CN111680076A - Block chain consensus method and system based on association rule model training - Google Patents

Block chain consensus method and system based on association rule model training Download PDF

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CN111680076A
CN111680076A CN202010317938.1A CN202010317938A CN111680076A CN 111680076 A CN111680076 A CN 111680076A CN 202010317938 A CN202010317938 A CN 202010317938A CN 111680076 A CN111680076 A CN 111680076A
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李引
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Guangzhou Zhongke Yide Technology Co ltd
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Abstract

The invention discloses a block chain consensus method and a system based on association rule model training, wherein the system comprises a consensus algorithm scheduler, a parameter and data acquirer, an association rule model trainer and a consensus verifier; the system can be butted with the existing block chain system to replace common recognition algorithms such as POW/POS/POA and the like. The invention introduces the excessive computing power of the block chain into the training of the association rule model, and leads investors to use the mining machines for training the artificial intelligence model through the excitation mechanism of the block chain, thereby being capable of guiding capital, computing power and energy to be put into more meaningful work and solving the problems of insufficient computing power and high cost. The invention uses the POW calculation power of the block chain to calculate the association rule of the big data, reduces the cost, saves social resources and uses the calculation power to meaningful work.

Description

Block chain consensus method and system based on association rule model training
Technical Field
The invention relates to the technical field of block chains, in particular to a block chain consensus method and system based on association rule model training.
Background
The development of the block chain technology is generally agreed by enterprises, research institutions, universities and the like at home and abroad, and is considered as the core of the next generation of valuable internet. The main stream block chain platform bit currency, Ethengfang, Laite currency and the like generally adopt a POW consensus algorithm, and determine the nodes of the block by repeatedly performing hash value operation by an ore machine, wherein the node has higher input calculation power and has higher probability to obtain block reward. This mechanism enables trust establishment and value consensus to be achieved through machines and algorithms, investors invest a lot of money to buy mining machines and power, the computing power of a whole bitcoin network has reached 70EH/S by 2019 and 9 months, the computing power and energy are used by investors to perform hash value operation so as to obtain rewards, and the computing power and energy waste in this mode are very serious and problematic.
The association rule is an implication of the form X → Y, defined as:
let I ═ { I ═ I1，I2，...，ImIs the set of items. Given a Transaction database D in which each Transaction (Transaction) t is a non-empty subset of I, i.e. each Transaction corresponds to a unique identifier tid (transactionid). The support (support) of the association rule in D is the percentage, i.e., probability, that D contains X, Y at the same time; the confidence (confidence) is the percentage of Y contained, i.e. the conditional probability, in the case that the transaction in D already contains X. An association rule is considered interesting if a minimum support threshold and a minimum confidence threshold are met. The mining of the association rule model is XY which needs to satisfy the condition relation under a certain threshold condition.
The association rule is widely applied to financial, retail, e-commerce and other industries, for example, in the financial industry, a bank binds the information of the local product which may be interested by the customer on the own ATM for the user to know. If a high credit limit customer changes address as shown in the database, the customer is likely to have newly purchased a larger home, and therefore may need a new credit card with a higher credit limit, a higher end, or a house for improved loan, all of which may be mailed to the customer via the credit card bill. The database may be powerful aids for telemarketing representatives when a customer makes a telephone call to consult. The sales representative's computer screen may display the characteristics of the customer and also what products the customer may be interested in.
For example, the data of the retail market is not only huge and complex, but also contains a lot of useful information, and some potential, useful and valuable information can be found from a database of a large supermarket, so that the data can be applied to the operation of the supermarket. Through the analysis of the accumulated sales data, sales information of various commodities can be obtained. Therefore, the ordering condition of various commodities can be formulated more reasonably, and the stock of various commodities can be controlled reasonably. In addition, according to the relevant conditions of various commodity sales, the sales relevance of the commodities can be analyzed, so that the basket analysis and the combined management of the commodities can be performed, and the commodity sales are facilitated.
Also, some well-known e-commerce sites benefit from strong association rule mining. These electronic shopping websites mine using rules in association rules and then set bundles that the user intends to buy together. There are also some shopping sites that use them to set up a corresponding cross-sell, i.e. a customer who purchases a certain product will see an advertisement for another product of interest.
The mainstream block chain generally adopts consensus algorithms such as POW (point of sale) and the like to determine block nodes, so that the problem of energy and computational complexity waste exists.
