WO2022105546A1 - 一种基于区块链的大数据交易方法、装置、介质及设备 - Google Patents

一种基于区块链的大数据交易方法、装置、介质及设备 Download PDF

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WO2022105546A1
WO2022105546A1 PCT/CN2021/126169 CN2021126169W WO2022105546A1 WO 2022105546 A1 WO2022105546 A1 WO 2022105546A1 CN 2021126169 W CN2021126169 W CN 2021126169W WO 2022105546 A1 WO2022105546 A1 WO 2022105546A1
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data
party
transaction
model
blockchain
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PCT/CN2021/126169
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English (en)
French (fr)
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薄辰龙
李宁
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深圳壹账通智能科技有限公司
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Publication of WO2022105546A1 publication Critical patent/WO2022105546A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
    • G06F21/64—Protecting data integrity, e.g. using checksums, certificates or signatures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • 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
    • G06F9/46—Multiprogramming arrangements
    • G06F9/54—Interprogram communication
    • G06F9/542—Event management; Broadcasting; Multicasting; Notifications
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange

Definitions

  • the present disclosure relates to the field of blockchain technology, and more particularly, the present disclosure relates to a method, device, medium and device for big data transaction based on blockchain.
  • the sharing and trading of data has become a hot topic in current technology and business. Due to the large differences between data and traditional commodities, such as easy to lose, easy to copy, and need to be kept confidential. Therefore, there are higher requirements for transaction processing capability, traceability of transaction process, integrity and reliability of transaction data. For companies with limited data collection capabilities, data trading will be a mutually beneficial endeavor that can boost the company's innovation.
  • Cloud computing The concept of Computing is a network application mode, which is the development of parallel computing, distributed computing and grid computing. Cloud computing has the characteristics of large scale, low cost, reliability and security. Integrate multiple computing entities into a system with powerful computing power through the network, and distribute this powerful computing power to end users.
  • the present disclosure provides a blockchain-based big data transaction method, including: the computing party, the data party and the model party participating in the transaction write their respective transaction data into the blockchain network to perform the above process. chain operation, and identify their respective feature types in the blockchain network; the computing party, the data party and the model party participating in the transaction broadcast the transaction on their respective nodes on the blockchain; start the data task, select the model party and the model party.
  • the computing side performs model training; obtains a training result based on the data of the data side, which is calculated by the computing side and trained with the model of the model side; and conducts transactions according to the training results.
  • the present disclosure also provides a blockchain-based big data transaction device, including: an on-chain module, used for the computing party, the data party and the model party participating in the transaction to write their respective transaction data into the blockchain network to perform On-chain operation, and identify their respective feature types in the blockchain network; transaction broadcasting module, for the computing party, data party and model party participating in the transaction to broadcast transactions on their respective nodes on the blockchain; training The module is used to start the data task, select the model side and the calculation side for model training; the training result acquisition module is used to obtain the training based on the data of the data side, calculated with the calculation side, and trained with the model of the model side result.
  • an on-chain module used for the computing party, the data party and the model party participating in the transaction to write their respective transaction data into the blockchain network to perform On-chain operation, and identify their respective feature types in the blockchain network
  • transaction broadcasting module for the computing party, data party and model party participating in the transaction to broadcast transactions on their respective nodes on the blockchain
  • training The module is used to start the data task
  • the present disclosure also provides an electronic device, including a memory and a processor, where the memory stores a computer program that can run on the processor, and when the processor executes the computer program, the above-mentioned blockchain-based large scale is realized.
  • a data transaction method includes: the computing party, the data party and the model party participating in the transaction write their respective transaction data into a blockchain network for on-chain operations, and identify their respective feature types in the blockchain network ; The computing party, the data party and the model party participating in the transaction broadcast transactions on their respective nodes on the blockchain; start the data task, select the model party and the computing party for model training; obtain the data based on the data of the data party, The training result of computing with the computing side and training with the model of the model side; conducting transactions according to the training results.
  • the present disclosure also provides a computer storage medium on which a computer program is stored, and when the program is executed by a processor, is used to implement the above-mentioned blockchain-based big data transaction method, the method comprising: a computing party participating in the transaction , the data party and the model party write their respective transaction data into the blockchain network for on-chain operations, and identify their respective feature types in the blockchain network; the computing party, data party and model participating in the transaction The parties broadcast transactions on their respective nodes on the blockchain; start the data task, select the model party and the computing party for model training; obtain the data based on the data of the data party, calculate with the computing party, and train with the model of the model party The training result; trade according to the training result.
  • the computing parties, data parties and model parties participating in the transaction can more easily find the target object and realize cooperation; the method of the present application can also realize customized selection by broadcasting its own historical data.
  • FIG. 1 shows a schematic flowchart 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.
  • This application may relate to the field of artificial intelligence and/or big data technology, for example, relevant data may be acquired and processed based on artificial intelligence technology.
  • the technical solution of the present application can be applied to various data processing scenarios based on blockchain, such as data processing scenarios in digital medicine, and data processing scenarios in financial technology.
  • Blockchain (Block chain) is a distributed shared accounting technology. What it needs to do is to allow all parties involved to establish a trust relationship at the technical level.
  • the blockchain can be roughly divided into the underlying technology of the blockchain and the upper-layer application of the blockchain.
  • the so-called application of blockchain refers to the application of transformation, optimization or innovation based on blockchain technology.
  • the core meaning of blockchain technology is to establish data credit between participants.
  • Bitcoin is the first application of blockchain technology, but the application of blockchain technology goes far beyond the financial industry.
  • the communication industry is also enthusiastic, especially in the field of telecom operators, and blockchain technology is becoming a new favorite.
  • Blockchain technology can be understood as the role of a network infrastructure similar to the TCP/IP protocol, and will be one of the key infrastructure elements to support the new Internet format characterized by peer-to-peer openness in the future, which will affect many industries.
  • the specific application form is just like the impact the Web has brought to various industries around the world.
  • the present application provides a blockchain-based big data transaction method, which specifically includes the following steps.
  • S1 The computing party, the data party and the model party participating in the transaction write their respective transaction data into the blockchain network for on-chain operations, and identify their respective feature types in the blockchain network.
  • the feature type specifically includes: node type, resource information, file type and/or file path.
  • the feature type when being a computing party, also includes: the cpu type that participates in the calculation, the type of memory used for storage, the algorithm type that participates in the calculation such as common computer languages such as Java and C++, the input parameter input and the participation in the calculation. Calculated output parameters and so on.
  • the feature type mainly includes: the type of the node participating in storing the data, the data structure for storing the data, or the actual path information for storing the data, and the like.
  • the feature types mainly include: the node type of the model constructed by integrating the data of the data side and the computing power of the computing side, and the computer or other used to carry the model such as a deep learning network model or a decision tree model.
  • the linking operation includes: obtaining a data linking request sent by a user, where the data linking request includes the data to be linked; updating the data to be linked into a pre-built memory database, and sending the data linking to the user Accept feedback information; read the in-memory database according to the preset time period, detect the updated data in the in-memory database, and write the updated data into the blockchain in the order of the data update time, where the earlier the data update time , the sooner it is written to the blockchain.
  • updating the data to be linked to the pre-built in-memory database includes: updating the data to be linked to the pre-built in-memory database, and marking each updated data in the in-memory database with a timestamp, time The stamp is the corresponding data update time.
