High-Efficiency Computing Power Engine with Cloud-Edge Collaboration

Section 1: Solution Overview

The Seeed Intelligent Edge Computing Power Integrated Solution integrates edge computing and cloud computing technologies to deliver high-density, low-latency, and high-reliability computing power for enterprises. Centered on modular edge computing carrier boards, the solution supports multi-node cluster deployment and flexible configuration of heterogeneous computing units (CPU, GPU, and AI accelerators). It establishes a Kubernetes containerized architecture to enable end-to-end management of edge inference, data preprocessing, and lightweight model training. Onboard high-speed communication modules facilitate low-latency inter-node interaction, while intelligent scheduling algorithms dynamically allocate computing resources to meet real-time processing demands in scenarios such as smart manufacturing, smart healthcare, and smart cities.

Section 2: Core Advantages

1. High-Performance Edge Computing Architecture

• Hardware Foundation: Employs a heterogeneous computing platform where a single node supports multiple computing unit types (e.g., CPU, AI accelerators). Integrates high-density parallel processing cores to efficiently run AI models such as YOLOv8 and BERT.

• Cluster Scalability: The solution enables multi-node cluster deployment through edge computing carrier boards, supporting distributed training and inference. It reduces model inference latency to millisecond-level, meeting real-time demands in scenarios like industrial quality inspection and autonomous driving.

• Flexible Configuration: Features compatibility with computing modules of various specifications and supports wireless communication module expansion. The system adapts to complex edge environments (e.g., industrial-grade temperature range of -20°C to 60°C).
  

2. Intelligent Scheduling and Collaborative Computing

• Edge-Cloud Collaboration: Leverages Kubernetes container orchestration technology to dynamically schedule resources between edge nodes and the cloud. Edge nodes process real-time data (e.g., sensor signals), while the cloud handles model iteration and global optimization, reducing data transmission costs by 30%.

• Energy Efficiency Optimization: Features built-in dynamic voltage and frequency scaling algorithms that automatically adjust computing unit power consumption based on load. Significantly enhances overall energy efficiency ratio and supports wide voltage input for complex industrial power environments.

3. Green Energy Efficiency and Reliability Assurance

• Thermal Design: Utilizes cold plate liquid cooling technology to maintain critical component temperatures below 50°C. Increases single-cabinet power density to over 25 kW with a PUE as low as 1.15, achieving 40% energy savings compared to traditional solutions.

• High Availability: Supports dual power supply redundancy and data mirroring backup. Ensures recovery time objective (RTO) of less than 5 minutes for critical business systems, meeting financial-grade reliability standards.

4. Security Compliance and Ecosystem Support

• Data Privacy: Incorporates a federated learning module to ensure data remains “usable yet invisible”, complying with EU GDPR and China’s Data Security Law.

• Open-Source Ecosystem: Provides a pre-integrated development environment, acceleration libraries, and toolchains that support end-to-end workflows from model training to edge deployment. The solution is compatible with mainstream operating systems and communication protocols.

Section 3: Industry Solutions

1. Smart Manufacturing

• Predictive Maintenance: Edge clusters analyze equipment vibration, temperature, and other data in real-time. When integrated with machine learning algorithms, the system achieves over 95% failure prediction accuracy, reducing equipment downtime by 70%.

• Intelligent Quality Inspection: Deploys visual inspection systems supporting defect identification with 0.01 mm precision. This increases product yield by 2% and improves inspection efficiency eightfold compared to manual methods.

2. Smart Healthcare

• Medical Imaging Analysis: Edge clusters equipped with 3D convolutional neural networks (3D CNN) achieve 99.2% accuracy in pulmonary nodule detection. This reduces per-case CT diagnosis time from 30 minutes to 2 minutes, lowering misdiagnosis rates to 0.3%.

• Remote Diagnosis: Leverages edge-cloud collaboration architecture for real-time transmission and analysis of multimodal data. Supports remote surgical guidance over 5G networks with latency of less than 200 milliseconds.

3. Smart City

• Traffic Management: Edge clusters deployed at intersections utilize multi-camera fusion object tracking algorithms to identify vehicles, pedestrians, and traffic violations in real-time. This improves incident response speed by 40%.

• Environmental Monitoring: Integrates sensors and edge computing units to monitor PM2.5, noise, and other data in real-time. When combined with cloud-based AI models to predict pollution diffusion trends, the system provides decision-making support for urban governance.

Section 4: Technical Specifications

Section 5: Service Support

• Full Lifecycle Management: Delivers end-to-end services from demand analysis and solution design to deployment and operation. Provides 24/7 expert support covering hardware failures, software debugging, and compliance audits.

• Certification System: Certified by the China Academy of Information and Communications Technology (CAICT) “Data Center Green Level Certification”. Complies with the EU Digital Services Act (DSA) and green computing standards, supporting custom industry certifications (e.g., ISO 27001).

• Ecosystem Collaboration: Jointly establishes a computing alliance with industry leaders, sharing technical resources and domain expertise while offering multi-system compatibility services.