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Raspberry Pi CM5 (BCM2712) vs. RK3588: From Open-Source Ecosystem to AI Computing Power

With the rapid development of edge computing and AIoT (Artificial Intelligence of Things), ARM-based SoCs have become the core of devices such as industrial controls, AI vision systems, and smart gateways. Currently, two highly representative processors are Broadcom's BCM2712 (used in the Raspberry Pi Compute Module 5, or CM5) and Rockchip's RK3588 (widely applied in high-performance ARM edge computers, such as the BL450 series). The former represents an ecosystem-driven general-purpose platform, while the latter emphasizes AI-driven high-performance computing. This article provides an in-depth comparison across five dimensions: architecture, performance, AI capabilities, ecosystem, and application directions, to help developers and engineers make informed selections.

CPU Architecture and Process Technology

The BCM2712 features a 4-core ARM Cortex-A76 architecture with a maximum clock speed of 2.4GHz and an approximately 16nm process node, making it a typical mid-to-high-performance embedded platform suitable for power-sensitive lightweight applications.

In contrast, the RK3588 employs an 8-core heterogeneous architecture (4×Cortex-A76 + 4×Cortex-A55) with a maximum clock speed of 2.4GHz, but advanced to an 8nm process. This provides superior power efficiency and computational performance.

In terms of raw computing power, the RK3588 outperforms the BCM2712 in multi-threaded tasks and heterogeneous processing, particularly in industrial AI applications requiring parallel tasks or background scheduling.

Graphics and Multimedia Performance

For GPU, the BCM2712 integrates the VideoCore VII graphics engine, supporting dual 4K video output, which is ideal for standard displays and lightweight graphics processing.

The RK3588 is equipped with the Mali-G610 MP4 GPU, enabling 8K video encoding/decoding, multi-channel display outputs, and advanced interfaces (e.g., HDMI 2.1, DP 1.4), excelling in video streaming and multi-screen applications.

As a result, in scenarios like video analytics, AI vision detection, and smart display terminals, the RK3588 demonstrates stronger image processing and display capabilities.

AI Computing Power and Edge Intelligence

AI performance shows the most significant gap between the two.

BCM2712: Lacks an integrated NPU, relying on CPU or GPU for AI inference, which is suitable for simple models but less efficient.

RK3588: Includes a built-in 6 TOPS NPU, supporting mainstream frameworks like TensorFlow, ONNX, and RKNN. It enables real-time on-device inference for neural networks such as YOLO, ResNet, and MobileNet.

In edge intelligence tasks like AI vision detection, object recognition, and speech processing, the RK3588 achieves millisecond-level local responses, significantly reducing latency and bandwidth usage without relying on cloud computing.

System Ecosystem and Software Support

The Raspberry Pi series is renowned for its vast open-source ecosystem, making the BCM2712 (CM5) platform highly developer-friendly:

Comprehensive official support for Raspberry Pi OS with strong compatibility;

Abundant reusable open-source projects, peripherals, and drivers;

Extensive documentation, tutorials, and community resources for quick onboarding.

By comparison, the RK3588's ecosystem leans toward industrial and professional development. Mainstream systems include Ubuntu, Buildroot, Yocto, and Debian, often provided as vendor-customized images for applications like industrial AI gateways and edge servers. While not as mature as Raspberry Pi's, it is optimized for high-load scenarios.

Application Directions and Selection Recommendations

The following table summarizes the advantages of each in typical application scenarios:Application ScenarioBCM2712 (CM5) BL460 Series Advantages RK3588 BL450 Series AdvantagesEducation & ResearchLow cost, rich ecosystem, easy developmentOverkill performance, poor cost-effectivenessIndustrial Data AcquisitionLow power consumption, long runtimeSupports more complex task processingAI Vision DetectionRequires external NPU or GPU reliance, moderate efficiencyBuilt-in 6 TOPS NPU, faster inferenceMultimedia DisplaySupports dual 4K screensSupports 8K multi-screen, high-definition decodingEdge ServersSuitable for lightweight edge computingIdeal for AIoT edge inference and video analytics

In summary, if the project prioritizes open-source ecosystem and rapid prototyping (e.g., educational development or lightweight industrial control), the Raspberry Pi CM5 BL460 series (BCM2712) is the top choice. For AI inference, video stream analysis, smart surveillance, or industrial automation requiring high computing power, ARM edge computers based on RK3588 (e.g., BL450 series) offer greater competitiveness.

Conclusion

The BCM2712 and RK3588 embody two distinct design philosophies: ecosystem-oriented and AI-performance-oriented. The former leverages Raspberry Pi's robust community for low barriers and high usability; the latter harnesses an advanced 8nm process and integrated NPU for efficient AI inference, ideal for industrial AI environments.

Looking ahead, as demand for AI edge computing continues to grow, the RK3588 will power more industrial and AIoT devices, while the Raspberry Pi CM5 maintains its unique strengths in education, R&D, and lightweight control domains.

The BCM2712 and RK3588 embody two distinct design philosophies: ecosystem-oriented and AI-performance-oriented. The former leverages Raspber

#Raspberry Pi CM5 #BCM2712 #RK3588 #Open-Source Ecosystem #AI Computing Power

ARM Embedded Computers in Integrated Fertilizer and Irrigation Systems

As modern agriculture rapidly advances toward digitization and intelligence, traditional irrigation and fertilization methods can no longer meet the demands of precision farming. Faced with challenges such as labor shortages, low resource utilization, and unstable crop quality, ARM architecture-based embedded computers are becoming the core computing unit for integrated water and fertilizer management systems. With their high reliability, low power consumption, and flexible scalability, they enable all-weather, precise, and visualized intelligent management in complex environments such as open fields, greenhouses, and orchards.

Unique Advantages of ARM Edge Computing Computer BL335

Low power consumption and high stability, ideal for unattended environments ARM processors naturally feature low energy use, minimal heat generation, and stable operation, making them perfect for remote agricultural scenarios. When paired with solar power systems, they can operate continuously for extended periods, suitable for distributed irrigation nodes.

Rich interfaces for seamless connection to sensors and actuators ARM embedded computers typically integrate RS485, CAN, GPIO, ADC, MODBUS, 4G/Wi-Fi, and other interfaces, enabling direct data collection from:

Soil moisture and temperature

EC (electrical conductivity), pH

Weather station data (wind speed, rainfall, evapotranspiration) And direct control of:

Solenoid valves

Irrigation pumps

Dosing pumps (fertilizer injection)

Proportional valves and fertilizer mixing controllers Achieving a complete closed-loop from sensing to actuation.

Support for edge intelligent decision-making After installing Node-RED, Python inference modules, or custom algorithms, the ARM controller can locally perform:

Automated irrigation strategies

Fertilizer ratio calculation (closed-loop control based on EC and flow)

Predictive irrigation (using ET models or AI models) Ensuring independent operation even during network instability or offline conditions.

Cloud connectivity for visualized management Through MQTT, HTTP, or LoRaWAN protocols, it connects to backend platforms and integrates with systems like EMQX, ThingsBoard, Grafana, or Home Assistant to provide:

Remote monitoring via mobile apps or web dashboards

Real-time alerts (pressure loss, abnormal flow, low fertilizer level, pump failure)

Centralized management of multiple sites Allowing farm managers to monitor production status anytime, anywhere.

Typical System Architecture

A standard integrated fertilizer + irrigation system usually consists of the following modules:

ARM edge controller (system core)

Soil sensor cluster (moisture/temperature/EC/pH)

Weather and environmental monitoring system

Water pump and solenoid valve control unit

Fertilizer dosing pumps and mixing pipelines

Flow meters and pressure sensors for closed-loop control

Cloud platform or local management server

Data flow: Sensors → ARM controller → Edge computing → Valve/pump control → Data upload to cloud → Visualization & alerts The controller calculates irrigation duration and fertilizer dosage in real time based on collected moisture, EC, flow, and other data, ensuring crops receive precise water and nutrients.

Operating Mechanism of Intelligent Water-Fertilizer Integration

Precision irrigation logic

Set target soil moisture levels for different crop growth stages

Automatically start irrigation when real-time moisture falls below threshold

Automatically adjust irrigation duration based on flow meter feedback

Skip irrigation cycles when rainfall is detected or evapotranspiration is low

Automated fertilizer control The system dynamically controls dosing pumps using EC, conductivity, and flow data to achieve precise nutrient solution ratios:

Automatically calculate fertilizer injection percentage based on target EC

Real-time adjustment of fertilizer amount according to water volume, achieving milliliter-level precision

Trigger alarms and stop fertilization if EC fluctuates abnormally or solution is insufficient

Safety interlock and protection mechanisms The ARM controller continuously monitors system health:

Low pressure → stop irrigation and send alert

Abnormal flow (blockage/leak) → automatically close valves

Pump overload or dry running → automatic power cutoff protection Ensuring long-term stable and safe operation.