  • the method further includes: assigning an on-chain status flag to the data to be uploaded, and initializing the on-chain status flag to the first a value.
  • the method further includes: changing the on-chain status flag bit from the first value to the second value.
  • the first numerical value is different from the second numerical value.
  • allocating an on-chain status flag bit for the data to be linked it also includes: when receiving the on-chain progress query request sent by the client, reading the on-chain status flag bit; Get the first value of the status flag bit on the chain, send the indication information that the data on-chain is not completed to the client; if the read status flag bit is the second value, send the data that the data on-chain has been completed to the user. Instructions.
  • the method before updating the data to be uploaded into the pre-built in-memory database, the method further includes: transferring the original data corresponding to the data to be uploaded in the in-memory database to the pre-built original data sequence.
  • the method further includes: if the updated data fails to be written into the blockchain, reading the original data sequence, and storing the updated data in the in-memory database. The updated data reverts to the corresponding original data recorded in the original data sequence.
  • S2 The computing party, the data party and the model party participating in the transaction broadcast the transaction on their respective nodes on the blockchain.
  • transaction broadcasting is the process of "broadcasting" transaction information in the blockchain network and verified by nodes.
  • the S2 is specifically: the data party participating in the transaction broadcasts data requirements; the model party participating in the transaction broadcasts the risk control requirements and risk identification requirements; the computing party participating in the transaction broadcasts the computing power requirements.
  • the content of the transaction broadcast of the computing party, the data party and the model party participating in the transaction on their respective nodes on the blockchain is defined, and the respective needs of the three parties involved in the transaction are clarified. Deals can be reached more quickly.
  • the data demand of blockchain means that blockchain technology is inseparable from data, because blockchain technology must be digital and inseparable from data.
  • the hash value stored in the blockchain is presented after the data is encrypted.
  • the hash packaged in the block of the blockchain may be cryptographic content related to various data, such as the user's purchase bill, the transfer bill, or the copyright hash of a picture uploaded by the user.
  • Risk control requirements specifically refer to: a range of users' actual risk acceptance. Due to the nature of Internet finance, its loan customers are all from the Internet, and compared with the offline approval of traditional small loan business, there are more risks. Some of these companies or individuals who cannot obtain life-saving food from banks turn to Internet finance for high-interest financing. However, under the background of small and micro enterprises operating difficulties, these companies or individuals are under increased repayment pressure, so the bad debt rate of some Internet financial platforms has appeared. rapidly increasing trend. In this disclosure, the risk control requirement may specifically refer to the bad debt ratio of the user to the Internet financial platform/account of the transaction node in the blockchain network.
  • the computing power requirement specifically refers to the computing power that the computing party in the blockchain network can provide to complete the transaction operation. For example, to complete a transaction, at least 30 memory units with CPUs with a certain computing power and not less than a certain value are required. The first-phase calculation of the computer is used to complete the actual computing needs of users.
  • the characteristics of the computing party, data party and model party participating in the transaction in the blockchain network are clarified, so that the information of the three parties involved in the transaction is publicly visible in the blockchain network, and the transparent information makes the transaction to be conducted more secure. .
  • S3 Start the data task, and select the model side and the computing side for model training.
  • the model training provided by the model party can be implemented based on common model training methods such as decision tree or deep learning.
  • common model training methods such as decision tree or deep learning.
  • the following is a detailed explanation of the technical solutions of the present disclosure by using the model training method for blockchain consensus based on deep learning.
  • the transaction subject collects the file information and stores it in the file system and returns the uniform resource locator.
  • the subject constructs a data acquisition transaction and sends it to the node according to the format, and the node broadcasts the information to adjacent nodes.
  • the subject obtains the stored file information, annotates it, generates an annotation file and stores it in the file system and returns the uniform resource locator annotation.
  • the subject constructs a data annotation transaction and sends it to the node according to the format. .
  • the deep learning model is used to train the consensus algorithm for calculation and verification.
  • the accounting node verifies the validity of the transaction and puts it into the buffer pool until the number m of data sets A in the buffer pool reaches the threshold a.
  • the accounting node uses parameters and data acquirers to obtain all data annotation transactions stored on the chain and all data annotation transactions in the node buffer pool, obtain their corresponding files and annotation information, and at the same time, obtain the previous block or the storage in the previous block.
  • the parameter values of use the parameter values to initialize the deep learning neural network, start supervised learning, and use the AutoML method to automatically adjust the network structure and parameters until the prediction accuracy of the model is greater than the threshold B set by the system.
  • the accounting node completes the calculation of the model, stores the model parameters in the block header, and generates the first block transaction to record the node’s acquisition of accounting rewards.
  • the block header is merged with the block body to generate a block and broadcast to the whole network to find the verification signature of other verification nodes.
  • Other verification nodes receive the information of the new block, and the consensus validator verifies it; when the deep learning consensus model is adopted, the verification node will use the parameters and data acquirer to obtain all the data annotation transactions stored on the chain, and obtain the "URI" among them. At the same time, obtain the parameter value stored in the block, use the parameter value to initialize the deep learning neural network to predict the file, and compare it with the standard information to calculate the model The correctness threshold Y; if the threshold Y is met, the verification node signs it and returns to the accounting node.
  • the computing party, the data party and the model party participating in the transaction are realized in the form of no less than one carrier.
  • the carrier can be embodied as the specific configuration of the computer participating in the calculation, the cpu with the main frequency of how many Ghz, or how much memory, hard disk space and so on.
  • the carrier may be embodied as a data structure for storing data, or a hard disk or memory resource for storing data.
  • the carrier may be embodied as a software model program constructed on the computer where the called computing model, such as deep learning or decision tree, etc., is located, and a software model program carrying the software model program for deep learning or decision-making model.
  • Computer physical hardware device For the model side, the carrier may be embodied as a software model program constructed on the computer where the called computing model, such as deep learning or decision tree, etc., is located, and a software model program carrying the software model program for deep learning or decision-making model.
  • Computer physical hardware device for the model side, the carrier may be embodied as a software model program constructed on the computer where the called computing model, such as deep learning or decision tree, etc., is located, and a software model program carrying the software model program for deep learning or decision-making model.
  • the method further includes: the computing party, the data party and the model party participating in the transaction prove that their performance is good by verifying the broadcast.
  • the computing party, the data party and the model party participating in the transaction prove that their performance is good and can meet the needs of the transaction through broadcasting. Because it is in the form of broadcasting, all nodes in the blockchain need to be verified to ensure the security and reliability of the transaction. more guaranteed.
  • the computing party, the data party and the model party participating in the transaction prove that their performance is good by verifying the broadcast.
  • the data party participating in the transaction broadcasts itself to other nodes in the blockchain through the node in the blockchain.
  • the data has been used for many times of training to prove that the data party has a high data recognition rate and a good data set;
  • the computing party participating in the transaction broadcasts its computing history to other nodes in the blockchain through the nodes in the blockchain , to prove that the computing party has a high carrying capacity for the task;
  • the model party's node in the blockchain broadcasts its own model scoring data to other nodes in the blockchain to prove that the model party is the preferred model.
  • the computing party, the data party and the model party can easily find the target object, realize cooperation, and realize customized selection by broadcasting their own historical data.
  • the content broadcasted by the computing party, the data party and the model party participating in the transaction is limited, and the three parties that are suitable for participating in the transaction can be selected for the transaction.
