, energy assets and the like, the assets are copied into corresponding mirror image assets through prices of the distributed prophetic machine to be stored in the intelligent contract of the exchange chain crossing, and a user calls the intelligent contract on the chain to realize mutual chain crossing of the multi-party assets.
As shown in FIG. 2, the multi-party cross-chaining method comprises the following steps:
step 1: creating a transaction circulation chain, deploying an intelligent contract on the chain, associating the intelligent contract with a distributed type prediction machine, forming a decentralized prediction machine node network by using a plurality of prediction machines as nodes, and synthesizing required assets into corresponding mirror image assets through the distributed type prediction machine and transmitting the mirror image assets to the intelligent contract on the chain;
step 2: establishing a multi-party mirror image asset list, monitoring a predictive machine node by the intelligent contract, recording the mirror image asset formed each time in real time, and synchronously updating the multi-party mirror image asset list; the multi-party mirror image assets are sequentially compared with inherent assets on the chain to form asset exchange rates, and dynamic asset exchange rates are generated in the multi-party mirror image asset list;
step 3: and responding to any cross-chain request, calling the mirror image assets of the intelligent contract associated demand assets, and generating circulation values according to the exchange rate of the mirror image assets to perform cross-chain asset conversion, so that multi-party asset mirror image cross-chain is realized.
As shown in fig. 3, which is a schematic flow diagram of a distributed predictive engine, a flow of synthesizing the multi-party assets under the chain into corresponding mirror assets and transmitting the mirror assets to the intelligent contract on the chain by the distributed predictive engine is specifically implemented as follows:
responding to a cross-chain demand, calling an intelligent contract to set a request protocol on a transaction flow transfer chain, registering the cross-chain demand into an event by the request protocol, and then creating a plurality of sub-contracts of the corresponding event on the chain, wherein the sub-contracts comprise a verification contract, a matching contract and an aggregation contract;
starting a verification contract, distributing a signature verification task of nodes of the language prediction machine, selecting the language prediction machine capable of providing service, verifying the authenticity and the historical performance of the language prediction machine according to the selection standard by checking the historical service level of a service provider of the language prediction machine, making reliability evaluation, recording the evaluation record in the nodes of the language prediction machine, and granting the language prediction machine to participate in the mirror image synthesis transaction of the required assets after more than half of the nodes of the language prediction machine give verification signatures;
the matching contract sends the cross-chain requirement in the intelligent contract to the nodes of the prediction machines, the nodes of the prediction machines participating in the competitive bidding in the distributed node network of the prediction machines participate in the competitive bidding by utilizing a POW consensus mechanism, and then the matching contract selects the prediction machines with proper quantity and type by utilizing an external adapter to copy the market value of the required assets;
the aggregate contract collection prediction machine acquires market value data of all assets and sets a market value threshold value [ n ]1,ni]Market value data to be within threshold range n1,n2,…niAggregate avg n1,n2,…niThe final composite mirror image asset value constant N ═ avg { N }1,n2,…niAnd transmitting the data to an intelligent contract storage.
In addition, the specific flow of the prediction machine for copying the property market value and synthesizing the mirror image property is as follows:
the language prediction machine is essentially a turing machine connected with an oracle, and one language prediction machine is composed of four multi-element groups to form the following function: m ═<Q,&,q0,F>The number of states, which is a finite number of states,
&：Q×{B，1}2→Q×{B，1}×{L，R}2is a partial function called transformation function (translation function), where L stands for a left shift and R stands for a right shift.
q0E.g. Q represents the starting state,
is a set of stop states.
The prediction machine starts with a work band (work tape) containing a limited but many 1's, the rest being blank, a prediction band A containing the unique function of prediction, and a prediction band at q0In a state of the turing machine, the read/write head is reading the first non-space grid of the working tape, and the predictive read head is reading the grid of the predictive tape corresponding to \ display \ chi \ { A } (0) }.
