edge
From edge to cloud: Trinity architecture reconstructs
1. Technical logic of the trinity architecture
Edge layer: deploy intelligent sensors and acquisition terminals to complete preliminary data cleaning and event filtering;
Control layer: network controller implements data routing, security strategy and resource scheduling;
Cloud layer: cloud IO platform provides big data analysis, model training and global decision feedback.
2. Cross-level collaboration case
Wind power operation and maintenance: wind turbine vibration sensor (edge acquisition) detects abnormal frequency → edge gateway compresses data and selects low-latency link through network controller → cloud IO platform calls AI model to diagnose faults and sends maintenance instructions to on-site robots.
Retail intelligence: in-store cameras count customer flow (edge AI) → network controller prioritizes sales data upload bandwidth → cloud IO generates heat map to guide product selection optimization.
3. Core technology breakthroughs
Edge lightweight AI: Google Coral series TPU acceleration chips enable edge devices to run ResNet-18 models locally;
Cloud-edge collaboration protocol: Apache Kafka's MirrorMaker 2.0 supports data synchronization across edge clusters;
Deterministic network: TSN (time-sensitive network) guarantees microsecond latency for industrial control instructions.
4. Industry impact and future
According to IDC, more than 50% of enterprise data will be processed on the edge by 2025. The trinity architecture will accelerate this process and give rise to new formats in the fields of intelligent manufacturing, vehicle-road collaboration, etc. For example, Tesla's "edge training-cloud iteration" Autopilot mode has achieved closed-loop value mining of vehicle data.
