CN112364102A - Block chain-based big data transaction method, device, medium and equipment - Google Patents
Block chain-based big data transaction method, device, medium and equipment Download PDFInfo
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
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Abstract
The invention relates to the technical field of block chains, and provides a big data transaction method, a device, a medium and equipment based on a block chain, wherein the method comprises the following steps: the method comprises the following steps that a calculating party, a data party and a model party participating in transaction write respective transaction data into a block chain network to carry out uplink operation, and respective characteristic types are identified in the block chain network; the calculation party, the data party and the model party participating in the transaction broadcast the transaction at respective nodes on the block chain; starting a data task, and selecting a model party and a calculating party for model training; obtaining a training result which is based on the data of the data side, calculated by the calculating side and trained by the model of the model side; and carrying out transaction according to the training result. By applying the block chain-based big data transaction method, a calculator, a data side and a model side can easily find a target object and realize cooperation, and customized selection is realized by broadcasting self historical data.
Description
Technical Field
The present disclosure relates to the field of blockchain technologies, and more particularly, to a method, an apparatus, a medium, and a device for big data transaction based on blockchain.
Background
Big data plays an increasingly important role. Data sharing and trading has become a hotspot in current technologies and businesses. Due to the fact that data have large differences compared with traditional commodities, the data are prone to loss, copying and secrecy and the like. Thus, there are higher demands on the processing power of the transaction, traceability of the transaction process, integrity of the transaction data and reliability. For enterprises with limited data collection capabilities, data trading will be a reciprocal job that can promote innovation in companies.
The concept of Cloud Computing is a network application mode, and is the development of parallel Computing, distributed Computing and grid Computing, and Cloud Computing has the characteristics of large scale, low cost, reliability, safety and the like. Multiple computing entities are integrated into a system with powerful computing power over a network and the powerful computing power is distributed to end users.
Cloud computing companies in the prior art often have strong cloud computing capacity, business companies have much data, small companies make models quickly, and when a calculator, a data party and a model party are blind, target objects are difficult to find and cooperation is difficult to realize.
Disclosure of Invention
The method aims to solve the technical problems that in the prior art, when a calculator, a data party and a model party are blind areas, a target object is difficult to find and cooperation is difficult to realize.
In order to achieve the technical purpose, the present disclosure provides a big data transaction method based on a block chain, including:
the method comprises the following steps that a calculating party, a data party and a model party participating in transaction write respective transaction data into a block chain network to carry out uplink operation, and respective characteristic types are identified in the block chain network;
the calculation party, the data party and the model party participating in the transaction broadcast the transaction at respective nodes on the block chain;
starting a data task, and selecting a model party and a calculating party for model training;
obtaining a training result which is based on the data of the data side, calculated by the calculating side and trained by the model of the model side;
and carrying out transaction according to the training result.
Further, the uplink operation specifically includes:
acquiring a data uplink request sent by a user, wherein the data uplink request comprises data to be uplink;
updating the data to be uplink into a pre-constructed memory database, and sending data uplink acceptance feedback information to a user;
reading the memory database according to a preset time period, detecting updated data in the memory database, and writing the updated data into the block chain according to the sequence of the data updating time, wherein the earlier the data updating time is, the block chain is written into.
Further, the broadcasting of the transaction by the computing party, the data party and the model party participating in the transaction at the respective nodes on the block chain specifically includes:
the data parties participating in the transaction broadcast data requirements;
broadcasting a wind control demand and a risk identification demand by a model party participating in the transaction;
the computing parties involved in the transaction broadcast the computing power requirements.
Further, the feature types specifically include: node type, resource information, file type, and/or file path.
Further, the calculator, the data party and the model party participating in the transaction are realized in the form of not less than one carrier.
Further, before the starting of the data task and the selection of the model party and the calculation party for model training, the method further comprises the following steps: the computer party, the data party and the model party participating in the transaction prove good performance by verifying the broadcast.