Typical Application Scenarios

Open-field agriculture (corn, wheat, vegetable bases) Zone-based valve control enables automated irrigation scheduling across multiple areas.

Smart greenhouses (strawberry, tomato cultivation) Precise control of nutrient solution EC and pH for high-quality crop production.

Orchards and fruit plantations (grapes, blueberries, citrus) Precise irrigation cycles based on seasonal evapotranspiration and real-time soil data.

Distributed irrigation systems (hilly areas, pipeline networks) Controllers communicate with central platforms via 4G/LoRaWAN, ideal for large-scale distributed deployments.

Summary of System Benefits

Water savings of 20%–40%: on-demand supply avoids over-irrigation

Fertilizer efficiency improvement of 20%–30%: precise formulation reduces waste

Increased yield and quality: more stable water and nutrient status

Significant labor cost reduction: automated operation and remote management

Enhanced reliability: industrial-grade ARM controllers withstand harsh outdoor conditions

Future Development Trends

With the growing adoption of AI and IoT in agriculture, ARM embedded computers will enable more advanced functions in water-fertilizer integration systems:

AI-based crop growth models for predicting irrigation needs

Vision-based diagnosis of pests, diseases, and nutrient deficiencies

Multi-site collaborative scheduling to optimize water resource efficiency

Deeper integration with comprehensive digital farm management platforms

ARM controllers will continue to serve as the core computing nodes of smart agriculture, driving the transformation from “experience-based fertilization” to “data-driven, scientific water and fertilizer management.”

ARM controllers will as the core computing nodes of smart agriculture, driving the transformation from “experience-based fertilization” to “

#ARMxy SBC #ARMxy based SBC #ARM Embedded Computers #Fertilizer system #Irrigation Systems

ARMxy equipped with ThingsBoard IoT Gateway solution

Solution Overview

The ARMxy series industrial edge gateways (e.g., BL340, BL410, BL450) can be seamlessly integrated with the ThingsBoard IoT Gateway to enable multi-protocol device access, edge intelligence, and cloud collaboration. This architecture is ideal for industrial automation, smart cities, smart agriculture, and other IoT deployments.

Powered by high-performance ARM processors (RK3568, RK3588J, TI AM62x, NXP, etc.) and rich industrial interfaces (RS485, CAN, Ethernet, WiFi/4G), combined with ThingsBoard’s Modbus, OPC-UA, and MQTT support, the system forms a complete end–edge–cloud IoT solution.

Hardware Selection

LevelRecommended ModelKey SpecsTypical ScenariosEntry-levelBL340（T507-H）2×100M Ethernet, RS485/DI/DO, low powerSmall-scale data acquisition & protocol conversionMid-rangeBL370 (RK3562)RK3562, multiple RS485/CAN, 4G/WiFi, rich I/OMedium-sized factories, building automationHigh-performance / AIBL450 (RK3588J)8-core (4×A76 + 4×A55) @ 2.4 GHz, 6 TOPS NPU, Gigabit Ethernet, multi-busAI edge inference, predictive maintenance, complex scenarios

Software Deployment (ARMxy + ThingsBoard Gateway)

Install required packages

sudo apt install python3-dev python3-pip python3-venv libglib2.0-dev git

Download ThingsBoard Gateway

git clone --recurse-submodules https://github.com/thingsboard/thingsboard-gateway.git --depth 1

After installation, type ls, and you will see a folder named thingsboard-gateway.

Prepare Python environment

Check version:

python --version

If lower than 3.8:

sudo apt install python3.9 python3.9-venv python3.9 -m venv venv source venv/bin/activate

Create a Gateway on ThingsBoard Cloud

Configure on ARMxy

cd thingsboard-gateway

Entering a virtual environment python3 -m venv venv && source venv/bin/activate Python installation requirements

thingsboard-gateway/thingsboard_gateway/configOpen `vi tb_gateway.json` and modify the values of "host" and "accessToken" to the Platform host and Access token you just recorded. Start the Gateway

Data Flow & Protocol Conversion

ARMxy collects Modbus data via RS485

The gateway converts register values → MQTT messages → cloud device shadow

Supports local SQLite data persistence

Offline buffering and automatic sync after reconnection

Typical Application Scenarios

Predictive maintenance for automotive production lines

Energy optimization in chemical plants

Smart water pump station monitoring and remote control

Cold-chain container environmental monitoring

Wind turbine fault prediction and remote diagnostics

Smart campus security & energy automation

Water treatment dosing and safety-energy linkage control

Intelligent warehouse sorting & equipment maintenance automation

ARMxy industrial edge gateways, combined with ThingsBoard Gateway, provide ready-to-use integrated "device-edge-cloud" IoT solution. This en

#ARMxy SBC #ARMxy based SBC #ThingsBoard IoT Gateway #Thingsboard Edge

ARM Controllers: The Core Platform and Industry Value for Edge Deep Learning

In the era of rapid AI advancement, deep learning has become a key driving force behind industrial automation, robotics, smart manufacturing, and IoT innovation. While traditionally associated with high-performance GPUs, an increasing number of enterprises are turning their attention to ARM-based edge controllers — efficient, cost-effective computing platforms capable of running AI inference directly on edge devices.

With continuous improvements in processor architecture and the widespread adoption of NPUs, ARM controllers are becoming the mainstream carrier for "edge deep learning," demonstrating immense potential in real-world industrial applications.

Why Are ARM Controllers Ideal for Deep Learning?

Compared to traditional x86 industrial PCs, ARM controllers offer three major advantages:

Ultra-low Power Consumption and High Energy Efficiency Enables fanless design and stable operation in sealed, high-temperature, dusty, or battery-powered environments.

Powerful On-device Inference Capabilities Powered by multi-core Cortex-A architectures, NEON SIMD instructions, and dedicated NPUs from vendors such as Rockchip, NXP, and Qualcomm.

Rich Industrial I/O Integration Native support for RS485, CAN, DI/DO, multiple Ethernet variants — enabling a single device to perform both AI inference and automation control.

Thus, ARM controllers are best suited for hybrid applications combining edge inference + local closed-loop control.

Deep Learning Capabilities Enabled by ARM Controllers

Industrial Vision AI (Edge Vision) The most widely adopted application. Paired with industrial cameras, ARM controllers become compact AI vision controllers capable of real-time execution of lightweight networks such as:

YOLOv8n/s, YOLOv11n, YOLO-Fastest

MobileNetV3, EfficientNet-Lite

PP-PicoDet, RT-DETR-R18 Typical tasks include:

Surface defect detection (scratches, dents, bubbles)

Product counting and dimensional measurement

Workpiece localization and robotic picking guidance

Safety helmet/workwear compliance detection

Barcode/QR code reading and OCR

Equipment Condition Monitoring and Predictive Maintenance Local analysis of vibration, acoustic, current, and temperature data enables:

Anomaly detection and early fault warning

Remaining Useful Life (RUL) prediction Commonly used models: 1D-CNN, LSTM/GRU, Tiny Transformers, Autoencoders

On-device Speech Recognition and Voice Control Effortlessly runs offline:

Keyword Spotting (KWS)

Command recognition in noisy environments

Voice-controlled HMI panels and robotic voice interaction

Lightweight Natural Language Processing (NLP) Capable of running BERT-Tiny, DistilBERT, etc., for:

Alarm log classification and parsing

Local FAQ systems

Sentiment and intent analysis

Hybrid OpenCV + Deep Learning Vision Algorithms The most common and robust architecture in industrial vision: Deep learning for detection/classification → OpenCV for geometric measurement and coordinate transformation → Structured output to PLC or robot

High-Performance Inference via NPU (Rockchip Platforms)ModelSoCNPU ComputeTypical PerformanceBL410RK35681 TOPS4×720p@30fps YOLOv8nBL440RK35766 TOPS4×1080p or 1×4K multi-taskBL450RK35886 TOPS8×1080p or 2×4K + multiple models

Applicable Industries and Scenarios

IndustryTypical ApplicationsManufacturingDefect detection, part localization, predictive maintenanceRoboticsVisual picking, obstacle avoidance, collaborative safetySmart Logistics/AGVPallet recognition, navigation, people-vehicle detectionSecurity & SurveillanceBehavior analysis, perimeter protection, face recognitionRetailProduct recognition, unattended cabinets, smart checkoutSmart AgricultureFruit grading, pest & disease detectionSmart Home/BuildingVoice control, home security, elderly monitoring

ARM is evolving from a supporting role to the core intelligent brain.