Setting a function M for computing the market value of multiple assets in each preloader node, carrying out Turing search statistics on the mainstream circulation websites related to the assets according to the specific demand asset keywords of the cross-chain demand, copying the market value in a threshold range, and outputting a market value data set { n } by using the computing function1,n2,…niAnd aggregate to avg { n }1,n2,…niThe final composite mirror image asset value constant N ═ avg { N }1,n2,…niThe data is transmitted to an intelligent contract which is associated with a multi-party mirror image asset columnThe table presents the mirror asset worth constant N.
In addition to utilizing the prediction machine to search the value of related mainstream websites, a plurality of fixed third-party searching websites can be set for the prediction machine in advance, the fixed websites belong to large websites which are recognized by the public, for example, required assets are educational resources, domestic third-party websites can be set in advance to be large websites such as google and hundredth degrees, or common e-commerce websites, and the third-party websites are directly searched in the related third-party websites in response to the cross-link requirement.
The intelligent contract monitors the nodes of the prediction machines, records the image assets formed each time in real time, creates a multi-party image asset list and updates in real time; the multi-party mirror image assets are sequentially compared with inherent assets on the chain to form asset exchange rates, and the dynamic asset exchange rates generated in the multi-party mirror image asset list are as follows:
the intelligent contract associated prediction machine nodes monitor the output state of each prediction machine, receive all image asset value constants output by the prediction machine, and correspondingly store the image asset value constants in a multi-party image asset list according to the required asset key words and update the image asset value constants in real time;
and when receiving each composite asset value constant, the intelligent contract calculates the ratio of the composite asset value constant to the inherent assets on the chain, namely the asset exchange rate, and presents the asset exchange rate in the multi-party mirror image asset list, wherein the asset exchange rate is dynamically changed along with the real-time adjustment of the composite asset value constant to form the dynamic asset exchange rate.
In this embodiment, the multi-party mirrored asset list may be as shown in table 1, including its value constant, exchange rate, asset key word, and the list has a transaction index of cross-chain assets, and in the list, assets with different attributes are interactively related by asset exchange rate.
TABLE 1 Multi-party mirror asset List example
In table 1, the mirror asset value constant is a constant read and copied and aggregated according to the market value of the corresponding asset, the multi-party mirror asset has a constant unit unified with the intrinsic asset, and the asset conversion rate is the mirror asset value constant/intrinsic asset value, wherein the intrinsic asset value is generated according to the co-recognition of the circulation frequency and the user usage times of the multi-party mirror asset, for example, if the unified constant unit is U.S. dollar, and the digital currency BTC reproduces the existing market value as $ 18000 through a prediction machine, the value constant of the composite mirror asset is 18000, the transaction circulation chain fixed asset PCX is 100, and the asset conversion rate is 180; while another asset, such as gold, has an existing market value of $ 1800, its composite mirror asset value constant is 1800 and its asset exchange rate is 18, so that the value transaction of BTC and gold in digital currency on the chain can be realized.
As shown in fig. 4, a cross-chain flow chart of multi-party asset mirroring is provided, and the process includes casting, trading and destroying:
casting: responding to a cross-chain request, wherein the cross-chain request comprises a user owned asset keyword, a target asset keyword and a specific transaction event, and calling an intelligent contract to select mirror image assets of the owned asset and the target asset and corresponding asset exchange rates in a multi-party mirror image asset list according to the user owned asset keyword and the target asset keyword; when the existing multi-party mirror image asset list does not have mirror image assets of assets or target assets held by users, calling an intelligent contract association distributed prediction machine to synthesize the mirror image assets of the required assets and presenting the mirror image assets in the multi-party mirror image asset list;
trading: performing value transaction according to the mirror image asset exchange rate of the held asset and the target asset and a specific cross-chain transaction (such as cross-chain transaction amount), generating a cross-chain transaction certificate after the transaction is successful, storing the cross-chain transaction certificate on a chain and sending the cross-chain transaction certificate to a user account for storage, wherein the transaction certificate is used as a user cross-chain asset transaction evidence;
destroying: and after a new cross-chain transaction voucher is generated, replacing the historical transaction voucher with the new cross-chain transaction voucher, destroying the historical transaction voucher, and enabling the user to always hold the latest cross-chain transaction voucher as a cross-chain asset transaction evidence.