Further, the specific steps of verifying the broadcast by the calculator, the data party and the model party participating in the transaction to prove that the performance of the calculator, the data party and the model party is good are as follows:
the data parties participating in the transaction broadcast own data to other nodes in the blockchain through the nodes in the blockchain and are used for training for multiple times, so that the data identification rate of the data parties is high, and the data set is good;
the method comprises the following steps that a calculator participating in transaction broadcasts self calculation history to other nodes in a blockchain through nodes in the blockchain, so that the calculator is proved to have high bearing capacity for tasks;
the node of the model party in the block chain broadcasts self model scoring data to other nodes in the block chain to prove that the model party is the preferred model.
The present disclosure also provides a big data transaction device based on a block chain, including:
the uplink module is used for writing respective transaction data into the block chain network by a calculating party, a data party and a model party participating in the transaction so as to carry out uplink operation, and identifying respective characteristic types in the block chain network;
the transaction broadcasting module is used for performing transaction broadcasting on respective nodes of the calculation party, the data party and the model party participating in the transaction on the block chain;
the training module is used for starting a data task and selecting a model party and a calculating party to carry out model training;
and the training result acquisition module is used for acquiring a training result which is calculated by a calculator and trained by a model of a model party on the basis of the data party.
The present disclosure also provides an electronic device, which includes a memory and a processor, where the memory stores a computer program executable on the processor, and the processor executes the computer program to implement the steps of the above block chain-based big data transaction method.
The present disclosure also provides a computer storage medium having a computer program stored thereon, where the computer program is used to implement the steps corresponding to the above-mentioned blockchain-based big data transaction method when executed by a processor.
The beneficial effect of this disclosure does:
by adopting the method for trading, the target object can be found and cooperation can be realized more easily by the calculator, the data party and the model party which participate in the trading; by the method, customized selection can be realized by broadcasting self historical data.
Drawings
Fig. 1 shows a schematic flow diagram of embodiment 1 of the present disclosure;
fig. 2 shows a schematic structural diagram of embodiment 2 of the present disclosure;
fig. 3 shows a schematic structural diagram of embodiment 4 of the present disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.
Various structural schematics according to embodiments of the present disclosure are shown in the figures. The figures are not drawn to scale, wherein certain details are exaggerated and possibly omitted for clarity of presentation. The shapes of various regions, layers, and relative sizes and positional relationships therebetween shown in the drawings are merely exemplary, and deviations may occur in practice due to manufacturing tolerances or technical limitations, and a person skilled in the art may additionally design regions/layers having different shapes, sizes, relative positions, as actually required.
A Block chain (Block chain) is a distributed shared accounting technology, and what is needed is to enable participating parties to establish a trust relationship at a technical level. The blockchain can be roughly divided into a blockchain bottom layer technology and a blockchain top layer application. The application of the block chain is based on the modification, optimization or innovation of the block chain technology. The most central meaning of the blockchain technique is to establish data credit between the participants.
Bitcoin is the first application of blockchain technology, but the application field of blockchain technology is far beyond the financial industry. The telecommunications industry, especially in the field of telecommunications carriers, is also enthusiastic that blockchain technology is becoming a new favorite. The blockchain technology can be understood as the role of a network infrastructure like a TCP/IP protocol, which is one of key facility elements for supporting a new Internet industry characterized by peer-to-peer opening in the future, and further influences specific application forms in a plurality of industry fields, like the influence brought by the Web to all industries around the world.
In the field of communication, information in a traditional mode is completed through point-to-point transmission, so that a tracker can intercept information by tracking a path of information transmission, which brings a security problem, and thus, an urgent need for ensuring absolute security of an information transmission path is generated. The principles of blockchain technology may just as well help to solve this problem. The brand new application of the block chain in the communication field can completely change the channel of information transmission, fundamentally solves the problem of path safety of information transmission, and opens a gate for changing the communication information transmission mode in the future by the block chain technology.
