Shared computing power data processing method and system based on block chain and storage medium

Abstract

The invention provides a shared computing power data processing method and system based on a block chain and a storage medium, and the method comprises the following steps: sending a task request to aserver node according to the occupation condition of computing power resources; obtaining task blocks which are executed in parallel; executing a computing task according to the task blocks, generating a computing result, and generating a new block in a block chain according to the actual workload proof; wherein the actual workload proof is the workload which can be completed by computing power resources under the condition that the computing task is not executed; and verifying the computing result to obtain a verification result, and returning the verification result to the server node; idlecomputing nodes in a distributed network are fully and reasonably utilized, optimal distribution is guaranteed, powerful decentralized computing power resources are provided for high-strength computing type tasks, and the data processing efficiency is higher; Self control of the non-differential computing nodes is achieved. The invention is more practical and can be widely applied to the technical field of distributed computing networks.

Description

technical field [0001] The invention relates to the technical field of distributed computing networks, in particular to a blockchain-based shared computing power data processing method, system and storage medium. Background technique [0002] Distributed computing is a computing method, which is relative to centralized computing. With the development of computing technology, some applications require huge computing power to complete. If centralized computing is used, it will take a long time to complete. Distributed computing breaks down the application into many small parts, which are distributed to multiple computers for processing. This can save the overall calculation time and greatly improve the calculation efficiency. [0003] In the current production and life, when faced with high-intensity computing tasks, a centralized computing method is usually adopted, and the central computer needs to perform all computing tasks; therefore, when there are many terminals, the ...

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Problems solved by technology

[0003] In the current production and life, when faced with high-intensity computing tasks, a centralized computing method is usually adopted, and the central computer needs to perform all computing tasks; therefore, when there are many terminals, the response speed will slow down; if the terminal Users have different needs, and it is not only difficult but also inefficient to handle each user's programs and resources individually on a centralized system

In addition, some distributed computing networks are also used in the prior art to complete high-intensity computing tasks; however, they simply use a balanced distribution of computing tasks, regardless of the performance status of the computing nodes and the occupancy of computing resources of the nodes. Under the same conditions, it will also bring huge pressure to a single computing node in the network

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