#arm embedded controller

With the rapid adoption of electric vehicles, the number of public and private charging stations is growing exponentially. The stability and safety of charging facilities directly affect user experience and grid operation. Traditional methods relying on manual inspection and centralized monitoring can no longer meet the demands of large-scale charging networks. Deploying an ARM AI embedded controller with edge intelligence for real-time fault detection and automatic isolation has become a key trend in the industry.

Background

Common issues in EV chargers include:

Hardware failures: relay stuck, communication interruption, controller board malfunction.

Electrical anomalies: overcurrent, short circuit, grounding faults, leakage.

Environmental & safety risks: overheating, smoke, water ingress.

If these problems are not identified and isolated in time, they may cause charging interruption, user complaints, or even power safety incidents. Therefore, deploying an ARM-based controller with local AI inference capability at the charging station enables real-time monitoring and automatic isolation.

Solution Overview

The ARM AI embedded controller integrates data acquisition, AI inference engine, decision-making logic, and communication modules, and can be directly deployed at the charging site:

Real-time Fault Detection Collects signals from current, voltage, temperature, and leakage sensors, then applies AI models to detect abnormal patterns and identify potential faults quickly.

Automatic Isolation & Recovery When a critical fault with high confidence is detected, the controller immediately disconnects the output circuit of the faulty charger for safe isolation. Automatic restart attempts can be executed; if recovery fails, the charger remains isolated and the event is reported to the cloud.

Edge Intelligence + Cloud Collaboration Local decision-making ensures low-latency safety response, while diagnostic logs and event details are synchronized with the cloud platform for centralized monitoring, trend analysis, and remote command dispatch.

Graded Alarms & Traceability Fault events are classified by severity, and detailed diagnostic reports are generated. Operators can quickly locate issues through a visual interface.

Security & Reliability Device certificates, encrypted communication, and audit logs protect the system from cyber threats and misoperations.

System Architecture

Physical Layer: EV chargers, circuit breakers, sensors (current, voltage, temperature, leakage).

Edge Layer (ARM AI Controller BL440 Series):

Data collection and preprocessing

AI inference (TFLite/ONNX models)

Decision-making & isolation execution

Local logging and alarm reporting

Cloud Platform: O&M dashboard, historical data analysis, AI model updates, OTA firmware upgrades.

User Layer: Operators receive alarms and can issue remote commands via mobile app or web portal.

Implementation Benefits

Reduced Fault Handling Time: from hours of manual inspection to millisecond-level automated isolation.

Enhanced Safety: prevents escalation of electrical incidents and ensures system stability.

Lower O&M Costs: fewer on-site inspections and reduced emergency repair costs.

Improved User Experience: keeps more chargers online and available.

Conclusion

The ARM AI embedded controller brings self-diagnosis, self-isolation, and self-recovery capabilities to EV charging stations, transforming operations from “manual monitoring” to “AI-driven autonomy.” This enhances equipment reliability, improves operational efficiency, and provides a strong foundation for the large-scale expansion of future EV charging infrastructure.

Case Details

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Introduction: Intelligent Upgrades for Automated Assembly Lines

In modern manufacturing, automated assembly lines are pivotal for boosting capacity, reducing labor costs, and ensuring product consistency. As production shifts toward flexibility and intelligence, assembly lines demand millisecond-level precision control and real-time responsiveness. ARM-based embedded controllers, with their low power consumption, high performance, and abundant industrial interfaces, serve as key enablers for intelligent upgrades.

Current State and Challenges

While traditional PLC and host computer systems dominate assembly lines, they face key pain points:

Limited Control Precision: Motor speeds and conveyor beats struggle to achieve millisecond synchronization, leading to assembly errors.

Poor Scalability: Adding sensors or actuators incurs high hardware/software expansion costs.

Lack of Real-Time Data: Equipment monitoring is confined to data collection, missing edge intelligence for fault prediction.

Complex Operations and Maintenance: Remote diagnostics, parameter tuning, and upgrades often require machine downtime and manual intervention.

These issues drive the need for more flexible, intelligent control solutions.

