CN114154982B - Real-time settlement and supervision method based on block chain and big data platform - Google Patents
Real-time settlement and supervision method based on block chain and big data platform Download PDFInfo
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Abstract
The invention provides a real-time settlement and supervision system and method based on a block chain and a big data platform, which realizes an algorithm for transaction settlement and anti-money laundering supervision, and comprises the following steps: the block chain system is used for executing the digital asset transaction and reporting the transaction information to the big data platform in real time; the big data platform is used for storing the transaction information reported by the blockchain system in a data table or a data graph and quickly evaluating whether the transaction passes the transaction; immediately after the evaluation, sending the evaluation decision to the blockchain system in real time; the blockchain system determines to complete or reject the transaction according to the supervision report; the block chain system immediately puts the relevant information into the block, and uplinks after the consensus; the information is automatically reported to a supervision unit after the transaction is refused; the storage system stores transaction information and supervision information by using a block-in-block mechanism or a traditional storage mechanism; and the network system is used for connecting the block chain system, the big data platform and the storage system.
Description
Technical Field
The invention belongs to the technical field of block chain digital asset transaction, big data and supervision technologies, and particularly relates to a real-time settlement and supervision method based on a block chain and a big data platform, which is required by transaction and big data platform interaction on a block chain system to complete settlement and supervision processes.
Background
Traditional blockchain systems such as bitcoin or ether house evasion policing, and super ledgers also do not consider policing mechanisms. However, the blockchain system must be considered regulatory, and according to the legal regulations of each country, a blockchain system which is not regulated or cannot be regulated later cannot be used. However, the content supervision mechanism of the traditional financial system is attached to the traditional financial system, and is implemented in the background of the financial system by using technologies such as big data, artificial intelligence contracts and the like. Many times, the day market deals and the night background systems engage in regulatory checks, i.e. many regulatory mechanisms are not performed in real time.
However, the block chain-based digital currency and digital finance are 365/7/24 transactions, and real-time supervision is needed, namely supervision check must be executed at transaction time, so that the novel block chain system is embedded in a protocol layer for supervision. This also represents a change in the working mode of the blockchain, which is the blockchain as long as it can be traded before, and now needs to be supervised to complete the trade. Such a requirement is clearly shown and described in the white paper of version 2 of the digital stable currency of face book in 2020, the face book declares that the "Travel Rule" (Travel Rule) is required to be obeyed, and a supervision mechanism is embedded in a block chain protocol layer, so that a real-time supervision mechanism is necessary to be confirmed, and the real-time supervision mechanism is also necessary infrastructure of a novel digital economy. Moreover, embedded policing at the blockchain protocol layer requires that the policing checks be completed in a short time, while the policing checks require processing of large amounts of data. The embedded supervisory mechanism changes blockchain architecture and operations, including transaction flow changes, data structure changes, and super-fusion with big data platforms. The blockchain platform and the big data platform need to be seamlessly and synchronously collocated on each transaction to complete. However, since the big data platform needs to perform a lot of data analysis, the big data platform needs to perform transaction verification and arrange data in real time to prepare for the transaction that is going on.
Some designs of interaction between the blockchain system and the large data platform have appeared in the prior art, but most of the two platforms work independently, exchange information with each other and cooperate to complete tasks, so that a new blockchain architecture needs to be designed to meet the requirement.
Disclosure of Invention
The invention aims to solve one or more technical problems in the prior art, and creatively provides a new architecture, which is connected with a traditional block chain platform and a big data platform, finishes settlement and supervision at the fastest speed in a transaction process, can finish connection in a cluster, uses a high-speed network inside, is connected with the big data platform, a server and other equipment, cannot be connected with the two platforms only, and the operation of the two platforms can affect each other: the block chain operation determines whether the transaction can be carried out or not according to the monitoring result of the big data platform; while the big data platform must 365/7/24 accept the blockchain data, update the data in the platform, then supervise the calculations from above and pass to the blockchain system supervision decisions in the shortest time. Thus, the manner in which both systems operate has changed. Due to mutual influence, the two closely interactive systems actually become a fused all-in-one system instead of two independent systems, the running of the block chain can be completed only by the participation of a large data platform, and the two systems run in real time instead of off-line running. Meanwhile, under the system architecture, the big data platform has enough calculation and storage servers to support real-time big data analysis.
The invention aims to provide a real-time settlement and supervision method based on a block chain and a big data platform, which comprises the following steps:
a transaction real-time settlement process and a transaction real-time supervision process;
the real-time settlement and supervision method based on the blockchain and the big data platform is realized by a real-time settlement and supervision system based on the blockchain and the big data platform, and the system comprises the following components: the blockchain system is used for executing digital asset transaction and reporting transaction information to the big data platform in real time, the blockchain system executes the transaction and adopts the same or different transaction processes, all information of the transaction is reported to the big data platform while stored in the blockchain system, the blockchain system comprises a plurality of nodes, each node maintains a chain, a block is arranged on each chain, and the block is guaranteed not to be tampered by a hash algorithm; a big data platform for storing information of the transactions reported by the blockchain system in a data table or data graph, thereby facilitating querying and engaging in supervisory activities, the data table or data graph being dynamically generated and dynamically updated; the big data platform is also used for dynamically adjusting and maintaining the data index; the big data platform is positioned on a node of at least one block chain system, the node is a super node, the big data platform is connected with one or more supervision units, the big data platform collects data, and after the data is verified, the data is analyzed to obtain the current transaction situation as the basis of supervision decision; or the big data platform receives related supervision information obtained from other reliable units; or the big data platform collects and analyzes information; the big data platform rapidly evaluates whether the transaction is passed or not according to the blacklist and other rating data including data collected and analyzed by the big data platform; after the big data is evaluated, immediately sending an evaluation decision to the block chain system in real time; the blockchain system determines whether to complete the transaction or reject the transaction according to the supervision report; whether the transaction is approved to be completed or rejected, the blockchain system immediately puts the relevant information into the blocks, and after the identification, the blocks are linked up; if the final decision is to reject the transaction, the relevant entity or individual, if involved in money laundering activities, may automatically report the information to the supervising entity on a large data platform, a blockchain system, or both; the storage system stores transaction information and supervision information by using a block-in-block mechanism or a traditional storage mechanism; the network system is a high-speed network system and is used for connecting the block chain system, the big data platform and the storage system;
the method comprises the following steps: the big data platform collects data, and after the data is verified, the big data platform analyzes the data to obtain the current transaction situation as the basis of supervision decision; or the big data platform receives related supervision information obtained from other reliable units; or the big data platform collects and analyzes information by itself; the big data platform rapidly evaluates whether the transaction is passed or not according to the blacklist and other rating data including data collected and analyzed by the big data platform; after the big data is evaluated, immediately sending an evaluation decision to the block chain system in real time; the blockchain system determines whether to complete the transaction or reject the transaction according to the supervision report; whether the transaction is approved to be completed or rejected, the blockchain system immediately puts the relevant information into the blocks, and after the identification, the blocks are linked up; if the final decision is to reject the transaction, the relevant organization or the individual, if participating in money laundering activity, is a big data platform, or a blockchain system, or both automatically report the information participating in money laundering activity to the supervising organization; therefore, real-time settlement and real-time supervision of the transaction are realized.
