CN111680099B - Block chain consensus method and system based on decision tree model training - Google Patents
Block chain consensus method and system based on decision tree model training Download PDFInfo
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Abstract
The invention discloses a block chain consensus method and system based on decision tree model training, wherein the system comprises a consensus algorithm dispatcher, a parameter and data acquirer, a decision tree model trainer and a consensus verifier; the system can be in butt joint with the existing block chain system to replace the consensus algorithm such as POW/POS/POA and the like. According to the invention, excessive computing power of the blockchain is introduced into the training of the decision tree model, so that investors can conduct more meaningful work of funds, computing power and energy when training the artificial intelligence model through the excitation mechanism of the blockchain, and the problem of insufficient computing power and high cost is solved. The invention uses the POW computing power of the block chain to carry out decision tree computation of big data, reduces the cost, saves social resources and uses the computing power to meaningful work.
Description
Technical Field
The invention relates to the technical field of blockchains, in particular to a blockchain consensus method and system based on decision tree model training.
Background
The development of the blockchain technology is widely accepted by domestic and foreign enterprises, research institutions, universities and the like, and is considered to be the core of the next-generation value Internet. The mainstream blockchain platform generally adopts a POW consensus algorithm, and determines the nodes of the block by repeatedly carrying out hash value operation, and the nodes have higher investment calculation power and higher probability to obtain the block rewards. This mechanism enables trust establishment and value consensus to be achieved through machines and algorithms, investors invest a lot of money to purchase electricity, these algorithms and energy sources are used by investors to perform hash value operations to obtain rewards, and the waste of algorithms and energy sources in this mode is greatly impaired.
The Decision Tree (Decision Tree) is a Decision analysis method for evaluating the risk of an item and judging the feasibility of the item by constructing the Decision Tree to obtain the probability that the expected value of the net present value is greater than or equal to zero on the basis of the occurrence probability of various known situations, and is a graphical method for intuitively applying probability analysis. Since such decision branches are drawn in a pattern much like the branches of a tree, the decision tree is called decision tree. In machine learning, a decision tree is a predictive model that represents a mapping between object properties and object values.
The mainstream blockchain generally adopts a common-knowledge algorithm such as POW and the like to determine block nodes, so that the problems of energy and calculation power waste exist.
Disclosure of Invention
In view of the above, in order to solve the problem of energy and computational power waste in the block chain consensus algorithm in the prior art, the invention provides a block chain consensus method and system based on decision tree model training.
The invention solves the problems by the following technical means:
in one aspect, the invention provides a blockchain consensus method based on decision tree model training, comprising the following steps:
the main body generates big data acquisition information and sends the big data acquisition information to the nodes, and the nodes broadcast the big data acquisition information to adjacent nodes;
training a consensus algorithm by adopting a decision tree model to calculate and verify, and verifying the validity of the big data acquisition information by the 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 in the chain by using a parameter and data acquirer, combines the big data acquisition information Z with the current data set A in the buffer pool to generate D, and maps the items contained in the D to a matrix M np Applying; adopting decision tree algorithm to respectively calculate A j (1. Ltoreq.j. Ltoreq.p) as classification category, { A 1 ，A 2 ，…A j-1 ，A j+1 ，…A p -as a feature attribute decision tree;
the node completes the calculation of the decision tree model, stores the decision tree model parameters into a block head, generates a first block cast transaction for recording that the node obtains accounting rewards, packages n data acquisition transactions generated by the data set A and other transfer transactions together into a block body, combines the block head and the block body to generate a block and performs whole network broadcasting;
the other nodes receive the information of the new block, and the common identification verifier verifies the informationA syndrome; when the decision tree model is adopted, the node utilizes the parameter and data acquirer to acquire all big data acquisition information Z stored in the chain, combines the big data acquisition information Z with the current data set B in the node buffer pool to generate D ', and predicts the pair A in the D' by utilizing the decision tree model j The classification accuracy is verified, and whether the accuracy is larger than the parameter requirement or not is judged; if the accuracy parameter index is reached, the block is put into the local block chain.