Solution: ARM Embedded Controller BL350 Series

ARM-based embedded controllers act as core nodes for assembly line control, acquisition, and analysis, supporting protocols like Modbus, CANopen, and EtherCAT. Key functions include:

Motor and Conveyor Monitoring:

Real-time communication for speed/torque adjustments to ensure smooth operation.

Encoder feedback for closed-loop control, enhancing precision.

Sensor Integration and Safety Management:

Collect data from photoelectric/proximity switches for position detection and beat synchronization.

Integrate emergency stops, safety doors, and other I/Os to protect personnel and equipment.

Edge Intelligence and Data Analytics:

Onboard PID algorithms for speed/position closed-loop control.

Real-time yield statistics, anomaly detection (e.g., downtime, overload); preliminary fault trend identification to mitigate risks.

Cloud Connectivity and Remote O&M:

Upload production data and alerts to MES/SCADA via Ethernet/4G/Wi-Fi, enabling IIoT integration.

Support remote configuration and OTA upgrades to cut on-site maintenance costs.

Application Value

Value DimensionSpecific BenefitsQuantified Example (Industry Case)Efficiency BoostPrecise control minimizes beat errors, enhancing throughput.15% production efficiency gain.Downtime ReductionReal-time monitoring and edge analytics enable early anomaly detection.20-30% cut in unplanned downtime.Consistency ImprovementCoordinated station beats reduce quality variations.Assembly errors within ±0.5%.O&M OptimizationRemote diagnostics reduce manual inspections.20% maintenance cost drop.Flexible ScalabilityMulti-protocol support for quick integration of new devices.Seamless adaptation to flexible lines.

Application Scenario Example

In a home appliance manufacturer's automated assembly line:

Motor/conveyor speed variances were precisely controlled to ±0.5%, significantly reducing assembly errors.

Edge computing analyzed current/vibration in real-time, preemptively detecting bearing wear and averting sudden line stops.

MES integration yielded 15% efficiency gains and 20% lower maintenance costs.

Conclusion

ARM embedded controllers combine the stability and real-time capabilities of traditional PLCs with edge computing and IoT connectivity, delivering end-to-end support from low-level control to data fusion. They empower manufacturers to achieve higher efficiency, lower costs, and greater competitiveness in the intelligent transformation era.

Case Details

Dams, as critical components of hydraulic engineering, undertake essential tasks such as flood control, water supply, power generation, and irrigation. However, structural weaknesses or operational anomalies in dams can pose severe safety risks. Traditional manual inspection methods suffer from long monitoring cycles and delayed responses, failing to meet the real-time and reliability demands of modern smart water management.

In this context, intelligent monitoring solutions based on ARM embedded controllers have emerged, enabling continuous, comprehensive, and intelligent safety monitoring of dams.

Role of ARM Embedded Controllers BL335 series

ARM embedded controllers serve as the core nodes of the monitoring system, offering low power consumption, high performance, versatile interfaces, and strong environmental adaptability. They can directly interface with various dam sensors to facilitate data acquisition, edge computing, and remote communication. Their key roles include:

Multi-Source Data Acquisition: Supports multiple interface standards (4–20mA, RS485, SDI-12, CAN) for sensors such as displacement gauges, piezometers, water level gauges, rain gauges, meteorological sensors, and accelerometers.

Edge Intelligence Analysis: Performs local data filtering, anomaly detection, and trend analysis to reduce unnecessary data uploads and optimize network bandwidth.

Real-Time Alerts: Triggers local audible and visual alarms immediately when monitored values exceed thresholds or show abnormal trends, while transmitting alerts to the monitoring center via 4G/5G or Ethernet.

Redundant Communication: Supports multiple communication links (Ethernet, fiber optics, cellular networks, LoRa, satellite) to ensure stable operation in extreme environments.

Remote Maintenance: Simplifies maintenance costs through over-the-air (OTA) updates and remote configuration, enhancing the system's lifecycle value.

System Architecture

The dam monitoring system consists of four layers:

Sensor Layer: Sensors installed on and around the dam collect real-time structural and environmental parameters.

