CN106326641A - Data processing method for block chain system based on compressed sensing and sparse reconstruction algorithm - Google Patents
Data processing method for block chain system based on compressed sensing and sparse reconstruction algorithm Download PDFInfo
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- CN106326641A CN106326641A CN201610670838.0A CN201610670838A CN106326641A CN 106326641 A CN106326641 A CN 106326641A CN 201610670838 A CN201610670838 A CN 201610670838A CN 106326641 A CN106326641 A CN 106326641A
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Abstract
The invention provides a data processing method for block chain system based on compressed sensing and a sparse reconstruction algorithm. The block chain system data processing method comprises the following steps: (1) converting transaction data of the block chain system into multi-temporal and spatial and multi-dimensional data, and carrying out compression and sparse representation on the converted data; (2) constructing an M*N sparse calculation matrix which is not related to a sparse transformation matrix and carrying out linear projection on the multi-temporal and spatial and multi-dimensional data to acquire a sensing data calculated value; (3) reconstructing transaction information: accurately reconstructing multi-temporal and spatial and multi-dimensional original transaction data through utilizing low-dimensional transaction data acquired by the compressed sensing through adopting a sparse algorithm. By adopting the data processing method, the technical problems that the processing speed of block chain data is slow and data redundancy and resource wastes are generated are solved.
Description
Technical field
The present invention relates to the processing method of block catenary system data, particularly one calculate based on compressed sensing and sparse reconstruct
The block catenary system data processing method of method.
Background technology
The appearance of bit coin in 2009 brings a kind of subversive achievement--and block chain technology, block chain is a safety
Account book class data base, be made up of data block one by one, user can this constantly update upgrading platform search number
According to, for financial institution, block chain can accelerate trading processing process, reduce cost, reduce go-between, improve market see clearly
Power, increases business transparency.
Block chain, as the Floor layer Technology of encryption currency bit coin, is a great innovation, and block chain technology can be used
In hitting swindle and illegal transaction, the most a lot of industries all begin to use block chain technology, how to obtain faster trading processing
Speed, is the principal element affecting privately owned block chain widespread adoption.Such as in financial field, NASDAQ processes the speed of transaction
Reach 1,000,000 times per second, VISA trading processing peak processing speed be 45000 times per second.It can be seen that improve block chain
The trading processing ability of system is extremely important.Transactions velocity all ratios of the privately owned block chain occurred in the market are relatively low.
Therefore, trading processing speed is a very important problem.In most applications, substantial amounts of transaction is one
Moment produces, and this large-scale application is also the key character of blockchain.In order to improve trading processing speed, various countries grind
The person of studying carefully has been carried out substantial amounts of work.Juan EduardoEt al. propose a simple competition analysis side
Method, by the number of computations of segmentation block catenary system node to reduce the transaction verification time.Scholar is also had to build a kind of safety
The Distributed Application platform of two-forty, it is possible to promote that the network trading of block chain processes.These researchs all have been achieved for one
Fixed effect, but, the transaction high speed processing problem of privately owned chain is still without effectively being solved.
The sampling rate that the birth of compressive sensing theory is is relevant to the structure of signal and content, and with less than Nyquist
Frequency sampling that sampling thheorem requires, encoding and reconstruct, this theory is mostly used for traditional industries originally, such as machinery, electronics, navigates
Sky, optical field, however very slow for current block chain data processing speed, the bottleneck producing data redundancy and the wasting of resources is asked
Inscribing serious technical problem, this theory provides new opportunity for its development, significantly affects the development of block chain data fusion.
The proposition of sparse restructing algorithm is to be all by orthogonal basis complete for signal decomposition to group based on traditional signal decomposition method,
There is significant limitation, expressing arbitrary signal when, need more effective nonopiate algorithm to propose.Signal is in redundancy word
Its Sparse Decomposition under allusion quotation, in sparse base or dictionary construction process, it is ensured that the expression of signal is sparse the most sparse, so that it is guaranteed that directly
The fewest with nonzero coefficient associated compression measurement number, the reconstruct data of high probability.
Summary of the invention
It is an object of the invention to provide a kind of block catenary system intersection number evidence based on compressed sensing and sparse restructing algorithm
Processing method, comprises the steps: that block catenary system transaction data is converted to multi-space multidimensional data by (1), then will conversion
After data be compressed and rarefaction representation, linear decomposition will be carried out by multi-space multidimensional data；(2) structure one and sparse change
Changing the incoherent M*N of matrix sparse to calculating matrix, multi-space multidimensional data carries out linear projection, acquisition perception data is to calculation
Value, so that transaction data object will be tieed up, wherein M is far smaller than N, and perception data is M*1 rank matrixes to calculation value；(3)
Transaction Information reconstructs, and the low-dimensional transaction data utilizing compressed sensing to obtain uses Corresponding Sparse Algorithm Accurate Reconstruction multi-space multidimensional original
Transaction data, i.e. utilizes M to tie up perception reconstructed N-dimensional transaction data.
