Di Shan Technology AI Edge Sensing Platform Technology Solution: Empowering Intelligent Edges, Driving Perception Revolution

Overview

In the era of edge intelligence, sensing and AI are deeply converging. With the rapid development of artificial intelligence, the Internet of Things (IoT), and 5G technologies, smart terminals are evolving from cloud-dependent to edge-autonomous operation. Across scenarios like smart manufacturing, smart cities, autonomous driving, and healthcare, there is an increasingly urgent demand for real-time, low-latency, and highly secure perception and decision-making capabilities.

 

The traditional "perception-transmission-cloud processing" model can no longer meet the requirements for efficient responsiveness in complex environments. This is especially true amid explosive data growth, constrained network bandwidth, and stringent privacy compliance mandates—factors that have made edge computing indispensable. According to Gartner’s forecast, by 2025, 75% of global enterprise data will be generated and processed at the edge, with the edge computing market set to exceed USD 300 billion in size. Edge intelligence not only eases cloud-side load pressures but also enables real-time local data analysis, safeguards data privacy, and cuts transmission costs, emerging as a pivotal enabler of intelligent transformation.

 

Against this backdrop, the AI Edge Sensing Platform has emerged as the core carrier for closing the loop on intelligent perception. As a pioneer in edge intelligence, Dishan Technology leverages its profound expertise in advanced packaging and system integration to launch an innovative technical solution for the AI Edge Sensing Platform. By deeply integrating sensors, AI computing power, algorithms, and communication modules, the platform builds an all-in-one "perception-computation-decision-making" intelligent edge node. It empowers customers to break through traditional technical bottlenecks, shifting from passive data collection to proactive intelligence and driving the intelligent upgrading of industries. Through this platform, Dishan Technology is committed to building the "nerve endings" of the intelligent world, making edge intelligence ubiquitous, and propelling industries and society toward higher-level intelligence.

 

Platform Positioning: A Hardware-Software Coordinated Intelligent Perception Hub

Dishan Technology’s AI Edge Sensing Platform is a highly integrated, low-power, and scalable intelligent perception system solution tailored for multi-scenario applications. Guided by the core design philosophy of "edge-side intelligence, real-time response, and security & reliability", the platform delivers localized processing of perceptual data and intelligent decision-making through deep hardware-software synergy.

 

It integrates high-precision sensors, edge AI processors, embedded operating systems, and dedicated algorithm libraries, supporting the fusion processing of diverse perception modalities—including vision, voice, inertial sensing, and environmental monitoring. This creates a closed, end-to-end workflow spanning data collection, preprocessing, feature extraction, and intelligent decision-making.

 

For example, in smart transportation scenarios, the platform uses cameras and radar sensors to monitor road conditions in real time, rapidly identifying vehicles, pedestrians, and traffic signs while processing data locally at the edge. When potential hazards are detected, the system triggers immediate decisions and sends alerts to relevant vehicles or traffic management centers, effectively preventing accidents. This use case vividly demonstrates the platform’s high-efficiency perception and real-time decision-making capabilities. Whether deployed for precision detection in industrial settings or real-time monitoring in urban traffic, the platform delivers high-performance, highly reliable edge intelligence services.

 

Its core strengths are threefold:

Hardware: Advanced packaging technologies enable miniaturization and low power consumption.

Software: Algorithm optimization and real-time operating systems ensure ultra-fast response speeds.

Security: Encryption and authentication mechanisms protect data privacy.

 

The platform supports rapid deployment and secondary development, seamlessly adapting to the needs of different industries. It helps customers build agile, responsive intelligent systems, achieving cost reduction, efficiency gains, and intelligent transformation.

 

Core Technical Architecture: Modular Design Enabling Flexible Deployment

The platform adopts a modular, reconfigurable system architecture, with core components including the multi-modal sensing layer, edge AI computing engine, intelligent algorithm stack, real-time operating system & communication interface, and low-power & reliability design. All modules work in tandem to deliver peak edge intelligence performance.

 

1. Multi-Modal Sensing Layer: The Physical Foundation for Precision Perception

Full Sensor Coverage: The platform supports an extensive range of sensors, including:

  CMOS image sensors (up to 4K resolution)

  Microphone arrays (for far-field voice recognition)

  Inertial Measurement Units (IMUs) with ±0.5°/h accuracy

  Temperature and humidity sensors (±0.3℃ accuracy)

  Air pressure sensors (±0.1Pa accuracy)

  Gas sensors (detecting 10+ harmful gases such as VOCs)

  Infrared sensors (640×480 thermal imaging resolution)

  These cover multi-dimensional perception needs for vision, hearing, motion tracking, and environmental monitoring.