Edge Control Layer (ARM Embedded Controller): Handles data acquisition, preprocessing, storage, and local alert logic.

Communication Layer: Transmits critical data and alerts to the platform via Ethernet, 4G/5G, or satellite links.

Monitoring Platform Layer: Provides a visualization interface, historical data storage, trend analysis, and risk warnings to support decision-making by management authorities.

Application Value

Enhanced Safety: Continuous, real-time monitoring of dam conditions to identify potential risks in advance.

Cost Reduction: Decreases the frequency of manual inspections and reduces operational costs through centralized remote management.

Improved Reliability: Enhances system resilience through edge intelligence and redundant communication.

Support for Smart Water Management: Integrates with cloud platforms and big data analytics to provide scientific decision-making support for water authorities.

Conclusion

Case Details

With the continuous expansion of urban heating systems and the increasing complexity of pipeline networks, achieving remote monitoring, intelligent scheduling, and efficient maintenance of heating networks has become a critical challenge for heating enterprises and operation departments. Traditional manual inspections and localized monitoring are not only costly and inefficient but also struggle to respond promptly to emergencies. The ARM-based Embedded Remote controller, with its low power consumption, high reliability, and flexible scalability, provides an ideal solution for the digital management of heating networks.

System Architecture

ARM embedded remote controllers BL370 are deployed at heat exchange stations, regional nodes, and main pipeline sections, connecting to various sensors for temperature, pressure, flow, valve position, and pump status. Data is collected through interfaces such as RS-485, 4-20mA, pulse, and M-Bus. The controller performs data cleaning, aggregation, and preliminary analysis at the edge, securely transmitting critical data and alerts to heating scheduling, metering, and maintenance departments via Ethernet or 4G networks.

Key Functions

Remote Data Acquisition

Real-time collection of supply/return water temperature, pressure, flow, and energy consumption data, providing a comprehensive view of heating network operations.

Edge Computing and Local Decision-Making

Equipped with anomaly detection and a rule engine, the controller can issue local alerts for issues like insufficient temperature difference, abnormal pressure, or sudden flow changes, reducing reliance on cloud systems and improving response speed.

Intelligent Scheduling and Energy-Saving Control

The scheduling center can adjust pipeline balance based on real-time data, automatically optimizing valve openings and pump operation modes to enhance heating efficiency and reduce energy consumption.

Metering and Billing Support

Accurate collection of regional heat meter data, enabling multidimensional energy consumption analysis and report generation to support user billing and energy efficiency evaluation.

Remote Maintenance and Work Order Dispatch

When equipment anomalies or alerts are triggered, the system automatically pushes information to maintenance departments, enabling automated work order dispatch and reducing fault resolution time.

Application Value

Enhanced Heating Safety: Real-time monitoring of critical parameters with early warnings to minimize the risk of large-scale heating disruptions.

Reduced Operational Costs: Remote diagnostics replace some manual inspections, improving maintenance accuracy.

Improved Energy Efficiency: Data-driven scheduling optimizes on-demand heating and energy-saving operations.

Support for Smart Heating Transformation: Integration with cloud platforms to build a digital heating management system combining scheduling, metering, and maintenance.

Typical Application Scenarios

Operational monitoring of urban centralized heating networks

Remote data acquisition and control for regional heat exchange stations

Energy consumption metering and efficiency analysis for industrial park heating systems

Front-end data source for smart city heating big data platforms

Conclusion

The ARM Embedded Remote Controller BL370 transforms traditional heating networks into intelligent, monitorable, schedulable, and maintainable systems, enhancing the reliability of heating services and user satisfaction. It also supports efficient energy utilization and urban green development. In the future, with further integration of IoT and AI technologies, the heating industry will achieve more intelligent, precise, and low-carbon management.

Case Details

The ARMxy BL450 Series, an industrial-grade ARM embedded computer is designed for flexible and robust industrial applications. By integrating OpenPLC and OpenCV/YOLO, the BL450 becomes a powerful platform for automated product quality inspection on production lines, detecting defects such as scratches, stains, or dimensional deviations. This article outlines the technical implementation, communication methods, visual system selection, and PLC logic design for such applications, with case studies in electronic component soldering and pharmaceutical packaging inspection.