Preferably, the one during the described rarefaction representation of step (1) has following multiple method: sparse transformation, Fourier
Conversion, wavelet transformation, Gabor transformation, Curvelet converts, and Bandelet converts, contourlet transformation.
Preferably, the rarefaction representation algorithm of step (1) includes tracing algorithm, and greedy matching pursuit algorithm, orthogonal coupling chases after
Track algorithm.
Preferably, in step (2) sparse to calculate matrix need to meet the capacitive condition such as incoherence and restriction, i.e. use dilute
Dredge conversion based on incoherent sparse to calculate matrix to Signal Compression extract, the transaction data signal of the most original acquisition can pass through
Rarefaction representation after certain conversion.
Preferably, in step (2) sparse to calculate matrix can from random Gaussian sparseness measuring matrix, random shellfish make great efforts measure
Matrix, partial orthogonality matrix and sparse random matrix select.
Preferably, step (3) restructing algorithm has three kinds: convex optimized algorithm based on L1 norm, greediness based on L0 norm
Algorithm and combinational algorithm.
Preferably, convex optimized algorithm based on L1 norm includes base tracing algorithm, gradient projection method, and convex set alternating projection is calculated
Method and interior some iterative method.
Preferably, greedy algorithm based on L0 norm includes matching pursuit algorithm, orthogonal matching pursuit algorithm, and stagewise is just
Hand over matching pursuit algorithm.
Preferably, combinational algorithm includes chain type tracing algorithm, HHS tracing algorithm and I-wen algorithm.
Use the data processing method of the present invention, efficiently solve block chain data processing speed slow, produce data redundancy
Technical problem with the wasting of resources.
According to below in conjunction with the accompanying drawing detailed description to the specific embodiment of the invention, those skilled in the art will be brighter
Above-mentioned and other purposes, advantage and the feature of the present invention.
Accompanying drawing explanation
Describe some specific embodiments of the present invention the most by way of example, and not by way of limitation in detail.
Reference identical in accompanying drawing denotes same or similar parts or part.It should be appreciated by those skilled in the art that these
Accompanying drawing is not necessarily drawn to scale.The target of the present invention and feature will be apparent from view of the description below in conjunction with accompanying drawing,
In accompanying drawing:
Fig. 1 is the privately owned block chain network model according to prior art；
Fig. 2 is that the block catenary system data based on compressed sensing and sparse restructing algorithm according to the embodiment of the present invention process
Method flow diagram.
Detailed description of the invention
Before carrying out the explanation of detailed description of the invention, the content discussed for apparent expression, first define
Some very important concepts.
Transaction: the essence of transaction is a relational data structure, comprises transaction participant's value Transfer in this data structure
Relevant information.These Transaction Informations are referred to as ledger of keeping accounts.Transaction need to create through three, verify, write block chain.Hand over
Easily have to pass through digital signature, it is ensured that the legitimacy of transaction.
Block: all of Transaction Information is deposited in block, a Transaction Information is exactly a record, as an independence
Record deposit in block chain.Block is made up of block head and data division, and block head field comprises each of block itself
Plant characteristic, the most previous block information, merkle value and timestamp etc..Wherein block head cryptographic Hash and block height are tag slots
The topmost two indices of block.Block primary identifier is its cryptographic hash, and one carries out two by SHA algorithm to block head
Secondary Hash calculation and the digital finger-print that obtains.The 32 byte cryptographic Hash produced are referred to as block cryptographic Hash, or block head Hash
Value, only block head are used for calculating.Block cryptographic Hash can uniquely, specifically identify a block, and any node leads to
Cross and simply block head is carried out Hash calculation can obtain this block cryptographic Hash independently.
Block chain: the data structure being chained up in order according to chain structure by block.Block chain is vertical just as one
Storehouse, first block is placed on other blocks as the first block at the bottom of stack, the most each block.When block writes
To change never after block chain, and backup on other block chain server.
Method and algorithm involved by the preferred embodiment of the present invention propose on the basis of privately owned block chain.
Bitcoin uses public block chain technology, and it runs on p2p network, and every PC accessed can participate in, and this is in reality
Border application process brings many drawbacks, such as, efficiency is low, it is slow to produce block, trading processing not in time etc..Concrete at some
Application is not allowed to.Privately owned block chain node runs in express network, and transfer rate is fast, and book keeping operation is instant, safety
Higher.These features ensure that privately owned block chain technology is applied in large-scale trading processing.