Why Deep Learning Must Run at the Edge

Extremely low latency requirements (e.g., <30ms for robotic grasping)

Unreliable or unavailable networks

Strict data privacy and security (production data must not leave premises)

Total cost of ownership constraints (mass deployment cannot afford GPUs)

ARM controllers achieve the optimal balance among cost, power consumption, industrial interfaces, and AI performance.

Conclusion: ARM Controllers Are Becoming the Mainstream Platform for Edge AI

ARM controllers are not designed for training large models, but they are one of the best platforms for deploying and running deep learning models.

Driven by three megatrends:

Continuous improvement in CPU performance

NPU becoming standard in ARM SoCs (from 1 → 6 → 20+ TOPS)

Advances in model compression and quantization (INT8/INT4)

In the next 3–5 years, all edge devices will have built-in AI inference capabilities, and ARM controllers will be the most important carrier of this transformation.

ARM controllers are not only the hardware support for deep learning, but also the strategic core driving industrial intelligence. From smart

#ARMxy SBC #ARMxy based SBC #ARM Controllers #Edge Computing #Deep Learning

Why CM5 ReComputer BL460 Is the Best Choice for the Next-Generation Industrial Controller？

In today’s rapidly evolving industrial automation and IoT landscape, enterprises demand more than simple logic control. The requirements now extend to edge computing, intelligent gateways, protocol conversion, and remote operations. Traditional PLCs and industrial PCs are showing limitations in performance, scalability, and ecosystem compatibility. The ARMxy BL460 Series from BLIIoT is designed precisely to meet these challenges, positioning itself as the next-generation industrial controller.

High-Performance Architecture for Complex Computing

The BL460 Series is powered by the Broadcom BCM2712 quad-core Cortex-A76 processor running at 2.4GHz, delivering performance far beyond conventional ARM controllers. With up to 16GB LPDDR4X RAM and 64GB eMMC storage, it easily handles industrial logic control, edge computing, data analytics, and even AI inference. Its integrated VideoCore VII GPU supports Vulkan 1.2, OpenGL ES 3.1, and 4K@60fps video decoding, making it ideal for industrial applications requiring human-machine interfaces and visualization.

Modular Expansion for All Scenarios

One of BL460’s greatest strengths lies in its flexible I/O expansion design:

Multiple Ethernet Ports: Configurations with 1–3 ports, supporting both gigabit and fast Ethernet, adaptable to diverse network topologies.

X/Y Series IO Boards: Modular options for RS485, RS232, DI/DO, AI/AO, relay outputs, PWM, temperature sensors, and more—covering nearly all industrial interface needs.

Wireless & Storage Expansion: Support for 4G/5G/WiFi/GPS modules, M.2 SSDs, Mini PCIe slots, and SIM cards, ensuring future-proof connectivity and storage.

This modular design allows BL460 to be customized for different application scenarios, truly achieving “one device, multiple uses.”

Complete Software Ecosystem for Fast Development

BL460 is fully compatible with the Raspberry Pi ecosystem, supporting Linux, Raspberry Pi OS, Docker, Node-Red, and Qt. It comes with BLIoTLink protocol conversion software, enabling seamless communication across Modbus, PLC, BACnet, IEC104, MQTT, OPC UA, and integration with major cloud platforms such as AWS IoT, ThingBoard, Ignition, Alibaba Cloud, and Huawei Cloud. With BLRAT remote access tools and the QuickConfig setup utility, developers and operators can deploy and maintain systems with ease, significantly reducing costs.

Industrial-Grade Reliability

BL460 has passed rigorous electromagnetic compatibility and environmental adaptability tests:

Operating Temperature: -20℃ to +85℃, suitable for extreme environments.

EMC Compliance: Certified to GB/T and IEC Class A standards, with ESD, EFT, surge, and RF immunity.

Robust Design: Aluminum alloy + stainless steel casing, DIN-rail or wall mounting, IP30 protection rating, ensuring long-term stability in industrial deployments.

These features guarantee reliable operation across power, transportation, manufacturing, and medical industries.

Wide Application Coverage

The BL460 Series is already applied across multiple industries:

Industrial control and PLC replacement

Edge computing gateways

Energy storage systems and EV charging stations

Smart manufacturing and robotic control

Medical devices (blood analyzers)

Rail transit and communication management units

It is not just a controller—it is an intelligent industrial platform.

Conclusion

With its high-performance ARM architecture, modular expansion, complete software ecosystem, and industrial-grade reliability, the BL460 Series redefines the standard for industrial controllers. It not only replaces traditional PLCs but also serves as a core node for edge computing and IoT gateways, truly embodying the future of industrial intelligence.

BL460 is more than a controller—it is the cornerstone of next-generation industrial automation.

With its high-performance ARM architecture, modular expansion, comprehensive software ecosystem, and industrial-grade reliability, the BL460

#ReComputer #CM5 #Compute Module 5 #Broadcom BCM2712 #Raspberry Pi #Industrial Controller

Developing a Data Acquisition Device Based on the SoM340 ARM SoM Board

In the era of rapid development of the Industrial Internet of Things (IIoT) and smart manufacturing, data acquisition devices play a crucial role in bridging the physical world and the digital world. They are responsible for collecting sensor data, performing preprocessing, storing information, and transmitting it to higher-level systems. The SoM340 ARM Embedded Core Board developed by BLIIoT, with its high-performance processor, rich interface resources, and industrial-grade reliability, provides an ideal platform for building advanced data acquisition systems.

Core Advantages of SoM340

High-Performance Processor: Powered by Allwinner T507-H, quad-core ARM Cortex-A53, up to 1.416GHz, supporting hardware-level 4K video encoding/decoding.

Rich Interface Resources: Equipped with 6 UARTs, 2 SPIs, 6 TWIs, 4 GPADCs, USB, and dual Ethernet ports, enabling flexible sensor and peripheral connections.

Industrial-Grade Reliability: Operating temperature range from -40℃ to 85℃, with low power consumption (0.95W in idle mode), ensuring long-term stable operation.

Comprehensive Software Ecosystem: Supports Linux-RT, Ubuntu, and Android operating systems, along with Qt, Docker, and Node-Red, making application development efficient and versatile.

System Architecture of Data Acquisition Device

Sensor Layer

Analog signals: voltage, current, temperature, pressure → connected via GPADC/LRADC.

Digital signals: serial sensors, CAN bus devices → connected via UART/SPI/TWI.

Video/Image: cameras → connected via MIPI CSI.

Acquisition & Processing Layer

SoM340 runs Linux drivers to handle data collection, buffering, and preprocessing.

DDR4 memory provides high-speed data buffering, while eMMC storage ensures reliable local data saving.

Communication Layer

Dual Gigabit Ethernet, 4G/WiFi/Bluetooth enable data transmission to cloud platforms or edge gateways.

Supports industrial protocols such as MQTT and EtherCAT.

Application Layer

Local monitoring: Qt-based GUI for real-time visualization.

Cloud applications: Docker containers for data analytics, Node-Red for data flow orchestration.

Typical Application Scenarios

Power Systems: Collect voltage, current, and power parameters for remote monitoring and fault diagnosis.

Rail Transit: Ticket readers and track equipment status monitoring.

Industrial Automation: Vehicle control consoles, motion controllers, and industrial tablets.

New Energy: Data acquisition and efficiency management in photovoltaic and energy storage systems.

IoT Gateways: Acting as edge computing nodes to aggregate multi-source data and upload to the cloud.

Development Case References

ARM-FPGA communication acquisition (SPI/SDIO).

Multi-channel synchronous AD acquisition (compatible with AD7606/AD7616).

Docker container and MQTT protocol for cloud data transmission.

Dual-display and OpenCV-based video acquisition.

Conclusion

The SoM340 ARM Embedded Core Board, with its high performance, low power consumption, versatile interfaces, and industrial-grade reliability, provides a solid foundation for developing data acquisition systems. Leveraging its flexible software ecosystem and comprehensive development resources, engineers can rapidly build efficient solutions for power systems, transportation, industrial automation, and renewable energy applications.