Application Scenario

In modern manufacturing, ensuring product quality is critical. The BL450, combined with OpenPLC and OpenCV/YOLO, enables real-time defect detection and automated sorting on production lines. Typical use cases include:

Electronic Component Soldering Quality Inspection: Detecting incomplete solder joints, excess solder, or misalignments.

Pharmaceutical Packaging Integrity Check: Identifying damaged, misprinted, or incomplete packaging.

The system uses a vision-based approach to detect defects and communicates results to a PLC, which controls mechanical arms to remove defective items or redirect them to a separate line.

Technical Implementation

Hardware Setup

The BL450’s flexible I/O configuration and robust hardware make it ideal for industrial environments. Key hardware features include:

Processor: Rockchip RK3588 (based on datasheet), offering high computational power for running OpenCV/YOLO and OpenPLC.

Interfaces:

Ethernet ports (1–3, 10/100/1000M) for industrial communication protocols.

USB ports (2) for connecting cameras.

I/O slots (X and Y boards) for direct I/O signals or RS485 communication.

Dimensions: Compact design (e.g., 42×83×110mm for single Ethernet port models) suitable for production line integration.

Optional Modules: WiFi (BL450W) or 4G (BL450L) for enhanced connectivity.

Example Configuration:

Model: BL450-SOM450-X10 (1 Ethernet port, 32GB eMMC, 4GB LPDDR4X, 2 RS485 ports).

Camera: 2D industrial camera for surface defect detection or 3D camera for depth-based inspection (e.g., bin picking).

Communication Methods

The BL450 supports multiple communication methods to integrate the vision system with the PLC and mechanical systems:

Direct I/O Signal:

Used for simple scenarios, such as sending OK/NG (pass/fail) signals.

Configured via the BL450’s 6-pin I/O slot (X board) for direct connection to OpenPLC.

Example: A high/low signal indicates whether a product passes inspection.

Industrial Communication Protocols:

Ethernet/IP, Profinet, Modbus TCP: Suitable for complex data interactions, such as transmitting defect coordinates or confidence scores from YOLO.

The BL450’s Ethernet port supports these protocols, enabling seamless integration with industrial networks.

Example: Modbus TCP transmits defect type and location to OpenPLC for precise mechanical arm control.

3. Selection of Visual System

The vision system is critical for defect detection. The BL450 supports both traditional and deep learning-based approaches:

Traditional Algorithms (OpenCV):

Techniques: Threshold segmentation, contour detection, edge detection.

Use Case: Suitable for regular objects with predictable defects (e.g., scratches or stains on flat surfaces).

Implementation: OpenCV runs on the BL450’s Debian or Ubuntu OS, processing images from a 2D camera to identify defects based on predefined thresholds.

Advantages: Fast, computationally efficient, and suitable for well-defined inspection tasks.

Deep Learning (YOLO):

Techniques: YOLO (You Only Look Once) for real-time object detection and classification.

Use Case: Complex defect detection (e.g., irregular solder joints or varied packaging defects).

Implementation: YOLO models are deployed on the BL450’s Cortex-A53 cores, leveraging the NPU for accelerated inference. Pre-trained models can be fine-tuned for specific defects.

Advantages: High accuracy for complex patterns, adaptable to diverse defect types.

Camera Selection:

2D Camera: Used for surface-level defect detection (e.g., scratches, stains). Connected via USB or Ethernet.

3D Camera: Used for depth-based inspection (e.g., bin picking or dimensional verification). Provides depth data for complex geometries.

PLC Logic Design (OpenPLC)

OpenPLC, running on the BL450’s Linux-based OS, handles control logic for mechanical systems based on vision system outputs.

Trigger Mechanism:

The vision system (OpenCV/YOLO) processes images and sends a pulse signal to OpenPLC upon completing defect detection.

Example: A detected defect triggers a high signal on a designated I/O pin, which OpenPLC interprets to actuate a mechanical arm.