The first embodiment is as follows:
as shown in fig. 1:
the application provides a big data transaction method based on a block chain, which specifically comprises the following steps:
s1: the method comprises the following steps that a calculating party, a data party and a model party participating in transaction write respective transaction data into a block chain network to carry out uplink operation, and respective characteristic types are identified in the block chain network;
wherein the feature types specifically include: node type, resource information, file type, and/or file path.
Further, when the calculator is the calculator, the feature type further includes:
the type of cpu participating in the calculation, the type of memory used for storing the memory, the type of algorithm participating in the calculation, such as Java, C + +, and other common computer languages, the type of input participating in the calculation, the type of output participating in the calculation, and the like.
When the data side is used, the feature types mainly include:
the type of nodes participating in storing the data, the data structure used to store the data, or the actual path information of the stored data, etc.
When the model side is used, the feature types mainly include:
the type of the node fusing the data side data and the model built by the computational power, the specific physical location information of the computer or other electronic equipment used for carrying the model such as a deep learning network model or a decision tree model, and the like.
Specifically, the uplink operation includes:
acquiring a data uplink request sent by a user, wherein the data uplink request comprises data to be uplink;
updating the data to be uplink into a pre-constructed memory database, and sending data uplink acceptance feedback information to a user;
reading the memory database according to a preset time period, detecting updated data in the memory database, and writing the updated data into the block chain according to the sequence of the data updating time, wherein the earlier the data updating time is, the block chain is written into.
Optionally, updating the data to be uplink into a pre-constructed in-memory database includes:
updating the data to be uplink into a pre-constructed memory database, and marking a timestamp for each updated data in the memory database, wherein the timestamp is the corresponding data updating time.
Optionally, after updating the data to be uplink into a pre-constructed in-memory database, the method further includes:
allocating an uplink state flag bit for the data to be uplink, and initializing the uplink state flag bit to a first value;
correspondingly, after writing the updated data into the block chain according to the data updating time, the method further comprises the following steps:
changing the UL status flag bit from the first value to a second value;
wherein the first value is different from the second value.
Optionally, after allocating an uplink status flag bit for the data to be uplink, the method further includes:
reading the uplink state flag bit when receiving an uplink progress query request sent by the client;
if the uplink state flag bit is read to be a first numerical value, indicating information that data uplink is not finished is sent to the client;
if the uplink state flag bit is read to be the second value, an indication that the data uplink is completed is sent to the user.
Optionally, before updating the data to be linked into the pre-built in-memory database, the method further includes:
transferring original data corresponding to data to be linked in a memory database to a pre-constructed original data sequence;
after the updated data is written into the block chain according to the sequence of the data updating time, the method further comprises the following steps:
and if the updated data fails to be written into the block chain, reading the original data sequence, and restoring the updated data in the memory database into corresponding original data recorded in the original data sequence.
S2: the calculation party, the data party and the model party participating in the transaction broadcast the transaction at respective nodes on the block chain;
the transaction broadcast is a process of "broadcasting" transaction information in the blockchain network, and verifying and confirming the transaction information by the node.
Specifically, the S2 specifically includes:
the data parties participating in the transaction broadcast data requirements;
broadcasting a wind control demand and a risk identification demand by a model party participating in the transaction;
the computing parties involved in the transaction broadcast the computing power requirements.
The content of transaction broadcasting of the calculation party, the data party and the model party participating in the transaction on respective nodes on the block chain is specifically limited, respective requirements of the three parties participating in the transaction are defined, the transaction requirements of the three parties are broadcasted in the form of block chain broadcasting, and the transaction can be promoted by achieving the agreement more quickly.
The data requirement of the blockchain means that the blockchain technology is inseparable from the data secret, since the blockchain technology must be digitized and inseparable from the data secret. And the hash values stored in the block chain are all presented after the data are encrypted.