Preferably, the real-time settlement and supervision method employs a mechanism for separating transactions and settlements, including:
(1) after the transaction reaches the blockchain system to carry out the first uplink, and after the information of all transaction parties is checked out, the fund and the assets are confirmed to be true, the following queued uplink transaction is carried out;
(2) queuing the uplink transaction, wherein the transaction and consensus mechanism are separated, and the transaction and settlement mechanism are separated;
(3) after consensus, the transaction waits for completion: if the transaction and the consensus are separated, the transaction needs to wait for the blockchain system transaction to be completed; if the block chain system is in consensus and transaction binding, the transaction is completed after the consensus;
(4) after the transaction is completed, linking the completed transaction information for the second time;
if the transaction is not successful or the transaction is rejected by the system, the transaction is also linked up, indicating that the transaction failed;
(5) after the money is washed back and the like, the transaction is successfully settled on a proper account system, and the transaction information comprises the 3 rd chain of settlement completion time.
Preferably, the real-time settlement and supervision method employs a transaction, i.e. settlement mechanism, including:
(1) before consensus, the big data platform firstly conducts supervision and inspection, only can the big data platform participate in consensus through inspection, the block chain system sends the information of the transaction to the big data platform, and the big data platform evaluates whether the transaction is approved or not;
(2) after the big data platform is evaluated, the decision information is transmitted to the block chain system;
(3) after receiving a big data platform supervision approval notice, uplink the supervision approval notice, which is the second uplink; if the big data platform returns a rejection notice, the rejection notice is also uplink-linked; the blockchain system informs the related units of the information of the failure of the transaction with the customer;
(4) if the big data platform approves, the transaction enters a consensus mechanism; if the transaction and the consensus are separated, the transaction needs to wait for the completion of the blockchain system transaction; if the blockchain system consensus and the transaction are bound, the transaction is considered to be completed after the consensus; whether the transaction is identified in common or separated, the transaction information is linked for the third time after the transaction is completed; if the transaction fails due to an exception, the failed transaction is linked up, which is also the third time, and the relevant organization and customer are notified.
Preferably, the transaction in the transaction real-time settlement process has two data uplink processes, and the transaction data is processed in real time on the big data platform in the first uplink process to generate the settlement data of the account.
Preferably, the settlement data of the account includes account-related transaction information, the data structure of the account-related transaction information is a side chain structure, all the accounts form an account chain, each account has a side chain, and all the transactions related to the account are stored in the side chain.
Preferably, the transaction real-time settlement process adopts a MapReduce parallel algorithm, and is suitable for the condition that accounts are overlapped, namely one account participates in a plurality of transactions, and the method comprises the following steps:
(1) generating account transaction data, including:
(11) carrying out account word frequency analysis, and counting out all account information;
(12) processing all transactions in parallel, connecting each transaction to the account of a transaction related party (a transaction initiator and a transaction receiver);
(13) accounting account settlement information: if the account is a transaction initiator of a certain transaction, subtracting the corresponding transaction amount from the account amount, and if the account is a transaction receiver of the certain transaction, adding the corresponding transaction amount to the account amount;
(2) the method for circularly and parallelly calculating the side table data to settle the transaction comprises the following steps:
(21) counting the minimum value of the transfer-out amount of each account side table in parallel, namely calculating the minimum value of a single transaction in the transfer-out amount of the account, which is the transaction initiator in the transaction data of each account side table, and setting the minimum value as (S1, S2.. Si), wherein i is the number of the accounts and Si is the minimum value of a single transaction of the transfer-out amount of the accounts in the transaction of the time period;
(22) setting account balance of each account as (a1, a 2.. Ai), if Ai > Si, marking the roll-out transaction corresponding to the single minimum value in the transaction data of the account side table as successful and updating Ai = Ai-Si; if Ai < Si, the transaction data of the account side table is temporarily unchanged, and Ai is also unchanged;
(23) because each roll-out transaction of the account is bound to have a roll-in account, each transaction has two data in the account side table, namely each transaction is on the roll-out side table and is also transferred into the account side table, the transaction successfully marked in the second step is marked to ensure that the same transaction data on the roll-in account side table is successfully marked at the same time, the account balance table is updated, and the transfer amount is added to the corresponding balance of the roll-in account;
(24) re-executing the step 21, re-calculating the minimum value of the single transaction of each account which is not marked as a successful transaction as (S1, S2.. Si), the balance of the account as (A1, A2.. Ai), if an account with Ai > Si exists, transferring to the step 22 to continue calculation, and if no account with Ai > Si exists, namely all accounts, transferring to the step 24;
(25) and traversing the account side table, marking all transactions which are not marked as successful as failed, finishing the processing at the moment, and feeding back a transaction result to the lower layer block chain.