Disclosure of Invention
In view of this, in order to solve the problem that the consensus algorithm of the blockchain in the prior art wastes energy and computational power, the invention provides a method and a system for consensus of the blockchain based on association rule model training.
The invention solves the problems through the following technical means:
in one aspect, the invention provides a block chain consensus method based on association rule model training, which comprises the following steps:
the main body generates big data acquisition information and sends the big data acquisition information to the node, and the node broadcasts the big data acquisition information to the adjacent nodes;
calculating and verifying by adopting an association rule model training consensus algorithm, verifying the effectiveness of big data acquisition information by a node, and putting the big data acquisition information into a buffer pool until the number n of data sets A in the buffer pool reaches a threshold value alpha;
acquiring all big data acquisition information Z stored on a chain by a node through a parameter and data acquirer, merging the big data acquisition information Z with a current data set A in a buffer pool, mining an association rule model existing in the big data acquisition information Z by adopting an association rule algorithm, and stopping when the parameter threshold requirement is met;
the node completes calculation of the association rule model, stores parameters of the association rule model into a block head, generates a first block coinage transaction for recording the node to obtain an accounting reward, generates n data acquisition transactions and other transfer transactions from a data set A, packs the n data acquisition transactions and other transfer transactions into a block body, combines the block head and the block body to generate a block, and broadcasts the block in the whole network;
other nodes receive the information of the new block, and the common identification verifier verifies the new block; when the adopted association rule consensus model is adopted, the node acquires all big data acquisition information Z stored on the chain by using a parameter and data acquirer, combines the big data acquisition information Z with the current data set B in the node buffer pool to generate D ', calculates whether the support degree and the confidence coefficient of the frequent item set in D ' meet the requirements of a minimum support degree threshold value and a minimum confidence coefficient threshold value according to the frequent item set calculated by the association rule model, and puts the block into a local block chain if the support degree and the confidence coefficient of the frequent item set in D ' meet the requirements of the minimum support degree threshold value and the minimum confidence coefficient threshold value.
Further, before the calculation and verification by using the association rule model training consensus algorithm, the method further comprises the following steps:
and the node 'consensus algorithm scheduler' selects a consensus algorithm, if the consensus algorithm is trained for the association rule model, the association rule model training consensus algorithm is adopted for calculation and verification, and otherwise, the traditional block chain consensus algorithm is adopted for calculation and verification.
Further, data information is directly stored on the block chain in a data contribution transaction mode, and the form of the data contribution transaction is expressed as { a subject wallet address, a data acquisition reward amount and data information }; or the data information is combed and sorted and then put into an external data storage system, and a resource locator for data storage is returned.
Further, if the underlying block chain is based on the block chain of bitcoin and Lei-Tech, a new data contribution transaction type is expanded, wherein the 'data information' is stored by adopting a reserved field in bitcoin transaction; if the block chain platform with the intelligent contract mechanism is based on the Etherns and the EOS, the data information is packaged to serve as parameters and is called for the method of the intelligent contract, when the data information passes the verification of the intelligent contract, the intelligent contract is used for transferring accounts to the main wallet address, and the amount is the data acquisition reward amount.
Further, an association rule algorithm adopts an Apriori algorithm or an FP-tree frequency set algorithm; the parameter threshold refers to a minimum support threshold and a minimum confidence threshold.
Further, the threshold value alpha is decided by means of intelligent contracts and voting by all node participants; the minimum support degree threshold value and the minimum confidence degree threshold value are voted and decided by all node participants in an intelligent contract mode.
In another aspect, the present invention further provides a block chain consensus system based on association rule model training, including:
the parameter and data acquirer is used for generating big data acquisition information by the main body and sending the big data acquisition information to the node, and the node broadcasts the big data acquisition information to the adjacent node;
the association rule model trainer is used for calculating and verifying by adopting an association rule model training consensus algorithm, verifying the effectiveness of the big data acquisition information by the nodes, and putting the big data acquisition information into the buffer pool until the number n of the data sets A in the buffer pool reaches a threshold value alpha; acquiring all big data acquisition information Z stored on a chain by a node through a parameter and data acquirer, merging the big data acquisition information Z with a current data set A in a buffer pool, mining an association rule model existing in the big data acquisition information Z by adopting an association rule algorithm, and stopping when the parameter threshold requirement is met; the node completes calculation of the association rule model, stores parameters of the association rule model into a block head, generates a first block coinage transaction for recording the node to obtain an accounting reward, generates n data acquisition transactions and other transfer transactions from a data set A, packs the n data acquisition transactions and other transfer transactions into a block body, combines the block head and the block body to generate a block, and broadcasts the block in the whole network;
the consensus verifier is used for verifying the new block by the other nodes after receiving the information of the new block; when the adopted association rule consensus model is adopted, the node acquires all big data acquisition information Z stored on the chain by using a parameter and data acquirer, combines the big data acquisition information Z with the current data set B in the node buffer pool to generate D ', calculates whether the support degree and the confidence coefficient of the frequent item set in D ' meet the requirements of a minimum support degree threshold value and a minimum confidence coefficient threshold value according to the frequent item set calculated by the association rule model, and puts the block into a local block chain if the support degree and the confidence coefficient of the frequent item set in D ' meet the requirements of the minimum support degree threshold value and the minimum confidence coefficient threshold value.