The hash value packed in the block of the block chain can be the related cryptology content of various data, the bill of the user for buying things, the bill of transfer, or the copyright hash value of a picture uploaded by the user.
The wind control requirements specifically mean:
a range of users' actual acceptance of risk. Due to the nature of internet finance, lending customers come from the internet, and there are more risks compared with the traditional examination and approval under the loan transaction line. Some enterprises or individuals who cannot obtain life-saving grains and grasses from banks turn to internet finance to seek high-interest financing, but under the background of difficult operation of small and micro enterprises, the repayment pressure of the enterprises or individuals is doubled, so that the bad account rate of some internet finance platforms is rapidly increased. The wind control requirements may be referred to in this disclosure as bad billing rates of users for internet financial platforms/accounts of transactional nodes in a blockchain network.
The calculation force requirement specifically means:
the calculation capability provided by the calculation party in the block chain network for completing the transaction operation, for example, at least 30 computers carrying memories with a certain operation capability of CPU and a certain value of CPU are required to be used for the first calculation to complete the actual calculation requirement of the user when completing a transaction.
The characteristic types of a calculating party, a data party and a model party participating in the transaction in the blockchain network are determined, so that the information of the three parties participating in the transaction is publicly visible in the blockchain network, and the transaction to be carried out is safer due to the transparent information.
S3: starting a data task, and selecting a model party and a calculating party for model training;
the model training provided by the model side can be realized based on common model training modes such as decision trees or deep learning, and the following model training method for block chain consensus only by deep learning is used for detailed explanation of the technical scheme of the disclosure:
the transaction main body collects file information and stores the file information in a file system, and then returns a uniform resource locator, the main body is constructed into data collection transaction and sends the data collection transaction to the nodes according to formats, and the nodes broadcast the information to adjacent nodes;
the method comprises the steps that a main body obtains stored file information, marks the stored file information, generates a marked file, stores the marked file in a file system, returns a uniform resource locator mark, is constructed into data marking transaction and sends the data marking transaction to nodes according to a format, and the nodes broadcast the information to adjacent nodes;
calculating and verifying by adopting a deep learning model training consensus algorithm, verifying the effectiveness of the transaction by an accounting node, and putting the transaction into a cache pool until the number m of the data sets A in the cache pool reaches a threshold value a;
the accounting node acquires all data marking transactions stored in a chain and all data marking transactions in a node buffer pool by using a parameter and data acquirer, acquires corresponding files and marking information, simultaneously acquires parameter values stored in a previous block or a previous block, initializes a deep learning neural network by using the parameter values, starts supervised learning, and automatically adjusts a network structure and parameters by using an AutoML method until the prediction accuracy of a model is greater than a threshold B set by a system;
the accounting node completes calculation of the model, stores model parameters to a block head, generates a first block transaction for recording accounting rewards obtained by the node, packs the transactions in the buffer pool into block bodies, combines the block head and the block bodies to generate the block, broadcasts the block in the whole network and searches for verification signatures of other verification nodes;
the other verification nodes receive the information of the new block, and the consensus verifier verifies the new block; when the deep learning consensus model is adopted, the verification node acquires all data marking transactions stored on a chain by using a parameter and data acquirer, acquires files and marking information corresponding to the URI file and the URI mark, acquires parameter values stored in the block, predicts the files after initializing the deep learning neural network by using the parameter values, compares the file with standard information, and calculates the accuracy threshold Y of the model; if the threshold Y requirement is met, the verification node signs the request and returns to the accounting node.
S4: obtaining a training result which is based on the data of the data side, calculated by the calculating side and trained by the model of the model side;
s5: and carrying out transaction according to the training result.
Further, the calculator, the data party and the model party participating in the transaction are realized in the form of not less than one carrier.
The method can be realized in the form of one carrier or a plurality of carriers, and the block chain-based big data transaction method has flexible and various realization forms.