Further, before the decision tree model is adopted to train the consensus algorithm for calculation and verification, the method further comprises the following steps:
the node 'consensus algorithm scheduler' selects a consensus algorithm, if the consensus algorithm is trained for the decision tree model, the decision tree model is adopted to train the consensus algorithm for calculation and verification, otherwise, the traditional block chain consensus algorithm is adopted for calculation and verification.
Further, the data information is directly stored on the blockchain in a data contribution transaction mode, and the data contribution transaction mode is expressed as { main wallet address, data acquisition rewarding amount and data information }; or the data information is put into an external data storage system after being carded and tidied, and a resource locator of the data storage is returned.
Further, if the bottom layer blockchain is based on the blockchain platform with the intelligent contract mechanism of the Ethernet and the EOS, the data information is encapsulated and then used as a parameter and is called by a method of the intelligent contract, and when the data information passes through the intelligent contract verification, the data information is transferred to the main wallet address through the intelligent contract, and the amount is the data acquisition rewarding amount.
Further, the threshold α is determined by voting by all node participants by way of a smart contract, and the accuracy parameter is determined by voting by all node participants by way of a smart contract.
Further, the decision tree is calculated using ID3, C4.5, C5.0 or an expansion algorithm.
In another aspect, the present invention provides a blockchain consensus system based on decision tree model training, 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 nodes;
the decision tree model trainer is used for adopting a decision tree model training consensus algorithm to calculate and verify, and the node verifies the validity of the big data acquisition information and puts 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 in the chain by using a parameter and data acquirer, combines the big data acquisition information Z with the current data set A in the buffer pool to generate D, and maps the items contained in the D to a matrix M np Applying; adopting decision tree algorithm to respectively calculate A j (1. Ltoreq.j. Ltoreq.p) as classification category, { A 1 ，A 2 ，…A j-1 ，A j+1 ，…A p -as a feature attribute decision tree; the node completes the calculation of the decision tree model, stores the decision tree model parameters into a block head, generates a first block cast transaction for recording that the node obtains accounting rewards, packages n data acquisition transactions generated by the data set A and other transfer transactions together into a block body, combines the block head and the block body to generate a block and performs whole network broadcasting;
the consensus verifier is used for verifying the information of the new block received by other nodes; when the decision tree model is adopted, the node utilizes the parameter and data acquirer to acquire all big data acquisition information Z stored in the chain, combines the big data acquisition information Z with the current data set B in the node buffer pool to generate D ', and predicts the pair A in the D' by utilizing the decision tree model j Classifying the correct rate, and verifying whether the correct rate is larger than the parameter requirement; if the accuracy parameter index is reached, the block is put into the local block chain.
Further, the blockchain consensus system trained based on the decision tree model further comprises:
and the consensus algorithm scheduling is used for selecting a consensus algorithm by the node, if the consensus algorithm is trained for the decision tree model, the decision tree model consensus algorithm is adopted for calculation and verification, otherwise, the traditional block chain consensus algorithm is adopted for calculation and verification.
Further, the data information is directly stored on the blockchain in a data contribution transaction mode, and the data contribution transaction mode is expressed as { main wallet address, data acquisition rewarding amount and data information }; or the data information is put into an external data storage system after being carded and tidied, and a resource locator of data storage is returned;
if the bottom layer blockchain is based on the blockchain platform with the intelligent contract mechanism of the Ethernet and the EOS, the data information is packaged and then used as a parameter and is called by a method of the intelligent contract, and when the data information passes through the intelligent contract verification, the data information is transferred to a main wallet address through the intelligent contract, and the amount is data acquisition rewarding amount.
Further, the decision tree is calculated by using ID3, C4.5, C5.0 or an expansion algorithm, the threshold alpha is determined by voting of all node participants by means of intelligent contracts, and the correct rate parameter is determined by voting of all node participants by means of intelligent contracts.
Compared with the prior art, the invention has the beneficial effects that at least:
the invention provides a method for achieving block chain consensus by using mining of big data decision tree models aiming at personal big data, enterprise big data, environment big data and the like, and can apply calculation force to valuable model mining.
According to the invention, excessive computing power of the blockchain is introduced into the training of the decision tree model, so that investors can conduct more meaningful work of funds, computing power and energy when training the artificial intelligence model through the excitation mechanism of the blockchain, and the problem of insufficient computing power and high cost is solved.
