A Blockchain Consensus Method Based on Deep Learning Training Tasks

A technology of deep learning and blockchain, applied in the field of blockchain consensus based on deep learning training tasks, to achieve the effects of ensuring randomness, protecting interests, and solving deep learning model training problems

Active Publication Date: 2019-09-24
山东浪潮数字能源科技有限公司
View PDF4 Cites 1 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The technical task of the present invention is to provide a block chain consensus method based on deep learning training tasks that solves the problem of deep learning model training

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A Blockchain Consensus Method Based on Deep Learning Training Tasks
  • A Blockchain Consensus Method Based on Deep Learning Training Tasks
  • A Blockchain Consensus Method Based on Deep Learning Training Tasks

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0043] A blockchain consensus method based on deep learning training tasks, including,

[0044] All blockchain participating nodes jointly maintain a ledger record. Any node can publish a transaction and distribute it to each node through the P2P consensus network. There will be deep learning task nodes in the P2P consensus network, and the deep learning task will be distributed to all Participating nodes and bookkeeping nodes combine deep learning tasks to reach consensus through the inter-node consensus mechanism and complete transaction confirmation.

[0045] The participating nodes are responsible for publishing inter-node transactions into the blockchain; the deep learning task nodes are also participating nodes of the blockchain and are responsible for publishing deep learning tasks; the P2P consensus network does not have a central node. A network system for user groups to exchange messages; the bookkeeping nodes are responsible for the bookkeeping tasks of distributed ...

Embodiment 2

[0061] Such as figure 1 As shown in , all blockchain participating nodes jointly maintain a ledger record, and any node can publish a transaction (transaction), and distribute it to each node through the P2P consensus network. There will be deep learning task nodes in the P2P consensus network, which will The deep learning tasks are distributed to all participating nodes, and the accounting nodes combine the deep learning tasks to reach a consensus through the inter-node consensus mechanism to complete the transaction confirmation. in,

[0062] The participating nodes are responsible for publishing inter-node transactions into the block chain; the deep learning task nodes are also participating nodes of the block chain and are responsible for publishing deep learning tasks; the P2P consensus network has no central node, through A network system for user groups to exchange messages; the bookkeeping node is responsible for the bookkeeping tasks of the distributed ledger, which ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a block chain consensus method based on deep learning training tasks, including that all block chain participating nodes jointly maintain a ledger record, any node can issue a transaction, and distribute it to each node through a P2P consensus network, There will be deep learning task nodes in the P2P consensus network, and the deep learning tasks will be distributed to all participating nodes. The accounting nodes will combine the deep learning tasks to reach a consensus through the inter-node consensus mechanism and complete the transaction confirmation. The present invention combines artificial intelligence and deep learning technology, adds the workload proof of the block chain public chain mining process into the deep learning task, effectively solves the traditional energy waste problem of obtaining bookkeeping rights through hash challenges, and to a certain extent It solves the problem of deep learning model training; at the same time, the workload proof of the mining process retains part of the challenge using the hash algorithm SHA256, which ensures the randomness of the mining process and avoids the monopoly of bookkeeping rights by deep learning giants.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence, in particular to a blockchain consensus method based on deep learning training tasks. Background technique [0002] In recent years, electronic currencies such as Bitcoin have become popular. Unlike most currencies, electronic currencies such as Bitcoin do not rely on specific currency issuers, and use many nodes in the entire P2P network to complete the transaction records of distributed ledgers. The security of each link of currency circulation is ensured, and the decentralized characteristics and consensus algorithm of P2P are used to ensure that the currency value cannot be artificially manipulated by mass production of bitcoins, forming a decentralized payment system. [0003] The core technology supporting Bitcoin is blockchain technology, also known as "distributed ledger technology", which is a technical solution for decentralization and collective maintenance of distribut...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Patents(China)
IPC IPC(8): H04L29/08H04L12/18G06Q40/00G06Q40/04G06Q20/38G06N3/04
CPCH04L12/185H04L67/1044G06Q20/382G06Q20/3827G06Q20/387G06Q20/389G06Q40/04G06Q40/128G06N3/045
Inventor 孙善宝于治楼徐驰
Owner 山东浪潮数字能源科技有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap