High-risk personnel analysis and recognition method based on big data platform

Abstract

The invention discloses a high-risk personnel analysis and recognition method based on a big data platform. The method comprises the steps of establishing a high-risk personnel portrayal; conducting modeling on high-risk personnel behavior properties according to a high-risk personnel feature library; conducting data simulation on a model through the big data platform, and recognizing suspected high-risk personnel; according to data correlation among the high-risk personnel, establishing a high-risk personnel relational network on the big data platform, analyzing club relationships of the high-risk personnel through a club network analysis method, accordingly determining roles played by the high-risk personnel in a club, and thus classifying extents of injury of the high-risk personnel. Compared with a traditional method, the high-risk personnel analysis and recognition method based on the big data platform introduces club network properties of the high-risk personnel, and accordingly 'key personnel and key monitoring' is achieved; processing capability of large-scale data of the big data platform is utilized, the analysis and recognition efficiency is effectively improved, and analysis results are updated dynamically; special precaution management of this kind of people is achieved, the crime rate of the whole society is reduced, and the social harmonious stability is improved.

Description

technical field [0001] The present invention relates to the field of computer programs, in particular to a method for analyzing and identifying high-risk personnel based on a big data platform. Background technique [0002] "Public security high-risk personnel" refers to groups of people who have existing or potential hazards to social order and public safety. Identifying and monitoring such groups requires the screening and analysis of a large number of ordinary people; at the same time, it is a huge and complicated task to monitor the people who are identified as "high-risk personnel" after screening. Usually, the screening and monitoring of such personnel mainly rely on reports from the masses and records kept by the public security department. This method is often inefficient, ineffective, and consumes a lot of manpower. [0003] With the digitization and digitalization of a large number of social records, it is possible to realize the automatic screening and dynamic mo...

AI Technical Summary

Problems solved by technology

[0005] At present, there have been a large number of algorithm results in academia on big data technology and social network analysis, but these algorithms are mostly too theoretical, and have not been combined with actual application scenarios for specific optimization, especially for police crime analysis.

In addition, there is no good solution for how to combine big data technology and social network analysis methods, and reasonably display the findings, so as to better assist security personnel

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