Further, the block chain consensus system trained based on the association rule model further includes:
and (4) scheduling the consensus algorithm, wherein the consensus algorithm is used for selecting the consensus algorithm for the nodes, if the consensus algorithm is trained for the association rule model, the association rule model is adopted to train the consensus algorithm for calculation and verification, and otherwise, the traditional block chain consensus algorithm is adopted for calculation and verification.
Further, data information is directly stored on the block chain in a data contribution transaction mode, and the form of the data contribution transaction is expressed as { a subject wallet address, a data acquisition reward amount and data information }; or the data information is combed and sorted and then is put into an external data storage system, and a resource locator for data storage is returned;
if the underlying layer block chain is based on the block chain of the bitcoin and the Leitexin, a new data contribution transaction type is expanded, and the 'data information' is stored by adopting a reserved field in the bitcoin transaction; if the block chain platform with the intelligent contract mechanism is based on the Etherns and the EOS, the data information is packaged to serve as parameters and is called for the method of the intelligent contract, when the data information passes the verification of the intelligent contract, the intelligent contract is used for transferring accounts to the main wallet address, and the amount is the data acquisition reward amount.
Further, an association rule algorithm adopts an Apriori algorithm or an FP-tree frequency set algorithm; the parameter threshold refers to a minimum support threshold and a minimum confidence threshold; the threshold alpha is determined by voting of all node participants in an intelligent contract mode; the minimum support degree threshold value and the minimum confidence degree threshold value are voted and decided by all node participants in an intelligent contract mode.
Compared with the prior art, the invention has the beneficial effects that at least:
the invention provides a method for mining the big data association rule models as block chain consensus achievement aiming at big data generated in scenes such as e-commerce, retail, tourism, catering and the like, and can apply the calculation power to valuable model mining.
The invention introduces the excessive computing power of the block chain into the training of the association rule model, and leads investors to use the mining machines for training the artificial intelligence model through the excitation mechanism of the block chain, thereby being capable of guiding capital, computing power and energy to be put into more meaningful work and solving the problems of insufficient computing power and high cost.
The invention uses the POW calculation power of the block chain to calculate the association rule of the big data, reduces the cost, saves social resources and uses the calculation power to meaningful work.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart of a block chain consensus method of the present invention based on association rule model training;
FIG. 2 is a schematic structural diagram of a block chain consensus system trained based on association rule models according to the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below. It should be noted that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments, and all other embodiments obtained by those skilled in the art without any inventive work based on the embodiments of the present invention belong to the protection scope of the present invention.
Example 1
As shown in fig. 1, the present invention provides a block chain consensus method based on association rule model training, which includes the following steps:
step S110: the main body generates big data acquisition information and sends the big data acquisition information to the nodes, and the nodes broadcast the information to adjacent nodes. Data information is directly stored on the blockchain in a data contribution transaction mode, and the form of the data contribution transaction can be expressed as { a subject wallet address, a data acquisition reward amount and data information }.
If the underlying layer block chain is based on the block chains of bitcoin, Leitexin and the like, a new data contribution transaction type is expanded, and the 'data information' can be stored by adopting a reserved field in bitcoin transaction; if the intelligent contract is based on a blockchain platform with an intelligent contract mechanism, such as an Ethernet, EOS and the like, the data information is packaged to be used as a parameter and used for calling a method of the intelligent contract, when the data information passes the verification of the intelligent contract, the intelligent contract is used for transferring accounts to a main wallet address, and the amount is the data acquisition reward amount.
Alternatively, the data information may be sorted and then put into an external data storage system, and a resource locator for data storage is returned.