For the calculation side, the carrier may be embodied as a specific configuration of a computer participating in the calculation, and how much cpu with Ghz as the main frequency is loaded, or how much memory and hard disk space are loaded.
For the data side, the carrier may embody a data structure for storing data, or a hard disk or memory resource for storing data.
For the model side, the carrier can be embodied as a software model program constructed on a computer of a called computing model such as a deep learning or decision tree and the like, and a computer entity hardware device carrying the software model program for the deep learning or decision model.
Before S3, the method further includes: the computer party, the data party and the model party participating in the transaction prove good performance by verifying the broadcast.
The calculation party, the data party and the model party participating in the transaction have good performance through broadcasting, and can meet the transaction requirements.
Specifically, the following steps are specifically performed by the computing party, the data party and the model party participating in the transaction through verification broadcast to prove that the performance of the model party is good:
the data parties participating in the transaction broadcast own data to other nodes in the blockchain through the nodes in the blockchain and are used for training for multiple times, so that the data identification rate of the data parties is high, and the data set is good;
the method comprises the following steps that a calculator participating in transaction broadcasts self calculation history to other nodes in a blockchain through nodes in the blockchain, so that the calculator is proved to have high bearing capacity for tasks;
the node of the model party in the block chain broadcasts self model scoring data to other nodes in the block chain to prove that the model party is the preferred model.
By applying the block chain-based big data transaction method, a calculator, a data side and a model side can easily find a target object and realize cooperation, and customized selection is realized by broadcasting self historical data.
The content of broadcasting by a calculating party, a data party and a model party participating in the transaction is particularly limited, and a suitable three party participating in the transaction can be preferably selected for transaction.
Example two:
as shown in fig. 2:
the present disclosure also provides a big data transaction device based on a block chain, including:
a chaining module 201, configured to write respective transaction data into the blockchain network for chaining by a calculator, a data party, and a model party participating in a transaction, and identify respective feature types in the blockchain network;
wherein the feature types specifically include: node type, resource information, file type, and/or file path.
Further, when the calculator is the calculator, the feature type further includes:
the type of cpu participating in the calculation, the type of memory used for storing the memory, the type of algorithm participating in the calculation, such as Java, C + +, and other common computer languages, the type of input participating in the calculation, the type of output participating in the calculation, and the like.
Specifically, the uplink operation includes:
acquiring a data uplink request sent by a user, wherein the data uplink request comprises data to be uplink;
updating the data to be uplink into a pre-constructed memory database, and sending data uplink acceptance feedback information to a user;
reading the memory database according to a preset time period, detecting updated data in the memory database, and writing the updated data into the block chain according to the sequence of the data updating time, wherein the earlier the data updating time is, the block chain is written into.
Optionally, updating the data to be uplink into a pre-constructed in-memory database includes:
updating the data to be uplink into a pre-constructed memory database, and marking a timestamp for each updated data in the memory database, wherein the timestamp is the corresponding data updating time.
Optionally, after updating the data to be uplink into a pre-constructed in-memory database, the method further includes:
allocating an uplink state flag bit for the data to be uplink, and initializing the uplink state flag bit to a first value;
correspondingly, after writing the updated data into the block chain according to the data updating time, the method further comprises the following steps:
changing the UL status flag bit from the first value to a second value;
wherein the first value is different from the second value.
Optionally, after allocating an uplink status flag bit for the data to be uplink, the method further includes:
reading the uplink state flag bit when receiving an uplink progress query request sent by the client;
if the uplink state flag bit is read to be a first numerical value, indicating information that data uplink is not finished is sent to the client;
if the uplink state flag bit is read to be the second value, an indication that the data uplink is completed is sent to the user.