Safety Redundancy:

Timeout Detection: If the vision system fails to send a signal within a specified time (e.g., 2 seconds), OpenPLC assumes a failure and halts the production line.

Exception Handling: OpenPLC monitors for vision system errors (e.g., camera disconnection) and executes fallback actions, such as redirecting products to a manual inspection station.

Implementation: OpenPLC ladder logic includes timers and error-checking routines to ensure robust operation.

Software Integration

The BL450’s development environment supports seamless integration of OpenPLC and OpenCV/YOLO:

Operating System: Debian or Ubuntu, with pre-installed libraries for OpenCV and Python for YOLO.

Development Examples (from datasheet):

OpenCV development cases for image processing.

Node-RED for IoT integration and data visualization.

Docker containers for deploying YOLO models.

MQTT for communication between vision and PLC systems.

Customization: The BL450’s flexible SOM and I/O board design allows tailored configurations for specific inspection needs.

Case Studies

Case 1: Electronic Component Soldering Quality Inspection

Setup: BL450-SOM450-X10 with a 2D camera and OpenCV for contour detection.

Process:

The camera captures images of solder joints.

OpenCV applies edge detection to identify irregularities (e.g., incomplete joints).

Results (OK/NG) are sent via Modbus TCP to OpenPLC.

OpenPLC controls a mechanical arm to remove defective components.

Outcome: Achieves high-speed inspection with minimal latency, suitable for high-throughput lines.

Case 2: Pharmaceutical Packaging Integrity Check

Setup: BL450W-SOM450-X10 with a 3D camera and YOLO for defect classification.

Process:

The 3D camera captures depth and surface data of packaging.

YOLO classifies defects (e.g., dents, misprints) with high accuracy.

Defect coordinates are sent via Ethernet/IP to OpenPLC.

OpenPLC directs defective packages to a reject line.

Outcome: Robust detection of complex defects, with WiFi enabling remote monitoring via BLiotLink.

Conclusion

The ARMxy BL450, combined with OpenPLC and OpenCV/YOLO, offers a versatile and powerful solution for product quality inspection. Its flexible hardware, support for industrial protocols, and rich development ecosystem make it ideal for automating defect detection and sorting in manufacturing. Whether using traditional OpenCV algorithms for simple tasks or YOLO for complex defect classification, the BL450 ensures reliable performance in demanding industrial environments.

Case Details

Beremiz is an open-source Integrated Development Environment (IDE) designed for Programmable Logic Controllers (PLCs), adhering to the IEC 61131-3 international standard. It supports multiple programming languages, including Ladder Diagram (LD), Structured Text (ST), Function Block Diagram (FBD), Instruction List (IL), and Sequential Function Chart (SFC). Beremiz provides an intuitive graphical programming interface, supports flexible data types, offers powerful debugging capabilities, and includes a simulation environment, allowing developers to test programs without physical hardware. Built on Python, C, and C++, Beremiz supports cross-platform operation on Windows and Linux systems and is widely used in industrial automation, equipment control, and data acquisition scenarios. Its open-source nature makes it highly extensible, enabling seamless integration with third-party tools and protocols for various industrial control needs.

Integration of ARMxy BL350 Embedded Controller with Beremiz

The ARMxy BL350 series embedded controller is a high-performance industrial control device based on the TI Sitara AM62x processor. It features a multi-core architecture (up to quad-core ARM Cortex-A53 at 1.4 GHz and a single-core ARM Cortex-M4F at 400 MHz), supports operating systems such as Linux-5.10.168, Yocto 3.1, and Ubuntu 20.04, and provides rich interfaces (e.g., 1–3 10/100M Ethernet ports, 2 USB 2.0 ports, optional HDMI 2.0, and various I/O expansion boards). Integrated with BLIoTLink industrial protocol conversion software, it supports protocols like Modbus, PLC, and MQTT, enabling seamless connectivity with mainstream IoT cloud platforms and SCADA systems. The BLRAT remote access tool further enhances remote operation and maintenance, making the BL350 ideal for industrial automation applications.