Preferably, the transaction real-time settlement process does not adopt a MapReduce parallel algorithm, and is suitable for the case that accounts are overlapped, that is, one account participates in a plurality of transactions less frequently, including:
(1) classifying all transaction data, wherein the classification one is a transaction without account overlapping condition, and the classification two is a transaction with account overlapping condition, the classification method adopts a hash method, namely, setting a hash table of all accounts, traversing all transactions, marking accounts related to the transactions, participating in a plurality of transactions by a plurality of marked accounts, classifying the accounts into the classification one, participating in only one transaction by the account marked once, and classifying the accounts into the classification two;
(2) performing parallel real-time settlement on the classified one transaction, wherein the transaction is successful when the balance of the transaction initiator is greater than the transfer amount, and the transaction is failed when the balance of the transaction initiator is insufficient;
(3) and (3) carrying out transfer operation on all the transactions classified into two, wherein after the transfer operation is successful, if the amount is larger than the transfer amount, the transactions are successful, and when the balance of the transaction initiator is insufficient, the transactions fail.
Preferably, if the transaction data is entered and the LSM is processed, step (1) can be omitted, since the LSM algorithm can find the accounts of the overlapping transactions and also preprocess the related transactions of the overlapping accounts, so that the big data platform can complete the transaction as long as the related accounts are found to have insufficient balance to pay.
Preferably, the account transaction chain obtained by the transaction real-time settlement algorithm in the transaction real-time supervision process may obtain a specific fund path, including:
(1) calculating settlement information of a specific account in a certain statistical time period according to transaction settlement data obtained by a transaction real-time settlement algorithm, wherein the settlement information of the account is positive and must have funds remitted finally, the settlement information of the account is negative and must have funds remitted out, and the funds are finally remitted into the positive account from the negative account through various channels;
(2) counting accounts of which the settlement information is a negative value, and executing transaction chain generation operation on all related transactions;
(3) after counting all fund transaction paths, maintaining a money laundering probability table (P1, P2.. Pi) for all accounts (A1, A2.., Ai) in which i is the number of accounts, setting an initial money laundering probability to be 0, and updating the money laundering probability table according to the money laundering probability in a black list for the accounts in the money laundering black list, wherein the money laundering probability is the proportion of money laundering amount to total transaction amount;
(4) traversing the transaction path, and updating the money laundering probability of all accounts on the same transaction path with the money laundering institution;
(5) updating the money laundering probability of the transaction account of the newly linked transaction in real time, and setting a transaction white list, wherein the white list is account information with the money laundering probability of 0 or less than a money laundering probability threshold, and the money laundering probability threshold can be set manually;
(6) the transaction data is linked twice, and when the transaction data is linked for the first time, if the transaction account is in the white list and the transaction amount S is less than A, the transaction directly passes, wherein A is a threshold value of the small amount transaction; checking the money laundering probability of the account during the second cochain, and if the money laundering probability Pi is greater than P, judging that the transaction fails, wherein P is a threshold value for controlling the money laundering probability;
said twice linking transaction data of said step (6) comprises:
the following operations are performed when transaction data is first uplink: the method comprises the steps of firstly, simply testing, if a transaction account is in a money laundering white list and the transaction amount meets the standard of small transaction, directly passing the transaction, if the money laundering probability of the transaction account is greater than a set value, directly failing the transaction, if the transaction amount is greater and the money laundering probability of the account is greater than the set value but not greater than the set value, performing second chain linking, updating transaction account chain data by using the transaction data subjected to the first chain linking, and updating the money laundering white list data and an account laundering probability table; and performing anti-money laundering inquiry and deep background investigation when the transaction data is linked up for the second time, wherein if the account is not in the anti-money laundering transaction chain, the transaction is successful, and if the account is in the anti-money laundering transaction chain, the transaction fails.
Preferably, the transaction chain generating operation comprises:
A. ordering all transaction information according to a time stamp sequence to obtain simplified transaction information, wherein the simplified transaction information comprises a time stamp, a transaction initiator, a transaction receiver and transaction amount;
B. regarding each transaction data as a data block, circularly traversing each data block from the initial data block, and linking two transaction blocks when the receiving party of the transaction block A is equal to the transaction initiating party of the transaction block B;
C. taking the transaction amount minimum value S of the data blocks in each transaction chain, subtracting the minimum value from the transaction amount of each transaction data of the chain to obtain (A1, A2.. Ai), wherein i is the transaction block number of the chain, deleting the data blocks in the data list when Ai =0 to obtain the information of the fund link, and then transferring to the step B to traverse again;
D. when there is no connection between the data blocks, each small data block is a separate transaction chain.
The invention has the beneficial effects that:
and the transaction on the blockchain system is interacted with the big data platform to complete the transaction and the supervision process, so that the security and the supervision of the transaction on the blockchain system are ensured.
The above and other objects, advantages and features of the present invention will become more apparent to those skilled in the art from the following detailed description of specific embodiments thereof taken in conjunction with the accompanying drawings.