Optionally, before updating the data to be linked into the pre-built in-memory database, the method further includes:
transferring original data corresponding to data to be linked in a memory database to a pre-constructed original data sequence;
after the updated data is written into the block chain according to the sequence of the data updating time, the method further comprises the following steps:
and if the updated data fails to be written into the block chain, reading the original data sequence, and restoring the updated data in the memory database into corresponding original data recorded in the original data sequence.
The transaction broadcasting module 202 is used for performing transaction broadcasting on respective nodes of the calculation party, the data party and the model party participating in the transaction on the block chain;
the English name of the Transaction Broadcast is Transaction Broadcast, and is a vocabulary related to the Transaction process in the block chain. Transaction information is "broadcast" in the blockchain network and verified, i.e., validated, by the nodes.
Specifically, the transaction broadcasting module 202 is specifically configured to:
the data parties participating in the transaction broadcast data requirements;
broadcasting a wind control demand and a risk identification demand by a model party participating in the transaction;
the computing parties involved in the transaction broadcast the computing power requirements.
The content of transaction broadcasting of the calculation party, the data party and the model party participating in the transaction on respective nodes on the block chain is specifically limited, respective requirements of the three parties participating in the transaction are defined, the transaction requirements of the three parties are broadcasted in the form of block chain broadcasting, and the transaction can be promoted by achieving the agreement more quickly.
The data requirement of the blockchain means that the blockchain technology is inseparable from the data secret, since the blockchain technology must be digitized and inseparable from the data secret. And the hash values stored in the block chain are all presented after the data are encrypted.
The hash value packed in the block of the block chain can be the related cryptology content of various data, the bill of the user for buying things, the bill of transfer, or the copyright hash value of a picture uploaded by the user.
The wind control requirements specifically mean:
a range of users' actual acceptance of risk. Due to the nature of internet finance, lending customers come from the internet, and there are more risks compared with the traditional examination and approval under the loan transaction line. Some enterprises or individuals who cannot obtain life-saving grains and grasses from banks turn to internet finance to seek high-interest financing, but under the background of difficult operation of small and micro enterprises, the repayment pressure of the enterprises or individuals is doubled, so that the bad account rate of some internet finance platforms is rapidly increased. The wind control requirements may be referred to in this disclosure as bad billing rates of users for internet financial platforms/accounts of transactional nodes in a blockchain network.
The calculation force requirement specifically means:
the calculation capability provided by the calculation party in the block chain network for completing the transaction operation, for example, at least 30 computers carrying memories with a certain operation capability of CPU and a certain value of CPU are required to be used for the first calculation to complete the actual calculation requirement of the user when completing a transaction.
The characteristic types of a calculating party, a data party and a model party participating in the transaction in the blockchain network are determined, so that the information of the three parties participating in the transaction is publicly visible in the blockchain network, and the transaction to be carried out is safer due to the transparent information.
The training module 203 is used for starting a data task and selecting a model party and a calculating party to carry out model training;
the model training provided by the model side can be realized based on common model training modes such as decision trees or deep learning, and the following model training method for block chain consensus only by deep learning is used for detailed explanation of the technical scheme of the disclosure:
the transaction main body collects file information and stores the file information in a file system, and then returns a uniform resource locator, the main body is constructed into data collection transaction and sends the data collection transaction to the nodes according to formats, and the nodes broadcast the information to adjacent nodes;
the method comprises the steps that a main body obtains stored file information, marks the stored file information, generates a marked file, stores the marked file in a file system, returns a uniform resource locator mark, is constructed into data marking transaction and sends the data marking transaction to nodes according to a format, and the nodes broadcast the information to adjacent nodes;
calculating and verifying by adopting a deep learning model training consensus algorithm, verifying the effectiveness of the transaction by an accounting node, and putting the transaction into a cache pool until the number m of the data sets A in the cache pool reaches a threshold value a;
the accounting node acquires all data marking transactions stored in a chain and all data marking transactions in a node buffer pool by using a parameter and data acquirer, acquires corresponding files and marking information, simultaneously acquires parameter values stored in a previous block or a previous block, initializes a deep learning neural network by using the parameter values, starts supervised learning, and automatically adjusts a network structure and parameters by using an AutoML method until the prediction accuracy of a model is greater than a threshold B set by a system;
the accounting node completes calculation of the model, stores model parameters to a block head, generates a first block transaction for recording accounting rewards obtained by the node, packs the transactions in the buffer pool into block bodies, combines the block head and the block bodies to generate the block, broadcasts the block in the whole network and searches for verification signatures of other verification nodes;
the other verification nodes receive the information of the new block, and the consensus verifier verifies the new block; when the deep learning consensus model is adopted, the verification node acquires all data marking transactions stored on a chain by using a parameter and data acquirer, acquires files and marking information corresponding to the URI file and the URI mark, acquires parameter values stored in the block, predicts the files after initializing the deep learning neural network by using the parameter values, compares the file with standard information, and calculates the accuracy threshold Y of the model; if the threshold Y requirement is met, the verification node signs the request and returns to the accounting node.
