Peking University

Renmin University of China

Xun Liang

Jian Yang

Hua Chen

Yang Xue

Kongbo Li

The invention relates to a contribution incentive-based blockchain collaboration method and system, which is characterized in, comprising: 1) applying from an ordinary node to a qualification assessment node for registered qualification; 2) dividing a task into a number of subject tasks and creating corresponding subject task ledgers respectively, and creating a number of sub-tasks corresponding to the subject task in each of the subject task ledgers; 3) applying from the ordinary node to the qualification assessment node for receiving the sub-tasks; 4) by the qualification assessment node, auditing all sub-tasks which are applied for reception by the ordinary node; 5) submitting completed sub-tasks from the ordinary node to a task quality audit node; 6) by the task quality audit node, calculating contribution incentives and qualification credits for the sub-tasks completed by the ordinary node; 7) by the task quality audit node, traversing through each subject task ledger respectively, for finding out a block with the highest content quality evaluation score for each completed sub-task, and giving reward to the ordinary node corresponding to the block. The invention can be widely used in the technical field of blockchain.

Contribution incentive-based blockchain collaboration method and system

The present application discloses a method for monitoring abnormal state of Internet information. The method includes obtaining frequency data for current date common words appearing on the current date web pages, combining with a hot words dictionary that Internet users focuses on to determine a list of current date keywords related to the Internet information, determining a weight of each current date keyword, determining an abnormal threshold of the current date keywords, and detecting an abnormal level of the current date keywords to determine current date hot Internet information. The disclose method further calculates an abnormal level of keywords by monitoring the change in the hot words frequency in the Internet information, and generates warning for the abnormal level of hot words frequency change, which allows the Internet users to respond at the first moment.

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Beijing, China

Xun Liang