#arm computer

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

Case Details

Why Choose AWS IoT Greengrass + ARM Edge Gateways?

Cost-Effective & Energy-Efficient ARM-based hardware (e.g., ARMxy, Raspberry Pi, NVIDIA Jetson) delivers ultra-low power consumption, ideal for large-scale deployments in streetlights, sensors, and cameras, slashing infrastructure and operational costs.

Millisecond-Level Real-Time Response Process data locally at the edge—traffic flow analysis, fire/smoke detection, or emergency alerts—without cloud latency. Enable split-second decision-making for critical scenarios.

Resilient Offline Operation Maintain functionality during network outages. Greengrass executes predefined logic (e.g., traffic signal control, environmental alarms) and syncs data to the cloud once connectivity resumes.

Seamless Cloud-Edge Synergy Filter and preprocess data locally, uploading only high-value insights to AWS (e.g., IoT Core, S3). Reduce bandwidth costs while enabling global analytics and AI model refinement.

Key Smart City Applications

1. Intelligent Traffic Management: Zero Tolerance for Congestion & Accidents

Adaptive Traffic Signals ARM gateways analyze real-time camera feeds to dynamically adjust traffic light cycles, increasing rush-hour throughput by 30%+.

Accident Detection in Seconds Deploy lightweight AI models (YOLO, TensorFlow Lite) at the edge to identify accidents within 5 seconds, triggering alerts on roadside displays for faster emergency response.

2. Environmental Governance: From Monitoring to Action

Real-Time Pollution Mapping Thousands of low-power sensors aggregate data via ARM gateways, generating live heatmaps of air/noise pollution to pinpoint sources (e.g., construction sites, factories).

Smart Waste Optimization Fill-level sensors in trash bins trigger edge-based route optimization for garbage trucks, cutting unnecessary trips by 30% and reducing carbon emissions.

3. Public Safety: Proactive Risk Mitigation

Fire & Flood Early Warning Edge AI analyzes camera feeds and environmental sensors to detect smoke, fire, or abnormal water levels, triggering instant alerts and emergency protocols.

Predictive Infrastructure Maintenance Vibration sensors detect manhole cover displacement, automatically generating repair orders to prevent accidents.

Proven Success Stories

Singapore’s "Smart Streetlights" Initiative 100,000 ARM-based streetlights with Greengrass reduce energy use by 40% and municipal costs by 25% through adaptive lighting and environmental monitoring.

Dubai Traffic Hub Optimization Real-time analysis of 2,000+ cameras by edge gateways dynamically adjusts lanes and public transit schedules, cutting peak-hour congestion by 22%.

Shanghai Industrial Zone Monitoring 500 edge nodes filter 95% of redundant data, lowering cloud storage costs by 70% and accelerating pollution response to under 2 minutes.

Core Advantages at a Glance

Pain PointsSolutionCustomer ValueHigh latency delays decisionsEdge processing + local AI inference50–90% faster incident responseCostly mass data transfersEdge filtering, only critical data to cloud60%+ lower bandwidth/storage costsFragmented device managementGreengrass unified OTA updates & deployment1 operator manages 1,000+ edge nodesUnstable network reliabilityOffline autonomy + data recovery99.99% system availability

Future Scalability: From Local to City-Wide Intelligence

Rapid Deployment Standardized ARM hardware and modular Greengrass components cut deployment time from weeks to hours.

Self-Evolving AI Edge data trains cloud-based models, with updated algorithms pushed back to devices—smarter with every iteration.

Case Details

1. Introduction

In modern industrial automation, embedded computing devices are widely used for production monitoring, equipment control, and data acquisition. ARM-based Industrial Embeddedd Controller, known for their low power consumption, high performance, and rich industrial interfaces, have become key components in smart manufacturing and Industrial IoT (IIoT). Python, as an efficient and easy-to-use programming language, provides a powerful ecosystem and extensive libraries, making industrial automation system development more convenient and efficient.

This article explores the typical applications of ARM Industrial Embeddedd Controller combined with Python in industrial automation, including device control, data acquisition, edge computing, and remote monitoring.

2. Advantages of ARM Industrial Embeddedd Controller in Industrial Automation

2.1 Low Power Consumption and High Reliability

Compared to x86-based industrial computers, ARM processors consume less power, making them ideal for long-term operation in industrial environments. Additionally, they support fanless designs, improving system stability.