For example, if the held asset is a digital currency Token and the target asset is a Copyright copy right, firstly checking whether the multi-party mirror image asset list contains S (Token) and the target asset is a Copyright S (copy right), if so, trading is performed by using the asset exchange rates of both parties, if not, the market value of the non-existing asset such as the digital currency Token is copied by associating the distributed prediction machine to synthesize the mirror image asset constant, finally outputting the mirror image asset constant in the multi-party asset list, and further generating trading by using the existing asset exchange rate.
After the transaction is successful, the intelligent contract issues a cross-chain transaction certificate to the cross-chain user, the cross-chain transaction certificate is stored in a transactional mode in a block on the chain, the cross-chain user holds the transaction certificate and can perform related entity asset exchange under the chain, for example, 2 tokens are used for trading an S (copy) on the chain, at the moment, the S (copy) is not really uplink, after the transaction on the chain is successful, both sides of the cross-chain transaction user obtain the transaction certificate, and both sides of the cross-chain transaction user use the transaction certificate to perform copyright transfer under the chain. Of course, in order to avoid affecting multiple transactions of the asset, only the latest cross-chain transaction certificate is valid, and the transaction certificate has a certificate generation timestamp, so as to determine whether the transaction certificate is the latest transaction certificate.
Example 2:
the invention discloses a multi-party chain crossing system based on mirror image chain crossing, which comprises: the system comprises a creation module, an association module, an updating module, a conversion module, an account module, a mirror image asset module, a prediction machine module, a list management module and a browser module; through the modular design, the multi-party chain crossing method forms an effective operating multi-party chain crossing system;
the system comprises a creating module, a processing module and a processing module, wherein the creating module is used for creating a transaction flow chain and deploying an intelligent contract;
the association module is used for associating the intelligent contract with the distributed prediction machine, synthesizing the required assets into corresponding mirror image assets through the distributed prediction machine and transmitting the mirror image assets to the intelligent contract on the chain;
the updating module monitors distributed prediction machine nodes through the intelligent contract, records image assets in real time, creates and updates a multi-party image asset list, forms asset exchange rates based on the multi-party image assets and inherent assets on the chain and forms dynamic asset exchange rates in the multi-party image asset list;
and the conversion module is used for responding to the cross-chain request, calling the mirror image assets of the intelligent contract associated demand assets, generating circulation values according to the dynamic asset exchange rate of the intelligent contract to convert the cross-chain assets, and further realizing multi-party asset mirror image cross-chain.
An account module: the system is used for registering and logging in a transaction chain by a user, checking a response historical list, asset details and storing a cross-chain transaction certificate, wherein the asset details comprise the held asset exchange rate and multi-party mirror image asset list details; the account module corresponds to an access port layer of a multi-party user, the user registers an account, and the system can distribute a user account address and generate a private key password of the user;
a mirror asset module: the system comprises a casting module, a transaction module and a destruction module, wherein the casting module is used for selecting or synthesizing mirror image assets according to a cross-chain request; the transaction module is used for performing cross-chain transaction by utilizing the mirror image asset exchange rate of each asset; the destroying module is used for updating the cross-chain transaction voucher and destroying the historical transaction voucher after transaction; the module corresponds to a casting, transaction and destruction protocol, and realizes a mirror image cross-chain flow by calling a mirror image asset module;
the prediction machine module: the associated casting module copies the market values of the multi-party assets, synthesizes a mirror image asset value constant, and carries out real-time statistical updating according to market value fluctuation; calling a predicting machine module to start a distributed predicting machine node network to synthesize mirror image assets;
a list management module: the association prediction machine module is used for managing a multi-party mirror image asset list and calculating and updating the mirror image assets and the exchange rate of the mirror image assets in real time; the list management module provides front and back interfaces for managing the multi-party mirror image asset list and updates the list content in real time according to the output state of the prediction machine;
a browser module: the cross-chain transaction overview is checked, cross-chain transactions are searched, the cross-chain transactions comprise cross-chain transaction details, casting details, destroying details and cross-chain request address details, the module serves as a display browsing module of the multi-party cross-chain system, other modules are associated, and a user can conveniently meet the requirements through one key.