SoM340 ARM Core Board, with its high performance, low power consumption, versatile interfaces, and industrial-grade reliability, provides a

#ARM Embedded SOM #SOM #System on module #Acquisition Device #ARM SoM #core board

ARM Edge Controller and ThingsBoard Edge: Building an Intelligent Production Equipment Status Monitoring System

In the wave of industrial digital transformation, real-time monitoring and intelligent management of production equipment have become key drivers for enterprises to enhance efficiency and reduce costs. While traditional cloud-based monitoring offers robust functionality, it faces limitations in real-time performance, reliability, and bandwidth consumption. With the rise of edge computing, the integration of ARM edge controllers and ThingsBoard Edge provides an efficient, flexible, and highly scalable solution for production equipment status monitoring. This approach shifts computational resources to the edge, enabling local intelligent processing alongside cloud collaboration, and empowers businesses to build a resilient industrial IoT ecosystem.

Architecture Design

The solution employs a dual-layer architecture of "edge controller + edge IoT platform" to ensure seamless data acquisition, processing, and management:

ARM Edge Controller BL370 Series

Deployed on-site in production environments, it directly collects equipment runtime data (e.g., temperature, current, vibration, RPM).

Supports multiple industrial protocols (Modbus, OPC UA, MQTT, CAN, etc.), enabling seamless integration with diverse devices.

Features built-in local computing capabilities for data preprocessing, anomaly detection, and filtering, significantly reducing the upload of redundant data and improving transmission efficiency.

ThingsBoard Edge

Deployed on the edge controller or local servers as an on-site IoT platform.

Provides device management, telemetry data collection, rule engines, and local visualization features, including real-time dashboards and report generation.

Synchronizes data with the cloud-based ThingsBoard Server, supporting centralized cross-factory management and advanced big data analytics.

Application Scenarios

Real-Time Status Monitoring: Equipment operational status can be viewed locally in the workshop, ensuring full transparency in the production process.

Anomaly Alerts: The edge rule engine instantly detects deviations (e.g., overheating or abnormal vibration) and triggers local or cloud-based notifications.

Data Buffering and Resumable Uploads: During network disruptions, the edge node automatically caches data, resuming seamless synchronization to the cloud upon recovery to maintain data integrity and continuity.

Layered Management: On-site ThingsBoard Edge handles device connectivity and initial processing, while the cloud platform focuses on cross-factory data aggregation, analysis, and reporting.

Advantages Analysis

Low-Latency Response: Local processing and alerting mechanisms meet the high real-time demands of industrial scenarios, achieving millisecond-level responses.

High Reliability: Even if cloud services are unavailable, the edge layer operates independently, ensuring uninterrupted monitoring and alerts.

Flexible Scalability: ARM controllers allow dynamic interface configuration based on device types; ThingsBoard Edge supports modular rule and visualization extensions, adapting to evolving production environments.

Cost Optimization: Dramatically reduces cloud computing loads and storage needs, minimizing bandwidth usage for more economical digital deployments.

Future Outlook

As demands for on-site intelligence continue to grow in industrial settings, the fusion of ARM edge controllers and ThingsBoard Edge will expand its application boundaries:

Predictive Maintenance: Integrating edge AI models for real-time data analysis to forecast potential failures in advance, extending equipment lifespan.

Energy Optimization: Monitoring equipment energy patterns and leveraging cloud algorithms to implement energy-saving strategies, advancing green manufacturing.

Cross-Industry Expansion: Beyond manufacturing, it extends to sectors like energy, power, and transportation, fostering universal innovation in the Industrial IoT.

Conclusion

The collaborative architecture of ARM edge controllers and ThingsBoard Edge infuses production equipment status monitoring with a potent blend of real-time capability, reliability, and scalability. It not only elevates the intelligence of production sites but also lays a solid foundation for enterprise digital transformation. Looking ahead, this solution will thrive across more industries, propelling the Industrial IoT toward an era of greater intelligence and autonomy.

The collaborative architecture of ARM edge controllers and ThingsBoard Edge infuses production equipment status monitoring with a potent ble

#ARM Edge Controller #Thingsboard gateway #Status Monitoring System #ARM Edge Gateway #Thingboard IoT Gateway

OpenPLC, Node-RED, BLRAT, and FUXA: A Complete Industrial Automation Workflow from Hardware Control to Visual Monitoring

The combination of OpenPLC, Node-RED, BLRAT, and FUXA enables a lightweight, open-source, end-to-end automation solution. It covers hardware control, logic orchestration, data processing, visualization, and remote maintenance—all without commercial software licensing. This makes the solution ideal for small and medium automation projects, remote operations, education, and secondary development.

Key Application Scenarios

1) EdgePLC Industrial Equipment Monitoring & Control

Goal: Real-time device monitoring, remote control, and automated alarms. Use Cases: Conveyors, motors, temperature/humidity regulation, auxiliary machine control.

Workflow:

OpenPLC: Collects sensor data (temperature, current) and drives actuators (relays, VFDs).

Node-RED: Implements logic such as “If temperature exceeds limit → shut down device → trigger alarm.”

BLRAT: Provides secure remote access and maintenance.

FUXA: Displays real-time charts, device status, alarms, and manual control buttons.

2) ARMxy Smart Home / Laboratory Automation

Goal: Low-cost automation and environment control without commercial smart-home platforms. Use Cases: Lighting control, HVAC, smart curtains, incubators, irrigation systems.

Workflow:

OpenPLC: Using ARMxy X23 (RS485), Y24 (relays), Y01 (DI/DO) to connect sensors and actuators.

Node-RED: Example logic: “Humidity < 40% → start humidifier” or “After 7 PM → open-door triggers auto-light.”

BLRAT: Remote device access.

FUXA: Mobile/PC interface for real-time values and manual control.

3) ARMxy Small-Scale Production Data Traceability

Goal: Capture production counts, runtime, and generate real-time and historical reports. Use Cases: Packaging lines, small assembly machines, product counters.

Workflow:

OpenPLC: Reads count pulses and equipment runtime.

Node-RED: Stores data to MySQL/InfluxDB and calculates metrics (hourly output, failure rate).

BLRAT: Ensures remote maintenance.

FUXA: Displays daily/weekly trends and supports historical queries.

System Architecture

A standard three-layer structure ensures modularity, flexibility, and easy expansion.LayerComponentRoleProtocolsHardware ControlOpenPLCI/O control and sensor acquisitionModbus TCP, MQTTLogic LayerNode-REDAutomation logic, event handling, DB integrationMQTT, HTTP, DB connectorsVisualization LayerFUXAHMI dashboard, data display, manual control, reportingMQTT, WebSocketRemote MaintenanceBLRATSecure remote access for all modulesEncrypted tunnel

The combination of OpenPLC, Node-RED, BLRAT, and FUXA makes it ideal for small to medium-sized automation projects, remote operation, educat

#ARMxy #ARM computer #OpenPLC #Node-RED #BLRAT #FUXA #Industrial Automation Workflow #Visual Monitoring

Industrial-Grade HMI Solution Based on SoM330 (T113-i, Dual-Core Cortex-A7)

In the context of rapidly advancing digital factories and intelligent equipment, industrial-grade Human-Machine Interfaces (HMIs) have become core interactive platforms for manufacturing, building control, mechanical equipment, and energy systems. Enterprises require not only cost-effective display and control platforms but also embedded hardware solutions with high stability, long lifecycle, and rich industrial interfaces.

The embedded platform based on SoM330 (T113-i, Dual-Core Cortex-A7) offers a highly competitive choice for building next-generation industrial HMIs, thanks to its abundant peripherals, low power consumption, high reliability, and comprehensive BSP support from the vendor.

Introduction to SoM330 (T113-i): Designed for Industrial Displays

The SoM330 series core board is powered by the Allwinner T113-i processor, featuring a dual Cortex-A7 architecture with a maximum clock speed of 1.2GHz and an integrated NEON acceleration engine, balancing stability and low power consumption.

Key features include:

✔ Abundant Display Capabilities

Supports multiple display outputs: RGB, LVDS, MIPI-DSI

Up to 1080p@60fps hardware display

Built-in 2D acceleration engine + video decoding unit, easily handling dynamic UIs, animated interfaces, and video playback

✔ Tailored for Industrial Communication

Integrated 2x CAN and 6x UART

Rich GPIO, SPI, I2C, PWM resources

Ideal for device status monitoring, sensor integration, motion control panels, and similar scenarios

✔ High Reliability and Industrial Standards

Wide temperature support: -20~70°C / -40~85°C (model-dependent)

ECC memory, power-loss protection, and stable eMMC storage

Mature Linux/Ubuntu/Buildroot BSP available

By adapting to a dedicated carrier board, SoM330 enables the construction of a highly reliable and scalable industrial-grade HMI platform.