 

Advanced Packaging Technology: System-in-Package (SiP) and Fan-Out Wafer Level Packaging (Fan-Out WLP) are employed to achieve sensor miniaturization, low noise, and high consistency. A single module’s volume is reduced by over 40% and power consumption by 20%, making it ideal for space-constrained scenarios like UAVs and wearable devices. For instance, integrating an IMU and gas sensor into a smart helmet results in a module one-third the size of traditional solutions.

 

Sensor Fusion Calibration: A built-in multi-sensor fusion algorithm (e.g., Kalman filtering) eliminates environmental interference (such as temperature drift and magnetic field distortion) via dynamic calibration, boosting data accuracy and robustness. In harsh environments with strong light or magnetic fields, fusing visual sensor and IMU data keeps positioning errors within 2cm.

 

2. Edge AI Computing Engine: The Art of Balancing Computing Power and Energy Efficiency

Heterogeneous Computing Architecture: Equipped with high-performance, low-power AI accelerator chips (supporting NPU/GPU/CPU collaborative computing), the platform offers scalable computing power ranging from 0.5 TOPS to 10 TOPS. It adapts flexibly to lightweight tasks (e.g., face detection with <30ms latency) and complex inference scenarios (e.g., industrial defect analysis with >99% accuracy). For example, in smart manufacturing, 8 TOPS of computing power can process 16 channels of high-definition video streams simultaneously.

 

AI Framework Compatibility: Fully compatible with mainstream frameworks (TensorFlow Lite, PyTorch Mobile, ONNX), the platform provides a model conversion toolchain to minimize migration costs. Developers can seamlessly deploy cloud-trained models to the edge—for example, quantizing and deploying the YOLOv5 object detection model boosts inference speed by 40%.

 

Real-Time Inference Optimization: Hardware acceleration (e.g., NPU fixed-point operations) and intelligent software scheduling (e.g., dynamic task allocation) keep model inference latency under 50ms, meeting millisecond-level requirements for scenarios like autonomous driving (collision warnings require <20ms response) and industrial control (1ms servo system cycle time).

 

3. Intelligent Algorithm Stack: Full-Scenario Coverage from General to Customized

Pre-trained Model Library: Hundreds of out-of-the-box pre-trained models cover scenarios including:

  Object detection (pedestrians, vehicles, industrial parts)

  Face recognition (>95% accuracy with masks)

  Behavior analysis (fall detection, anomaly alerts)

  Voice wake-up (>90% wake rate at 5 meters)

  Environmental anomaly detection (smoke, water leaks)

  In smart elderly care, for example, fall detection algorithms combined with skeleton key point recognition cut false alarm rates by 60% compared to traditional solutions.

 

Customized Development Support: An AutoML toolchain (one-stop data annotation, model training, and evaluation) enables customers to fine-tune models with limited data (e.g., 1,000 industrial defect images). Model compression techniques (distillation, quantization, pruning) reduce model size by over 50%, adapting to edge resource constraints. A semiconductor factory, for instance, compressed a defect detection model to 2MB and tripled its inference speed.

 

Edge Learning Capability: Select models support online learning, iteratively optimizing models using real-time feedback data. In smart retail, for example, passenger flow analysis models dynamically adjust accuracy based on seasonal trends, improving precision from 85% to 92%.

 

4. Real-Time Operating System & Communication Interface: The Bridge Connecting Edge and Cloud

Operating System Compatibility: Supports RTOS (FreeRTOS, μC/OS), Linux (Ubuntu, Debian), and lightweight containerized environments (EdgeOS), catering to diverse development preferences and performance needs. In industrial robot control, for example, μC/OS delivers microsecond-level task scheduling.

 

Communication Protocol Matrix: Integrates a comprehensive suite of interfaces:

  Wi-Fi 6 (up to 9.6 Gbps)

  Bluetooth 5.3 (300-meter transmission range)

  5G/NB-IoT (low-power wide-area coverage)

  CAN bus (industrial applications)

  Gigabit Ethernet

  It enables collaboration between edge devices (e.g., multi-robot linkage in smart workshops) and cloud-edge synergy (encrypted upload of critical data), while supporting IoT protocols (MQTT, HTTP, CoAP) for cross-platform interoperability.

