Ethernet Switch Supports Edge Computing: How to Achieve Localized Data Processing? Unlock a New Dimension of Production Efficiency
In the welding workshop of an automobile manufacturing plant, 100,000 pieces of temperature, pressure, and displacement data are generated every second. If all this data were uploaded to the cloud for processing, not only would it entail high bandwidth costs, but the 200ms network latency would also lead to fluctuations in welding quality. By deploying an Ethernet switch that supports edge computing, the plant has achieved "second-level" local data processing, resulting in a 12% increase in welding pass rate and annual cost savings exceeding ten million yuan. This case reveals the core value of industrial edge computing: enabling data processing close to the source and reshaping production efficiency with "real-time intelligence."
In the era of Industry 4.0, data has become a new production factor, but the traditional "centralized cloud processing" model is facing severe challenges:
The blast furnace monitoring system of a steel enterprise generates 50GB of data per second. If all this data were uploaded to the cloud:
Cost explosion: Daily traffic fees exceed ten thousand yuan, with annual costs reaching several million yuan;
Latency out of control: The 200ms network latency causes delays in control commands, leading to abnormal equipment shutdowns;
Reliability risks: Data loss during network interruptions results in loss of control over the production process.
The SCADA system of an energy enterprise once suffered a cloud data leak, allowing competitors to gain advance knowledge of production plans, resulting in significant economic losses. Industrial data contains core secrets such as process parameters and equipment status, and transmitting it to the cloud means entrusting the "digital lifeline" to others.
In robot collaboration scenarios, a 0.1-second delay can lead to robotic arm collisions; in power failure detection, a 1-second delay can expand the scope of the accident. Data processed in the cloud needs to go through the complete link of "collection-transmission-processing-feedback," making it difficult to meet the millisecond-level response requirements of industrial scenarios.
Edge computing constructs a closed-loop system of "perception-analysis-decision-making" by deploying computing power at the data source, thoroughly breaking the dilemmas of traditional models:
Real-time response: Complete data processing locally, compressing control latency from 200ms to less than 5ms. A semiconductor factory achieved real-time feedback on wafer inspection through edge computing, increasing defect identification accuracy to 99.97%.
Bandwidth liberation: Only upload critical data to the cloud, reducing bandwidth demand by 90%. A wind farm project demonstrated that edge computing reduced data transmission volume from 1TB per day to 100GB.
Security enhancement: Data is encrypted and stored locally, with access restricted to authorized devices. A chemical enterprise built a "data stays within the factory" system through edge computing, successfully passing the Level 3 certification of the Information Security Protection 2.0.
Edge computing is not a single device but an intelligent system collaborative by "end-edge-cloud":
End layer: Sensors, PLCs, and other devices collect raw data;
Edge layer: Ethernet switches equipped with edge computing modules perform data preprocessing, feature extraction, and model inference;
Cloud layer: Conducts global optimization, model training, and remote management.
Taking the USR-ISG series switches as an example, their built-in edge computing modules can simultaneously process 20 streams of 1080P video, support Python/C++ development environments, and enable rapid deployment of AI algorithms. In a smart logistics project, USR-ISG reduced package sorting error rates from 0.3% to 0.02% through edge computing.
Not all Ethernet switches can undertake the mission of edge computing; they need to possess the following core capabilities:
Multi-core processors: Adopt ARM Cortex-A series or x86 architectures, providing 1-16 core optional computing power. The USR-ISG series is equipped with a 4-core 1.8GHz processor, capable of running 5 AI models simultaneously.
GPU acceleration: Integrate NVIDIA Jetson or Huawei Ascend chips to support complex calculations such as image recognition and voice processing. A car factory achieved a 10-fold increase in welding defect detection speed through the GPU acceleration function of USR-ISG.
Memory expansion: Support 8GB-64GB DDR4 memory to meet large-scale data processing needs. A power project demonstrated that 32GB of memory can simultaneously process 100,000-level equipment status data.
Containerization technology: Support Docker containers to enable rapid deployment and isolation of algorithms. The container platform of USR-ISG can complete the launch of new algorithms within 5 minutes.
Development framework compatibility: Compatible with mainstream AI frameworks such as TensorFlow Lite and PyTorch Mobile, lowering development thresholds. A pharmaceutical enterprise developed a pill defect detection model within 3 weeks based on the PyTorch environment of USR-ISG.