Core Advantages of SoM330 for Industrial HMIs

High Cost-Performance Ratio, Ideal for Mass Production

The T113-i platform features ultra-low power consumption and stable performance, making it suitable for 4.3"~15.6" industrial touchscreens. Compared to traditional ARM9 or early Cortex-A7 solutions, SoM330 delivers stronger graphics rendering capabilities at a lower BOM cost.

Robust Display Drivers, Compatible with Various Industrial Panels

Direct compatibility with numerous off-the-shelf RGB/LVDS screens

Support for resistive and capacitive touch

Graphics acceleration via EGL / OpenGL ES 2.0

Thus, SoM330 is well-suited for applications such as:

✔ Industrial production line touchscreens

✔ Equipment front-panel displays

✔ Smart warehousing and robot interfaces

✔ Energy management screens

✔ Smart home/building control HMIs

Rich Industrial Interfaces, Reducing Peripheral Conversion Costs

With built-in dual CAN buses and multiple UARTs, SoM330 seamlessly connects to PLCs, inverters, servo controllers, and more, enabling device status monitoring and parameter adjustments.

These features provide inherent advantages in industrial environments:

CAN: Ideal for industrial control, robotics, and agricultural machinery

RS485 (via UART expansion): Supports Modbus RTU

Ethernet: Supports Modbus TCP / MQTT / OPC UA

Efficient Development with Linux / Buildroot / Qt

The vendor provides comprehensive hardware and software resources, including:

Linux 4.x BSP

Buildroot SDK

Qt/Qt Quick graphics libraries

Complete driver examples (LCD, touch, CAN, UART, etc.)

Development teams can rapidly prototype interfaces, shortening the time-to-market for HMI products.

Reference System Architecture for Industrial HMIs

A typical industrial HMI system based on SoM330 consists of the following components:

Processing Core: SoM330 (T113-i)

Handles UI rendering, logic control, communication protocols, and data processing

Display Module

LVDS / RGB screens

4.3”~15.6” industrial touchscreens

I2C capacitive touch (e.g., GT911/FT5x06)

System Interfaces

CAN0 / CAN1

RS485 (UART expansion)

USB Host

Ethernet

GPIO control ports

Software Framework

Linux + Qt HMI framework

Modbus / CANopen / Custom serial protocols

MQTT / HTTP/HTTPS / OPC UA

Local UI + OTA upgrade mechanism

Enclosure and Industrial Design

Metal housing

EMC interference resistance

Wide-temperature operation

Regulated power supply with surge protection

This architecture supports diverse needs, from device-level HMIs and gateway HMIs to edge-computing HMIs.

Typical Application Scenarios

Mechanical Equipment Operation Panels

For CNC machines, injection molding, packaging, and spraying equipment—used for parameter configuration, alarm display, and process monitoring.

Factory Smart Dashboard Interfaces

Connects sensors and PLCs to display real-time data like temperature/humidity, speed, current, voltage, and operational status.

Energy and Environmental Control Panels

Such as PV inverter screens, energy storage BMS panels, and building HVAC control interfaces.

Smart Home and Building Control

Smart access control, video intercoms, elevator operation panels, and home central control screens.

Agricultural and Transportation Equipment

Agricultural irrigation/feed machinery, sorting systems, and vehicle information terminals.

SoM330's low power consumption and high stability make it particularly popular in small- to medium-sized HMIs.

Development Recommendations for HMIs Based on SoM330

Hardware Carrier Board Design Suggestions

Prioritize LVDS interfaces for enhanced anti-interference

Use I2C capacitive touchscreens for easier calibration

Retain CAN transceivers and RS485 converters

Include USB Host for USB drive upgrades and peripheral expansion

Strengthen power filtering and TVS protection

Software Development Suggestions

Design modern industrial interfaces with Qt/QML

Leverage hardware acceleration to prevent UI lag

Separate UI and data threads to avoid blocking

Implement OTA updates (local/remote)

Use circular logging to prevent eMMC wear-out

Reliability Design

Watchdog timer mechanism

Boot-time self-diagnostics (network/sensors/peripherals)

Power-loss protection and abnormal restart recovery

Temperature monitoring with UI degradation modes

Conclusion

SoM330 (T113-i, Dual-A7) is a truly industrial-grade embedded platform tailored for HMIs. Its low power consumption, high stability, rich industrial interfaces, and strong display capabilities make it ideal for 4.3”~15.6” HMI scenarios, while supporting high-volume production.

Whether for equipment operation panels, smart factory interfaces, energy management screens, or building control panels, SoM330 empowers device manufacturers to quickly build reliable, economical, and flexible HMI product ecosystems.

SoM330 (T113-i, Dual-A7) is a truly industrial-grade embedded platform tailored for HMIs. Its low power consumption, high stability, rich in

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Advantages of Industrial-Grade Raspberry Pi CM5 Controllers

Industrial-grade Raspberry Pi CM5 controllers, such as the ARMxy BL460 series, are built on the Broadcom BCM2712 quad-core Cortex-A76 processor (the core of Raspberry Pi 5/CM5) and are specifically optimized for harsh industrial environments. Compared to standard consumer-grade Raspberry Pi CM5 modules, they offer superior durability, expandability, and compatibility. Below is a concise summary of their key advantages, categorized by hardware, software, environmental adaptability, and application flexibility.

High Performance and Flexible Configuration

Processor and Memory: Quad-core Cortex-A76 processor at 2.4GHz, with a GPU supporting OpenGL ES 3.1, Vulkan 1.2, and 4K@60fps decoding. It accommodates up to 64GB eMMC storage and 16GB LPDDR4X RAM, ideal for edge computing and multitasking demands.

Rich I/O Expansion: Features 1-3 Ethernet ports (with ESD/EFT protection), 2x USB 3.0, HDMI 2.1, M.2 SSD slot, Mini PCIe (for 4G modules), SIM slot, and antenna. The X/Y series I/O boards enable expansion for RS485/RS232, DI/DO, AI/AO, RTD, TC, and more, with customizable configurations (e.g., 4x RS485 or 8x DI).

Advantages: Far exceeds the standard CM5's I/O capabilities, facilitating seamless integration of industrial protocols like Modbus and OPC UA, while minimizing the need for external adapters.

Robust Software Ecosystem and Development Ease

Raspberry Pi Compatibility: Native support for Raspberry Pi OS, Linux 6.6.78, Docker, Node-RED, Qt 5.15.11, and WiringPi libraries, enabling effortless migration of existing projects.

Industrial-Specific Tools: Includes BLIoTLink (for protocol conversion supporting MQTT, IEC104, and cloud platforms like AWS IoT/Alibaba IoT), BLRAT (remote maintenance), and QuickConfig (AI-assisted setup). Offers 40-pin GPIO expansion and extensive demos (e.g., in Python, C#, MySQL).

Advantages: Lowers development barriers with support for industrial real-time control (via Linux-RT), plus open kernel source code and platform development kits to accelerate prototyping to mass production.

Industrial-Grade Durability and Environmental Resilience

Temperature Range: Operates from -20°C to +85°C, with verified low/high-temperature boot testing.

Protection and Mounting: Aluminum alloy and stainless steel enclosure, compatible with DIN35 rail or wall mounting; IP30 dust protection, built-in watchdog timer, reset button, and safeguards against reverse polarity and overcurrent.

Certifications and Testing: Compliant with GB/T and IEC electromagnetic compatibility standards (Class A for radiation/conduction, ESD Level III, EFT/Surge Level III) and environmental tests (2g vibration, 0.8m free fall, 40°C/85% RH constant humidity).

Advantages: Ensures reliable performance in harsh industrial settings (e.g., vibration or humidity), surpassing the consumer-grade limitations of standard CM5 for 24/7 continuous operation.

Versatile Applications and Cost-Effectiveness

Typical Use Cases: Industrial PLCs, edge gateways, EV charging stations, AGVs/industrial robots, energy storage, and smart manufacturing, with robust IoT data acquisition and cloud integration.

Flexible Product Options: Modular design (SOM + X/Y boards), e.g., BL461-SOM4612-X10 (2 ETH ports, 32GB eMMC, 16GB RAM, 4 RS485), with wireless extensions (4G/5G/WiFi). Lite versions omit eMMC for cost reduction.

Advantages: High customizability and excellent value; includes long-term after-sales support, R&D customization, and manufacturing services for diverse needs from test instruments to rail transit.