Drawings
Some specific embodiments of the invention will be described in detail hereinafter, by way of illustration and not limitation, with reference to the accompanying drawings. The same reference numbers in the drawings identify the same or similar elements or components. Those skilled in the art will appreciate that the drawings are not necessarily drawn to scale. The objects and features of the present invention will become more apparent in view of the following description taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a block chain system and big data platform fused into a curated digital asset transaction system schematic block diagram in accordance with a preferred embodiment of the invention;
FIG. 2 is a flow diagram of the upper chain of transactions with separation of transactions and settlement according to the preferred embodiment of the present invention;
FIG. 3 is a flow chart of the uplink transaction for "transaction is settlement" according to the preferred embodiment of the present invention;
FIG. 4 is a diagram illustrating a data structure of a first step in a transaction real-time settlement process according to a preferred embodiment of the present invention;
FIG. 5 is a flow diagram illustrating the calculation of transaction settlement by loop-parallel computation of collateral data in a transaction real-time settlement process according to a preferred embodiment of the present invention;
FIG. 6 is a diagram illustrating new account information or new transaction information during a transaction real-time settlement process according to a preferred embodiment of the present invention;
FIG. 7 is a schematic diagram illustrating a specific fund path that can be obtained from an account transaction chain obtained by a transaction real-time settlement algorithm in a transaction real-time supervision process according to a preferred embodiment of the present invention;
FIG. 8 is a schematic diagram of the linkage of transaction data in the transaction real-time supervision flow according to the preferred embodiment of the present invention;
FIG. 9 is a flow chart of transaction path generation in a transaction real-time supervision flow according to the preferred embodiment of the present invention;
FIG. 10 is a schematic diagram of a transaction path in accordance with a preferred embodiment of the present invention;
FIG. 11 is a diagram illustrating the update of money laundering probability table in the transaction real-time supervision flow according to the preferred embodiment of the present invention;
fig. 12 is a flowchart of probability table updating in the transaction real-time supervision process according to the preferred embodiment of the present invention.
Detailed Description
In order to make the present invention more comprehensible with respect to its gist, the present invention will be further described with reference to the accompanying drawings and examples. In the following description, numerous specific details and specific examples are set forth in order to provide a more thorough understanding of the present invention and to provide a thorough understanding of the present invention. While this invention is susceptible of embodiment in many different forms than that described herein, there will be many equivalents to those skilled in the art which incorporate such variations and modifications without departing from the spirit and scope of the invention as defined by the appended claims and their equivalents.
In the following description, numerous specific details and specific examples are set forth in order to provide a more thorough understanding of the present invention and to provide a thorough understanding of the present invention. While this invention is susceptible of embodiment in many different forms than that described herein, there will be many equivalents to those skilled in the art which incorporate such variations and modifications without departing from the spirit and scope of the invention as defined by the appended claims and their equivalents.
As shown in fig. 1, the present embodiment provides a real-time settlement and supervision system based on a blockchain and a big data platform, including:
the blockchain system is used for executing digital asset transaction and reporting transaction information to the big data platform in real time, the blockchain system executes the transaction and adopts the same or different transaction processes, all information of the transaction exists in the blockchain system and is reported to the big data platform at the same time, the blockchain system comprises a plurality of nodes, each node maintains a chain, a block is arranged on the chain, and the block is guaranteed not to be tampered through a Hash algorithm;
the big data platform is used for storing the information of the transaction reported by the blockchain system in a data table or a data graph so as to be convenient for inquiring and carrying out supervision activities, and the data table or the data graph is dynamically generated and dynamically updated; the big data platform is also used for dynamically adjusting and maintaining the data index; the big data platform is positioned on a node of at least one block chain system, the node is a super node, the big data platform is connected with one or more supervision units, the big data platform collects data, and after the data is verified, the big data platform analyzes the data to obtain the current transaction situation as the basis of supervision decision; or the big data platform receives related supervision information obtained from other reliable units; or the big data platform collects and analyzes information; the big data platform rapidly evaluates whether the transaction is passed or not according to the blacklist and other rating data including data collected and analyzed by the big data platform; after the big data is evaluated, immediately sending the evaluation decision to the block chain system in real time; the blockchain system determines whether to complete the transaction or reject the transaction according to the supervision report; whether the transaction is approved to be completed or rejected, the blockchain system immediately puts the relevant information into the blocks, and after the identification, the blocks are linked up; if the final decision is to reject the transaction, the relevant entity or the individual, if participating in the money laundering activity, automatically reports the information to the supervising entity on the big data platform, the blockchain system, or both;
the storage system stores transaction information and supervision information by using a block-in-block mechanism or a traditional storage mechanism; and
the network system is a high-speed network system and is used for connecting the block chain system, the big data platform and the storage system.
The embodiment also provides a real-time settlement and supervision method based on the block chain and the big data platform, which comprises the following steps:
the method comprises a transaction real-time settlement process and a transaction real-time supervision process, wherein a big data platform collects data, analyzes the data after verifying the data, and obtains the current transaction situation as the basis of supervision decision; or the big data platform receives related supervision information obtained from other reliable units; or the big data platform collects and analyzes information; the big data platform rapidly evaluates whether the transaction is passed or not according to the blacklist and other rating data including data collected and analyzed by the big data platform; after the big data is evaluated, immediately sending the evaluation decision to the block chain system in real time; the blockchain system determines whether to complete the transaction or reject the transaction according to the supervision report; whether the transaction is approved to be completed or rejected, the blockchain system immediately puts the relevant information into the blocks, and after the identification, the blocks are linked up; if the final decision is to reject the transaction, the relevant entity or the individual if involved in the money laundering activity, the big data platform, or the blockchain system, or both, automatically report the information of participation in the money laundering activity to the supervising entity.
The real-time settlement and supervision method adopts a alliance chain mode and a mechanism for separating transaction and settlement as shown in fig. 2, and comprises the following steps:
(1) when a transaction arrives at the blockchain system to carry out the first uplink, after the information of all transaction parties is checked out, the fund and the asset are confirmed to be true, and the following queued uplink transaction is carried out;
(2) queuing the uplink transaction, wherein the transaction and consensus mechanism are separated, and the transaction and settlement mechanism are separated;
(3) after consensus, the transaction waits for completion: if the transaction and the consensus are separated, the transaction needs to wait for the completion of the blockchain system transaction; if the consensus and the transaction are bound in the blockchain system, the transaction is completed after the consensus;
(4) after the transaction is completed, linking the completed transaction information for the second time;
if the transaction is not successful or the transaction is rejected by the system, the transaction is also linked up, indicating that the transaction failed;
(3) after the money is washed back and the like, the transaction is successfully settled on a proper account system, and the transaction information comprises the 3 rd chain of settlement completion time.