The invention uses the POW computing power of the block chain to carry out decision tree computation of big data, reduces the cost, saves social resources and uses the computing power to meaningful work.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a blockchain consensus method of the present invention based on decision tree model training;
FIG. 2 is a schematic diagram of a block chain consensus system based on decision tree model training in accordance with the present invention.
Detailed Description
In order to make the above objects, features and advantages of the present invention more comprehensible, the following detailed description of the technical solution of the present invention refers to the accompanying drawings and specific embodiments. It should be noted that the described embodiments are only some embodiments of the present invention, and not all embodiments, and that all other embodiments obtained by persons skilled in the art without making creative efforts based on the embodiments in the present invention are within the protection scope of the present invention.
Example 1
As shown in FIG. 1, the invention provides a block chain consensus method based on decision tree model training, comprising 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 big data acquisition information to the adjacent nodes. The data information is directly stored on the blockchain in the form of data contribution transactions, which can be expressed as { subject wallet address, data acquisition rewards amount, data information }.
If the bottom layer blockchain is based on a blockchain platform with an intelligent contract mechanism such as an Ethernet, an EOS and the like, the data information is packaged and then used as a parameter and is called by a method of an intelligent contract, and when the data information passes through the intelligent contract verification, the data information is transferred to a main wallet address through the intelligent contract, and the amount is data acquisition rewarding amount.
Alternatively, the data information may be put into an external data storage system after being carded and sorted, and returned to a resource locator of the data storage.
Step S120: the node "consensus algorithm scheduler" selects a consensus algorithm, if the consensus algorithm is trained for the decision tree model, then jumps to step S130, otherwise a conventional blockchain consensus algorithm is employed for computation and verification.
Step S130: 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 voting by all node participants by means of smart contracts or the like.
Step S140: 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 A in the buffer pool to generate D, and maps the items contained in the D to a matrix M np As shown in table 1. Adopting decision tree algorithm to respectively calculate A j (1. Ltoreq.j. Ltoreq.p) as classification category, { A 1 ，A 2 ，…A j-1 ，A j+1 ，…A p As a feature attribute decision tree. The decision tree is calculated by adopting algorithms such as ID3, C4.5, C5.0 or an expansion algorithm.
TABLE 1 matrix M np Example
Note that: the data source is not limited to personal big data, enterprise big data, environment big data, etc. In this example, the attributes of the matrix array are not limited to use of the application app, but can be extended to any personal big data, such as "walk per day", "purchase XX stock", and so on.
Step S150: the node completes the calculation of the decision tree model, the decision tree model parameters are stored in the block head, a first block coinage transaction is generated for recording that the node obtains the accounting rewards, meanwhile, the data collection A generates n data collection transactions and other transfer transactions to be packaged into a block body, and the block head and the block body are combined to generate a block and are broadcasted in the whole network.
Step S160: the other nodes receive the information of the new block, and the common identification verifier verifies the information. When the decision tree model is adopted, the node utilizes the parameter and data acquirer to acquire all big data acquisition information Z stored in the chain, combines the big data acquisition information Z with the current data set B in the node buffer pool to generate D ', and predicts the pair A in the D' by utilizing the decision tree model j And classifying the correct rate, and verifying whether the correct rate is larger than the parameter requirement. If the accuracy parameter index is reached, the block is put into the local block chain. The accuracy parameters may be determined by voting by all node participants by means of smart contracts, etc.
Example 2
As shown in FIG. 2, the invention also provides a block chain consensus system based on decision tree model training, which comprises a parameter and data acquirer, a consensus algorithm scheduler, a decision tree 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 node, and the node broadcasts the information to the adjacent nodes. The data information is directly stored on the blockchain in the form of data contribution transactions, which can be expressed as { subject wallet address, data acquisition rewards amount, data information }.
If the bottom layer blockchain is based on a blockchain platform with an intelligent contract mechanism such as an Ethernet, an EOS and the like, the data information is packaged and then used as a parameter and is called by a method of an intelligent contract, and when the data information passes through the intelligent contract verification, the data information is transferred to a main wallet address through the intelligent contract, and the amount is data acquisition rewarding amount.
Alternatively, the data information may be put into an external data storage system after being carded and sorted, and returned to a resource locator of the data storage.