2.2 Rich Industrial Interfaces

Industrial Embeddedd Controllerxy integrate GPIO, RS485/232, CAN, DIN/DO/AIN/AO/RTD/TC and other interfaces, allowing direct connection to various sensors, actuators, and industrial equipment without additional adapters.

2.3 Strong Compatibility with Linux and Python

Most ARM Industrial Embeddedd Controller run embedded Linux systems such as Ubuntu, Debian, or Yocto. Python has broad support in these environments, providing flexibility in development.

3. Python Applications in Industrial Automation

3.1 Device Control

On automated production lines, Python can be used to control relays, motors, conveyor belts, and other equipment, enabling precise logical control. For example, it can use GPIO to control industrial robotic arms or automation line actuators.

Example: Controlling a Relay-Driven Motor via GPIO

import RPi.GPIO as GPIO import time

# Set GPIO mode GPIO.setmode(GPIO.BCM) motor_pin = 18 GPIO.setup(motor_pin, GPIO.OUT)

# Control motor operation try:     while True:         GPIO.output(motor_pin, GPIO.HIGH)  # Start motor         time.sleep(5)  # Run for 5 seconds         GPIO.output(motor_pin, GPIO.LOW)   # Stop motor         time.sleep(5) except KeyboardInterrupt:     GPIO.cleanup()

3.2 Sensor Data Acquisition and Processing

Python can acquire data from industrial sensors, such as temperature, humidity, pressure, and vibration, for local processing or uploading to a server for analysis.

Example: Reading Data from a Temperature and Humidity Sensor

import Adafruit_DHT

sensor = Adafruit_DHT.DHT22 pin = 4  # GPIO pin connected to the sensor

humidity, temperature = Adafruit_DHT.read_retry(sensor, pin) print(f"Temperature: {temperature:.2f}°C, Humidity: {humidity:.2f}%")

3.3 Edge Computing and AI Inference

In industrial automation, edge computing reduces reliance on cloud computing, lowers latency, and improves real-time response. ARM industrial computers can use Python with TensorFlow Lite or OpenCV for defect detection, object recognition, and other AI tasks.

Example: Real-Time Image Processing with OpenCV

import cv2

cap = cv2.VideoCapture(0)  # Open camera

while True:     ret, frame = cap.read()     gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)  # Convert to grayscale     cv2.imshow("Gray Frame", gray)

    if cv2.waitKey(1) & 0xFF == ord('q'):         break

cap.release() cv2.destroyAllWindows()

3.4 Remote Monitoring and Industrial IoT (IIoT)

ARM industrial computers can use Python for remote monitoring by leveraging MQTT, Modbus, HTTP, and other protocols to transmit real-time equipment status and production data to the cloud or build a private industrial IoT platform.

Example: Using MQTT to Send Sensor Data to the Cloud

import paho.mqtt.client as mqtt import json

def on_connect(client, userdata, flags, rc):     print(f"Connected with result code {rc}")

client = mqtt.Client() client.on_connect = on_connect client.connect("broker.hivemq.com", 1883, 60)  # Connect to public MQTT broker

data = {"temperature": 25.5, "humidity": 60} client.publish("industrial/data", json.dumps(data))  # Send data client.loop_forever()

3.5 Production Data Analysis and Visualization

Python can be used for industrial data analysis and visualization. With Pandas and Matplotlib, it can store data, perform trend analysis, detect anomalies, and improve production management efficiency.

Example: Using Matplotlib to Plot Sensor Data Trends

import matplotlib.pyplot as plt

# Simulated data time_stamps = list(range(10)) temperature_data = [22.5, 23.0, 22.8, 23.1, 23.3, 23.0, 22.7, 23.2, 23.4, 23.1]

plt.plot(time_stamps, temperature_data, marker='o', linestyle='-') plt.xlabel("Time (min)") plt.ylabel("Temperature (°C)") plt.title("Temperature Trend") plt.grid(True) plt.show()

4. Conclusion

ARMxy BL410 is a Linux industrial computer. The embedded PC is based on the Rockchip RK3568J/RK3568B2 processor, featuring aquad-core ARM Cortex-A55 architecture with clock speed up to 1.8GHz/2.0GHz. The ARM based SBC device is equipped with 8/16/32GB eMMC storage and various combinations of 1/2/4GB LPDDR4X RAM. Additionally, it includes a built-in 1TOPS computing power Neural Processing Unit (NPU) for supporting deep learning tasks.

NPU (Neural Processing Unit) is a specialized coprocessor designed to accelerate artificial intelligence (AI) and machine learning (ML) tasks in ARM-based industrial control systems. It optimizes neural network computations, enhancing real-time performance, energy efficiency, and complex data processing capabilities in industrial automation.