As shown in fig. 5, a casting module, a transaction module and a destruction module are arranged in the multi-party chain-spanning system for multi-party chain spanning:
the casting module is configured to: responding to the cross-link request, and calling an intelligent contract to select the mirror image assets of the held assets and the target assets and corresponding asset exchange rates in a multi-party mirror image asset list according to the asset key words held by the user and the target asset key words; when the existing multi-party mirror image asset list does not have mirror image assets of user held assets or target assets, calling an intelligent contract associated distributed prediction machine to synthesize the mirror image assets of the required assets and displaying the mirror image assets in the multi-party mirror image asset list, wherein the cross-chain request comprises user held asset keywords, target asset keywords and specific transaction events;
the transaction module is configured to: performing value transaction according to the mirror image asset exchange rate of the held asset and the target asset and a specific cross-chain transaction, generating a cross-chain transaction certificate after the transaction is successful, storing the cross-chain transaction certificate on a chain and sending the cross-chain transaction certificate to a user account for storage, wherein the transaction certificate is used as a user cross-chain asset transaction evidence;
the destruction module is configured to: and after a new cross-chain transaction voucher is generated, replacing the historical transaction voucher with the new cross-chain transaction voucher, destroying the historical transaction voucher, and enabling the user to always hold the latest cross-chain transaction voucher as a cross-chain asset transaction evidence.
A user calls a creation module to create a transaction circulation chain and deploy an intelligent contract, responds to a cross-chain request, calls a correlation module to correlate the intelligent contract with a distributed preplan machine, calls a conversion module, calls a mirror image asset of the intelligent contract correlation demand asset, and forms a multi-party cross-chain passage;
registering a transaction chain account through an account module, acquiring a transaction right on a chain, and sending a cross-chain request, wherein the cross-chain request comprises an asset key word held by a user, a target asset key word and a specific transaction event;
a casting module of the image asset starting module calls a multi-party image asset list to check whether image assets of assets and target assets held by a user exist or not, and if yes, a transaction starting module selects a value constant of the corresponding image asset to perform cross-chain transaction by combining with a specific cross-chain transaction; after the transaction is successful, generating a cross-chain transaction certificate, storing the cross-chain transaction certificate to a chain, and sending the cross-chain transaction certificate to a user account for storage;
if not, associating the predictive machine module, entering a distributed predictive machine node network, synthesizing the mirror image assets of the required assets, starting a list management module to store the synthesized mirror image assets and the mirror image asset value constants to be presented in a multi-party mirror image asset list, and calling the transaction module again to perform a cross-chain transaction process;
and meanwhile, a browser module is called to check the cross-chain transaction overview and search the cross-chain transaction, wherein the cross-chain transaction overview comprises cross-chain transaction details, casting details, destroying details and cross-chain request address details.
The embodiments described above are presented to enable a person having ordinary skill in the art to make and use the invention. It will be readily apparent to those skilled in the art that various modifications to the above-described embodiments may be made, and the generic principles defined herein may be applied to other embodiments without the use of inventive faculty. Therefore, the present invention is not limited to the above embodiments, and those skilled in the art should make improvements and modifications to the present invention based on the disclosure of the present invention within the protection scope of the present invention.
Claims (10)
1. A multi-party chain crossing method based on mirror image chain crossing is used for creating a transaction flow chain and deploying an intelligent contract, and is characterized by comprising the following steps:
associating the intelligent contract with a distributed type language predictive machine, synthesizing the required assets into corresponding mirror image assets through the distributed type language predictive machine, and transmitting the mirror image assets to the intelligent contract on the chain;
the intelligent contract monitors distributed pre-senter nodes, records mirror image assets in real time, creates and updates a multi-party mirror image asset list, forms asset exchange rates based on the multi-party mirror image assets and inherent assets on a chain and forms dynamic asset exchange rates in the multi-party mirror image asset list;
and responding to the cross-chain request, calling the mirror image assets of the intelligent contract associated demand assets, and generating circulation values according to the dynamic asset exchange rate to perform cross-chain asset conversion so as to realize multi-party asset mirror image cross-chain.
