Medical data security consensus method based on credit rating

Abstract

The invention relates to a medical data security consensus method based on credit rating, which comprises the following steps: dividing nodes into data nodes, consensus nodes and supervision nodes, and quickly verifying node identities by adopting a node identity verification mechanism; improving an inter-node distance calculation method, a fitness value of a judgment radius and a cutting method in different optimization stages, and realizing rapid division of miner credit rating based on a self-optimization credit rating division method of an improved ocean grazing feeder; meanwhile, according to the situation that the nodes participate in the block consensus, providing a credit rating mechanism, which comprises a reward and punishment mechanism, a credit value model and a credit rating division algorithm, improving the enthusiasm of the nodes participating in the block consensus, and realizing reasonable credit rating division; providing a representative node election mechanism, andpreventing malicious nodes from becoming representative nodes as much as possible; and finally, proposing a representative node consensus mechanism to improve the block consensus efficiency. Accordingto the method, malicious nodes can be eliminated, the node participation block consensus enthusiasm is improved, the transaction delay is reduced, and the consensus efficiency is improved.

Description

Technical field: [0001] The present invention relates to the technical field of block chain, and specifically refers to a medical data security consensus method based on credit rating division. Background technique: [0002] With the development of science and technology and communication technology, smart healthcare is gradually becoming an indispensable part of the future medical development by virtue of the characteristics of interactive healthcare services. Among them, the hospital information system is an important part of smart medical care, which can digitize medical data such as electronic medical records, hospitalization records, prescriptions, and private health information. While the hospital information system brings benefits such as convenient management and high efficiency to the hospital, it also brings the following new problems: First, the system data sharing is difficult, the interoperability is low, and users cannot access / store electronic medical records ...

AI Technical Summary

Problems solved by technology

While the hospital information system brings benefits such as convenient management and high efficiency to the hospital, it also brings the following new problems: First, the system data sharing is difficult, the interoperability is low, and users cannot access / store electronic medical records and hospitalization records across regions , prescriptions and other medical data; second, private medical data such as health information and electronic medical records have become an asset. When patients and doctors access and share medical data, they are subject to strict restrictions, and it takes a lot of resources and time to authorize them. Review and data verification, so as to protect the privacy of patients and prevent the leakage of medical data; third, with the rapid growth of economic strength, people's awareness of legal rights protection gradually increases, and higher requirements are put forward for medical technology and service levels, leading to medical disputes increase, requiring medical forensic identification

However, these methods do not consider the attack of malicious nodes and the consensus efficiency

[0004] To sum up, due to the large scale of medical data, existing consensus algorithms such as DPOS and PBFT in the blockchain medical system currently have problems such as difficult identification and elimination of malicious nodes and low consensus efficiency, making it difficult for existing consensus methods to be safe and efficient. Achieving consensus on medical data

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