Comparison Table: Standard Raspberry Pi CM5 vs. Industrial-Grade BL460 Series

AspectStandard Raspberry Pi CM5Industrial-Grade BL460 SeriesKey Advantages of BL460 SeriesTemperature Range0°C ~ 50°C-20°C ~ +85°C, industrial wide-temperatureEnhanced resilience in extreme environmentsI/O ExpansionBasic GPIO/USBX/Y boards with 20+ I/O types, protocol conversionBroader compatibility and reduced adaptersProtection & CertificationsNone (consumer-grade)EMC Class A, IP30, vibration/drop testsMeets industrial safety and reliability standardsSoftware ToolsRaspberry Pi OS /DockerBLIoTLink/BLRAT + cloud integration demosStreamlined development and remote managementMountingBreadboard/customDIN rail standard, aluminum industrial enclosureEasy integration into industrial setups

Compared to the standard CM5, the BL460 series leads in temperature range, I/O expansion, protection certifications, and software tools. Combining the ease of use and industrial robustness of Raspberry Pi, it is ideal for IoT/automation upgrade projects.

Compared to the standard CM5, the BL460 series is superior in temperature range, I/O expansion, protection certifications and software tools

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SoM340 System on Module: Building a High-Performance ARM Motion Control Core Platform

In the era of rapid evolution in smart manufacturing and automation equipment, the motion controller serves as the "brain" of the machine, undertaking critical tasks such as coordinating multi-axis movements, executing precise trajectory planning, and delivering real-time responses. The SoM340 System on Module (SoM) launched by Beilai Technology, based on Allwinner Technology's T507-H quad-core ARM Cortex-A53 processor, stands out with its superior computing power, low power consumption, and abundant industrial interfaces, making it the ideal choice for next-generation domestic motion control platforms.

High-Performance ARM Core Driving Precise Control

The SoM340 features a 64-bit quad-core Cortex-A53 architecture with a clock speed up to 1.416GHz, supporting operating systems such as Linux, Linux-RT, Ubuntu, and Android, while being compatible with the Qt 5.12 graphical interface development environment. In multi-task real-time control scenarios, it efficiently handles path planning, servo signal analysis, and human-machine interface rendering, providing robust computing support for complex mechanical equipment.

CPU: T507-H, quad-core 64-bit ARM Cortex-A53

GPU: G31 MP2, supporting OpenGL / Vulkan / OpenCL

Memory Configuration: 1GB / 2GB DDR4 + 8GB / 16GB eMMC

Power Consumption: Only 2.15W at full load

This configuration not only suits high-speed motion control cores but also seamlessly integrates image display and algorithm computation, achieving deep fusion of control and vision.

Abundant Interfaces Meeting Complex Control Demands

Motion control imposes stringent requirements on interface real-time performance and precision. The SoM340 exposes over 170 pin resources via a stamp-hole design, covering all core interfaces essential for industrial control:

6 UARTs, 2 SPIs, 6 PWMs — Driving stepper motors and servo systems;

Multi-channel ADC (12-bit precision) — Precisely capturing analog signals like torque, current, and displacement;

Dual Ethernet ports (supporting gigabit RMII/RGMII) — Building EtherCAT or Modbus TCP networks;

CAN bus and RS485 expansion interfaces — Enabling communication with traditional PLCs and sensors;

4 USBs, HDMI, LVDS, MIPI-CSI — Supporting HMI screens and industrial camera connections.

Additionally, the SoM340 includes built-in IgH EtherCAT master and SPI-to-CAN driver examples, facilitating rapid deployment of EtherCAT servo control systems for multi-axis synchronization and real-time motion.

Real-Time Systems and Visualization Development Support

The SoM340 is compatible with various real-time operating systems and development tools, including:

Linux-RT Real-Time Kernel — Precisely managing servo synchronization and interrupt responses;

Docker + Node-RED — Rapidly building cloud-edge collaborative logic;

Qt 5.12.5 — Developing high-performance industrial human-machine interfaces (HMIs);

OpenCV / AI Modules — Supporting vision guidance and intelligent inspection.

These features empower the SoM340 to go beyond traditional motion control, seamlessly integrating AI, data acquisition, and remote monitoring to serve as a core computing node in smart factories.

Typical Application Scenarios

With its high reliability and flexible scalability, the SoM340 finds broad applications across automation equipment:

CNC Machining Centers: Multi-axis real-time interpolation and path optimization;

AGV / AMR Intelligent Mobile Platforms: Integration of motor drives, positioning, and path planning;

Robot Controllers: Multi-joint coordinated motion and force feedback;

SMT Placement and Laser Cutting Systems: High-frequency PWM control with nanosecond-level synchronous response;

Automation Production Line Equipment: EtherCAT bus control and visualization monitoring.

Leveraging the SoM340, developers can efficiently construct integrated software-hardware motion control solutions, significantly shortening R&D cycles.

Industrial-Grade Stability and Long-Term Reliability

The SoM340 employs industrial-grade components and an 8-layer PCB design, ensuring stable operation in temperatures from -40°C to +85°C. It supports DIN35 rail mounting for seamless integration into standard industrial cabinets, meeting 7×24-hour continuous operation demands. Barium Rhenium Technology also provides:

Editable baseboard schematics and 3D files;

System images and driver source code;

Example programs for FPGA communication, AD acquisition, CAN/EtherCAT;

Long-term technical support and custom development services.

Conclusion

The SoM340 System on Module from Beilai Technology, with its high-performance ARM architecture, abundant interface resources, real-time OS support, and industrial-grade reliability, delivers a stable and efficient core platform for domestic motion control systems. Whether for robots, CNC equipment, smart logistics, or automation production lines, the SoM340 can transform into the system's "brain," empowering enterprises to achieve a comprehensive upgrade from traditional control to intelligent operations.

SoM340 core board can be used in robots, CNC machine, logistics, and automated production lines, helping enterprises achieve a comprehensive

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ARM Edge Controllers in Three-Color Light Monitoring for CNC Machining Production Lines

In the wave of intelligent manufacturing, CNC machining production lines are progressively advancing toward digitalization and visualization management. The three-color light, as a traditional tool for indicating equipment status, is simple yet essential. By introducing ARM architecture-based edge controllers, three-color light signals can be transformed into collectible, analyzable, and remotely manageable data resources, thereby building an efficient and intelligent production line monitoring system.

Application Background and Challenges

CNC machining equipment is typically equipped with three-color lights to indicate running (green), standby (yellow), and fault (red) states. However, traditional three-color lights only provide local visual prompts and cannot achieve remote monitoring, data statistics, or intelligent responses, which hinders the transparency and automation level of production lines.

Key challenges include:

Inability to remotely acquire equipment status;

Inability to store and analyze status data;

Delayed fault responses, impacting production line efficiency.

Solution Architecture

The ARM edge controller BL335 Series serves as a bridge connecting physical devices and digital systems, offering advantages such as low power consumption, high performance, rich interfaces, and robust protocol support. Its core roles in the three-color light monitoring solution are as follows:

Signal Acquisition

Collect three-color light status signals via GPIO, RS485, IO modules, and other methods;

Support industrial-grade acquisition methods such as level triggering and photoelectric isolation.

Edge Processing

The controller runs embedded Linux or RTOS with built-in status recognition logic;

Enable local status display (e.g., via LCD, LED, or Web HMI);

Implement status change logging, anomaly detection, and local alarms.

Data Upload and Integration

Support protocols such as MQTT, Modbus, and OPC UA;

Integrate with CNC control systems, MES, and SCADA platforms;

Enable remote equipment status monitoring, statistical analysis, and fault tracing.

Typical Application Scenarios

The three-color light status controller can respond to backend processing based on different signals:

Green (Running): Record running time to update utilization rates and production statistics;

Yellow (Standby): Track idle duration to analyze bottlenecks and optimize scheduling;

Red (Fault): Trigger local buzzers and remote alarms to notify maintenance personnel and generate fault reports.

Additionally, the controller can be extended to collect data on temperature, vibration, current, and more, enabling multi-dimensional status monitoring and predictive maintenance.

Solution Advantages

Strong Real-Time Performance: Edge processing avoids cloud latency, achieving second-level responses to equipment status changes;

Flexible Deployment: Supports DIN rail mounting, PoE power supply, wireless communication, and other methods;

High Compatibility: Adapts to various CNC brands and three-color light models;

Robust Visualization: Supports Qt graphical interfaces, Web Dashboards, and mobile viewing.

Conclusion

ARM edge controllers empower three-color light monitoring, not only enhancing the visualization and responsiveness of CNC machining production lines but also laying a solid data foundation for intelligent manufacturing. In the future, this solution can further integrate AI analysis and industrial vision to achieve higher levels of production line intelligence.