2. The multi-party chain crossing method based on the mirror image chain crossing of claim 1, wherein the under-chain multi-party assets are synthesized into corresponding mirror image assets through a distributed prediction machine and transmitted to the on-chain intelligent contract, and the specific implementation flow is as follows:
setting a request protocol on a transaction flow chain, responding to a cross-chain request, calling an intelligent contract on the chain, and sending a cross-chain requirement;
requesting a protocol to register cross-chain requirements as events, creating a plurality of sub-contracts corresponding to the events on a chain, wherein the sub-contracts include a validation contract, a matching contract, and an aggregation contract;
the verification contract checks the historical service level of a predictive speech machine service provider, verifies the authenticity and the historical performance of the contract by adopting a multiple signature mechanism, and selects nodes participating in the distributed predictive speech machine;
the matching contract sends the cross-chain requirements in the intelligent contract to the nodes of the prediction machines, receives the bidding of the nodes of the prediction machines, and then selects the prediction machines with proper quantity and types to copy the market value of the target assets by using the external adapter;
and the aggregation contract acquires market value data of all target assets from the selected prediction machine, verifies and aggregates the data, and finally synthesizes a mirror image asset value constant and transmits the mirror image asset value constant to the intelligent contract for storage.
3. The mirrored-based multi-party chaining method according to claim 2, further comprising the steps of:
and the distributed prediction machine counts the market value of the target asset in real time, adjusts the corresponding composite asset value constant in real time according to the fluctuation of the market value and outputs the composite asset value constant to the intelligent contract.
4. The multi-party chain crossing method based on the mirror image chain crossing as claimed in claim 2, wherein the distributed prediction machine node is provided with a function for calculating the market value of the multi-party assets, and the function for calculating the market value of the multi-party assets is used for carrying out value search statistics on the main stream circulation website of the assets according to specific demand asset keywords of the chain crossing demand, copying the value constant in the threshold range, and forming the mirror image asset value constant through aggregation operation to be stored in an intelligent contract.
5. The multi-party mirror image cross-chaining method according to claim 1, wherein the real-time recording of the mirror image assets, the creation and updating of the multi-party mirror image asset list, the asset exchange rate formation based on the multi-party mirror image assets and the inherent assets in the chain and the dynamic asset exchange rate formation in the multi-party mirror image asset list are as follows:
the predicting machine node counts the market value of the target asset in real time, adjusts the corresponding composite asset value constant in real time according to the fluctuation of the market value and outputs the composite asset value constant to the intelligent contract;
based on all the received composite asset value constants, correspondingly storing the composite asset value constants in a multi-party mirror image asset list according to target asset key words, and updating the composite asset value constants in real time, wherein the target asset key words are key words of assets required by chain crossing;
and based on the received composite asset value constant, obtaining an asset exchange rate through the ratio of the composite asset value constant to the inherent assets on the chain, and adding the asset exchange rate into the multi-party mirror image asset list, wherein the asset exchange rate is dynamically changed along with the real-time adjustment of the composite asset value constant to form a dynamic asset exchange rate.
6. The multi-party chain crossing method based on the mirror image chain crossing of claim 1, wherein the multi-party asset mirror image chain crossing comprises a casting process, a transaction process and a destruction process, and the specific flow is as follows:
casting: responding to a cross-chain request, wherein the cross-chain request comprises a user owned asset keyword, a target asset keyword and a specific transaction event, and calling an intelligent contract to select mirror image assets of the owned asset and the target asset and corresponding asset exchange rates in a multi-party mirror image asset list according to the user owned asset keyword and the target asset keyword;
trading: performing value transaction according to the mirror image asset exchange rate of the held asset and the target asset and a specific cross-chain transaction, generating a cross-chain transaction certificate after the transaction is successful, storing the cross-chain transaction certificate on a chain and sending the cross-chain transaction certificate to a user account for storage, wherein the transaction certificate is used as a user cross-chain asset transaction evidence;
destroying: and after a new cross-chain transaction voucher is generated, replacing the historical transaction voucher with the new cross-chain transaction voucher, destroying the historical transaction voucher, and enabling the user to always hold the latest cross-chain transaction voucher as a cross-chain asset transaction evidence.
