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WEDA Cloud: The Cornerstone for Next-Generation Intelligent IoT

WEDA Cloud Features

WEDA Core is powered by an API-centric design, integrating advanced edge computing and AI capabilities. It’s purpose-built to deliver highly stable, scalable, and cross-domain solutions for both modern IoT and vertical AI applications. For developers and technology evaluators, WEDA Core addresses the most common pain points with the following features:

  1. Pain Point: Complex Large-Scale Device Deployment and Maintenance

Corresponding Feature: Mass Node Management

  • Node: Automatic Provisioning & Device-Centric Design

    • Simplifies the process of large-scale node deployment, reducing manual configuration errors.

    • Supports fast device mounting to speed up deployment times.

  • Container: Docker Ecosystem and AI Model Management

    • Natively supports Docker Compose (stack) and version control (including rollback), making it easy for technical teams to quickly copy, backup, and recover application stacks at scale.

    • Enhances centralized management and deployment of AI models, making machine learning and inference service rollout and maintenance flexible and efficient.

    • Provides secure remote container access and registry management for seamless and safe deployments.

  1. Pain Point: Equipment Health Monitoring, Diagnostics, and Remote Maintenance Challenges

Corresponding Feature: Digital Twin

  • Shadow: Real-Time Health Monitoring & Automated Diagnostic Analysis

    • Continuously monitors the health of each node and container, sending instant alerts and proactively warning of anomalies to avoid unexpected downtime.

    • TCP Tunnel enables remote control without the need for on-site support or VPN. IT staff can access diagnostic data and perform repairs anytime, reducing maintenance cycles and manpower costs.

  • Virtual Node: Multi-Protocol Data Integration & Remote Diagnosis

    • Natively connects with protocols like Modbus, OPC UA, MQTT, NATS, Webhook, HTTP Server, fully solving the problem of data silos from heterogeneous devices and distributed locations.

    • Supports full lifecycle management of virtual nodes, standardizing diagnostic and maintenance processes for fast replication and deployment across devices and industries.

    • Allows for customized diagnostic algorithms (JavaScript/Python templates), making it quick and flexible to deploy diverse anomaly detection logic.

    • Integrates historical diagnostic data and includes default models for fault inference, lifetime prediction, and health grading—helping IT staff quickly grasp device condition and boost preventive maintenance.

  1. Pain Point: High Barriers for Data Transformation, Stream Processing, and Time-Series Analytics

Corresponding Feature: Math Engine

  • Provides automatic conversion for univariate and multivariate data, reducing ETL workloads.

  • Includes built-in time series aggregation and analytics, enabling easy implementation for industrial monitoring and energy management.

  • Ensures low-latency processing with real-time data filtering.

  • Fully supports JavaScript and Python plugin logic for advanced analytics, such as AI vision and anomaly detection.

  1. Pain Point: Difficulties in Global Operations, Management Efficiency, and Flexible Scaling

Corresponding Feature: General Services & Operation Management

  • Settings

    • Highly flexible alert, notification, and scheduling configuration to meet automation needs for various scenarios.

  • Tenant

    • Supports OAuth2 and multi-tenancy, ensuring cross-department/customer data isolation and security, and enabling SaaS-based cloud management.

  • Operation

    • Offers both automatic and manual scaling with built-in load balancing, enabling high-availability enterprise deployments.

    • An integrated admin system, helping IT staff coordinate and maintain systems efficiently.

  1. Pain Point: UI Development and Ecosystem Integration

Corresponding Feature

  • WEDA UI Boilerplate (Opensource)

    • Open-source templates (C# & React) speed up custom UI delivery and lower frontend development barriers.

  • Third-Party Ecosystem Integration

    • Native integration with solutions like Grafana, NVIDIA Omniverse (visualization), Edge Impulse (MLOps), SimAI/Dify/n8n for AI agents and automation. This helps teams quickly bring in advanced technologies, reducing integration risks and costs.

Technical Foundation​

WEDA Core is built on international mainstream technology stacks such as ABP, DDD, DTDL, Docker, and K3s, ensuring scalability, standardization, and operational stability. This supports enterprises in building the blueprint for the future of intelligent IoT.

Summary​

With its forward-looking technology design, multifunction integration, and strong ecosystem support, WEDA Core precisely targets the pain points of modern IoT and edge AI industries. It is the ideal choice for application developers and system evaluators, helping users innovate rapidly, deploy flexibly, and manage IoT systems efficiently.

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