  • the present disclosure also provides a blockchain-based big data transaction device, including the following modules.
  • the on-chain module 201 is used for the computing party, the data party and the model party participating in the transaction to write their respective transaction data into the blockchain network for the on-chain operation, and to identify their respective feature types in the blockchain network.
  • the feature type specifically includes: node type, resource information, file type and/or file path.
  • the feature type when being a computing party, also includes: the cpu type that participates in the calculation, the type of memory used for storage, the algorithm type that participates in the calculation such as common computer languages such as Java and C++, the input parameter input and the participation in the calculation. Calculated output parameters and so on.
  • the linking operation includes: obtaining a data linking request sent by a user, where the data linking request includes the data to be linked; updating the data to be linked into a pre-built memory database, and sending the data linking to the user Accept feedback information; read the in-memory database according to the preset time period, detect the updated data in the in-memory database, and write the updated data into the blockchain in the order of the data update time, where the earlier the data update time , the sooner it is written to the blockchain.
  • updating the data to be linked to the pre-built in-memory database includes: updating the data to be linked to the pre-built in-memory database, and marking each updated data in the in-memory database with a timestamp, time The stamp is the corresponding data update time.
  • the method further includes: assigning an on-chain status flag to the data to be uploaded, and initializing the on-chain status flag to the first a value.
  • the method further includes: changing the on-chain status flag bit from the first value to the second value.
  • the first numerical value is different from the second numerical value.
  • allocating an on-chain status flag bit for the data to be linked it also includes: when receiving the on-chain progress query request sent by the client, reading the on-chain status flag bit; Get the first value of the status flag bit on the chain, send the indication information that the data on-chain is not completed to the client; if the read status flag bit is the second value, send the data that the data on-chain has been completed to the user. Instructions.
  • the method before updating the data to be uploaded into the pre-built in-memory database, the method further includes: transferring the original data corresponding to the data to be uploaded in the in-memory database to the pre-built original data sequence.
  • the method further includes: if the updated data fails to be written into the blockchain, reading the original data sequence, and storing the updated data in the in-memory database. The updated data reverts to the corresponding original data recorded in the original data sequence.
  • the transaction broadcasting module 202 is used for the computing party, the data party and the model party participating in the transaction to broadcast transactions on their respective nodes on the blockchain.
  • Transaction Broadcast is a vocabulary related to the transaction process in the blockchain.
  • the transaction broadcasting module 202 is specifically used for: data parties participating in transactions broadcast data requirements; model parties participating in transactions broadcasting risk control requirements and risk identification requirements; computing parties participating in transactions broadcasting computing power requirements.
  • the content of the transaction broadcast of the computing party, the data party and the model party participating in the transaction on their respective nodes on the blockchain is defined, and the respective needs of the three parties involved in the transaction are clarified. Deals can be reached more quickly.
  • the data demand of blockchain means that blockchain technology is inseparable from data, because blockchain technology must be digital and inseparable from data.
  • the hash value stored in the blockchain is presented after the data is encrypted.
  • the hash packaged in the block of the blockchain may be cryptographic content related to various data, such as the user's purchase bill, the transfer bill, or the copyright hash of a picture uploaded by the user.
  • Risk control requirements specifically refer to: a range of users' actual risk acceptance. Due to the nature of Internet finance, its loan customers are all from the Internet, and compared with the offline approval of traditional small loan business, there are more risks. Some of these companies or individuals who cannot obtain life-saving food from banks turn to Internet finance for high-interest financing. However, under the background of small and micro enterprises operating difficulties, these companies or individuals are under increased repayment pressure, so the bad debt rate of some Internet financial platforms has appeared. rapidly increasing trend. In this disclosure, the risk control requirement may specifically refer to the bad debt ratio of the user to the Internet financial platform/account of the transaction node in the blockchain network.
  • the computing power requirement specifically refers to the computing power that the computing party in the blockchain network can provide to complete the transaction operation. For example, to complete a transaction, at least 30 memory units with CPUs with a certain computing power and not less than a certain value are required. The first-phase calculation of the computer is used to complete the actual computing needs of users.
  • the characteristics of the computing party, data party and model party participating in the transaction in the blockchain network are clarified, so that the information of the three parties involved in the transaction is publicly visible in the blockchain network, and the transparent information makes the transaction to be conducted more secure. .
  • the training module 203 is used for starting the data task, and selecting the model side and the computing side for model training.
  • the model training provided by the model party can be implemented based on common model training methods such as decision tree or deep learning.
  • the following is a detailed explanation of the technical solution disclosed in the model training method of blockchain consensus based on deep learning:
  • the transaction subject collects the file information and stores it in the file
  • the main body constructs a data collection transaction, and sends it to the node according to the format, and the node broadcasts the information to the adjacent nodes;
  • the main body obtains the stored file information, annotates it, and generates a marked file and stores it in the file
  • the main body is structured as a data annotation transaction, which is sent to the node according to the format, and the node broadcasts the information to adjacent nodes;
  • the deep learning model is used to train the consensus algorithm for calculation and verification, and the accounting node verifies the transaction.
  • the accounting node uses parameters and data acquirers to obtain all data stored on the chain to mark transactions and all data in the node buffer pool Label the transaction, obtain its corresponding file and label information, and at the same time, obtain the parameter value stored in the previous block or the previous block, use the parameter value to initialize the deep learning neural network, start supervised learning, and use the AutoML method,
  • the network structure and parameters are automatically adjusted until the prediction accuracy of the model is greater than the threshold B set by the system;
  • the accounting node completes the calculation of the model, stores the model parameters in the block header, and generates the first block transaction to record the
  • the node receives the bookkeeping reward, and at the same time, the transactions in the buffer pool are packaged together into a block body, the block header and the block body are combined to generate a block and broadcast to the whole network, and the verification signature of other verification nodes is found; other verification nodes After receiving the information of the new block, the consensus
  • Mark the corresponding file and label information obtain the parameter value stored in the block, use the parameter value to initialize the deep learning neural network to predict the file, and compare it with the standard information to calculate the correctness of the model Threshold Y; if the threshold Y is met, the verification node signs it and returns to the accounting node.
  • the training result obtaining module 204 is configured to obtain a training result based on the data of the data side, calculated with the calculation side, and trained with the model of the model side.
  • the linking module 201 described in the present disclosure is sequentially connected with the transaction broadcasting module 202 , the training module 203 and the training result obtaining module 204 .
  • the present disclosure can also provide a computer storage medium on which a computer program is stored, and when the computer program is executed by a processor, is used to implement the steps of the above-mentioned blockchain-based big data transaction method.
  • the storage medium involved in this application may be a readable storage medium. Further optionally, the storage medium may be non-volatile or volatile.
  • the storage medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM, Read-Only Memory).
  • 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 the usage according to the block chain node. created data, etc.
  • the present disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor.
  • the processor executes the computer program, the above-mentioned block chain-based big data transaction method is implemented. step.
  • FIG. 3 is a schematic diagram of the internal structure of an electronic device in one embodiment.
  • 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 a control information sequence.
  • the processor can realize a block-based Chain's big data transaction method.
  • the processor of the electrical equipment is used to provide computing and control capabilities and support the operation of the entire computer equipment.
  • Computer-readable instructions may be stored in the memory of the computer device, and when the computer-readable instructions are executed by the processor, the processor may execute a blockchain-based big data transaction method.