Step S120: and selecting a consensus algorithm by the node 'consensus algorithm scheduler', jumping to the step S130 if the consensus algorithm is trained for the association rule model, and otherwise, calculating and verifying by adopting a traditional block chain consensus algorithm.
Step S130: and the node verifies the validity of the data acquisition information and puts the data acquisition information into the buffer pool until the number n of the data sets A in the buffer pool reaches a threshold value alpha. The threshold α may be determined by intelligent contracts and the like and voted on by all node participants.
Step S140: the node acquires all big data acquisition information Z stored on the chain by using a parameter and data acquirer, merges the big data acquisition information Z with the current data set A in the buffer pool, adopts an association rule algorithm, mines an association rule model existing in the node, and stops when the parameter threshold requirement is met. The association rule algorithm may employ Apriori algorithm, FP-tree frequency set algorithm, or the like. The parameter threshold mainly refers to a minimum support threshold and a minimum confidence threshold.
Step S150: the node completes calculation of the association rule model, stores parameters of the association rule model in a block head, generates a first block coinage transaction for recording the node to obtain an accounting reward, generates n data acquisition transactions and other transfer transactions from the data set A, packs the n data acquisition transactions and other transfer transactions into a block body, combines the block head and the block body to generate a block, and broadcasts the block in the whole network.
Step S160: the other nodes receive the information of the new block, and the consensus verifier verifies the new block. When the adopted association rule consensus model is adopted, the node acquires all big data acquisition information Z stored on the chain by using a parameter and data acquirer, combines the big data acquisition information Z with the current data set B in the node buffer pool to generate D ', calculates whether the support degree and the confidence coefficient of the frequent item set in D ' meet the requirements of a minimum support degree threshold value and a minimum confidence coefficient threshold value according to the frequent item set calculated by the association rule model, and puts the block into a local block chain if the support degree and the confidence coefficient of the frequent item set in D ' meet the requirements of the minimum support degree threshold value and the minimum confidence coefficient threshold value. The minimum support degree threshold value and the minimum confidence degree threshold value can be voted and decided by all node participants through intelligent contract and the like.
Example 2
As shown in fig. 2, the invention provides a block chain consensus system based on association rule model training, which includes a parameter and data acquirer, a consensus algorithm scheduler, an association rule model trainer, and a consensus verifier.
And the parameter and data acquirer is used for generating big data acquisition information by the main body and sending the big data acquisition information to the nodes, and the nodes broadcast the information to the adjacent nodes. Data information is directly stored on the blockchain in a data contribution transaction mode, and the form of the data contribution transaction can be expressed as { a subject wallet address, a data acquisition reward amount and data information }.
If the underlying layer block chain is based on the block chains of bitcoin, Leitexin and the like, a new data contribution transaction type is expanded, and the 'data information' can be stored by adopting a reserved field in bitcoin transaction; if the intelligent contract is based on a blockchain platform with an intelligent contract mechanism, such as an Ethernet, EOS and the like, the data information is packaged to be used as a parameter and used for calling a method of the intelligent contract, when the data information passes the verification of the intelligent contract, the intelligent contract is used for transferring accounts to a main wallet address, and the amount is the data acquisition reward amount.
Alternatively, the data information may be sorted and then put into an external data storage system, and a resource locator for data storage is returned.
And the consensus algorithm scheduler is used for selecting a consensus algorithm for the nodes, if the consensus algorithm is trained for the association rule model, the association rule model training consensus algorithm is adopted for calculation and verification, and otherwise, the traditional block chain consensus algorithm is adopted for calculation and verification.
And the association rule model trainer is used for verifying the validity of the data acquisition information by the node and putting the data acquisition information into the buffer pool until the number n of the data sets A in the buffer pool reaches a threshold value alpha. The threshold α may be determined by intelligent contracts and the like and voted on by all node participants.
The node acquires all big data acquisition information Z stored on the chain by using a parameter and data acquirer, merges the big data acquisition information Z with the current data set A in the buffer pool, adopts an association rule algorithm, mines an association rule model existing in the node, and stops when the parameter threshold requirement is met. The association rule algorithm may employ Apriori algorithm, FP-tree frequency set algorithm, or the like. The parameter threshold mainly refers to a minimum support threshold and a minimum confidence threshold.