Combining Beremiz with the ARMxy BL350 leverages the strengths of both, creating an efficient and flexible industrial automation solution. Below are specific application scenarios and implementation methods:

Industrial Control and PLC Programming

The BL350’s multi-core architecture and extensive I/O interfaces make it an excellent platform for soft PLC applications. Beremiz, compliant with IEC 61131-3, can run PLC programs on the BL350, using Ladder Diagram or Structured Text to implement control logic. For example, in industrial control scenarios, developers can use Beremiz to design control logic and leverage the BL350’s X-series I/O boards (supporting RS485, CAN, DI/DO, etc.) for sensor data acquisition and actuator control. The BL350’s Cortex-M4F core handles real-time tasks, ensuring efficient execution of control programs.

Edge Computing and Data Acquisition

As an edge computing gateway, the BL350 supports Docker containers and Node-Red tools. Combined with Beremiz’s data processing capabilities, it enables complex data acquisition and processing tasks. In smart manufacturing, Beremiz can develop programs to collect analog (e.g., PT100 temperature sensor data) or digital signals via the BL350’s Y-series I/O boards and upload data to cloud platforms (e.g., AWS IoT Core or ThingsBoard) using the MQTT protocol. Beremiz’s simulation feature allows developers to validate logic during development, reducing debugging costs.

Motion Control and Real-Time Processing

The BL350’s Cortex-M4F and PRU-ICSS units provide hardware support for real-time motion control. Beremiz can be used to develop motion control algorithms, such as controlling motor speed via PWM output or implementing position feedback through pulse counting. In applications like Automated Guided Vehicles (AGVs) or industrial robots, Beremiz’s Sequential Function Chart (SFC) can design complex motion sequences, while the BL350’s multi-core architecture ensures real-time performance and high efficiency.

Remote Monitoring and Maintenance

The BL350’s integrated BLRAT remote access tool, combined with Beremiz’s debugging capabilities, enables remote deployment and monitoring of PLC programs. For instance, in Energy Management Systems (EMS/BMS), developers can write Modbus protocol control programs using Beremiz, deploy them on the BL350, and use 4G/WiFi modules for remote data transmission and program updates. This approach significantly improves equipment maintenance efficiency and reduces on-site operational costs.

Development and Debugging Workflow

Environment Setup: Install Linux-5.10.168 or Ubuntu 20.04 on the BL350 and configure the Beremiz runtime environment (requires Python and related dependencies).

Program Development: Use Beremiz’s graphical interface to design PLC programs, selecting appropriate programming languages (e.g., FBD or ST) for control logic.

Hardware Interface Configuration: Configure RS485, CAN, or other interfaces via the BL350’s X/Y-series I/O boards to ensure communication with external devices.

Simulation and Testing: Use Beremiz’s simulation feature to test program logic on the BL350, verifying input and output signal accuracy.

Deployment and Monitoring: Compile the program into an executable, deploy it to the BL350, and monitor remotely using BLRAT or SCADA systems.

Case Study: Application in Smart Manufacturing

In a smart manufacturing factory, the ARMxy BL350 paired with Beremiz was used for production line automation control. The BL350 was configured as BL350B-SOM352-X10-Y63, featuring 1 Ethernet port, 8GB eMMC, 1GB DDR4, and 2 RS485 interfaces. Beremiz was used to develop Ladder Diagram programs to control sensors and actuators on the production line. Data was uploaded to an Ignition SCADA system via the MQTT protocol using the BL350’s BLIoTLink software, enabling real-time monitoring. The BLRAT tool allowed engineers to remotely update PLC programs, significantly improving production efficiency and equipment maintainability.

Advantages Summary

High-Performance Hardware: The BL350’s multi-core architecture and rich interfaces support complex industrial applications.

Flexible Programming: Beremiz’s multi-language support and simulation capabilities streamline development.

Seamless Integration: BLIoTLink and BLRAT tools enable connectivity with cloud platforms and remote systems.

Industrial-Grade Reliability: The BL350 passes electromagnetic compatibility and environmental adaptability tests, operating reliably in extreme conditions from -40°C to 85°C.