  • the network interface of the computer equipment is used for communication with the terminal connection.
  • FIG. 3 is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer equipment to which the solution of the present application is applied. Include more or fewer components than shown in the figures, or combine certain components, or have a different arrangement of components.
  • the electronic devices include, but are not limited to, smart phones, computers, tablet computers, wearable smart devices, artificial intelligence devices, power banks, and the like.
  • the processor may be composed of integrated circuits, such as a single packaged integrated circuit, or a plurality of integrated circuits packaged with the same function or different functions, including one or more central Processor (Central Processing Unit, CPU), microprocessor, digital processing chip, graphics processor and combination of various control chips, etc.
  • the processor is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect the various components of the entire electronic device, by running or executing the program or module stored in the memory (for example, executing a remote control unit). data read and write programs, etc.), and call the data stored in the memory to perform various functions of the electronic device and process data.
  • the bus may be a peripheral component interconnect (PCI for short) bus or an extended industry standard architecture (extended industry standard architecture, EISA for short) bus or the like.
  • PCI peripheral component interconnect
  • EISA extended industry standard architecture
  • the bus can be divided into address bus, data bus, control bus and so on.
  • the bus is configured to enable connection communication between the memory and at least one processor or the like.
  • FIG. 3 only shows an electronic device with components. Those skilled in the art can understand that the structure shown in FIG. 3 does not constitute a limitation on the electronic device, and may include fewer or more components than those shown in the drawings. , or a combination of certain components, or a different arrangement of components.
  • the electronic device may also include a power source (such as a battery) for powering the various components, preferably, the power source may be logically connected to the at least one processor through a power management device, so as to be implemented by the power management device Charge management, discharge management, and power management functions.
  • the power source may also include one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, and any other components.
  • the electronic device may further include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.
  • the electronic device may also include a network interface, optionally, the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.) Establish a communication connection between other electronic devices.
  • a network interface optionally, the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.) Establish a communication connection between other electronic devices.
  • the electronic device may further include a user interface
  • the user interface may be a display (Display), an input unit (such as a keyboard (Keyboard)), optionally, the user interface may also be a standard wired interface or a wireless interface.
  • the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, and the like.
  • the display may also be appropriately called a display screen or a display unit, which is used for displaying information processed in the electronic device and for displaying a visual user interface.
