Industrial Data Middle Platform and Edge Computing: Architectural Innovation from Device Interconnection to Real-Time Collaboration

Under the wave of Industry 4.0 and intelligent manufacturing, traditional industrial systems are undergoing a profound transformation driven by "data." PLC (Programmable Logic Controller), SCADA (Supervisory Control and Data Acquisition), and MES (Manufacturing Execution System), as the three core levels of industrial automation, have their collaboration efficiency directly determining the flexibility, response speed, and decision-making quality of production lines. However, with the surge in the variety of devices and the explosive growth of data volume, issues such as "data silos," "insufficient real-time performance," and "protocol barriers" in traditional architectures have become increasingly prominent. How to reconstruct a real-time collaboration architecture through technologies such as industrial data middle platforms and edge computing gateways has become a key proposition for enterprises' digital transformation.

1. Industrial Data Middle Platform: Breaking the Collaboration Dilemma of PLC, SCADA, and MES

1.1 Three Major Pain Points of Traditional Architectures

In traditional industrial automation architectures, PLC serves as the underlying control unit responsible for real-time control at the device level; the SCADA system undertakes data acquisition and visual monitoring; and the MES system focuses on production planning and execution management. Although the three have clear divisions of labor, their hierarchical segmentation leads to low collaboration efficiency:

• Data delay and consistency: PLC data collected by SCADA needs to be transmitted through multiple layers to MES, with delays reaching seconds or even minutes, making it difficult to support real-time scheduling.
• Protocol heterogeneity: PLCs from different manufacturers (such as Siemens S7, Mitsubishi FX, and Omron NJ) adopt proprietary protocols, requiring customized drivers for SCADA and resulting in high integration costs.
• Semantic gap: PLC register data (such as temperature values and switch states) lacks business semantics, and MES needs to develop additional mapping logic, which is prone to decision-making deviations due to configuration errors.

1.2 Collaborative Architecture Design of the Data Middle Platform

The industrial data middle platform reconstructs the collaboration mode of PLC, SCADA, and MES through a three-layer architecture of "data aggregation - semantic modeling - real-time services":

• Data aggregation layer: Based on industrial gateways (such as USR-M300), it enables multi-protocol parsing and edge preprocessing. The gateway supports over 200 industrial protocols such as Modbus TCP/RTU, OPC UA, and Profinet, and can unify scattered PLC data into JSON/MQTT formats, reducing the pressure on the SCADA system.
• Semantic modeling layer: It builds a three-level data model of "device - production line - factory," maps PLC register addresses to business semantic labels (such as "reactor temperature_Line A_Machine 1"), and associates process parameter thresholds to provide interpretable real-time data for MES.
• Real-time service layer: Through time-series databases (such as InfluxDB) and stream processing engines (such as Apache Flink), it achieves millisecond-level data calculation and anomaly detection. For example, when the "device vibration value" reported by SCADA exceeds the threshold, the data middle platform can immediately trigger an MES shutdown instruction to avoid unplanned downtime.

Case practice: After deploying the data middle platform, a chemical enterprise reduced the collaboration delay between PLC and MES from 15 seconds to 200 milliseconds, improved the response speed of production plan adjustments by 60%, and achieved a device fault prediction accuracy rate of 92%.

M300

4G Global BandIO, RS232/485, EthernetNode-RED, PLC Protocol

2. Industrial Gateway + SCADA: Paradigm Upgrade from Data Acquisition to System Integration

2.1 Limitations of Traditional SCADA Integration

Traditional SCADA systems mostly adopt a "C/S architecture + local deployment" model and face three major challenges:

• Poor scalability: Adding new devices requires manual configuration of drivers and screens, with project implementation cycles lasting for months.
• Insufficient openness: Integration with systems such as ERP and WMS relies on old interfaces such as OPC DA, resulting in high data synchronization delays.
• High maintenance costs: Distributed SCADA sites require dedicated personnel for inspections, and fault location efficiency is low.

2.2 Integration Solution Enabled by Industrial Gateways

Industrial gateways redefine the integration boundaries of SCADA systems through "edge computing + cloud-native" technologies:

• Protocol adaptability and edge processing: The gateway has a built-in protocol parsing engine that can automatically identify device types and match communication parameters. For example, the USR-M300 supports drag-and-drop configuration of PLC models through a web interface, enabling data mapping without coding. At the same time, the gateway can perform operations such as data cleaning (such as denoising and filtering) and aggregation (such as minute-level average value calculation) at the edge side, reducing 90% of invalid data uploads.
• Lightweight SCADA function integration: Some high-end gateways (such as the USR-M300) have built-in configuration software modules and support the rapid development of visualization dashboards through H5/Vue technology. Users can view device status locally on the gateway or push data to a cloud SCADA platform through MQTT/HTTP to achieve "edge-cloud" collaborative monitoring.
• Open APIs and low-code integration: The gateway provides RESTful APIs and Python script interfaces for seamless integration with systems such as MES and ERP. For example, by calling the gateway API to obtain real-time production data, MES can dynamically adjust production scheduling plans to avoid resource idleness.