The node completes calculation of the association rule model, stores parameters of the association rule model in a block head, generates a first block coinage transaction for recording the node to obtain an accounting reward, generates n data acquisition transactions and other transfer transactions from the data set A, packs the n data acquisition transactions and other transfer transactions into a block body, combines the block head and the block body to generate a block, and broadcasts the block in the whole network.
And the consensus verifier is used for verifying the new block by the other nodes after receiving the information of the new block. When the adopted association rule consensus model is adopted, the node acquires all big data acquisition information Z stored on the chain by using a parameter and data acquirer, combines the big data acquisition information Z with the current data set B in the node buffer pool to generate D ', calculates whether the support degree and the confidence coefficient of the frequent item set in D ' meet the requirements of a minimum support degree threshold value and a minimum confidence coefficient threshold value according to the frequent item set calculated by the association rule model, and puts the block into a local block chain if the support degree and the confidence coefficient of the frequent item set in D ' meet the requirements of the minimum support degree threshold value and the minimum confidence coefficient threshold value. The minimum support degree threshold value and the minimum confidence degree threshold value can be voted and decided by all node participants through intelligent contract and the like.
The invention provides a method and a system for achieving block chain node consensus by adopting association rules, which are divided into a consensus algorithm scheduler, a parameter and data acquirer, an association rule model trainer and a consensus verifier. The system can be butted with the existing block chain system to replace common recognition algorithms such as POW/POS/POA and the like.
The invention provides a method for mining the big data association rule models as block chain consensus achievement aiming at big data generated in scenes such as e-commerce, retail, tourism, catering and the like, and can apply the calculation power to valuable model mining.
The invention introduces the excessive computing power of the block chain into the training of the association rule model, and leads investors to use the mining machines for training the artificial intelligence model through the excitation mechanism of the block chain, thereby being capable of guiding capital, computing power and energy to be put into more meaningful work and solving the problems of insufficient computing power and high cost.
The invention uses the POW calculation power of the block chain to calculate the association rule of the big data, reduces the cost, saves social resources and uses the calculation power to meaningful work.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (10)

1. A block chain consensus method based on association rule model training is characterized by comprising the following steps:
the main body generates big data acquisition information and sends the big data acquisition information to the node, and the node broadcasts the big data acquisition information to the adjacent nodes;
calculating and verifying by adopting an association rule model training consensus algorithm, verifying the effectiveness of big data acquisition information by a node, and putting the big data acquisition information into a buffer pool until the number n of data sets A in the buffer pool reaches a threshold value alpha;
the node acquires all big data acquisition information Z stored on a chain by using a parameter and data acquirer, merges the big data acquisition information Z with a current data set A in a buffer pool, adopts an association rule algorithm, excavates an association rule model existing in the big data acquisition information Z, and stops when the parameter threshold requirement is met;
the node completes calculation of the association rule model, stores parameters of the association rule model into a block head, generates a first block coinage transaction for recording the node to obtain an accounting reward, generates n data acquisition transactions and other transfer transactions from a data set A, packs the n data acquisition transactions and other transfer transactions into a block body, combines the block head and the block body to generate a block, and broadcasts the block in the whole network;
the other nodes receive the information of the new block, and the consensus verifier verifies the new block; when the adopted association rule consensus model is adopted, the node acquires all big data acquisition information Z stored in the chain by using a parameter and data acquirer, combines the big data acquisition information Z with the current data set B in the buffer pool of the node to generate D ', calculates whether the support degree and the confidence coefficient of the frequent item set in D ' meet the requirements of a minimum support degree threshold value and a minimum confidence coefficient threshold value according to the frequent item set calculated by the association rule model, and puts the block into a local block chain if the support degree and the confidence coefficient of the frequent item set in D ' meet the requirements of the minimum support degree threshold value and the minimum confidence coefficient threshold value.
2. The association rule model training-based block chain consensus method of claim 1, wherein before the calculating and verifying by using the association rule model training consensus algorithm, the method further comprises:
and selecting a consensus algorithm by the node consensus algorithm scheduler, if the consensus algorithm is trained for the association rule model, calculating and verifying by adopting the association rule model training consensus algorithm, and otherwise, calculating and verifying by adopting a traditional block chain consensus algorithm.
3. The blockchain consensus method based on association rule model training of claim 1, wherein data information is directly stored on the blockchain in a form of data contribution transaction, the form of data contribution transaction is expressed as { subject wallet address, data collection reward amount, data information }; or the data information is combed and sorted and then put into an external data storage system, and a resource locator for data storage is returned.