  • the computer usable storage medium may mainly include a stored program area and a stored data area, wherein the stored program area may store an operating system, an application program required for at least one function, and the like; using the created data, etc.
  • modules described as separate components may or may not be physically separated, and components shown as modules may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution in this embodiment.
  • each functional module in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit.
  • the above-mentioned integrated units can be implemented in the form of hardware, or can be implemented in the form of hardware plus software function modules.

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Abstract

本申请涉及区块链技术领域，本申请提供一种基于区块链的大数据交易方法、装置、介质及设备，所述方法包括：参与交易的计算方、数据方以及模型方将各自的交易数据写入区块链网络中以进行上链操作，并在区块链网络中标识出各自的特征类型；所述参与交易的计算方、数据方以及模型方在区块链上各自的节点进行交易广播；启动数据任务，选择模型方和计算方进行模型训练；获得以数据方的数据为基础的、以计算方进行计算、以模型方的模型进行训练的训练结果；根据所述训练结果进行交易。应用本申请的基于区块链的大数据交易方法，计算方、数据方以及模型方容易找到目标对象、实现合作，通过广播自身历史数据，实现定制化选择。

Description

一种基于区块链的大数据交易方法、装置、介质及设备
本申请要求于2020年11月17日提交中国专利局、申请号为202011287493.3，发明名称为“一种基于区块链的大数据交易方法、装置、介质及设备”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本公开涉及区块链技术领域，更为具体来说，本公开涉及一种基于区块链的大数据交易方法、装置、介质及设备。
 背景技术
大数据扮演着越来越重要的角色。数据的分享与交易成为了当前技术和商业的一个热点。由于数据与传统商品相比有较大的差异，比如容易丢失，容易复制，需要保密等。因而，对交易的处理能力、交易过程的可追踪性、交易数据的完整性以及可靠性都有更高的要求。对于数据收集能力有限企业，数据交易将是一个互惠利工作可以促进公司的创新。
云计算(Cloud Computing)的概念是一个网络应用模式，是并行计算，分布式计算和网格计算的发展，云计算具有规模大，成本低，可靠安全等特点。通过网络把多个计算实体整合成一个具有强大计算能力的系统，并把这强大的计算能力分布到终端用户手中。
发明人发现，云计算公司往往都有着较强的云计算能力，业务公司数据很多、小公司制作模型很快，然而在计算方、数据方以及模型方互为盲区的时候，难以找到目标对象、实现合作。
 技术问题
为解决现有技术中计算方、数据方以及模型方互为盲区的时候，难以找到目标对象、实现合作的技术问题。
 技术解决方案
为实现上述技术目的，本公开提供了一种基于区块链的大数据交易方法，包括：参与交易的计算方、数据方以及模型方将各自的交易数据写入区块链网络中以进行上链操作，并在区块链网络中标识出各自的特征类型；所述参与交易的计算方、数据方以及模型方在区块链上各自的节点进行交易广播；启动数据任务，选择模型方和计算方进行模型训练；获得以数据方的数据为基础的、以计算方进行计算、以模型方的模型进行训练的训练结果；根据所述训练结果进行交易。
本公开还提供了一种基于区块链的大数据交易装置，包括：上链模块，用于参与交易的计算方、数据方以及模型方将各自的交易数据写入区块链网络中以进行上链操作，并在区块链网络中标识出各自的特征类型；交易广播模块，用于所述参与交易的计算方、数据方以及模型方在区块链上各自的节点进行交易广播；训练模块，用于启动数据任务，选择模型方和计算方进行模型训练；训练结果获取模块，用于获得以数据方的数据为基础的、以计算方进行计算、以模型方的模型进行训练的训练结果。
本公开还提供了一种电子设备，包括存储器、处理器，所述存储器上存储有可在处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现上述基于区块链的大数据交易方法，该方法包括：参与交易的计算方、数据方以及模型方将各自的交易数据写入区块链网络中以进行上链操作，并在区块链网络中标识出各自的特征类型；所述参与交易的计算方、数据方以及模型方在区块链上各自的节点进行交易广播；启动数据任务，选择模型方和计算方进行模型训练；获得以数据方的数据为基础的、以计算方进行计算、以模型方的模型进行训练的训练结果；根据所述训练结果进行交易。
本公开还提供了一种计算机存储介质，其上存储有计算机程序，所述程序被处理器执行时用于实现上述的基于区块链的大数据交易方法，该方法包括：参与交易的计算方、数据方以及模型方将各自的交易数据写入区块链网络中以进行上链操作，并在区块链网络中标识出各自的特征类型；所述参与交易的计算方、数据方以及模型方在区块链上各自的节点进行交易广播；启动数据任务，选择模型方和计算方进行模型训练；获得以数据方的数据为基础的、以计算方进行计算、以模型方的模型进行训练的训练结果；根据所述训练结果进行交易。
 有益效果
采用本申请的方法进行交易，参与交易的计算方、数据方以及模型方都更容易找到目标对象、实现合作；通过本申请的方法还可以通过广播自身历史数据，实现定制化选择。
 附图说明
图1示出了本公开的实施例1的流程示意图。
图2示出了本公开的实施例2的结构示意图。
图3示出了本公开的实施例4的结构示意图。
 本发明的实施方式
以下，将参照附图来描述本公开的实施例。但是应该理解，这些描述只是示例性的，而并非要限制本公开的范围。此外，在以下说明中，省略了对公知结构和技术的描述，以避免不必要地混淆本公开的概念。
在附图中示出了根据本公开实施例的各种结构示意图。这些图并非是按比例绘制的，其中为了清楚表达的目的，放大了某些细节，并且可能省略了某些细节。图中所示出的各种区域、层的形状以及它们之间的相对大小、位置关系仅是示例性的，实际中可能由于制造公差或技术限制而有所偏差，并且本领域技术人员根据实际所需可以另外设计具有不同形状、大小、相对位置的区域/层。
本申请可涉及人工智能和/或大数据技术领域，如可以基于人工智能技术对相关的数据进行获取和处理。可选的，本申请的技术方案可应用于基于区块链的各种数据处理场景，如数字医疗中的数据处理场景，又如金融科技中的数据处理场景。
区块链（Block chain）是一种分布式共享记账的技术，它要做的事情就是让参与的各方能够在技术层面建立信任关系。区块链可以大致分成区块链底层技术和区块链上层应用。所谓区块链的应用，就是基于区块链技术的改造、优化或者创新等应用。区块链技术最为核心的意义是参与方之间建立数据信用。
比特币是区块链技术的第一个应用，但区块链技术的应用领域远不止金融行业。通信业者同样投以热情，特别是电信运营商领域，区块链技术正在成为新宠。区块链技术可以理解为类似TCP/IP协议这样的一种网络基础设施的角色，将是支撑未来以对等开放为特征的新型互联网业态的关键设施要素之一，进而影响到众多行业领域的具体应用形态，就像Web带给全世界各行业的影响一样。
在通信领域，传统方式下的信息都是通过点对点传输来完成，这使得追踪者可以通过追踪信息传输的路径来拦截信息，这就带来了一个安全问题，由此也就产生了保障信息传输路径绝对安全的迫切需求。区块链技术的原理可能正好可以帮助解决这一问题。区块链在通信领域的全新应用可以完全改变信息传输的渠道，从根本上解决信息传递的路径安全问题，区块链技术为未来通信信息传递模式的改变打开了一扇大门。
实施例一。
如图1所示：本申请提供了一种基于区块链的大数据交易方法，具体包括以下步骤。
S1：参与交易的计算方、数据方以及模型方将各自的交易数据写入区块链网络中以进行上链操作，并在区块链网络中标识出各自的特征类型。
其中，所述特征类型具体包括：节点类型、资源信息、文件类型和/或文件路径。