Fig. 1 is the network model of privately owned block chain, its essence is a multi-client many-server model.In many services
Under device pattern, there is 3x+1 node, wherein have one or more leader node.Substantially it is a distributed treatment
System, the communication mechanism between BC node is the most loaded down with trivial details.A task is jointly performed, it is desirable to have message mechanism enters due to multinode
Row is linked up, and message during sending it is possible that lose, out of order, the situation such as repeat, this structure we term it
Client/Multi-Server pattern, is called for short C/MS pattern.The pattern of multiserver ensure that the safety of system, autgmentability
And fault-tolerance.This network model includes: client node: the promoter of transaction；Multiserver cluster: distributed task system
System, is made up of multiple nodes of the inside, and the task in node cooperates by message communicating to each other, consistent completes times
Business；Leader's node: host node, is responsible for receiving task from client nodes, and is distributed to other node；Block chain node, negative
Duty receives and completes the task that Leader distributes, meanwhile, if host node breaks down, and the choosing that multiple BC node can be spontaneous
Go out a new host node；Transaction: need checking and count the transaction of block；The message that inter-node communication uses.
By system node utilization rate, the transaction average processed, the average number of deals in system, system waits
Average number of deals, conclude the business average waiting time, transaction the average latency assess block chain transaction data processing speed and
Efficiency.
Therefore this algorithm is set about in terms of following three: (1) rarefaction representation；(2) sparseness measuring；(3) signal reconstruction.
According to Fig. 2, owing to can there are two kinds of data in system, one is all of transaction data of client user, this
Data will be aggregated in block chain node server, and the second data are the block datas after block is formed, and block data is often
Relatively big, and broadcast at the whole network upon formation, by great occupied bandwidth resource.Therefore used based on compressed sensing and dilute
Dredge the block catenary system transaction data processing method of restructing algorithm, comprise the steps: that block catenary system transaction data is turned by (1)
It is changed to multi-space multidimensional data, then the data after conversion is compressed and rarefaction representation, will enter by multi-space multidimensional data
Line linearity decomposes；(2) structure one is sparse with the incoherent M*N of sparse transformation matrix to calculating matrix, to multi-space multidimensional data
Carrying out linear projection, acquisition perception data is to calculation value, so that transaction data object will be tieed up, wherein M is far smaller than N, and
Perception data is M*1 rank matrixes to calculation value；(3) Transaction Information reconstruct, the low-dimensional transaction data utilizing compressed sensing to obtain uses
Corresponding Sparse Algorithm Accurate Reconstruction multi-space multidimensional original transaction data, i.e. utilizes M to tie up perception reconstructed N-dimensional transaction data
Embodiment:
Block chain applied environment is power grid electric transaction.Transaction is produced by the application program of client.By VLAN
Internal BC server process transaction.The bottleneck affecting trading processing speed is the network condition of BC server and calculates energy
Power.The number of cluster internal BC server is 3x+1.We respectively by the number of BC node respectively 10,20,30,40,50,
Test below 60 grades, the transaction data of every 10 node collections is carried out Fourier and wavelet transformation, construct sparse dictionary
For redundant dictionary, for the inherent feature of Transaction Information itself, each element of dictionary all mates, and the sparse word selected
Allusion quotation makes sparse the most sparse, and the compression number of transaction the most relevant to nonzero coefficient is the fewest, uses block chain data base certainly
Body mechanism characteristics, uses Its Sparse Decomposition orthogonal matching pursuit algorithm to carry out rarefaction representation.Signal is carried out QR decomposition, orthogonal transformation
Improve Random sparseness to calculate matrix a method, thus build with block chain transaction data intrinsic meet sparse to calculation square
Battle array, lays the foundation for Transaction Information reconstruct.The sparse of structure is y=af to calculating matrix, and wherein y is M*1 matrix so that it is right to measure
As dimensionality reduction, sparse is non-adaptive to calculation process, and the most sparse selection to calculating matrix a does not relies on Transaction Information f, structure
Sparse K the Transaction Information value obtained calculation matrix requirements Transaction Information during f is converted to y can retain original transaction
Full detail, it is ensured that the Accurate Reconstruction of Transaction Information, as long as wherein the acquisition of K quantity meets limited equidistant character, its
Depending on the selection of middle constant is according to block chain transaction data scale, use convex optimization method, rebuild Transaction Information, hand in big data
Easily after acquisition of information, use alternating direction algorithm and greedy tracing algorithm synchronous iteration to carry out, select suitable iterative initial value and
Threshold value, takes into account isolating construction.