Key Features of NPU

Dedicated Architecture Hardware-optimized for neural network operations (e.g., matrix multiplication, convolutions), enabling higher computational throughput at lower power consumption compared to CPUs/GPUs.

Massive Parallelism Utilizes parallel processing units (e.g., MAC arrays) to handle multidimensional data (images, sensor signals, audio) efficiently.

Low Latency & High Energy Efficiency Minimizes redundant instructions and memory access, achieving millisecond-level inference speeds critical for real-time industrial applications.

NPU vs. CPU/GPU

Benefits of NPU in Industrial PCs

Real-Time Performance: Meets strict latency requirements for robotics, production lines, etc.

Power Savings: Reduces energy costs for 24/7 operations and simplifies thermal management.

Edge Intelligence: Local data processing improves reliability in unstable network environments.

Cost Efficiency: Eliminates external AI accelerators, lowering hardware complexity.

Real-World Examples

Smart Warehousing: NPU-powered robots improve picking accuracy by 30% via real-time object recognition.

Power Grid Inspection: Drones analyze infrared images locally to detect faults (e.g., broken insulators).

Food Packaging: Vision systems with NPU reduce sealing defect rates from 0.5% to 0.02%, cutting GPU costs.

Typical Use Cases in Industrial Control

Machine Vision & Quality Inspection

Example: NPU accelerates vision models (e.g., YOLO, ResNet) to detect product defects (scratches, misalignment) on production lines, replacing manual inspection.

Advantage: Higher frame rates (FPS) for high-resolution images with lower power consumption.

Predictive Maintenance

Example: Analyzes sensor data (vibration, temperature) using time-series models (e.g., LSTM) to predict equipment failures (motors, bearings).

Advantage: Real-time processing of multi-sensor data streams, reducing downtime.

Autonomous Robotics

Example: AGVs (Automated Guided Vehicles) leverage NPU-accelerated SLAM algorithms for obstacle avoidance and path planning via LiDAR/camera data.

Advantage: Ultra-low latency ensures safe navigation in dynamic environments.

Voice & NLP Integration

Example: Enables voice-controlled machinery (e.g., "Start Line B") using on-device speech recognition models.

Advantage: Offline operation ensures privacy and reliability without cloud dependency.

IIoT Edge Computing

Example: NPU processes video/sensor data at the edge, transmitting only critical insights to the cloud.

Advantage: Reduces bandwidth usage and enhances data security.

Conclusion

NPUs empower ARM-based industrial computers with localized AI capabilities, driving efficiency and reliability in automation, quality control, and predictive maintenance. As Industry 4.0 demands smarter edge devices, NPUs are becoming essential for next-generation industrial systems.

With the rapid development of the Internet of Things (IoT), how to quickly build an efficient, reliable and low-cost system has become a core challenge facing the industry. The solution consisting of the ARMxy Industrial Edge Gateway, the Node-RED low-code platform and the ThingsBoard IoT platform can meet 99% of the IoT scenario requirements with its flexibility, development efficiency and comprehensive functions. Whether it is industrial automation, smart agriculture, or smart cities, this combination has demonstrated strong adaptability. The following analyzes its advantages from three aspects: hardware, development tools and platforms.

ARMxy: Combined Industrial Edge Gateway, flexible hardware selection to meet different project requirements

As the "nerve endings" of the IoT system, the performance and reliability of the hardware acquisition module are crucial. Through the flexible combination of X board and Y board, the ARMxy series can support tens of thousands of IO interface configurations, including RS485, RS232, CAN, Ethernet port, analog/digital input and output, thermal resistance, etc., covering almost all access requirements of industrial sensors and equipment.

Its multi-level configuration strategy (high/medium/low configuration) further optimizes the cost: the high-profile version is suitable for industrial scenarios with many data points and strict real-time requirements; while the medium and low-profile versions can meet lightweight requirements such as agricultural monitoring or small environmental monitoring. In addition, ARMxy adopts industrial-grade design, with dustproof, vibration-resistant, wide temperature operation and other characteristics, ensuring long-term stable operation in harsh environments, laying the foundation for the reliability of the Internet of Things system.

Node-RED: Low-code development, opening up the "highway" of data flow

Node-RED, with its visual drag-and-drop programming and rich protocol support, has become a key bridge connecting hardware and the cloud.