Alternatively, the real-time settlement and supervision method adopts a real-time transaction control method in which the transaction shown in fig. 3 is settlement, including:
(1) before consensus, the big data platform firstly conducts supervision and inspection, only can the big data platform participate in consensus through inspection, the block chain system sends the information of the transaction to the big data platform, and the big data platform evaluates whether the transaction is approved or not;
(2) after the big data platform is evaluated, the decision information is transmitted to the block chain system;
(3) after receiving the supervision approval notice of the big data platform, uplink the supervision approval notice, which is the second uplink; if the big data platform returns the rejection notice, the rejection notice is also uplink-linked; the blockchain system informs the related units of the information of the failure of the transaction with the customer;
(4) if the big data platform approves, the transaction enters a consensus mechanism; if the transaction and the consensus are separated, the transaction needs to wait for the completion of the blockchain system transaction; if the blockchain system is identified and the transaction is bound, the transaction is considered to be completed after the identification; whether the transaction is identified in common or separated, the transaction information is linked for the third time after the transaction is completed; if the transaction fails due to an exception, the failed transaction is linked up, which is also the third time, and the relevant organization and customer are notified.
As a preferred embodiment, the transaction real-time settlement process adopts a MapReduce parallel algorithm, is suitable for the condition that accounts are overlapped frequently, and adopts the system architecture shown in FIG. 1, and a big data platform has enough calculation and storage servers to support real-time big data analysis. The system is composed of a block chain and a big data platform, the transaction is a data uplink process which is carried out twice, the transaction data is processed in real time on the big data platform in the first uplink process to generate the settlement data of the account, the process is shown as figure 4, and figure 4 is a data structure of the first step in the transaction real-time settlement process. The generated account related transaction information has a data structure of a side chain structure, all accounts form an account chain, each account has a side chain, and all transactions related to the account are stored in the side chains, and because the algorithm is executed in parallel in the whole process, the speed is much higher than that of the traditional clearing algorithm:
(1) account transaction data is generated, and the algorithm steps (MapReduce parallel execution) are as follows:
(11) carrying out account word frequency analysis, and counting out all account information;
(12) processing all transactions in parallel, connecting each transaction to the account of a transaction related party (a transaction initiator and a transaction receiver);
(13) accounting account settlement information: if the account is a transaction initiator of a certain transaction, subtracting the corresponding transaction amount from the account amount, and if the account is a transaction receiver of the certain transaction, adding the corresponding transaction amount to the account amount;
(2) the side table data is circularly and parallelly calculated to settle the transaction, and the algorithm steps are as follows:
(21) counting the minimum value of the transfer-out amount of each account side table in parallel, namely calculating the minimum value of a single transaction in the transfer-out amount of the account, which is the transaction initiator in the transaction data of each account side table, and setting the minimum value as (S1, S2.. Si), wherein i is the number of the accounts and Si is the minimum value of a single transaction of the transfer-out amount of the accounts in the transaction of the time period;
(22) setting the account balance of each account as (a1, a 2.. Ai), if Ai > Si, marking the roll-out transaction corresponding to the single minimum value in the transaction data of the account-side table as successful and updating Ai = Ai-Si; if Ai < Si, the transaction data of the account side table is temporarily unchanged, and Ai is also unchanged;
(23) because each roll-out transaction of the account is bound to have a roll-in account, each transaction has two data in the account side table, namely each transaction is on the roll-out side table and is also transferred into the account side table, the transaction successfully marked in the second step is marked to ensure that the same transaction data on the roll-in account side table is successfully marked at the same time, the account balance table is updated, and the transfer amount is added to the corresponding balance of the roll-in account;
(24) re-executing the step 21, re-calculating the minimum value of the single transaction of each account which is not marked as a successful transaction as (S1, S2.. Si), the balance of the account as (A1, A2.. Ai), if an account with Ai > Si exists, transferring to the step 22 to continue calculation, and if no account with Ai > Si exists, namely all accounts, transferring to the step 24;
(25) and traversing the account side table, marking all transactions which are not marked as successful as failed, finishing the processing at the moment, and feeding back a transaction result to the lower layer block chain. The algorithm flow is shown in fig. 5.
Since the settlement is performed in real time and 365/7/24 is performed, the settlement information of the account is constantly updated. For the newly added transaction, the uplink operation can also be performed in parallel, if the account of the newly added transaction is not in the main chain, the new account is added at the tail of the main chain, and if the transaction account is in the main chain, the transaction is added to the account side chain corresponding to the newly added transaction, as shown in fig. 5.
As a preferred embodiment, the transaction real-time settlement process does not adopt a MapReduce parallel algorithm, and is applicable to a case where accounts are overlapped (i.e. one account participates in multiple transactions) less, including:
(1) classifying all transaction data, wherein the classification one is a transaction without account overlapping condition, and the classification two is a transaction with account overlapping condition, the classification method adopts a Hash method, namely, a Hash table of all accounts is set, all transactions are traversed, the accounts involved in the transactions are marked, the accounts with a plurality of marks participate in a plurality of transactions, the classification one is a classification one, the accounts with only one mark participate in only one transaction, and the classification two is a classification two;
(2) performing parallel real-time settlement on the classified one transaction, wherein the transaction is successful when the balance of the transaction initiator is greater than the transfer amount, and the transaction is failed when the balance of the transaction initiator is insufficient;
(3) and (3) carrying out transfer operation on all the transactions classified into two, wherein after the transfer operation is successful, if the amount is larger than the transfer amount, the transactions are successful, and when the balance of the transaction initiator is insufficient, the transactions fail.
As a preferred embodiment, if the transaction data is entered and the LSM is processed, the step (1) can be omitted, since the LSM algorithm can find the accounts of the overlapped transactions and also preprocess the related transactions of the overlapped accounts, so that the large data platform can complete the transaction as long as the related accounts are searched for whether there is enough balance to pay.
