Scientific Information Research
Keywords
scenario based services; intelligence services; multi source data aggregation; human machine hybrid enhanced intelligence; Russia-Ukraine conflict
Abstract
[Purpose/significance]Faced with the challenges of big data and artificial intelligence technology, knowledge services are undergoing profound changes, starting from the task scenarios of user needs, this paper explores the scenario-based intelligence service process that supports and matches different industries and their business scenarios. [Method/process]This paper takes the scenario-based intelligence service R-S model as the core, builds the scenario-based intelligence service solutions generation and evaluation process framework based on multi-source data aggregation, briefly describes the main contents and operation of multi-source data aggregation, and takes an organization's "Russia-Ukraine conflict" equipment information quick perception intelligence service as an example, discusses in detail the operation process and main contents from scenario service demand analysis, service element scenario, service solutions generation to service solutions evaluation. [Result/conclusion]By identifying scene elements and formalizing them, and using low-cost computational methods, then simulate human-computer interaction to establish a configuration response relationship,generate service solutions and optimize them through evaluation. The proposed scenario-based intelligence service solutions generation and evaluation process in this article can meet the needs of service users in task scenarios.
First Page
56
Last Page
68
Digital Object Identifier (DOI)
10.19809/j.cnki.kjqbyj.2024.03.005
Recommended Citation
WANG, Yuefen; DONG, Xiaoyi; and HE, Jin
(2024)
"Research on the Solutions Generation and Evaluation in Scenario-based Intelligence Service Based on Multi-source Data Aggregation,"
Scientific Information Research: Vol. 6:
Iss.
3, Article 5.
DOI: 10.19809/j.cnki.kjqbyj.2024.03.005
Available at:
https://eng.kjqbyj.com/journal/vol6/iss3/5
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