Enterprise AI Analysis
An Adaptive Cloud-Edge Collaborative Framework Based on Multi-Source Heterogeneous Information Fusion
This paper introduces an adaptive cloud-edge collaborative framework for efficient and reliable multi-source heterogeneous information fusion and intelligent decision-making. Utilizing a Faster R-CNN target recognition network, the framework combines supervised and unsupervised multi-perspective basic belief assignment methods to extract target state features. Reinforcement learning is employed for model inference updates and cloud-edge model migration collaboration. Experimental results show a mAP of 90.61% for the cloud fusion model and 90% consistency in cloud-edge collaborative decision-making, demonstrating its effectiveness.
Key Metrics & Executive Impact
Our analysis highlights significant advancements in AI-driven decision-making, offering tangible benefits for enterprise operations.
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Cloud Fusion mAP
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Decision Consistency
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Model Adaptation
Deep Analysis & Enterprise Applications
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90.61%
mAP achieved by Cloud Fusion Model
Cloud-Edge Collaboration Workflow
Edge Model Detection
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Upload Results to Cloud
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Cloud-side Fusion
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Evaluate Consistency
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RL Decision (Transfer/Skip)
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Edge Model Adaptation
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90%
Consistency of Cloud-Edge Decisions
Target Recognition Performance (Boeing 747)
The proposed framework demonstrated exceptional performance in target recognition for Boeing 747 aircraft, achieving a 96.00% AP in cloud-edge fusion. This highlights the framework's ability to maintain high accuracy even for specific, complex object types across heterogeneous sensor data. The system's adaptive nature ensures robust detection capabilities in varied environmental conditions.
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Your AI Implementation Roadmap
A phased approach to integrate adaptive cloud-edge AI into your operations.
Phase 1: Discovery & Strategy
Assess current infrastructure, define objectives, and tailor the framework to specific needs.
Phase 2: Data Integration & Model Training
Integrate multi-source data, train initial models, and establish cloud-edge communication.
Phase 3: Deployment & Optimization
Deploy models to edge devices, enable RL for adaptive updates, and continuous performance tuning.
Phase 4: Scaling & Expansion
Expand framework to new use cases and integrate with existing enterprise systems.
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