Context Uncertainty Elimination via QoX Adaptive Management
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Solution Overview
Problem
Heterogeneous context-aware networks face inefficiencies due to redundant data and uncertainty issues like incompleteness, inaccuracy, and inconsistency in context information, leading to reduced resource efficiency and user satisfaction, as existing technologies lack dynamic adaptive processing mechanisms.
Innovation Solution
A context information uncertainty elimination system based on Quality of Experience (QoX) adaptive management, utilizing a hierarchical combination of Quality of Device (QoD), Quality of Context (QoC), and Quality of Service (QoS) indexes, with error correction mechanisms in the context application layer, to improve credibility and adaptability by selectively eliminating uncertainty through real-time feedback and algorithm adaptation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If direct uncertainty elimination for multi-source context information is performed, then computing resource efficiency is improved, but the system makes inappropriate reasoning decisions and affects service quality
Solution Approach 1:
The patent changes the parameters of context information by introducing quality indexes (completeness, accuracy, consistency) and dynamically adjusting elimination strategies based on these parameters. The system evaluates multiple quality dimensions and selects appropriate elimination methods accordingly, preventing inappropriate reasoning decisions while maintaining computing efficiency.
Solution Approach 2:
The patent implements a feedback mechanism where the system continuously monitors the quality of context information and adjusts its uncertainty elimination strategy in real-time. By feedback on quality indexes and reasoning outcomes, the system optimizes its processing approach to avoid incorrect decisions while efficient resource utilization.
2Device complexity
If simple and fixed uncertainty elimination mechanisms are adopted, then system complexity is reduced, but the system cannot meet dynamic requirements of complex and changeable uncertainty context information processing
Solution Approach 1:
The patent transforms fixed uncertainty elimination mechanisms into dynamic adaptive mechanisms. The system continuously adjusts its processing strategies based on real-time quality assessments of context information, enabling it to handle complex and changeable uncertainty scenarios effectively while maintaining manageable complexity through structured quality-index-based decision making.
3Loss of information
If multi-source context information is collected from heterogeneous context-aware networks, then information completeness is improved, but redundancy increases and resource efficiency decreases
Solution Approach 1:
The patent applies local quality assessment by evaluating different quality indexes (completeness, accuracy, consistency) for different sources and types of context information. The system selectively processes and eliminates uncertainties based on local quality characteristics of each information source, maintaining information completeness while optimizing resource efficiency through targeted processing.
Data Source
AI summary
The invention relates to a QoX adaptive management-based context information uncertainty elimination system and a working method thereof. The system includes a sensor module, a context information acquisition and modeling module, a context threshold information preset module, an original context information detection module, an adaptive management module, an uncertainty elimination module, a context information correlation analysis module, an original context information flow reconstruction module, a composite context information flow module, a fusion and reasoning module, a context application layer adjustment module and a knowledge base.