Industrial internet data sharing optimization method based on intelligent fragmentation decision block chain

By introducing intelligent sharding decision-making and deep reinforcement learning algorithms into the industrial internet system, combined with mobile edge computing, the data sharing latency and blockchain transaction throughput are optimized, solving the problems of high computing resource consumption and low efficiency, and realizing low-latency, high-throughput data sharing.

CN117478697BActive Publication Date: 2026-06-26BEIJING UNIV OF TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
BEIJING UNIV OF TECH
Filing Date
2023-10-26
Publication Date
2026-06-26

AI Technical Summary

Technical Problem

Existing blockchain technology in industrial internet systems suffers from problems such as high computational resource consumption, low computational efficiency, poor system scalability, and insufficient security. In particular, in dynamic scenarios, the lack of initiative in sharding strategies leads to low system throughput, high latency, and increased security risks.

Method used

By introducing intelligent sharding decision-making, combining deep reinforcement learning algorithms and mobile edge computing, the data sharing latency and blockchain transaction throughput are optimized. The asynchronous dominant actor critique algorithm (A3C) is used for joint optimization, and the sharding strategy is dynamically adjusted to reduce latency and improve throughput.

Benefits of technology

In large-scale, dynamic industrial internet systems, it effectively reduces the total latency of the data sharing process, increases blockchain transaction throughput, and improves the system's computing efficiency and security.

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Abstract

The application discloses an industrial internet data sharing optimization method based on intelligent sharding decision blockchains. Through adaptive sharding decision of a blockchain system, the method can effectively break through the scalability bottleneck of the blockchain. Specifically, the method adds a blockchain sharding decision factor to an action space of a deep reinforcement learning algorithm, jointly considers sharding decision, unloading decision, block size and block interval, introduces a cloud-edge collaboration computing paradigm, models an optimization problem as a Markov decision process, and solves the optimization problem by using a deep reinforcement learning method. Simulation results show that the total delay of industrial internet data sharing and the blockchain transaction throughput obtained by the method are better than those of other methods.
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