进一步地，当为计算方时，所述特征类型还包括：参与计算的cpu类型、用于存储memory类型、参与计算的算法类型如Java、C++等常用计算机语言、参与计算的入参input和参与计算的出参output等等。
当为数据方时，所述特征类型主要包括：参与存储数据的节点的类型、用于存储数据的数据结构或者存储数据的实际路径信息等。
当为模型方时，所述特征类型主要包括：融合了数据方数据和计算方算力构建的模型的节点的类型以及用于搭载所述模型如深度学习网络模型或决策树模型的计算机或者其他电子设备的具体物理位置信息等等。
具体地，所述上链操作包括：获取用户发送的数据上链请求，数据上链请求包括待上链数据；将待上链数据更新到预先构建的内存数据库中，并向用户发送数据上链受理反馈信息；按照预设的时间周期读取内存数据库，检测内存数据库中出现更新的数据，并将出现更新的数据按照数据更新时刻的先后顺序写入区块链，其中，数据更新时刻越早，越先写入区块链。
可选地，将待上链数据更新到预先构建的内存数据库中，包括：将待上链数据更新到预先构建的内存数据库中，并为内存数据库中每一个出现更新的数据标记时间戳，时间戳为对应的数据更新时刻。
可选地，在将所述待上链数据更新到预先构建的内存数据库中之后，还包括：为所述待上链数据分配一个上链状态标记位，并将上链状态标记位初始化为第一数值。
相应地，在将出现更新的数据按照数据更新时刻写入区块链之后，还包括：将所述上链状态标记位由所述第一数值变更为第二数值。
其中，第一数值不同于所述第二数值。
可选地，在为待上链数据分配一个上链状态标记位之后，还包括：当接收到所述客户端发送的上链进度查询请求时，读取所述上链状态标记位；若读取到上链状态标记位为第一数值，则向客户端发送数据上链未完成的指示信息；若读取到上链状态标记位为第二数值，则向用户发送数据上链已完成的指示信息。
可选地，在将待上链数据更新到预先构建的内存数据库中之前，还包括：将内存数据库中与待上链数据对应的原始数据转存到预先构建的原始数据序列中。
在将出现更新的数据按照数据更新时刻的先后顺序写入区块链之后，还包括：若出现更新的数据写入区块链失败，则读取所述原始数据序列，并将内存数据库中出现更新的数据还原为原始数据序列中记录的相应原始数据。
S2：所述参与交易的计算方、数据方以及模型方在区块链上各自的节点进行交易广播。
其中，交易广播是将交易信息在区块链网络中“广播”，并由节点验证即确认的过程。
具体地，所述S2具体为：参与交易的数据方广播数据需求；参与交易的模型方广播风控需求以及风险识别需求；参与交易的计算方广播算力需求。
具体限定了参与交易的计算方、数据方以及模型方在区块链上各自的节点进行交易广播的内容，明确了参与交易的三方各自的需求，通过区块链广播的形式广播三方交易需求，可以更快的达成一致促成交易。
区块链的数据需求是指区块链技术与数据密不可分，因为区块链技术一定是数字化的，与数据密不可分。区块链存储的哈希值，都是数据被加密后呈现的。
在区块链的区块里打包的哈希，可能是各类数据相关的密码学内容，可以是用户买东西的账单，转账的账单，也可以是用户上传的一张图片的版权哈希。
风控需求具体是指：用户的实际对于风险接受程度的一个范围。由于互联网金融本身的属性，其借贷客户均来自于互联网，和传统小贷业务线下审批相比，存在更多的风险。其中一些无法从银行获得救命粮草的企业或个人，转向互联网金融寻求高息融资，但小微企业经营困难的背景下，这些企业或个人还款压力倍增，因此一些互联网金融平台的坏账率出现了快速增加的态势。风控需求在本公开中可以特指用户对于区块链网络中的交易节点的互联网金融平台/账户的坏账率。
算力需求具体是指：区块链网络中计算方完成交易运算所能提供的计算能力，比如完成一笔交易至少需要使用30台搭载不低于一定运算能力cpu、不低于一定数值的内存的计算机一期计算以完成用户的计算实际需求。
明确了参与交易的计算方、数据方以及模型方在区块链网络中的特征类型，使参与交易的三方信息在区块链网络中公开可见，透明化的信息使将要进行的交易更为安全。
S3：启动数据任务，选择模型方和计算方进行模型训练。
模型方提供的模型训练可以基于决策树或深度学习等常用的模型训练方式实现，以下仅以深度学习进行区块链共识的模型训练方法详解本公开的技术方案。
交易主体采集文件信息存储到文件系统后返回统一资源定位符，主体构造成数据采集交易，按照格式发送给节点，节点将该信息广播到相邻节点。
主体获取到存储的文件信息，对其进行标注，生成标注文件存储到文件系统后返回统一资源定位符标注，主体构造成数据标注交易，按照格式发送给节点，节点将该信息广播到相邻节点。
采用深度学习模型训练共识算法进行计算和验证，记账节点验证交易的有效性，将其放入缓存池，直到缓冲池中的数据集合A的数量m达到阈值a。
记账节点用参数和数据获取器获取链上存储的所有数据标注交易和节点缓冲池中所有数据标注交易，获取其对应的文件和标注信息，同时，获取上一个区块或者之前区块中存储的参数值，利用参数值对深度学习神经网络进行初始化，开始进行监督学习，并采用AutoML方法，自动对网络结构和参数进行调整，直到模型的预测准确度大于系统设定的阈值B。
记账节点完成模型的计算，将模型参数存储到区块头，生成第一条区块交易用于记录该节点获得记账奖励，同时将缓冲池中的交易一起进行打包成区块体，将区块头与区块体进行合并生成区块并进行全网广播，寻找其他验证节点的验证签名。
其他验证节点收到新区块的信息，共识验证器对其进行验证;当采用的深度学习共识模型时，验证节点将利用参数和数据获取器获取链上存储的所有数据标注交易，获取其中"URI文件+URI标注"对应的文件和标注信息，同时，获取该区块中存储的参数值，利用参数值对深度学习神经网络进行初始化后对文件进行预测，并与标准信息进行对比，计算该模型的正确度阈值Y；如果达到阈值Y要求，验证节点对其进行签名，并返回记账节点。
S4：获得以数据方的数据为基础的、以计算方进行计算、以模型方的模型进行训练的训练结果。
S5：根据所述训练结果进行交易。
进一步地，所述参与交易的计算方、数据方以及模型方通过不少于一个载体的形式实现。
可以是一个载体的形式实现，也可以是多个载体的形式实现，本申请的基于区块链的大数据交易方法实现形式灵活多样。
对于计算方而言，所述载体可以体现为参与计算的计算机的具体配置，搭载了主频为多少Ghz的cpu，或搭载了多大的内存，硬盘空间等。
对于数据方而言，所述载体可以体现为存储数据的数据结构，或存储数据的硬盘或者内存资源。
对于模型方而言，所述载体可以体现为调用的计算模型例如深度学习或决策树等等所在计算机上构建的软件模型程序，以及搭载了所述用于深度学习或决策模型的软件模型程序的计算机实体硬件设备。
所述S3之前，还包括：所述参与交易的计算方、数据方以及模型方通过验证广播以证明自身性能良好。
参与交易的计算方、数据方以及模型方通过广播证明自身性能良好，能够满足交易的需求，由于是通过广播的形式，区块链中的所有节点都需要进行验证，交易的安全性和可靠性更能得到保证。
具体地，所述参与交易的计算方、数据方以及模型方通过验证广播以证明自身性能良好具体为：参与交易的数据方通过在区块链中的节点向区块链中的其他节点广播自身数据曾被用于多次训练，以证明所述数据方的数据识别率高、数据集好；参与交易的计算方通过在区块链中的节点向区块链中的其他节点广播自身计算历史，以证明所述计算方对于任务的承载能力高；模型方在区块链中的节点向区块链中的其他节点广播自身模型评分数据，以证明所述模型方为优选模型。
应用本申请的基于区块链的大数据交易方法，计算方、数据方以及模型方容易找到目标对象、实现合作，通过广播自身历史数据，实现定制化选择。
具体限定了参与交易的计算方、数据方以及模型方进行广播的内容，更能优选出适合参与交易的三方进行交易。
实施例二。
如图2所示：本公开还提供了一种基于区块链的大数据交易装置，包括以下模块。
上链模块201，用于参与交易的计算方、数据方以及模型方将各自的交易数据写入区块链网络中以进行上链操作，并在区块链网络中标识出各自的特征类型。
其中，所述特征类型具体包括：节点类型、资源信息、文件类型和/或文件路径。
进一步地，当为计算方时，所述特征类型还包括：参与计算的cpu类型、用于存储memory类型、参与计算的算法类型如Java、C++等常用计算机语言、参与计算的入参input和参与计算的出参output等等。
具体地，所述上链操作包括：获取用户发送的数据上链请求，数据上链请求包括待上链数据；将待上链数据更新到预先构建的内存数据库中，并向用户发送数据上链受理反馈信息；按照预设的时间周期读取内存数据库，检测内存数据库中出现更新的数据，并将出现更新的数据按照数据更新时刻的先后顺序写入区块链，其中，数据更新时刻越早，越先写入区块链。
可选地，将待上链数据更新到预先构建的内存数据库中，包括：将待上链数据更新到预先构建的内存数据库中，并为内存数据库中每一个出现更新的数据标记时间戳，时间戳为对应的数据更新时刻。