7. The mirrored-bride-based multi-party bridal method of claim 6, further comprising, during the casting process:
when the image assets of the assets or the target assets held by the user do not exist in the existing multi-party image asset list, the intelligent contract association distributed prediction machine is called to synthesize the image assets of the required assets and present the image assets in the multi-party image asset list.
8. A multi-party cross-chain system based on mirror image cross-chain is characterized by comprising a creating module, an association module, an updating module and a conversion module;
the creating module is used for creating a transaction flow chain and deploying an intelligent contract;
the association module is used for associating the intelligent contract with the distributed prediction machine, synthesizing the required assets into corresponding mirror image assets through the distributed prediction machine and transmitting the mirror image assets to the chain intelligent contract;
the updating module monitors distributed prediction machine nodes through the intelligent contract, records image assets in real time, creates and updates a multi-party image asset list, forms asset exchange rates based on the multi-party image assets and inherent assets on the chain and forms dynamic asset exchange rates in the multi-party image asset list;
the conversion module is used for responding to the cross-chain request, calling the mirror image assets of the intelligent contract associated demand assets, generating circulation values according to the dynamic asset exchange rate of the intelligent contract to convert the cross-chain assets, and further realizing multi-party asset mirror image cross-chain.
9. The multi-party mirror-image-based chaining system of claim 8, further comprising an account module, a mirror-image asset module, a predictive player module, a list management module, and a browser module;
an account module: the system is used for registering and logging in a transaction chain by a user, checking a response historical list, asset details and storing a cross-chain transaction certificate, wherein the asset details comprise the held asset exchange rate and multi-party mirror image asset list details;
a mirror asset module: the system comprises a casting module, a transaction module and a destruction module, wherein the casting module is used for selecting or synthesizing mirror image assets according to a cross-chain request; the transaction module is used for performing cross-chain transaction by utilizing the mirror image asset exchange rate of each asset; the destroying module is used for updating the cross-chain transaction voucher and destroying the historical transaction voucher after transaction;
the prediction machine module: the associated casting module copies the market values of the multi-party assets, synthesizes a mirror image asset value constant, and carries out real-time statistical updating according to market value fluctuation;
a list management module: the association prediction machine module is used for managing a multi-party mirror image asset list and calculating and updating the mirror image assets and the exchange rate of the mirror image assets in real time;
a browser module: and viewing the cross-chain transaction overview, and searching the cross-chain transaction, wherein the cross-chain transaction comprises cross-chain transaction details, casting details, destroying details and cross-chain request address details.
10. The mirrored-bride-based multi-party bride system of claim 9, wherein the casting module is configured to: responding to the cross-link request, and calling an intelligent contract to select the mirror image assets of the held assets and the target assets and corresponding asset exchange rates in a multi-party mirror image asset list according to the asset key words held by the user and the target asset key words; when the existing multi-party mirror image asset list does not have mirror image assets of user held assets or target assets, calling an intelligent contract associated distributed prediction machine to synthesize the mirror image assets of the required assets and displaying the mirror image assets in the multi-party mirror image asset list, wherein the cross-chain request comprises user held asset keywords, target asset keywords and specific transaction events;
the transaction module is configured to: performing value transaction according to the mirror image asset exchange rate of the held asset and the target asset and a specific cross-chain transaction, generating a cross-chain transaction certificate after the transaction is successful, storing the cross-chain transaction certificate on a chain and sending the cross-chain transaction certificate to a user account for storage, wherein the transaction certificate is used as a user cross-chain asset transaction evidence;
the destruction module is configured to: and after a new cross-chain transaction voucher is generated, replacing the historical transaction voucher with the new cross-chain transaction voucher, destroying the historical transaction voucher, and enabling the user to always hold the latest cross-chain transaction voucher as a cross-chain asset transaction evidence.
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