Practice of an auto parts manufacturer: After adopting the gateway + SCADA integration solution, the deployment cycle of new production lines was shortened from 3 months to 2 weeks, the cross-system data synchronization delay was reduced to below 500 milliseconds, and the operation and maintenance labor costs were reduced by 40%.

3. Edge Computing Gateway: Accelerator for the Integration of SCADA and Configuration Software

3.1 Three Major Requirements for Configuration Software Integration

Modern industrial scenarios impose higher requirements on the integration of SCADA and configuration software:

• Real-time performance: It needs to support millisecond-level data refresh to meet the needs of high-speed motion control.
• Heterogeneous compatibility: It needs to be compatible with configuration software from different manufacturers (such as WinCC, Intouch, and iFIX) to avoid vendor lock-in.
• Intelligent analysis: It needs to embed AI models (such as device health scores) in the configuration interface to achieve a "monitoring - analysis - decision-making" closed loop.

3.2 Core Value of Edge Computing Gateways

Edge computing gateways provide key support for the integration of configuration software through "hardware acceleration + software-defined" technologies:

• High-performance data processing: Using ARM Cortex-A series processors and hardware encryption chips, it can simultaneously process data from over 2,000 points and support the OPC UA Pub/Sub mode to achieve nanosecond-level delay control. For example, the USR-M300 achieved real-time data acquisition of SMT placement machines in an electronics manufacturing plant, with a configuration screen refresh rate of 50 ms, meeting the needs of high-speed placement.
• Virtualized configuration runtime environment: The gateway can virtualize and run a lightweight configuration engine (such as Node-RED + D3.js), supporting the construction of visualization logic through drag-and-drop. Users can achieve device monitoring, alarm pushing, and other functions locally on the gateway without purchasing expensive commercial configuration software.
• AI model edge deployment: The gateway integrates TensorFlow Lite/ONNX Runtime runtimes and can directly run pre-trained AI models. For example, by analyzing the vibration spectrum data collected by SCADA, the gateway can calculate the device health score (0-100 points) in real time and dynamically display it on the configuration interface to assist operation and maintenance personnel in early intervention.

Practice of a steel enterprise: After deploying the edge computing gateway, the communication delay between the configuration software and PLC was reduced from 1 second to 50 ms, the AI model inference speed was increased by 10 times, and the annual equipment fault losses were reduced by 2 million yuan.

4. Future Outlook: From Real-Time Collaboration to Autonomous Decision-Making

With the integration of technologies such as 5G, digital twins, and large models, the collaborative architecture of industrial data middle platforms and edge computing will evolve to a higher level:

• Dynamic resource scheduling: Based on AI load prediction algorithms, it can automatically adjust the allocation of gateway computing resources, such as prioritizing the transmission of control instructions when devices are under high load.
• Digital twin integration: Real-time data collected by edge gateways can drive the synchronous operation of cloud-based digital twins, enabling "virtual-real"联动 (virtual-real linkage) predictive maintenance.
• Large model empowerment: Using industrial gateways as edge AI nodes, industry-specific large models (such as predictive maintenance and quality inspection) can be deployed, achieving a decision-making upgrade from "experience-driven" to "data + model-driven."

The rise of industrial data middle platforms and edge computing gateways marks a new stage of "real-time collaboration and intelligent integration" in industrial automation. By breaking the hierarchical barriers of PLC, SCADA, and MES and building a three-level architecture of "device - edge - cloud," enterprises can not only achieve transparency and flexibility in the production process but also continuously optimize processes, reduce energy consumption, and improve quality based on data-driven approaches. In this transformation, technology selection needs to take into account "real-time performance, openness, and ease of use," and the emergence of industrial gateway products such as the USR-M300 undoubtedly provides a cost-effective transformation path for small and medium-sized enterprises. In the future, with the deep integration of AI and industrial scenarios, the real-time collaboration architecture will further empower intelligent manufacturing and drive industrial production towards a higher level of autonomy and intelligence.