In a preferred embodiment, the new account information or the new transaction information is shown in fig. 6, and the account transaction chain obtained by the transaction real-time settlement algorithm in the transaction real-time supervision process may obtain a specific fund path, as shown in fig. 7. The algorithm comprises the following steps:
(1) calculating settlement information of a specific account in a certain statistical time period according to transaction settlement data obtained by a transaction real-time settlement algorithm, wherein the settlement information of the account is positive and must have funds remitted finally, the settlement information of the account is negative and must have funds remitted out, and the funds are finally remitted into the positive account from the negative account through various channels;
(2) counting accounts with negative account settlement information, and executing the following operations on all related transactions:
A. and sequencing all transaction information according to the time stamp sequence. The simplified transaction information format is as follows:
B. each transaction datum is a block of data, each block of data is traversed from the very beginning, and when the receiver of transaction block a is equal to the initiator of the transaction of transaction block B, the two transaction blocks are linked, as shown in fig. 8.
C. Taking the minimum value S of the transaction amount of the data block in each transaction chain, subtracting the minimum value from the transaction amount of each transaction data block in the chain to obtain (a1, a 2.. Ai), where i is the number of transaction blocks in the chain, deleting the data block in the data list when Ai =0 to obtain the information of the fund link, and then transferring to step B to traverse again, as shown in fig. 9.
D. When there is no connection between the data blocks, each small data block is an independent transaction chain, and the final transaction chain is shown in fig. 10.
(3) After counting all fund transaction paths, for all accounts (a1, a 2...., Ai), where i is the number of accounts, a money laundering probability table (P1, P2.. Pi) is maintained, an initial money laundering probability table is set to 0, and for accounts in the money laundering blacklist, the money laundering probability table is updated according to the money laundering probability in the blacklist (the money laundering probability is the proportion of money laundering amount to total transaction amount), as shown in fig. 11 (assuming that C and D are in the money laundering blacklist):
(4) traversing a transaction path, updating money laundering probabilities of all accounts on the same transaction path with a money laundering organization, if the money laundering probability Pi =0 before updating of the account, directly assigning the money laundering probability Pa of a blacklist organization on the transaction path to the account, namely Pi = Pa, if the money laundering probability Pi! =0 before updating of the account, namely the account already has the money laundering probability, updating the money laundering probability according to a transaction amount weight, wherein the formula is as follows:
Pi = （Pi*S + Pa*A）/ (S+A)
S = S+A
wherein Pi is the money laundering probability of the account, Pa is the money laundering probability of the blacklist organization in the current transaction path, a is the transaction amount of the current transaction path, S is the total transaction amount of the previous account, and S is updated after updating Pi, taking the fund path of the embodiment as an example, fig. 12 shows the updating step of the probability table.
(5) For the newly linked transaction, the money laundering probability of the transaction account is updated in real time, and a transaction white list (account information with money laundering probability of 0 or less than the money laundering probability threshold in the white list, which may be set manually) is set.
(6) The transaction is linked twice, when the transaction is linked for the first time, if the transaction account is in a white list and the transaction amount S < A (A is a threshold value of small transaction and can be set manually), the transaction directly passes through, when the transaction is linked for the second time, the money laundering probability of the account is checked, and if the money laundering probability Pi > P (P is a threshold value for controlling the money laundering probability and can be set manually), the transaction is judged to be failed.
In this embodiment, the specific supervision implementation steps are as follows:
the following operations are performed when transaction data is first uplink: the first step is simple test, if the transaction account is in the money laundering white list and the transaction amount meets the standard of the small transaction, the transaction can directly pass, if the money laundering probability of the transaction account is greater than a set value, the transaction directly fails, if the transaction amount is greater and the money laundering probability exists in the account (but not greater than the set value), the second chain linking is carried out, the transaction data linked for the first time updates the transaction account chain data, and updates the money laundering white list data and the account money laundering probability table.
And (4) performing anti-money laundering inquiry during the second chain linking, and performing deep background investigation, wherein if the account is not in the anti-money laundering transaction chain, the transaction is successful, and if the account is in the anti-money laundering transaction chain, the transaction fails.
The specific computer code involved in this embodiment is as follows:
wordCount= rdddata.flatMap(lambda line: onlyname(line)).map(lambda word:(word,1)).reduceByKey(lambda a, b : a + b)
wordsets = wordCount.map(lambda x:x[0]).collect()
namedata = rdddata.filter(lambda x: word in x).map(lambda x:(x,word)).map(lambda x: productdata(x)).map(lambda x:(x[0],int(x[1]))).reduceByKey(lambda a, b : a + b)
in this embodiment, transactions on the blockchain system and interactions on the big data platform are completed, and the transaction and supervision processes are completed, so that the security and the supervision of the transactions on the blockchain system are ensured.
While the present invention has been described with reference to the particular illustrative embodiments, it is not to be restricted by the embodiments but only by the appended claims. It will be understood by those skilled in the art that variations and modifications of the embodiments of the present invention can be made without departing from the scope and spirit of the invention.