A training result obtaining module 204, configured to obtain a training result that is calculated by a calculator and trained by a model of a model side based on data of a data side.
The uplink module 201 of the present disclosure is sequentially connected to the transaction broadcasting module 202, the training module 203, and the training result obtaining module 204.
Example three:
the present disclosure can also provide a computer storage medium having stored thereon a computer program for implementing the steps of the above-described blockchain-based big data transaction method when executed by a processor.
The storage medium may be non-volatile or volatile. The storage medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
Further, the storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.
Example four:
the present disclosure also provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the steps of the above block chain-based big data transaction method are implemented.
Fig. 3 is a schematic diagram of an internal structure of an electronic device in one embodiment. As shown in fig. 3, the electronic device includes a processor, a storage medium, a memory, and a network interface connected through a system bus. The storage medium of the computer device stores an operating system, a database and computer readable instructions, the database can store control information sequences, and the computer readable instructions can enable the processor to realize a big data transaction method based on the block chain when being executed by the processor. The processor of the electrical device is used to provide computing and control capabilities to support the operation of the entire computer device. The memory of the computer device may have stored therein computer readable instructions that, when executed by the processor, may cause the processor to perform a blockchain-based big data transaction method. The network interface of the computer device is used for connecting and communicating with the terminal. Those skilled in the art will appreciate that the architecture shown in fig. 3 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
The electronic device includes, but is not limited to, a smart phone, a computer, a tablet, a wearable smart device, an artificial smart device, a mobile power source, and the like.
The processor may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor is a Control Unit of the electronic device, connects various components of the electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by running or executing programs or modules (for example, executing remote data reading and writing programs, etc.) stored in the memory and calling data stored in the memory.
The bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connected communication between the memory and at least one processor or the like.
Fig. 3 shows only an electronic device having components, and those skilled in the art will appreciate that the structure shown in fig. 3 does not constitute a limitation of the electronic device, and may include fewer or more components than those shown, or some components may be combined, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor through a power management device, so that functions such as charge management, discharge management, and power consumption management are implemented through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
Further, the electronic device may further include a network interface, and optionally, the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a bluetooth interface, etc.), which are generally used to establish a communication connection between the electronic device and other electronic devices.
Optionally, the electronic device may further comprise a user interface, which may be a Display (Display), an input unit (such as a Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.
Further, the computer usable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
The embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.
Claims (10)
1. A big data transaction method based on a block chain is characterized by comprising the following steps:
the method comprises the following steps that a calculating party, a data party and a model party participating in transaction write respective transaction data into a block chain network to carry out uplink operation, and respective characteristic types are identified in the block chain network;
the calculation party, the data party and the model party participating in the transaction broadcast the transaction at respective nodes on the block chain;
starting a data task, and selecting a model party and a calculating party for model training;
obtaining a training result which is based on the data of the data side, calculated by the calculating side and trained by the model of the model side;
and carrying out transaction according to the training result.