Carry out the speed being separately recorded in different server number lower node to trading processing the most respectively, it can be seen that prolong
Performance is significantly improved late, and saving of time, process number of transaction per second is greatly improved.
Although the present invention is described by reference to specific illustrative embodiment, but will not be by these embodiments
Restriction and only limited by accessory claim.Skilled artisan would appreciate that can be without departing from the present invention's
In the case of protection domain and spirit, embodiments of the invention can be modified and revise.
Claims (10)
1. a block catenary system data processing method based on compressed sensing and sparse restructing algorithm, it is characterised in that include as
Lower step:
(1) block catenary system transaction data is converted to multi-space multidimensional data, then will conversion after data be compressed and
Rarefaction representation, will carry out linear decomposition by multi-space multidimensional data；
(2) structure one is sparse with the incoherent M*N of sparse transformation matrix to calculating matrix, carries out multi-space multidimensional data linearly
Projection, acquisition perception data is to calculation value, so that transaction data object will be tieed up, wherein M is far smaller than N, and perception data
It is M*1 rank matrixes to calculation value；
(3) Transaction Information reconstruct, the low-dimensional transaction data utilizing compressed sensing to obtain uses Corresponding Sparse Algorithm Accurate Reconstruction multi-space
Multidimensional original transaction data, i.e. utilizes M to tie up perception reconstructed N-dimensional transaction data.
A kind of block catenary system data process side based on compressed sensing and sparse restructing algorithm the most according to claim 1
Method, it is characterised in that: the described rarefaction representation of step (1) has an one in following multiple method: sparse transformation, Fourier
Conversion, wavelet transformation, Gabor transformation, Curvelet converts, and Bandelet converts, contourlet transformation.
A kind of block catenary system data process side based on compressed sensing and sparse restructing algorithm the most according to claim 1
Method, it is characterised in that: the rarefaction representation algorithm of described step (1) includes tracing algorithm, greedy matching pursuit algorithm, orthogonal coupling
Tracing algorithm.
A kind of block catenary system data process side based on compressed sensing and sparse restructing algorithm the most according to claim 1
Method, it is characterised in that: in described step (2) sparse to calculate matrix need to meet the capacitive condition such as incoherence and restriction, even if
With sparse transformation based on incoherent sparse to calculate matrix to Signal Compression extract, the transaction data signal of the most original acquisition is permissible
Rarefaction representation after certain converts.
A kind of block catenary system data process side based on compressed sensing and sparse restructing algorithm the most according to claim 1
Method, it is characterised in that: in described step (2) sparse to calculate matrix can be from random Gaussian sparseness measuring matrix, random shellfish effort
Calculation matrix, partial orthogonality matrix and sparse random matrix select.
A kind of block catenary system data process side based on compressed sensing and sparse restructing algorithm the most according to claim 1
Method, it is characterised in that: described step (3) restructing algorithm has three kinds: convex optimized algorithm based on L1 norm, based on L0 norm greedy
Greedy algorithm and combinational algorithm.
A kind of block catenary system data process side based on compressed sensing and sparse restructing algorithm the most according to claim 6
Method, it is characterised in that: described convex optimized algorithm based on L1 norm includes base tracing algorithm, gradient projection method, and convex set is alternately thrown
Shadow algorithm and interior some iterative method.
A kind of block catenary system data process side based on compressed sensing and sparse restructing algorithm the most according to claim 6
Method, it is characterised in that: described greedy algorithm based on L0 norm includes matching pursuit algorithm, orthogonal matching pursuit algorithm, segmentation
Formula orthogonal matching pursuit algorithm.
A kind of block catenary system data process side based on compressed sensing and sparse restructing algorithm the most according to claim 6
Method, it is characterised in that: described combinational algorithm includes chain type tracing algorithm.
A kind of block catenary system data based on compressed sensing and sparse restructing algorithm the most according to claim 6 process
Method, it is characterised in that: described combinational algorithm also includes HHS tracing algorithm and I-wen algorithm.
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| PCT/CN2016/095572 WO2018032368A1 (en) | 2016-08-13 | 2016-08-16 | Block chain system data processing method based on compressed sensing and sparse reconstruction algorithm |
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| CN107181797A (en) * | 2017-05-11 | 2017-09-19 | 中国农业银行股份有限公司 | The block compression method and system of a kind of block chain |
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| CN110532329A (en) * | 2019-09-02 | 2019-12-03 | 智慧谷(厦门)物联科技有限公司 | A kind of Intelligent bracelet data processing and sharing method based on block chain technology |
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| CN107689797A (en) * | 2017-09-14 | 2018-02-13 | 中南大学 | Compressed sensing signal reconstruction method based on the arbitrary piecemeal Hadamard calculation matrix of dimension |
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