Conclusion

The combination of the ARMxy BL350 embedded controller and Beremiz provides a robust hardware and software platform for industrial automation. Whether for industrial control, edge computing, or motion control, this combination delivers efficient and reliable solutions. Developers can leverage Beremiz’s open-source flexibility and the BL350’s hardware versatility to rapidly build customized industrial automation systems, meeting diverse needs in smart manufacturing, energy management, and IoT applications.

Case Details

Main Application Scenarios

Fire Detection and Alarming: ARM controllers integrate sensors (e.g., smoke, temperature, gas sensors) to monitor environmental data in real-time, using algorithms to assess fire risks and trigger alarms.

Fire Equipment Control: Manage fire pumps, sprinkler systems, fire doors, smoke exhaust fans, etc., ensuring rapid response during a fire.

Communication and Networking: Support multiple communication protocols (e.g., Modbus, CAN, Ethernet, 4G, Wifi) for connectivity with fire control centers, cloud platforms, or other smart devices.

Data Processing and Storage: Process large amounts of sensor data and log events for post-incident analysis and system optimization.

Human-Machine Interaction: Drive displays or touchscreens to provide status monitoring and operation interfaces for fire protection systems.

Emergency Power Management: Manage backup power sources (e.g., UPS or batteries) to ensure system operation during power outages.

Advantages of ARM Embedded Controllers ARMxy series

High Performance and Low Power Consumption: ARM Cortex-M series (e.g., Cortex-M4, M7) or Cortex-A series offer strong computing power while maintaining low energy consumption, ideal for continuous operation in fire systems.

Rich Peripheral Support: ARMxy series Integrated RS485, GPIO, DI, DO, AI, AO. RS232, CAN, RTD, etc., facilitate connections to various sensors and actuators.

Real-Time Capability: Support real-time operating systems (RTOS, e.g., FreeRTOS, uC/OS) to ensure low-latency fire detection and response.

Scalability: ARMxy series supports modular design, enabling easy system upgrades or feature expansions.

Cost-Effectiveness: ARM chips are relatively affordable, suitable for large-scale deployment in building fire systems.

Security: Support encryption modules and secure boot to prevent malicious tampering, ensuring system reliability.

Typical Implementation Cases

Smoke Alarm System: Use ARM Cortex-A7 controllers BL330 with smoke sensors, collecting data via DI/AI to trigger buzzers or networked alarms.

Smart Fire Hydrant Monitoring: ARM controllers monitor hydrant water pressure and status, uploading data to the cloud via Ethernet, 4G or WiFi.

Building Fire System Integration: Cortex-A55 controllers BL410 running Linux integrate video surveillance, fire alarms, and evacuation guidance for comprehensive management.

Development Considerations

Reliability: Fire systems require 24/7 operation; select industrial-grade ARM chips to ensure high temperature resistance and anti-interference.

Redundancy Design: Include backup controllers or communication channels to prevent single-point failures.

Certification Standards: Ensure compliance with fire protection standards.

Power Optimization: Use sleep modes or dynamic frequency scaling to reduce energy consumption and extend device lifespan.

Software Security: Regularly update firmware to mitigate potential cyberattacks.

Summary

ARM embedded controllers, with their high performance, low power consumption, and flexibility, have become core components of building fire protection systems. Through proper chip selection and design, they enable efficient and reliable fire detection, equipment control, and emergency response, significantly enhancing building fire safety.

Case Details

ARM Embedded Computers and Prometheus Overview

1. ARM Embedded Computers

ARM embedded computers are low-power, high-performance microcomputers based on ARM architecture, designed for IoT and edge computing scenarios. Key features include:

Low Power Consumption: Utilizes ARM processors (e.g., Cortex-A series), ideal for long-term embedded applications.

Compact Design: Small form factor, easily integrated into devices like sensor gateways or industrial controllers.

Rich Interfaces: Supports GPIO, I2C, SPI, UART, USB, Ethernet, etc., enabling seamless connectivity with sensors and peripherals.