ARM edge controllers empower three-color light monitoring, not only enhancing the visualization and responsiveness of CNC machining producti

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Home Assistant + ARM Controller: The Perfect Synergy for Smart Homes and Building Automation

Home Assistant is a globally renowned open-source platform for managing smart homes, focused on delivering a "localized, customizable, and secure" automation control system. It integrates thousands of brands and protocols for smart devices—including lights, air conditioners, cameras, sensors, access controls, and voice assistants—enabling unified visualization, management, and automated logic orchestration.

Key features of Home Assistant include:

🧩 Openness: Supports over 2,000 devices and protocols (e.g., MQTT, Modbus, Zigbee, Z-Wave, Wi-Fi, RS485);

🔒 Data Privacy and Security: All data is stored locally on your server by default, with no cloud uploads required;

⚙️ High Customizability: Complex automation logic can be built using YAML, Python, or Node-RED;

🌐 Multi-Platform Accessibility: Compatible with web browsers, mobile apps, and voice assistants (e.g., Google Home, Alexa).

In essence, Home Assistant acts as a "central hub," allowing users to manage all smart devices in their home or facility through a unified "digital brain."

Why Choose an ARM Controller for Running Home Assistant?

Selecting the right hardware is crucial when deploying Home Assistant. Compared to traditional x86 servers or Raspberry Pi boards, ARM-based industrial controllers (such as Barium ReTech's BL series) offer superior stability, interface compatibility, power efficiency, and local processing capabilities.

For instance, models like the BL440 (powered by RK3576) or BL450 (powered by RK3588) provide:

Powerful Processing: Multi-core ARM Cortex CPUs, with some models featuring NPUs delivering up to 26 TOPS of AI compute power;

Rich Industrial Interfaces: RS485, RS232, DI/DO, CAN, Ethernet, Wi-Fi, 4G modules, and more;

Low-Power, Reliable Operation: Enables 7x24-hour continuous runtime, ideal for edge deployments;

System Flexibility: Compatible with Debian, Ubuntu, Buildroot, Yocto, and easy Docker installation;

Edge Computing Capabilities: Supports on-device data analysis, AI recognition, and automated decision-making, reducing cloud dependency.

These attributes make ARM controllers an ideal hardware foundation for Home Assistant.

Benefits of Integration

Localized Smart Control No cloud dependency—all logic and data reside within the ARM controller for fast, secure, and reliable responses.

Strong Edge Computing Run AI models or data analytics directly on the controller to enable features like device recognition, energy forecasting, and environmental adjustments.

Multi-Protocol Fusion Seamlessly integrates industrial and home protocols (e.g., Modbus RTU, MQTT, Zigbee, RS485), allowing interoperability across diverse brands.

Stability and Energy Efficiency Low power consumption, minimal heat generation, and fanless design support 24/7 operation, perfect for smart buildings or remote sites.

High Scalability Docker-based deployment allows co-running tools like Node-RED, Grafana, and InfluxDB on the same device for advanced data analytics and visualization.

Typical Application Scenarios

Smart Home Systems: Centralized control of lighting, HVAC, security cameras, and access systems;

Smart Buildings and Hotels: Automated energy optimization and guest room monitoring;

Industrial Environmental Monitoring: RS485-based sensor data collection and visualization in Home Assistant;

Smart Agriculture: Integration with temperature/humidity sensors, fans, and pumps for automated irrigation and climate control;

AI Security (RK3588 Platform): Combine OpenCV with Home Assistant for automated alerts and threat detection.

Conclusion

The combination of Home Assistant and ARM controllers goes beyond a simple smart home solution—it's a versatile edge intelligence platform. It addresses automation needs across residential, commercial, and industrial settings with lower power use, higher reliability, and enhanced customizability, making true smart control a reality.

Looking ahead, as ARM platforms advance in compute power and ecosystem maturity, this integration will emerge as a cornerstone architecture in the smart automation landscape.

The combination of Home Assistant and an ARM controller transcends a simple smart home solution, becoming a fully functional edge intelligen

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Why ARM Industrial PCs Serve as the Core Brain of Robot Control？

In today's rapidly advancing landscape of intelligent manufacturing and industrial automation, robot systems are evolving toward higher performance, lower power consumption, and greater intelligence. At the heart of this technological revolution, ARM-based industrial PCs (IPCs) stand out with their superior architecture and high integration capabilities, making them an indispensable "core brain" in robot control systems.

Inherent Advantages of ARM Architecture

The ARM (Advanced RISC Machine) architecture, with its streamlined instruction set, high energy efficiency, and flexible System-on-Chip (SoC) design, is widely adopted in embedded systems. Compared to traditional x86 architectures, ARM IPCs offer distinct advantages for robot control:

Low Power, High Performance: Ideal for deployment in mobile robots and collaborative robots where extended battery life is critical.

High Integration: SoC design consolidates CPU, GPU, NPU, ISP, and other modules into a single chip, reducing space requirements and enhancing response times.

Strong Real-Time Capabilities: Supports real-time operating systems like Linux-RT, meeting millisecond-level control demands.

The BL440 Series: ARM IPCs Tailored for Robotics

Take the BL440 series from BLIIoT as an example—its hardware specifications and software ecosystem are perfectly aligned with robot control needs:

✅ Core Performance

Processor: RK3576J/RK3576, an 8-core architecture (4×A72 + 4×A53 + 1×M0), with a maximum clock speed of 2.2GHz.

NPU: 6 TOPS computing power, compatible with mainstream deep learning frameworks like TensorFlow and PyTorch.

GPU & ISP: Supports OpenGL/Vulkan for graphics rendering and 16MP image processing, suitable for machine vision applications.

✅ Communication and Expansion Capabilities

Network Interfaces: Up to 3 RJ-45 ports, supporting Gigabit Ethernet.

I/O Modules: Compatible with X/Y series expansion boards, covering industrial interfaces such as RS485, CAN-FD, DI/DO, AI/AO, PWM, PT100, and TC.

Wireless Communication: Supports WiFi, 4G, and 5G modules for remote control in mobile robots.

✅ Software Ecosystem

Operating Systems: Compatible with Linux, Ubuntu, Docker, and Node-RED.

Protocol Support: Built-in BLIoTLink for industrial protocols including Modbus, MQTT, OPC UA, and IEC104.

Remote Management: Features BLRAT for remote access and QuickConfig for rapid setup.

Typical Applications in Robot Control

ARM IPCs play pivotal roles across diverse robot control scenarios. The following table outlines key applications:Application ScenarioRole of ARM IPCAGV CartsPath planning, navigation, and motion controlCollaborative RobotsMulti-axis linkage control, visual recognition, and task schedulingIntelligent Assembly LinesReal-time data acquisition, actuator control, and status feedbackMachine Vision InspectionImage processing, AI recognition, and result determinationAutonomous Navigation RobotsSensor fusion, obstacle avoidance algorithms, and environmental mapping

Reliability Assurance in Industrial Environments

The BL440 series undergoes rigorous electromagnetic compatibility (EMC) and environmental adaptability testing to ensure stable operation in harsh industrial settings:

Operating Temperature Range: -40°C to 85°C

Interference Resistance: Passes ESD, EFT, and Surge tests at Level 3 standards

Protection Rating: IP30, dust- and debris-resistant

Mounting Options: Supports DIN35 rail and wall mounting

Conclusion

ARM IPCs are not merely the "brain" of robot control; they act as the central nervous system, bridging perception, decision-making, and execution. As AIoT technologies continue to converge, high-performance ARM IPCs like the BL440 series will drive robot systems toward greater intelligence, efficiency, and reliability.

ARM IPCs are not merely the "brain" of robot control; they act as the central nervous system, bridging perception, decision-making, and exec

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Application of ARM Controllers in Smart Hydroponics Systems

As modern agriculture moves toward intelligence and sustainability, Hydroponics, a hybrid system that combines aquaculture and plant cultivation, has become an important direction for future farming. However, Hydroponics systems are highly sensitive to water conditions. Parameters such as temperature, pH, dissolved oxygen (DO), electrical conductivity (EC), and ammonia levels must be continuously monitored and dynamically regulated. Traditional manual monitoring is inefficient and error-prone, making it difficult to manage complex environments. Therefore, integrating an ARM-based intelligent controller has become the key to automation and optimization in such systems.

Advantages of the ARM Controller

The ARM controller is known for its low power consumption, high performance, and high integration, making it ideal for deployment in humid and variable Hydroponics environments. Its advantages include:

⚙️ High Stability – Industrial-grade ARM processors can operate reliably in high-humidity and water-vapor environments.

📶 Multi-Interface Compatibility – Supports RS485, CAN, I²C, SPI, and UART interfaces for connecting various sensors and actuators.