可选地，在将所述待上链数据更新到预先构建的内存数据库中之后，还包括：为所述待上链数据分配一个上链状态标记位，并将上链状态标记位初始化为第一数值。
相应地，在将出现更新的数据按照数据更新时刻写入区块链之后，还包括：将所述上链状态标记位由所述第一数值变更为第二数值。
其中，第一数值不同于所述第二数值。
可选地，在为待上链数据分配一个上链状态标记位之后，还包括：当接收到所述客户端发送的上链进度查询请求时，读取所述上链状态标记位；若读取到上链状态标记位为第一数值，则向客户端发送数据上链未完成的指示信息；若读取到上链状态标记位为第二数值，则向用户发送数据上链已完成的指示信息。
可选地，在将待上链数据更新到预先构建的内存数据库中之前，还包括：将内存数据库中与待上链数据对应的原始数据转存到预先构建的原始数据序列中。
在将出现更新的数据按照数据更新时刻的先后顺序写入区块链之后，还包括：若出现更新的数据写入区块链失败，则读取所述原始数据序列，并将内存数据库中出现更新的数据还原为原始数据序列中记录的相应原始数据。
交易广播模块202，用于所述参与交易的计算方、数据方以及模型方在区块链上各自的节点进行交易广播。
其中，交易广播的英文名为Transaction Broadcast，是区块链中与交易过程相关的词汇。将交易信息在区块链网络中“广播”，并由节点验证即确认的过程。
具体地，所述交易广播模块202具体用于：参与交易的数据方广播数据需求；参与交易的模型方广播风控需求以及风险识别需求；参与交易的计算方广播算力需求。
具体限定了参与交易的计算方、数据方以及模型方在区块链上各自的节点进行交易广播的内容，明确了参与交易的三方各自的需求，通过区块链广播的形式广播三方交易需求，可以更快的达成一致促成交易。
区块链的数据需求是指区块链技术与数据密不可分，因为区块链技术一定是数字化的，与数据密不可分。区块链存储的哈希值，都是数据被加密后呈现的。
在区块链的区块里打包的哈希，可能是各类数据相关的密码学内容，可以是用户买东西的账单，转账的账单，也可以是用户上传的一张图片的版权哈希。
风控需求具体是指：用户的实际对于风险接受程度的一个范围。由于互联网金融本身的属性，其借贷客户均来自于互联网，和传统小贷业务线下审批相比，存在更多的风险。其中一些无法从银行获得救命粮草的企业或个人，转向互联网金融寻求高息融资，但小微企业经营困难的背景下，这些企业或个人还款压力倍增，因此一些互联网金融平台的坏账率出现了快速增加的态势。风控需求在本公开中可以特指用户对于区块链网络中的交易节点的互联网金融平台/账户的坏账率。
算力需求具体是指：区块链网络中计算方完成交易运算所能提供的计算能力，比如完成一笔交易至少需要使用30台搭载不低于一定运算能力cpu、不低于一定数值的内存的计算机一期计算以完成用户的计算实际需求。
明确了参与交易的计算方、数据方以及模型方在区块链网络中的特征类型，使参与交易的三方信息在区块链网络中公开可见，透明化的信息使将要进行的交易更为安全。
训练模块203，用于启动数据任务，选择模型方和计算方进行模型训练。
模型方提供的模型训练可以基于决策树或深度学习等常用的模型训练方式实现，以下仅以深度学习进行区块链共识的模型训练方法详解本公开的技术方案：交易主体采集文件信息存储到文件系统后返回统一资源定位符，主体构造成数据采集交易，按照格式发送给节点，节点将该信息广播到相邻节点;主体获取到存储的文件信息，对其进行标注，生成标注文件存储到文件系统后返回统一资源定位符标注，主体构造成数据标注交易，按照格式发送给节点，节点将该信息广播到相邻节点;采用深度学习模型训练共识算法进行计算和验证，记账节点验证交易的有效性，将其放入缓存池，直到缓冲池中的数据集合A的数量m达到阈值a;记账节点用参数和数据获取器获取链上存储的所有数据标注交易和节点缓冲池中所有数据标注交易，获取其对应的文件和标注信息，同时，获取上一个区块或者之前区块中存储的参数值，利用参数值对深度学习神经网络进行初始化，开始进行监督学习，并采用AutoML方法，自动对网络结构和参数进行调整，直到模型的预测准确度大于系统设定的阈值B;记账节点完成模型的计算，将模型参数存储到区块头，生成第一条区块交易用于记录该节点获得记账奖励，同时将缓冲池中的交易一起进行打包成区块体，将区块头与区块体进行合并生成区块并进行全网广播，寻找其他验证节点的验证签名;其他验证节点收到新区块的信息，共识验证器对其进行验证;当采用的深度学习共识模型时，验证节点将利用参数和数据获取器获取链上存储的所有数据标注交易，获取其中"URI文件+URI标注"对应的文件和标注信息，同时，获取该区块中存储的参数值，利用参数值对深度学习神经网络进行初始化后对文件进行预测，并与标准信息进行对比，计算该模型的正确度阈值Y；如果达到阈值Y要求，验证节点对其进行签名，并返回记账节点。
训练结果获取模块204，用于获得以数据方的数据为基础的、以计算方进行计算、以模型方的模型进行训练的训练结果。
本公开所述的上链模块201依次与所述交易广播模块202、所述训练模块203以及所述训练结果获取模块204相连接。
实施例三。
本公开还能够提供一种计算机存储介质，其上存储有计算机程序，计算机程序被处理器执行时用于实现上述的基于区块链的大数据交易方法的步骤。
可选的，本申请涉及的存储介质可以可读存储介质。进一步可选的，所述存储介质可以是非易失性的，也可以是易失性的。所述存储介质可以包括：能够携带所述计算机程序代码的任何实体或装置、记录介质、U盘、移动硬盘、磁碟、光盘、计算机存储器、只读存储器（ROM，Read-Only Memory）。
进一步地，所述存储介质可主要包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需的应用程序等；存储数据区可存储根据区块链节点的使用所创建的数据等。
实施例四。
本公开还提供了一种电子设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，处理器执行计算机程序时实现上述的基于区块链的大数据交易方法的步骤。
图3为一个实施例中电子设备的内部结构示意图。如图3所示，该电子设备包括通过系统总线连接的处理器、存储介质、存储器和网络接口。其中，该计算机设备的存储介质存储有操作系统、数据库和计算机可读指令，数据库中可存储有控件信息序列，该计算机可读指令被处理器执行时，可使得处理器实现一种基于区块链的大数据交易方法。该电设备的处理器用于提供计算和控制能力，支撑整个计算机设备的运行。该计算机设备的存储器中可存储有计算机可读指令，该计算机可读指令被处理器执行时，可使得处理器执行一种基于区块链的大数据交易方法。该计算机设备的网络接口用于与终端连接通信。本领域技术人员可以理解，图3中示出的结构，仅仅是与本申请方案相关的部分结构的框图，并不构成对本申请方案所应用于其上的计算机设备的限定，具体的计算机设备可以包括比图中所示更多或更少的部件，或者组合某些部件，或者具有不同的部件布置。
该电子设备包括但不限于智能电话、计算机、平板电脑、可穿戴智能设备、人工智能设备、移动电源等。
所述处理器在一些实施例中可以由集成电路组成，例如可以由单个封装的集成电路所组成，也可以是由多个相同功能或不同功能封装的集成电路所组成，包括一个或者多个中央处理器（Central Processing unit，CPU）、微处理器、数字处理芯片、图形处理器及各种控制芯片的组合等。所述处理器是所述电子设备的控制核心（Control Unit），利用各种接口和线路连接整个电子设备的各个部件，通过运行或执行存储在所述存储器内的程序或者模块（例如执行远端数据读写程序等），以及调用存储在所述存储器内的数据，以执行电子设备的各种功能和处理数据。
所述总线可以是外设部件互连标准（peripheral component interconnect，简称PCI）总线或扩展工业标准结构（extended industry standard architecture，简称EISA）总线等。该总线可以分为地址总线、数据总线、控制总线等。所述总线被设置为实现所述存储器以及至少一个处理器等之间的连接通信。
图3仅示出了具有部件的电子设备，本领域技术人员可以理解的是，图3示出的结构并不构成对所述电子设备的限定，可以包括比图示更少或者更多的部件，或者组合某些部件，或者不同的部件布置。
例如，尽管未示出，所述电子设备还可以包括给各个部件供电的电源（比如电池），优选地，电源可以通过电源管理装置与所述至少一个处理器逻辑相连，从而通过电源管理装置实现充电管理、放电管理、以及功耗管理等功能。电源还可以包括一个或一个以上的直流或交流电源、再充电装置、电源故障检测电路、电源转换器或者逆变器、电源状态指示器等任意组件。所述电子设备还可以包括多种传感器、蓝牙模块、Wi-Fi模块等，在此不再赘述。