Full coverage of protocols: Built-in Modbus, Siemens PLC protocol, power industry DL/T645 and other industrial protocol nodes, easily connect to various devices; uplink supports MQTT and HTTP, seamlessly integrates mainstream cloud platforms such as Alibaba Cloud and AWS.

Edge computing capabilities: Through custom nodes or plug-in extensions, data can be pre-processed (such as filtering and compression), reducing cloud bandwidth pressure and improving system efficiency.

Rapid development: Non-professional developers can also build data collection, processing and transmission logic by dragging nodes, greatly shortening the development cycle, especially suitable for prototype verification and agile iteration.

ThingsBoard: An all-round platform for refined management of data and devices

As an open source IoT platform, ThingsBoard provides one-stop services from data storage to visualization:

Real-time monitoring and visualization: Through custom dashboards, users can intuitively view device status and generate dynamic charts or reports, such as temperature curves of factory equipment and heat maps of urban traffic flow.

Large-scale device management: Supports batch access, remote control and OTA upgrades of thousands of devices to meet industrial-grade scenario requirements.

Highly scalable: Open source features allow developers to customize functional modules according to business needs, such as integrating AI algorithms to achieve predictive maintenance.

ARMxy + Node-RED + ThingsBoard synergistic advantages release the potential of the IoT

The combination of ARMxy, Node-RED and ThingsBoard builds a complete closed loop from hardware to cloud:

Seamless collaboration: ARMxy is responsible for data collection, Node-RED performs edge processing and protocol conversion, ThingsBoard realizes centralized management and visualization, and the three form an efficient data flow.

Extreme cost optimization: ARMxy's flexible configuration reduces hardware costs, and the open source features of Node-RED and ThingsBoard eliminate software licensing fees. The overall solution cost is only 1/3 of the traditional solution.

Full coverage of scenarios: Industrial production lines, farmland environmental monitoring, urban infrastructure management and other scenarios can be quickly adapted to meet 99% of the Internet of Things needs.

Rapid deployment: Node-RED's low-code development and ThingsBoard's ready-to-use functions can compress project cycles from months to weeks.

Conclusion: The future of IoT begins with an efficient tool combination

The golden combination of ARMxy + Node-RED + ThingsBoard not only solves the compatibility problem in the fragmented IoT scenario, but also becomes the preferred solution for enterprise digital transformation through its advantages of low cost, high flexibility and rapid implementation. Whether it is a startup team or a large enterprise, this combination can help them seize the initiative in the IoT wave and truly realize the vision of "Internet of Everything".

The ARMxy BL330 series is a versatile industrial ARM controller designed for flexible I/O configuration. It is based on the Allwinner Technology T113-i dual-core ARM Cortex-A7 + XuanTie C906 RISC-V + HiFi4 DSP heterogeneous multi-core processor. This industrial-grade computer features an ARM Cortex-A7 processor with clock speed of up to 1.2GHz. It is equipped with either 256Mbyte NAND Flash or 4/8GByte eMMC storage options and offers various combinations of 128M/256M/512M/1GByte DDR3 RAM and ROM. The BL330 series ARM embedded computer offers a rich array of interfaces including 1-3 10/100M Ethernet ports, 2 x USB 2.0 ports,1 x optional HDMI 2.0a port,1 x optional X series IO board,2 x optional Y series I/O boards.These interfaces support various functions such as communication, PWM output, pulse counting, and data acquisition and control. The BL330 also supports hardware decoding of 1080P@60fps videos. Additionally, it features a built-in Mini PCIe interface to support communication modules such as Bluetooth, WiFi, and 4G modules. The BL330 series ARM embedded computer supports a range of operating systems anddevelopment tools, including Linux-5.4.61 and Linux-RT-5.4.61 kernels,Ubuntu 20.04 operating system,Docker containers, Node-Red,Qt-5.12.5 and Qt-5.11.3 for graphical interface development.In addition, BL330 is compatible with BLIoTLink industrial protocol conversion software for data collection and transformation, and can seamless integration withvarious mainstream IoT cloud platforms and industrial SCADA software. Users can leverage the BLRAT for remote access and maintenance of the BL330 embedded computer. Additionally, with support for Node-Red, users can rapidly develop IoT applications on the BL330. The BL330 series ARM embedded computer has undergone rigorous electrical performance design and high-low temperature testing to ensure stability and reliability. It is designed for DIN35 rail mounting, making it suitable for various industrial application environments. This embedded computer is widely used in industrial IoT, photovoltaic power generation and energy storage systems, automation control, and transportation and rail applications.