SUNFLOWER
  • Introduction
    • What is Sunflower?
    • Mission & Vision
    • Key Features
  • Getting Started
    • System Requirements
    • Installation Guide
    • Creating an Account
    • Setting up a Node
  • User Guide
    • Dashboard Overview
    • Sharing Bandwidth
    • Monitoring Earnings
    • Token Management
    • Participating in Governance
  • Architecture
    • Overview
    • User Layer
    • Client Layer
    • P2P Network Layer
    • Consensus & Mining
    • Smart Contract & Governance Layer
    • Application & Data Consumption Layer
  • Tokenomics
    • Token Distribution Model
    • Mining Rewards & Incentives
    • Use Cases
    • Inflation & Deflation Strategies
  • Sunflower Data
    • Sunflower Architecture
      • Client Layer
      • RPC/Aggregator Nodes
      • Validator
      • Router
      • Sunflower Nodes
      • Public Web Servers
      • ZK Processor / PoB Aggregator
      • Sunflower Data Ledger
      • L1 Settlement Layer (Optional)
      • AI / Edge Features (Optional)
    • Data Flow Overview
      • Step-by-Step Data Flow
      • Data Flow Diagram
      • Key Features and Professional Highlights
  • Security
    • Security Measures
    • Privacy Protection
    • Smart Contract Audits
  • Roadmap
    • Phase 1: Foundation Building
    • Phase 2: Test Iteration & Audits
    • Phase 3: Mainnet Launch
    • Phase 4: Ecosystem Expansion
    • Phase 5: Decentralized Governance
  • FAQ
    • General Questions
    • Technical Questions
    • Mining and Rewards
    • Token & Transactions
    • Security & Privacy
    • Support & Community
    • Future Development
  • Contributing
    • How to Contribute
    • Development Guidelines
    • Reporting Issues
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On this page
  1. Sunflower Data
  2. Sunflower Architecture

AI / Edge Features (Optional)

Functionality:

  • Data Processing: Utilizes AI models to analyze and optimize data flows within the network.

  • Edge Computing: Implements edge computing strategies to enhance real-time data processing and reduce latency.

  • Predictive Analytics: Employs machine learning to predict network demands and optimize resource allocation.

Workflow:

  1. Collect Data: Gathers real-time data from various network nodes.

  2. Process with AI Models: Analyzes data using AI algorithms to derive insights and optimize operations.

  3. Implement Edge Solutions: Deploys edge computing nodes to handle data-intensive tasks locally, improving overall network performance.

Diagram:

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AI / Edge Features (Optional)
 ├─> Collect Real-Time Data from Network Nodes
 ├─> Process Data with AI Models
 └─> Implement Edge Computing Solutions for Optimization
PreviousL1 Settlement Layer (Optional)NextData Flow Overview

Last updated 3 months ago