Claims (9)
1. A real-time settlement and supervision method based on a block chain and a big data platform is characterized by comprising the following steps:
a transaction real-time settlement process and a transaction real-time supervision process;
the real-time settlement and supervision method based on the blockchain and the big data platform is realized by a real-time settlement and supervision system based on the blockchain and the big data platform, and the system comprises: the blockchain system is used for executing digital asset transaction and reporting transaction information to a big data platform in real time, the blockchain system executes the transaction and adopts the same or different transaction processes, all information of the transaction exists in the blockchain system and is reported to the big data platform at the same time, the blockchain system comprises a plurality of nodes, each node maintains a chain, a block is arranged on the chain, and the block is guaranteed not to be tampered through a Hash algorithm; a big data platform for storing information of the transactions reported by the blockchain system in a data table or data graph, thereby facilitating querying and engaging in supervisory activities, the data table or data graph being dynamically generated and dynamically updated; the big data platform is also used for dynamically adjusting and maintaining the data index; the big data platform is positioned on a node of at least one block chain system, the node is a super node, the big data platform is connected with one or more supervision units, the big data platform collects data, and after the data is verified, the data is analyzed to obtain the transaction situation at that time as the basis of supervision decision; or the big data platform receives related supervision information obtained from other reliable units; or the big data platform collects and analyzes information by itself; the big data platform rapidly evaluates whether the transaction is passed or not according to the blacklist and other rating data including data collected and analyzed by the big data platform; after the big data platform finishes evaluation, immediately sending an evaluation decision to the block chain system in real time; the blockchain system determines whether to complete the transaction or reject the transaction according to the supervision report; whether the transaction is approved to be completed or rejected, the blockchain system immediately puts the relevant information into the blocks, and after the identification, the blocks are linked up; if the final decision is to reject the transaction, the relevant entity or the individual, if participating in money laundering activities, automatically reports this information to the supervising entity on the big data platform, or the blockchain system, or both systems; the storage system stores transaction information and supervision information by using a block-in-block mechanism or a traditional storage mechanism; the network system is a high-speed network system and is used for connecting the block chain system, the big data platform and the storage system;
the method comprises the following steps: the big data platform collects data, and after the data is verified, the big data platform analyzes the data to obtain the current transaction situation as the basis of supervision decision; or the big data platform receives related supervision information obtained from other reliable units; or the big data platform collects and analyzes information by itself; the big data platform rapidly evaluates whether the transaction is passed according to the blacklist and other rating data, including data collected and analyzed by the big data platform; immediately sending an evaluation decision to the block chain system in real time after the big data is evaluated; the blockchain system determines whether to complete the transaction or reject the transaction according to the supervision report; whether the transaction is approved to be completed or rejected, the blockchain system immediately puts the relevant information into the blocks, and after the identification, the blocks are linked up; if the final decision is to reject the transaction, the relevant entity or the individual if participating in money laundering activity, the big data platform, or the blockchain system, or both automatically report the information participating in money laundering activity to the supervising entity; therefore, real-time settlement and real-time supervision of the transaction are realized;
the transaction real-time settlement process adopts a MapReduce parallel algorithm, is suitable for the condition that accounts are overlapped, namely one account participates in a plurality of transactions, and comprises the following steps:
(1) generating account transaction data, including:
(11) performing account word frequency analysis, and counting all account information;
(12) processing all transactions in parallel, connecting each transaction to the account of a transaction related party (a transaction initiator and a transaction receiver);
(13) and (3) account settlement information is counted: if the account is a transaction initiator of a certain transaction, subtracting the corresponding transaction amount from the account amount, and if the account is a transaction receiver of the certain transaction, adding the corresponding transaction amount to the account amount;
(2) the method for circularly and parallelly calculating the side table data to settle the transaction comprises the following steps:
(21) counting the minimum value of the transfer-out amount of each account side table in parallel, namely calculating the minimum value of a single transaction in the transfer-out amount of the account, which is the transaction initiator in the transaction data of each account side table, and setting the minimum value as (S1, S2.. Si), wherein i is the number of the accounts and Si is the minimum value of a single transaction of the transfer-out amount of the accounts in the transaction of the time period;
(22) setting account balance of each account as (a1, a 2.. Ai), if Ai > Si, marking the roll-out transaction corresponding to the single minimum value in the transaction data of the account side table as successful and updating Ai = Ai-Si; if Ai < Si, the transaction data of the account side table is temporarily unchanged, and Ai is also unchanged;
(23) because each roll-out transaction of the account is bound to have a roll-in account, each transaction has two data in the account side table, namely each transaction is on the roll-out side table and is also transferred into the account side table, the transaction successfully marked in the second step is marked to ensure that the same transaction data on the roll-in account side table is successfully marked at the same time, the account balance table is updated, and the transfer amount is added to the corresponding balance of the roll-in account;
(24) re-executing the step (21), re-calculating the minimum value of the single transaction of each account which is not marked as a successful transaction as (S1, S2.. Si), and the balance of the account as (A1, A2.. Ai), if the account with Ai > Si exists, continuing the calculation in the step (22), and if the account with Ai > Si does not exist, namely all accounts, continuing the calculation in the step (24);
(25) and traversing the account side table, marking all transactions which are not marked as successful as failed, finishing the processing at the moment, and feeding back a transaction result to the lower layer block chain.
2. The real-time settlement and supervision method based on blockchain and big data platform according to claim 1, characterized in that the real-time settlement and supervision method adopts a mechanism of transaction and settlement separation, comprising:
(1) after the transaction arrives at the blockchain system to carry out the first uplink, after the information of all transaction parties is checked out, the capital and the assets are confirmed, and then the following queued uplink transaction is carried out;
(2) queuing the uplink transaction, wherein the transaction and consensus mechanism are separated, and the transaction and settlement mechanism are separated;
(3) after consensus, the transaction waits for completion: if the transaction and the consensus are separated, the transaction needs to wait for the blockchain system transaction to be completed; if the block chain system is in consensus and transaction binding, the transaction is completed after the consensus;
(4) after the transaction is completed, linking the completed transaction information for the second time;
if the transaction is not successful or the transaction is rejected by the system, the transaction is also linked up, indicating that the transaction failed;
(5) after the money back-washing process, the transaction is successfully settled on the appropriate account system, and the transaction information includes the 3 rd chain of settlement completion time.