Real-Time Capabilities: Some models support real-time operating systems (RTOS) or Linux, meeting industrial control requirements. Typical applications include smart homes, industrial automation, and energy management.

2. Prometheus

Prometheus is an open-source monitoring and alerting tool maintained by the Cloud Native Computing Foundation (CNCF). Core functionalities include:

Time-Series Database: Efficiently stores and queries metrics (e.g., CPU usage, energy consumption).

Data Collection: Uses HTTP pull mode to gather metrics from targets (e.g., ARM devices).

PromQL Query Language: Enables flexible data analysis and aggregation.

Visualization & Alerting: Integrates with Grafana for dashboards and Alertmanager for anomaly notifications. Prometheus excels in distributed systems and IoT real-time monitoring.

Application Cases: ARM Embedded Computers + Prometheus in Lighting System Energy Monitoring

System Architecture

Hardware Layer

ARM embedded computers (e.g., Raspberry Pi, NVIDIA Jetson Nano, Rockchip boards) act as edge nodes, connected to light sensors, current/voltage sensors, and smart meters.

Collects real-time data (power, current, on/off status) from lighting devices (e.g., LED lights, smart switches) via Modbus, MQTT, or LoRa protocols.

Data Collection Layer

Deploy Prometheus Exporters (e.g., Node Exporter, custom exporters) on ARM devices to convert sensor data into Prometheus-compatible metrics (e.g., light_power_consumption{watt="50", location="room1"}).

Transmits data to a Prometheus server (deployed locally or in the cloud) via Wi-Fi/Ethernet.

Monitoring & Analytics Layer

Prometheus periodically pulls metrics from ARM devices and stores them in its time-series database.

Grafana dashboards visualize real-time energy usage, historical trends, and device status.

Typical Use Cases

Case 1: Commercial Building Energy Optimization

Requirement: Reduce lighting energy consumption in an office building while ensuring comfortable illumination.

Solution:

Deploy ARM devices on each floor to collect light intensity and power data.

Use Prometheus to monitor real-time power usage and automatically adjust LED brightness based on ambient light.

Trigger alerts via Alertmanager for high-energy zones (e.g., lights left on in empty meeting rooms).

Outcome: 30% reduction in energy consumption; remote lighting strategy management.

Case 2: Industrial Lighting Health Monitoring

Requirement: Prevent production line downtime caused by lighting failures in a factory.

Solution:

ARM devices monitor current fluctuations to detect anomalies (e.g., voltage drops).

Prometheus stores historical data; machine learning models (e.g., TensorFlow Lite) predict lamp lifespan.

Dashboards display device health scores for proactive maintenance.

Outcome: 50% fewer failures; 20% lower maintenance costs.

Case 3: Smart City Streetlight Management

Requirement: Enable remote control and time-based dimming for municipal streetlights.

Solution:

ARM devices with LoRa modules act as streetlight controllers.

Prometheus collects status and energy data, adjusts brightness based on time (e.g., dimming at night).

GPS-integrated maps pinpoint faulty streetlights.

Outcome: 40% lower energy usage; 60% reduction in public complaints.

Key Advantages

Real-Time Insights: ARM edge computing + Prometheus enables sub-second data collection for rapid responses.

Cost Efficiency: Low-cost ARM hardware + free/open-source Prometheus suits large-scale deployments.

Scalability: Easily expandable—add sensors or zones without system overhauls.

Data-Driven Decisions: Historical analysis supports energy-saving initiatives (e.g., replacing inefficient fixtures).

Future Prospects

Integrating AI algorithms (e.g., time-series prediction) could further optimize lighting strategies, 

The BL370 series, with its high-performance hardware, industrial protocol stack, and edge AI capabilities, combined with Prometheus’ real-time monitoring, flexible querying, and visualization, delivers the following core benefits for lighting energy management:

Precision Control: End-to-end low latency from data acquisition to action.

Reliable Operations: Resilient in harsh environments with remote diagnostics and long-term stability.

Intelligent Decision-Making: AI-driven energy optimization and predictive maintenance.

Rapid Deployment: Out-of-the-box hardware/software with strong protocol compatibility, shortening project timelines.