2. The method of claim 1 wherein the uplink operation specifically comprises:
acquiring a data uplink request sent by a user, wherein the data uplink request comprises data to be uplink;
updating the data to be uplink into a pre-constructed memory database, and sending data uplink acceptance feedback information to a user;
reading the memory database according to a preset time period, detecting the updated data in the memory database, and writing the updated data into the block chain according to the sequence of the data updating time.
3. The method according to claim 1, wherein the broadcasting of the transaction by the computing party, the data party and the model party at the respective nodes on the blockchain specifically comprises:
the data parties participating in the transaction broadcast data requirements;
broadcasting a wind control demand and a risk identification demand by a model party participating in the transaction;
the computing parties involved in the transaction broadcast the computing power requirements.
4. The method according to claim 1, wherein the feature type specifically comprises: node type, resource information, file type, and/or file path.
5. The method of claim 1, wherein the computational, data, and modeling parties involved in the transaction are implemented in the form of no less than one carrier.
6. The method according to any one of claims 1 to 5, wherein before the initiating the data task, selecting the model side and the calculator side for model training, the method further comprises: the computer party, the data party and the model party participating in the transaction prove good performance by verifying the broadcast.
7. The method of claim 6, wherein the parties involved in the transaction, the data party and the modeling party, through verification of the broadcast, prove themselves well-behaved by:
the data parties participating in the transaction broadcast own data to other nodes in the blockchain through the nodes in the blockchain and are used for training for multiple times, so that the data identification rate of the data parties is high, and the data set is good;
the method comprises the following steps that a calculator participating in transaction broadcasts self calculation history to other nodes in a blockchain through nodes in the blockchain, so that the calculator is proved to have high bearing capacity for tasks;
the node of the model party in the block chain broadcasts self model scoring data to other nodes in the block chain to prove that the model party is the preferred model.
8. A big data transaction device based on a block chain is characterized by comprising:
the uplink module is used for writing respective transaction data into the block chain network by a calculating party, a data party and a model party participating in the transaction so as to carry out uplink operation, and identifying respective characteristic types in the block chain network;
the transaction broadcasting module is used for performing transaction broadcasting on respective nodes of the calculation party, the data party and the model party participating in the transaction on the block chain;
the training module is used for starting a data task and selecting a model party and a calculating party to carry out model training;
and the training result acquisition module is used for acquiring a training result which is calculated by a calculator and trained by a model of a model party on the basis of the data party.
9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and operable on the processor, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer storage medium having a computer program stored thereon, wherein the program, when executed by a processor, is adapted to implement the steps corresponding to the blockchain-based big data transaction method according to any one of claims 1 to 7.
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| PCT/CN2021/126169 WO2022105546A1 (en) | 2020-11-17 | 2021-10-25 | Big data transaction method and apparatus based on blockchain, and medium and device |
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| WO2022105546A1 (en) * | 2020-11-17 | 2022-05-27 | 深圳壹账通智能科技有限公司 | Big data transaction method and apparatus based on blockchain, and medium and device |
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| CN107229864A (en) * | 2017-06-26 | 2017-10-03 | 上海优刻得信息科技有限公司 | Data processing method, device, system and storage medium |
| US11157833B2 (en) * | 2018-03-14 | 2021-10-26 | International Business Machines Corporation | Learning service blockchain |
| CN109146635A (en) * | 2018-08-24 | 2019-01-04 | 华东师范大学 | The exchange of geographical big data and transaction system and method based on block chain |
| CN111858753B (en) * | 2020-06-30 | 2023-11-17 | 全链通有限公司 | Training parameter processing method, device and storage medium based on block chain |
| CN112364102A (en) * | 2020-11-17 | 2021-02-12 | 深圳壹账通智能科技有限公司 | Block chain-based big data transaction method, device, medium and equipment |
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