AISim
  • 1. Market Background
    • 1.1 Development Prospects
    • 1.2 Potential Challenges
  • 2. AISim: The World’s First Web3 IoE Network
    • 2.1 AIA Protocol (AISim Intelligent Access Protocol)
    • 2.2 Decentralized Identity Authentication (DID) System
    • 2.3 Distributed AI Acceleration Engine
    • 2.4 Intelligent Privacy Computing Module
    • 2.5 DeAI Client (Decentralized AI Client)
    • 2.6 IoE Data Management and Intelligent Caching System
  • 3. Technical Architecture
    • 3.1 AIA Protocol
      • 3.1.1 Protocol Adaptation Layer
      • 3.1.2 Distributed Task Scheduling Engine
      • 3.1.3 Decentralized Communication Network
      • 3.1.4 Cross-layer Data Encryption and Privacy Protection
      • 3.1.5 Dynamic Resource Scheduling and Optimization
    • 3.2 Distributed AI Acceleration
      • 3.2.1 Edge Node Computing Optimization
      • 3.2.2 Multi-Node Distributed Execution
      • 3.2.3 Privacy-Preserving Collaborative Training
    • 3.3 Decentralized Identity and Access Management
      • 3.3.1 Identity Verification and Access Level Grading
      • 3.3.2 Multi-level Data Protection
  • 4. Application Scenario
    • 4.1 Smart Healthcare and Health Management
    • 4.2 Autonomous Driving and Intelligent Transportation
    • 4.3 Agricultural Internet of Things and Precision Agriculture
    • 4.4 Industrial Automation and Intelligent Manufacturing
    • 4.5 Smart City and Public Services
    • 4.6 Edge Computing and Distributed AI
  • 5. IoE Web3 Ecosystem Construction
    • 5.1 DID Physical Nodes
      • 5.1.1 Types of Smart SIM Cards
      • 5.1.2 Rights of DID Physical Nodes
    • 5.2 MVNO Integration
    • 5.3 Ecosystem Incentives
  • 6. Tokenomics
    • 6.1 Token Distribution
    • 6.2 AST Token Use Cases
  • 7.Roadmap
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  1. 4. Application Scenario

4.3 Agricultural Internet of Things and Precision Agriculture

In the application of agricultural IoT, AISim utilizes IoE devices to collect data such as soil moisture and climate change, aiding farmers in intelligent irrigation. AI Agents dynamically adjust irrigation strategies based on this data, ensuring optimal use of water resources and enhancing crop growth outcomes. This not only improves agricultural production efficiency but also reduces water waste.

Crop health monitoring is another key application of AISim in agriculture. Using drones and ground sensors, AI Agents can monitor the health of crops in real-time, detecting issues such as pests, diseases, or nutrient deficiencies. By analyzing this data, AI Agents can promptly provide farmers with advice to ensure optimal growing conditions for crops.

AISim can also optimize the use of agricultural resources based on land use conditions and market demand. AI Agents analyze land conditions and climate changes to adjust planting, fertilizing, and harvesting schedules, ensuring the maximum utilization of agricultural resources and reducing unnecessary costs.

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