3. The real-time settlement and supervision method based on blockchain and big data platform according to claim 1, wherein the real-time settlement and supervision method adopts a mechanism that transaction is settlement, including:
(1) before consensus, the big data platform firstly conducts supervision and inspection, only can the big data platform participate in consensus through inspection, the block chain system sends the information of the transaction to the big data platform, and the big data platform evaluates whether the transaction is approved or not;
(2) after the big data platform is evaluated, the decision information is transmitted to the block chain system;
(3) after receiving a big data platform supervision approval notice, uplink the supervision approval notice, which is the second uplink; if the big data platform returns a rejection notice, the rejection notice is also uplink-linked; the blockchain system informs the relevant units and the information of the transaction failure of the customer;
(4) if the big data platform approves, the transaction enters a consensus mechanism; if the transaction and the consensus are separated, the transaction needs to wait for the completion of the blockchain system transaction; if the blockchain system consensus and the transaction are bound, the transaction is considered to be completed after the consensus; whether the transaction is identified in common or separated, the transaction information is linked for the third time after the transaction is completed; if the transaction fails due to an exception, the failed transaction is linked up, which is also the third time, and the relevant organization and customer are notified.
4. The method as claimed in claim 1, wherein the transaction has two data linking processes, and the transaction data will be processed in real time on the big data platform during the first linking process to generate the settlement data of the account.
5. The real-time settlement and supervision method based on blockchain and big data platform as claimed in claim 4, wherein the settlement data of the accounts includes account-related transaction information, the data structure of the account-related transaction information is a side chain structure, all the accounts form an account chain, each account has a side chain, and all the transactions related to the account are stored in the side chain.
6. The real-time settlement and supervision method based on blockchain and big data platform as claimed in claim 1, wherein the transaction real-time settlement process does not adopt MapReduce parallel algorithm, and is adapted to the case of account overlap, i.e. one account participates in a plurality of transactions less often, including:
(a) classifying all transaction data, wherein the classification one is a transaction without account overlapping condition, and the classification two is a transaction with account overlapping condition, the classification method adopts a Hash method, namely, a Hash table of all accounts is set, all transactions are traversed, the accounts involved in the transactions are marked, the accounts with a plurality of marks participate in a plurality of transactions, the classification one is a classification one, the accounts with only one mark participate in only one transaction, and the classification two is a classification two;
(b) performing parallel real-time settlement on the classified one transaction, wherein the transaction is successful when the balance of the transaction initiator is greater than the transfer amount, and the transaction is failed when the balance of the transaction initiator is insufficient;
(c) and (3) carrying out transfer operation on all the transactions classified into two, wherein after the transfer operation is successful, if the amount is larger than the transfer amount, the transactions are successful, and when the balance of the transaction initiator is insufficient, the transactions fail.
7. A blockchain and big data platform based real-time settlement and supervision method according to claim 6, wherein if the transaction data is already processed by LSM at the time of entering, said step (a) can be omitted, since LSM algorithm can find the accounts of overlapping transactions and also pre-process the related transactions of these overlapping accounts, so that the big data platform can complete as long as it finds whether the related accounts have enough balance to pay.
8. The real-time settlement and supervision method based on blockchain and big data platform as claimed in claim 1, wherein the account transaction chain obtained by the transaction real-time settlement algorithm in the transaction real-time supervision process can obtain a specific fund path, comprising:
(1) calculating settlement information of a specific account in a certain statistical time period according to transaction settlement data obtained by a transaction real-time settlement algorithm, wherein the settlement information of the account is positive and is bound with funds finally, the settlement information of the account is negative and is bound with funds finally, and the funds are bound into the positive account from the negative account through various channels finally;
(2) counting accounts of which the settlement information is a negative value, and executing transaction chain generation operation on all related transactions;
(3) after counting all fund transaction paths, maintaining a money laundering probability table (P1, P2.. Pi) for all accounts (A1, A2.., Ai) in which i is the number of accounts, setting an initial money laundering probability to be 0, and updating the money laundering probability table according to the money laundering probability in a black list for the accounts in the money laundering black list, wherein the money laundering probability is the proportion of money laundering amount to total transaction amount;
(4) traversing the transaction path, and updating the money laundering probability of all accounts on the same transaction path with the money laundering institution;
(5) updating the money laundering probability of the transaction account of the newly linked transaction in real time, and setting a transaction white list, wherein the white list is account information with the money laundering probability of 0 or less than a money laundering probability threshold, and the money laundering probability threshold can be set manually;
(6) the transaction data is linked twice, and when the transaction data is linked for the first time, if the transaction account is in the white list and the transaction amount S is less than A, the transaction directly passes, wherein A is a threshold value of the small amount transaction; checking the money laundering probability of the account during the second chain linking, and if the money laundering probability Pi is greater than P, judging that the transaction fails, wherein P is a threshold value for controlling the money laundering probability;
said linking transaction data twice of step (6) comprises:
the following operations are performed when transaction data is first uplink: the method comprises the steps of firstly, simply testing, if a transaction account is in a money laundering white list and the transaction amount meets the standard of small transaction, directly passing the transaction, if the money laundering probability of the transaction account is greater than a set value, directly failing the transaction, if the transaction amount is greater and the money laundering probability of the account is greater than the set value but not greater than the set value, performing second chain linking, updating transaction account chain data by using the transaction data subjected to the first chain linking, and updating the money laundering white list data and an account laundering probability table; and performing anti-money laundering inquiry and deep background investigation when the transaction data is linked up for the second time, wherein if the account is not in the anti-money laundering transaction chain, the transaction is successful, and if the account is in the anti-money laundering transaction chain, the transaction fails.
9. The method of claim 8, wherein the transaction chain generation operation comprises:
A. ordering all transaction information according to a time stamp sequence to obtain simplified transaction information, wherein the simplified transaction information comprises a time stamp, a transaction initiator, a transaction receiver and transaction amount;
B. regarding each transaction data as a data block, circularly traversing each data block from the initial data block, and linking two transaction blocks when the receiving party of the transaction block A is equal to the transaction initiating party of the transaction block B;
C. taking the transaction amount minimum value S of the data blocks in each transaction chain, subtracting the minimum value from the transaction amount of each transaction data of the chain to obtain (A1, A2.. Ai), wherein i is the transaction block number of the chain, deleting the data blocks in the data list when Ai =0 to obtain the information of the fund link, and then transferring to the step B to traverse again;
D. when there is no connection between the data blocks, each small data block is a separate transaction chain.
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