Design of a community risk prevention big data platform with multi-agent collaboration

Jiangtao Wang; Yaming Luo; Nan Jia

doi:10.1117/12.2641465

10 November 2022 Design of a community risk prevention big data platform with multi-agent collaboration

Jiangtao Wang, Yaming Luo, Nan Jia

Proceedings Volume 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022); 123481O (2022) https://doi.org/10.1117/12.2641465
Event: 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 2022, Zhuhai, China

Abstract

Community risk prevention is the foundation of social stability and harmony. Meanwhile, the research about community risk prevention big data platform is crucial for community safety management. On this basis, the design research of community risk prevention big data platform with multi-agent collaboration was carried out. First of all, the current situation of the construction and application of big data platform in grassroots communities in China was fully investigated, its shortcomings of poor multi-agent linkage and low intelligence degree was clarified, and the research direction of this paper was further clarified. Then, on the basis of analyzing the connotation and correlation of multi-subject community and multi-dimensional data of community risk, the functional requirements of community risk prevention platform were analyzed. Finally, the design method of multi-agent linkage big data platform for community risk prevention was proposed from the aspects of main structure, module division and design. Theoretical and methodological support for community management were provided by this study.

Citation Download Citation

Jiangtao Wang, Yaming Luo, and Nan Jia "Design of a community risk prevention big data platform with multi-agent collaboration", Proc. SPIE 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 123481O (10 November 2022); https://doi.org/10.1117/12.2641465

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