4. The association rule model training-based blockchain consensus method according to claim 1, wherein the underlying blockchain, if based on the blockchain of bitcoin and leybu, extends new data contribution transaction types, where data information is stored using reserved fields in bitcoin transaction; if the intelligent contract is a blockchain platform with an intelligent contract mechanism based on the Etherns and the EOS, the data information is packaged to be used as a parameter and used for calling a method of the intelligent contract, when the data information passes the verification of the intelligent contract, the account is transferred to the address of a main wallet through the intelligent contract, and the amount is the data acquisition reward amount.
5. The association rule model training-based block chain consensus method of claim 1, wherein an association rule algorithm employs Apriori algorithm or FP-tree frequency set algorithm; the parameter threshold refers to a minimum support threshold and a minimum confidence threshold.
6. The association rule model training-based block chain consensus method as claimed in claim 1, wherein the threshold α is determined by means of an intelligent contract and voted by all node participants; the minimum support degree threshold value and the minimum confidence degree threshold value are voted and decided by all node participants in an intelligent contract mode.
7. A block chain consensus system trained based on association rule models, comprising:
the parameter and data acquirer is used for generating big data acquisition information by the main body and sending the big data acquisition information to the node, and the node broadcasts the big data acquisition information to the adjacent node;
the association rule model trainer is used for calculating and verifying by adopting an association rule model training consensus algorithm, verifying the effectiveness of the big data acquisition information by the nodes, and putting the big data acquisition information into the buffer pool until the number n of the data sets A in the buffer pool reaches a threshold value alpha; the node acquires all big data acquisition information Z stored on a chain by using a parameter and data acquirer, merges the big data acquisition information Z with a current data set A in a buffer pool, adopts an association rule algorithm, excavates an association rule model existing in the big data acquisition information Z, and stops when the parameter threshold requirement is met; the node completes calculation of the association rule model, stores parameters of the association rule model into a block head, generates a first block coinage transaction for recording the node to obtain an accounting reward, generates n data acquisition transactions and other transfer transactions from a data set A, packs the n data acquisition transactions and other transfer transactions into a block body, combines the block head and the block body to generate a block, and broadcasts the block in the whole network;
the consensus verifier is used for verifying the new block by the other nodes after receiving the information of the new block; when the adopted association rule consensus model is adopted, the node acquires all big data acquisition information Z stored in the chain by using a parameter and data acquirer, combines the big data acquisition information Z with the current data set B in the buffer pool of the node to generate D ', calculates whether the support degree and the confidence coefficient of the frequent item set in D ' meet the requirements of a minimum support degree threshold value and a minimum confidence coefficient threshold value according to the frequent item set calculated by the association rule model, and puts the block into a local block chain if the support degree and the confidence coefficient of the frequent item set in D ' meet the requirements of the minimum support degree threshold value and the minimum confidence coefficient threshold value.
8. The association rule model-trained blockchain consensus system as claimed in claim 7, further comprising:
and (4) scheduling the consensus algorithm, wherein the consensus algorithm is used for selecting the consensus algorithm for the nodes, if the consensus algorithm is trained for the association rule model, the association rule model is adopted to train the consensus algorithm for calculation and verification, and otherwise, the traditional block chain consensus algorithm is adopted for calculation and verification.
9. The association rule model-trained blockchain consensus system of claim 7, wherein data information is directly stored on the blockchain in a form of data contribution transactions expressed as { subject wallet address, data collection reward amount, data information }; or the data information is combed and sorted and then is put into an external data storage system, and a resource locator for data storage is returned;
if the underlying layer block chain is based on the block chain of the bitcoin and the Leitexin, a new data contribution transaction type is expanded, and data information is stored by adopting a reserved field in bitcoin transaction; if the intelligent contract is a blockchain platform with an intelligent contract mechanism based on the Etherns and the EOS, the data information is packaged to be used as a parameter and used for calling a method of the intelligent contract, when the data information passes the verification of the intelligent contract, the account is transferred to the address of a main wallet through the intelligent contract, and the amount is the data acquisition reward amount.
10. The association rule model training-based block chain consensus system as claimed in claim 7, wherein the association rule algorithm employs Apriori algorithm or FP-tree frequency set algorithm; the parameter threshold refers to a minimum support threshold and a minimum confidence threshold; the threshold alpha is determined by voting of all node participants in an intelligent contract mode; the minimum support degree threshold value and the minimum confidence degree threshold value are voted and decided by all node participants in an intelligent contract mode.
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