进一步地，所述电子设备还可以包括网络接口，可选地，所述网络接口可以包括有线接口和/或无线接口（如WI-FI接口、蓝牙接口等），通常用于在该电子设备与其他电子设备之间建立通信连接。
可选地，该电子设备还可以包括用户接口，用户接口可以是显示器（Display）、输入单元（比如键盘（Keyboard）），可选地，用户接口还可以是标准的有线接口、无线接口。可选地，在一些实施例中，显示器可以是LED显示器、液晶显示器、触控式液晶显示器以及OLED（Organic Light-Emitting Diode，有机发光二极管）触摸器等。其中，显示器也可以适当的称为显示屏或显示单元，用于显示在电子设备中处理的信息以及用于显示可视化的用户界面。
进一步地，所述计算机可用存储介质可主要包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需的应用程序等；存储数据区可存储根据区块链节点的使用所创建的数据等。
在本申请所提供的几个实施例中，应该理解到，所揭露的设备，装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，所述模块的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式。
所述作为分离部件说明的模块可以是或者也可以不是物理上分开的，作为模块显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部模块来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能模块可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用硬件加软件功能模块的形式实现。
以上对本公开的实施例进行了描述。但是，这些实施例仅仅是为了说明的目的，而并非为了限制本公开的范围。本公开的范围由所附权利要求及其等价物限定。不脱离本公开的范围，本领域技术人员可以做出多种替代和修改，这些替代和修改都应落在本公开的范围之内。

Claims (20)

  1. 一种基于区块链的大数据交易方法，包括：
    参与交易的计算方、数据方以及模型方将各自的交易数据写入区块链网络中以进行上链操作，并在区块链网络中标识出各自的特征类型；
    所述参与交易的计算方、数据方以及模型方在区块链上各自的节点进行交易广播；
    启动数据任务，选择模型方和计算方进行模型训练；
    获得以数据方的数据为基础的、以计算方进行计算、以模型方的模型进行训练的训练结果；
    根据所述训练结果进行交易。
  2. 根据权利要求1所述的方法，其中，所述上链操作具体包括：
    获取用户发送的数据上链请求，数据上链请求包括待上链数据；
    将待上链数据更新到预先构建的内存数据库中，并向用户发送数据上链受理反馈信息；
    按照预设的时间周期读取内存数据库，检测内存数据库中出现更新的数据，并将出现更新的数据按照数据更新时刻的先后顺序写入区块链。
  3. 根据权利要求1所述的方法，其中，所述参与交易的计算方、数据方以及模型方在区块链上各自的节点进行交易广播具体为：
    参与交易的数据方广播数据需求；
    参与交易的模型方广播风控需求以及风险识别需求；
    参与交易的计算方广播算力需求。
  4. 根据权利要求1所述的方法，其中，所述特征类型具体包括：节点类型、资源信息、文件类型和/或文件路径。
  5. 根据权利要求1所述的方法，其中，所述参与交易的计算方、数据方以及模型方通过不少于一个载体的形式实现。
  6. 根据权利要求1~5任一项中所述的方法，其中，所述启动数据任务，选择模型方和计算方进行模型训练之前，还包括：所述参与交易的计算方、数据方以及模型方通过验证广播以证明自身性能良好。
  7. 根据权利要求6所述的方法，其中，所述参与交易的计算方、数据方以及模型方通过验证广播以证明自身性能良好具体为：
    参与交易的数据方通过在区块链中的节点向区块链中的其他节点广播自身数据曾被用于多次训练，以证明所述数据方的数据识别率高、数据集好；
    参与交易的计算方通过在区块链中的节点向区块链中的其他节点广播自身计算历史，以证明所述计算方对于任务的承载能力高；
    模型方在区块链中的节点向区块链中的其他节点广播自身模型评分数据，以证明所述模型方为优选模型。
  8. 一种基于区块链的大数据交易装置，包括：
    上链模块，用于参与交易的计算方、数据方以及模型方将各自的交易数据写入区块链网络中以进行上链操作，并在区块链网络中标识出各自的特征类型；
    交易广播模块，用于所述参与交易的计算方、数据方以及模型方在区块链上各自的节点进行交易广播；
    训练模块，用于启动数据任务，选择模型方和计算方进行模型训练；
    训练结果获取模块，用于获得以数据方的数据为基础的、以计算方进行计算、以模型方的模型进行训练的训练结果。
  9. 一种电子设备，包括存储器、处理器，所述存储器上存储有可在处理器上运行的计算机程序，其中，所述处理器执行所述计算机程序时实现以下方法：
    参与交易的计算方、数据方以及模型方将各自的交易数据写入区块链网络中以进行上链操作，并在区块链网络中标识出各自的特征类型；
    所述参与交易的计算方、数据方以及模型方在区块链上各自的节点进行交易广播；
    启动数据任务，选择模型方和计算方进行模型训练；
    获得以数据方的数据为基础的、以计算方进行计算、以模型方的模型进行训练的训练结果；
    根据所述训练结果进行交易。
  10. 根据权利要求9所述的电子设备，其中，所述上链操作具体包括：
    获取用户发送的数据上链请求，数据上链请求包括待上链数据；
    将待上链数据更新到预先构建的内存数据库中，并向用户发送数据上链受理反馈信息；
    按照预设的时间周期读取内存数据库，检测内存数据库中出现更新的数据，并将出现更新的数据按照数据更新时刻的先后顺序写入区块链。
  11. 根据权利要求9所述的电子设备，其中，所述参与交易的计算方、数据方以及模型方在区块链上各自的节点进行交易广播具体为：
    参与交易的数据方广播数据需求；
    参与交易的模型方广播风控需求以及风险识别需求；
    参与交易的计算方广播算力需求。
  12. 根据权利要求9所述的电子设备，其中，所述特征类型具体包括：节点类型、资源信息、文件类型和/或文件路径。
  13. 根据权利要求9所述的电子设备，其中，所述参与交易的计算方、数据方以及模型方通过不少于一个载体的形式实现。
  14. 根据权利要求9~13任一项中所述的电子设备，其中，所述启动数据任务，选择模型方和计算方进行模型训练之前，还包括：所述参与交易的计算方、数据方以及模型方通过验证广播以证明自身性能良好。
  15. 一种计算机存储介质，其上存储有计算机程序，其中，所述程序被处理器执行时用于实现基于区块链的大数据交易方法，所述方法包括：
    参与交易的计算方、数据方以及模型方将各自的交易数据写入区块链网络中以进行上链操作，并在区块链网络中标识出各自的特征类型；
    所述参与交易的计算方、数据方以及模型方在区块链上各自的节点进行交易广播；
    启动数据任务，选择模型方和计算方进行模型训练；
    获得以数据方的数据为基础的、以计算方进行计算、以模型方的模型进行训练的训练结果；
    根据所述训练结果进行交易。
  16. 根据权利要求15所述的计算机存储介质，其中，所述上链操作具体包括：
    获取用户发送的数据上链请求，数据上链请求包括待上链数据；
    将待上链数据更新到预先构建的内存数据库中，并向用户发送数据上链受理反馈信息；
    按照预设的时间周期读取内存数据库，检测内存数据库中出现更新的数据，并将出现更新的数据按照数据更新时刻的先后顺序写入区块链。
  17. 根据权利要求15所述的计算机存储介质，其中，所述参与交易的计算方、数据方以及模型方在区块链上各自的节点进行交易广播具体为：
    参与交易的数据方广播数据需求；
    参与交易的模型方广播风控需求以及风险识别需求；
    参与交易的计算方广播算力需求。
  18. 根据权利要求15所述的计算机存储介质，其中，所述特征类型具体包括：节点类型、资源信息、文件类型和/或文件路径。
  19. 根据权利要求15所述的计算机存储介质，其中，所述参与交易的计算方、数据方以及模型方通过不少于一个载体的形式实现。
  20. 根据权利要求15~19任一项中所述的计算机存储介质，其中，所述启动数据任务，选择模型方和计算方进行模型训练之前，还包括：所述参与交易的计算方、数据方以及模型方通过验证广播以证明自身性能良好。
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