API open interfaces: Provide RESTful API, Modbus TCP, and other interfaces for seamless integration with existing systems. A smart park project achieved data interconnection with the energy management system through the API interfaces of USR-ISG.
Data cleaning: Filter noise data and extract effective features. USR-ISG can automatically identify and eliminate sensor outliers, increasing data accuracy to 99.9%.
Real-time analysis: Support streaming computing frameworks (such as Apache Flink) to achieve millisecond-level responses. A rail transit project shortened train fault warning times from minutes to seconds through the streaming computing function of USR-ISG.
Model inference: Run pre-trained models locally without relying on the cloud. A textile enterprise deployed a fabric defect detection model through USR-ISG, achieving an identification speed of 300 meters per minute.
Time-Sensitive Networking (TSN): Ensure low-latency transmission of critical data. USR-ISG supports the IEEE 802.1Qbv standard, stabilizing control command transmission latency within 10μs.
5G/Wi-Fi 6 integration: Provide high-speed wireless connections to support mobile device access. A port project achieved real-time scheduling of AGV unmanned forklifts through the 5G module of USR-ISG.
SDN (Software-Defined Networking): Dynamically adjust network resource allocation. The SDN function of USR-ISG can automatically allocate bandwidth based on business priorities to ensure priority transmission of critical data.
Among numerous Ethernet switches, the USR-ISG series has become an ideal carrier for edge computing due to its "high-performance computing power + simplified development" characteristics:
Computing power options: Provide 1-core/4-core/8-core processor versions, supporting up to 64GB of memory;
Rich interfaces: Support 12 Gigabit electrical ports + 4 10-Gigabit optical ports to meet large-scale device access needs;
Environmental adaptability: Operate in a wide temperature range of -40℃ to 85℃ and pass the IEC 60068-2 series certification.
Edge computing suite: Pre-install USR EdgeOS, integrating data cleaning, model inference, streaming computing, and other modules;
Development toolchain: Provide Python SDK, C++ libraries, and visual development tools to lower development thresholds;
Cloud management platform: Support remote management through the USR Cloud, enabling batch configuration deployment and firmware online upgrades.
Smart manufacturing: In a car welding production line, USR-ISG analyzes welding current and voltage data in real-time, automatically adjusting parameters to reduce the welding defect rate from 0.5% to 0.03%;
Energy and power: In a wind farm project, the edge computing module of USR-ISG processes wind turbine vibration data, predicting bearing failures 48 hours in advance and reducing unplanned downtime by 80%;
Smart city: In a traffic signal control system, USR-ISG analyzes traffic flow data through edge computing and dynamically adjusts signal timing, improving intersection throughput efficiency by 35%.
Even with high-performance switches, a lack of a systematic deployment strategy can still lead to failure. The following five-step method can help enterprises quickly realize the value of edge computing:
Priority ranking: Select scenarios with high requirements for real-time performance and security, such as equipment predictive maintenance and quality inspection;
ROI calculation: Evaluate benefits such as bandwidth cost savings and pass rate improvements to ensure an ROI greater than 150%.
Computing power demand assessment: Select appropriate switch models based on data processing volume;
Network topology design: Adopt star, ring, or hybrid topologies to ensure redundancy backup of critical nodes.
Model lightweighting: Compress large cloud models into lightweight models suitable for edge operation;
Containerization packaging: Use Docker to package algorithms into independent containers for rapid deployment.
End-to-end verification: Test the full-link latency from data collection to decision feedback;
Stress testing: Simulate peak data volumes to verify system stability.
Remote monitoring: Monitor edge node status in real-time through the cloud platform;
Automatic updates: Enable remote iteration of algorithm models and firmware.
Is your production system facing the following challenges?
Control command delays due to cloud processing?
High bandwidth costs eating into profits?
Core data at risk of leakage during transmission?
Lack of real-time analysis capabilities affecting decision quality?
The USR-ISG series switches offer you:
Flagship-level computing power to support local operation of complex AI models
Out-of-the-box edge computing suite to lower development thresholds
2-year warranty and lifetime one-on-one technical support
Contact us to receive:
A free copy of the "White Paper on Industrial Edge Computing Deployment"
Customized 1-on-1 scenario design by dedicated engineers
A limited-time trial of the USR-ISG Ethernet switch (with edge computing functionality)