And the consensus algorithm scheduler is used for selecting a consensus algorithm by the nodes, if the consensus algorithm is trained for the decision tree model, the decision tree model is used for training the consensus algorithm to calculate and verify, otherwise, the conventional block chain consensus algorithm is used for calculating and verifying.
And the decision tree model trainer is used for verifying the validity of the data acquisition information by the nodes, 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 voting by all node participants by means of smart contracts or the like.
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 A in the buffer pool to generate D, and maps the items contained in the D to a matrix M np As shown in table 1. Adopting decision tree algorithm to respectively calculate A j (1. Ltoreq.j. Ltoreq.p) as classification category, { A 1 ，A 2 ，…A j-1 ，A j+1 ，…A p As a feature attribute decision tree. The decision tree is calculated by adopting algorithms such as ID3, C4.5, C5.0 or an expansion algorithm.
TABLE 1 matrix M np Example
Note that: the data source is not limited to personal big data, enterprise big data, environment big data, etc. In this example, the attributes of the matrix array are not limited to use of the application app, but can be extended to any personal big data, such as "walk per day", "purchase XX stock", and so on.
The node completes the calculation of the decision tree model, the decision tree model parameters are stored in the block head, a first block coinage transaction is generated for recording that the node obtains the accounting rewards, meanwhile, the data collection A generates n data collection transactions and other transfer transactions to be packaged into a block body, and the block head and the block body are combined to generate a block and are broadcasted in the whole network.
And the consensus verifier is used for verifying the information of the new block received by other nodes. When the decision tree model is adopted, the node uses the parameter and data acquirer to acquire all big data acquisition information Z stored in the chain, and combines the big data acquisition information Z with the current data set B in the node buffer pool to generate D', and the decision tree is usedModel to predict pair A in D j And classifying the correct rate, and verifying whether the correct rate is larger than the parameter requirement. If the accuracy parameter index is reached, the block is put into the local block chain. The accuracy parameters may be determined by voting by all node participants by means of smart contracts, etc.
The invention provides a method and a system for achieving block link point consensus by adopting a decision tree, which are divided into a consensus algorithm dispatcher, a parameter and data acquirer, a decision tree model trainer and a consensus verifier. The system can be in butt joint with the existing block chain system to replace the consensus algorithm such as POW/POS/POA and the like.
The invention provides a method for achieving block chain consensus by using mining of big data decision tree models aiming at personal big data, enterprise big data, environment big data and the like, and can apply calculation force to valuable model mining.
According to the invention, excessive computing power of the blockchain is introduced into the training of the decision tree model, so that investors can conduct more meaningful work of funds, computing power and energy when training the artificial intelligence model through the excitation mechanism of the blockchain, and the problem of insufficient computing power and high cost is solved.
The invention uses the POW computing power of the block chain to carry out decision tree computation of big data, reduces the cost, saves social resources and uses the computing power to meaningful work.
The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (7)
1. A block chain consensus method based on decision tree 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 nodes, and the nodes broadcast the big data acquisition information to adjacent nodes;
training a consensus algorithm by adopting a decision tree model to calculate and verify, and verifying the validity of the big data acquisition information by the 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 in the chain by using the parameter and data acquirer, combines the big data acquisition information Z with the current data set A in the buffer pool to generate D, and maps the items contained in the D to a matrix M np Applying; adopting decision tree algorithm to respectively calculate A j (1. Ltoreq.j. Ltoreq.p) as classification category, { A 1 ，A 2 ，…A j-1 ，A j+1 ，…A p -as a feature attribute decision tree;
the node completes the calculation of the decision tree model, stores the decision tree model parameters into a block head, generates a first block cast transaction for recording that the node obtains accounting rewards, packages n data acquisition transactions generated by the data set A and other transfer transactions together into a block body, combines the block head and the block body to generate a block and performs whole network broadcasting;
other nodes receive the information of the new block, and the consensus verifier verifies the information; when the decision tree model is adopted, the node combines all big data acquisition information Z stored in the acquisition chain of the parameter and data acquirer with the current data set B in the node buffer pool to generate D ', and the decision tree model is used for predicting the pair A in the D' j The classification accuracy is verified, and whether the accuracy is larger than the parameter requirement or not is judged; if the accuracy parameter index is reached, the block is put into a local block chain;
before the decision tree model is adopted to train the consensus algorithm for calculation and verification, the method further comprises the following steps:
the node consensus algorithm scheduler selects a consensus algorithm, if the consensus algorithm is trained for the decision tree model, the decision tree model is adopted to train the consensus algorithm for calculation and verification, otherwise, the traditional block chain consensus algorithm is adopted for calculation and verification;
the threshold alpha is determined by voting by all node participants by way of a smart contract, and the accuracy parameter is determined by voting by all node participants by way of a smart contract.
