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
Powered by GitBook
On this page
  1. 4. Application Scenario

4.1 Smart Healthcare and Health Management

In the field of smart healthcare, AISim collects patients' health data in real-time through IoE devices, such as physiological indicators like heart rate, blood pressure, and blood glucose. AI Agents can analyze these data in real-time to provide personalized health advice, helping patients manage their health. For example, when the heart rate is too high or blood glucose fluctuates, AI Agents will automatically remind patients to take corresponding measures or contact doctors, thereby reducing health risks and reducing hospital pressure.

Telemedicine also benefits from the promotion of AISim technology. Doctors can obtain patients' health data in real-time through AI Agents and diagnose and guide surgery through remote platforms. This allows doctors to provide efficient medical services even in remote areas, while ensuring the accuracy and timeliness of diagnosis.

Epidemic monitoring is another important application scenario. AISim can identify the spread trend of epidemics in real-time by analyzing the data of distributed sensor networks and issue early warnings. AI Agents can propose prevention and control suggestions based on the analysis results, helping public health institutions to formulate effective intervention measures to reduce the spread of the epidemic.

Previous4. Application ScenarioNext4.2 Autonomous Driving and Intelligent Transportation

Last updated 1 month ago