🤖 Edge Intelligence – Built-in AI inference capability allows local algorithm execution and intelligent control.

☁️ IoT Connectivity – Supports 4G/5G, Ethernet, for real-time cloud communication and remote maintenance.

System Architecture

A typical ARM-based smart Hydroponics system consists of four main layers:

Data Acquisition Layer

Includes pH sensors, DO probes, temperature sensors, EC probes, and water level sensors.

The ARM controller collects real-time data through ADC or serial interfaces.

Intelligent Control Layer (Core)

ARM controllers (e.g., BL410 based on RK3568 or BL440 based on RK3576) run on Linux and support Node-RED or Python control logic.

Real-time control decisions:

Low DO → Start aeration pump

Low water level → Trigger water refill

Abnormal pH → Activate regulation system

Enables edge AI analysis for anomaly detection and predictive control.

Communication & Cloud Layer

Uses MQTT, HTTP, or Modbus TCP protocols for cloud connectivity.

Cloud services handle data storage, visualization, alerts, and remote configuration.

Monitoring & Operation Layer

Operators can access real-time water quality data via web dashboards or mobile apps.

The system automatically generates reports and energy consumption summaries for easy management.

Key Features

✅ Fully Automated Control – Manages aeration, lighting, and water circulation intelligently. ✅ AI-Based Predictive Maintenance – Edge AI forecasts potential water quality deterioration. ✅ Cloud Visualization – Real-time dashboards for performance and analytics. ✅ Offline Reliability – Local logic continues to run even when the network is down. ✅ Energy Efficiency – Smart scheduling reduces energy usage while improving yield.

Application Example

In a smart agriculture demonstration project, a BL410 ARM controller was used to build a smart hydroponics system that achieved:

Water quality accuracy within ±0.1 units,

A 12% increase in fish survival rate,

18% reduction in energy consumption,

24-hour unmanned operation with remote monitoring and automatic alarms.

Conclusion

The integration of ARM controllers transforms hydroponics from passive monitoring to active intelligent control. With strong computational performance, low power consumption, and edge AI capability, ARM controllers are becoming the central brain of smart agriculture and circular ecosystems. In the future, as AI hydroponics and IoT technologies continue to evolve, ARM-based hydroponics systems will lead the way in delivering more efficient, eco-friendly, and intelligent agricultural solutions.

With strong computational performance, low power consumption, and edge AI capability, ARM controllers are becoming the central brain of smar

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Empowering Juice Production Lines with ARM AI Edge Computers: A New Paradigm for Intelligent Fault Diagnosis and Efficient Debugging

In the modern food processing industry, the filtration and storage stages of juice production are critical to product quality and operational efficiency. However, traditional automation systems often suffer from delayed responses and cumbersome debugging when dealing with complex faults, equipment aging, or multi-variable interferences, thereby limiting overall capacity and stability. The integration of edge computing and artificial intelligence is transforming this landscape, with ARM architecture-based AI edge computers emerging as the key enabler for intelligent upgrades in juice production lines.

Challenges: The Complexity of Faults in Juice Filtration and Storage

Equipment such as filters, pumps, storage tanks, and temperature control systems in juice production lines are highly interdependent, leading to common faults including:

Filter Clogging: Causes abnormal pressure differentials, impacting flow rates and filtration efficiency.

Storage Tank Level Sensor Drift or Failure: Results in level misjudgments and overflow risks.

Temperature Control System Malfunction: Disrupts juice preservation and fermentation control.

Pump Flow Rate Anomalies: Leads to uneven liquid supply or equipment idling.

Failure to identify and address these issues promptly can directly compromise product quality and production safety.

Solution: Intelligent Response Mechanisms of ARM AI Edge Computers BL440

Real-Time Data Acquisition and Edge Preprocessing By interfacing with sensors for pressure, flow rate, liquid level, and temperature, the edge computer performs local denoising, outlier removal, and trend analysis, mitigating cloud latency and bandwidth constraints.

AI Model-Driven Fault Detection Deploying lightweight AI models (e.g., TensorFlow Lite, ONNX Runtime) enables precise identification of fault patterns such as filter clogging, pump efficiency degradation, and temperature imbalances, supplemented by root cause analysis using historical data.

Automated Debugging Recommendations and Interlocked Control The system generates tailored debugging suggestions based on fault types (e.g., switching to backup filters, adjusting pump frequencies, restarting temperature modules) and integrates with PLC/SCADA systems via protocols like Modbus, CAN, and OPC UA for closed-loop automated control.

Visualization and Operations Optimization Supports local HMI displays or remote Web Dashboards to present fault analyses, debugging processes, and recovery logs in real time, empowering maintenance personnel for swift responses and continuous improvements.

Deployment Architecture Recommendations

ModuleRecommended Configuration DescriptionEdge HardwareARM Cortex-A72/A76 processors, integrated NPU/GPU, supporting industrial interfaces (e.g., RS-485, Ethernet).Software PlatformLinux + Docker + AI Inference Engines (TensorFlow Lite/ONNX) + MQTT/OPC UA Communication Stack.Security MechanismsLocal data encryption, authentication, remote access controls (VPN/SSL).Operations SupportOTA updates, log transmission, remote debugging interfaces (SSH/Web).

Application Value and Future Outlook

The adoption of ARM AI edge computers not only accelerates fault response times and debugging efficiency in juice production lines but also provides a replicable paradigm for intelligent transformation across the food processing industry. Looking ahead, as AI model accuracy improves and edge collaboration capabilities advance, applications will extend to filling, packaging, and cold chain logistics, paving the way for fully realized "edge intelligent factories."

ARM AI edge computer can not only accelerate fault response time and debugging efficiency in juice production lines, also promote the intell

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T507-H SoM340 Core Board: The Ideal Embedded Platform for Smart Vending Machines

In the era of rapid smart retail development, smart vending machines—as unmanned, intelligent terminal devices—are increasingly integrated into every corner of urban life. Providing stable, efficient, and scalable embedded platforms for these devices has become an industry focus. The T507-H SoM340 series ARM embedded core board from Beilai Technology, with its powerful performance, abundant interfaces, and industrial-grade stability, stands out as the premier solution for smart vending machine systems.

High-Performance Processor for Seamless Experience

The SoM340 core board features the Allwinner T507-H processor, powered by a quad-core ARM Cortex-A53 architecture clocked up to 1.416GHz, paired with a G31 MP2 GPU. It supports graphics standards like OpenGL ES and Vulkan, enabling smooth handling of multimedia ads, product display interfaces, and backend logic processing. With hardware-accelerated 4K video encoding and decoding, it fully meets high-definition content playback needs.

Abundant Interface Resources for Multimodal Interaction

Smart vending machines typically integrate peripherals such as scanners, cameras, printers, touchscreens, and payment modules. The SoM340 core board exposes up to 170 pins via a stamp-hole design, supporting MIPI CSI cameras, HDMI/LVDS/RGB displays, USB, UART, SPI, I2S, EMAC, and more. This allows effortless connectivity for various devices, building a complete interactive system.

Industrial-Grade Design for Harsh Environments

Given that vending machines are often deployed outdoors or in extreme temperature conditions, the SoM340 core board uses industrial-grade components for wide-temperature operation from -40°C to 85°C. Its 8-layer PCB design and compatibility with DIN35 rail mounting ensure reliable performance in demanding settings.

Comprehensive Software Ecosystem for Rapid Development and Deployment

The SoM340 supports mainstream OS like Linux, Ubuntu, and Android, along with Qt graphical development tools, Docker container technology, and extensive demo cases. Developers can quickly build applications for remote management, data collection, intelligent recognition, and more. MQTT protocol integration facilitates seamless cloud platform connectivity for device networking and centralized control.

Typical Application Scenarios

Multi-compartment automatic vending machines (beverages, snacks, pharmaceuticals)

Smart retail cabinets (RFID recognition, facial recognition)

Cold-chain vending machines (temperature monitoring, remote alerts)

Central control systems for mobile vending vehicles

Intelligent restocking and inventory management terminals

Value-Added Services and Technical Support

Beilai Technology offers customization services including carrier board design, embedded software development, and project collaboration. We also assist with system debugging, troubleshooting, and secondary development to help customers swiftly implement product solutions.

The T507-H SoM340 core board is more than just embedded hardware—it's a powerful accelerator for intelligent vending machine upgrades. By integrating high-performance computing, multimedia processing, industrial-grade stability, and flexible scalability, it provides robust technical support for smart retail devices.

#SoM340 #T507-H SoM340 Core Board #Core Board #system on Module #Embedded Platform #Smart Vending Machines

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