2. The blockchain consensus method based on decision tree model training of claim 1, wherein the data information is directly stored on the blockchain in the form of data contribution transactions expressed as { subject wallet address, data acquisition reward amount, data information }; or the data information is put into an external data storage system after being carded and tidied, and a resource locator of the data storage is returned.
3. The blockchain consensus method based on decision tree model training according to claim 1, wherein if the underlying blockchain is based on the blockchain platform with the intelligent contract mechanism of ethernet and EOS, the data information is a method call for encapsulating the data information as a parameter and transferring the data information to the main wallet address through the intelligent contract after the data information passes the intelligent contract verification, and the amount is the data acquisition rewarding amount.
4. The blockchain consensus method based on decision tree model training of claim 1, wherein the decision tree is calculated using an ID3, C4.5, C5.0 or expansion algorithm.
5. A blockchain consensus system based on decision tree model training, 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 nodes;
the decision tree model trainer is used for adopting a decision tree model training consensus algorithm to calculate and verify, and the node verifies the validity of the big data acquisition information and puts 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 in the chain by using the parameter and data acquirer, combines the big data acquisition information Z with the current data set A in the buffer pool to generate D, and maps the items contained in the D to a matrix M np Applying; adopting decision tree algorithm to respectively calculate A j (1. Ltoreq.j. Ltoreq.p) as classification category, { A 1 ，A 2 ，…A j-1 ，A j+1 ，…A p -as a feature attribute decision tree; the node completes the calculation of the decision tree model, stores the decision tree model parameters into a block head, generates a first block cast transaction for recording that the node obtains accounting rewards, packages n data acquisition transactions generated by the data set A and other transfer transactions together into a block body, combines the block head and the block body to generate a block and performs whole network broadcasting;
the consensus verifier is used for verifying the information of the new block received by other nodes; when the decision tree model is adopted, the node combines all big data acquisition information Z stored in the acquisition chain of the parameter and data acquirer with the current data set B in the node buffer pool to generate D ', and the decision tree model is used for predicting the pair A in the D' j Classifying the correct rate, and verifying whether the correct rate is larger than the parameter requirement; if the accuracy parameter index is reached, the block is put into a local block chain;
the block chain consensus system based on decision tree model training further comprises:
the common-mode algorithm scheduling is used for selecting a common-mode algorithm by the node, if the common-mode algorithm is trained for the decision tree model, the decision tree model common-mode algorithm is adopted for calculation and verification, otherwise, the traditional block chain common-mode algorithm is adopted for calculation and verification;
the threshold alpha is determined by voting by all node participants by way of a smart contract, and the accuracy parameter is determined by voting by all node participants by way of a smart contract.
6. The blockchain consensus system trained based on a decision tree model of claim 5, wherein the data information is stored directly on the blockchain in the form of data contribution transactions expressed as { subject wallet address, data acquisition reward amount, data information }; or the data information is put into an external data storage system after being carded and tidied, and a resource locator of data storage is returned;
if the bottom layer blockchain is based on the blockchain platform with the intelligent contract mechanism of the Ethernet and the EOS, the data information is packaged and then used as parameters, the method of the intelligent contract is called, and when the data information passes through the verification of the intelligent contract, the data information is transferred to the main wallet address through the intelligent contract, and the amount is the data acquisition rewarding amount.
7. The blockchain consensus system based on decision tree model training of claim 5, wherein the decision tree is calculated using an ID3, C4.5, C5.0 or expansion algorithm.
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| 袁勇 ; 倪晓春 ; 曾帅 ; 王飞跃 ; .区块链共识算法的发展现状与展望.自动化学报.2018,(11),93-104. * |
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