 

Secure Communication Mechanism: Built-in TLS/SSL encryption and a Hardware Security Module (HSM) supporting national cryptographic algorithms (SM2/SM3/SM4) ensure data confidentiality and integrity during transmission and storage. The platform complies with GDPR, ISO 27001, and China’s Cybersecurity Level 2.0 standards—for example, medical imaging data is desensitized locally, with only diagnostic results uploaded to the cloud.

 

5. Low-Power & Reliability Design: Guarantee for Sustained Edge Operation

Dynamic Power Management: Supports multi-level sleep modes (deep sleep power consumption <0.5mW) and event-triggered wake-up (sound, vibration, light). In smart agriculture, sensors activate only during critical crop growth stages (flowering, ripening), extending battery life to 3 years.

 

Industrial-Grade Reliability Certification: Passes AEC-Q100 automotive-grade certification (10g vibration resistance at 10–2000Hz), IP68 waterproof/dustproof testing (1.5m water immersion for 30 minutes), and wide-temperature operation validation (-40℃ to 85℃). It is suitable for harsh environments such as industrial sites and outdoor monitoring—for example, equipment operates stably in salt spray conditions at port terminals.

 

Self-Maintenance & OTA Upgrade: A built-in self-diagnostic system monitors CPU temperature, memory usage, and sensor status, supporting remote over-the-air (OTA) upgrades. Differential upgrades save 90% of bandwidth. In smart city camera clusters, for instance, algorithm firmware can be batch-updated to reduce maintenance costs.

 

#Platform Advantages: Breaking Through Core Pain Points of Edge Intelligence

1.  Ultimate Integration: 3D packaging and SiP technology integrate sensors, AI chips, storage, and power management into a single module (≤50cm³)—40% smaller and 30% more power-efficient than traditional solutions. In UAV inspection applications, the 20g module extends flight time by 25%. A leading UAV manufacturer reported significant performance gains and a 20% increase in inspection efficiency after deployment.

 

2.  Millisecond-Level Low Latency: The edge-side real-time processing architecture delivers an average response time ≤50ms, avoiding risks from cloud latency in scenarios like industrial robot collision prevention (<15ms response) and autonomous driving obstacle detection (20ms braking latency). An autonomous driving firm noted a 30% improvement in emergency braking response speed and enhanced vehicle safety.

 

3.  Data Sovereignty & Privacy Protection: Sensitive data (medical images, industrial formulas) is analyzed locally, with only key results (diagnostic reports, statistics) uploaded—eliminating data leakage risks. A pharmaceutical factory reduced compliance costs by 50% through local formula processing, while significantly enhancing data security.

 

4.  Flexible Deployment & Ecosystem Compatibility: Standardized SDKs, APIs, and DevKit development kits (including simulators and debugging tools) enable developers to complete POC validation within 3 weeks. The platform is compatible with domestic chip platforms (Huawei Ascend, Hygon, Zhaoxin), supporting the development of China’s independent tech ecosystem. Developers praised the streamlined workflow, which drastically shortened project cycles.

 

5.  Full-Lifecycle Services: End-to-end support spans demand definition (on-site industry expert analysis), hardware customization (PCB design, thermal optimization), algorithm development (joint modeling), mass production delivery (production line testing), and long-term maintenance (24/7 technical support with <2-hour fault response). Dishan Technology has delivered customized edge intelligence solutions to dozens of industry leaders, including Sany Heavy Industry, BYD, and China Mobile. Sany Heavy Industry commended the full-lifecycle services, citing them as a solid guarantee for equipment efficiency.

 

Typical Application Scenarios: Reshaping Industry Value

1. Smart Manufacturing

Predictive Equipment Maintenance: Vibration sensors and AI models monitor machine tool conditions in real time, issuing bearing wear alerts 3 days in advance and reducing unplanned downtime losses.

Online Quality Inspection: Visual sensors on production lines identify PCB solder joint defects (cold solder, insufficient solder) with 99.5% accuracy, replacing manual inspection and boosting efficiency by 5x.

Human-Machine Collaboration Safety: LiDAR and AI algorithms define safe zones; robots slow down or stop automatically when personnel enter hazardous areas, cutting accident rates by 90%.

 

2. Smart Cities

Smart Traffic Optimization: Multi-modal sensors at intersections fuse traffic flow and pedestrian data to dynamically adjust traffic light timing, reducing congestion by 20% (a pilot city saw 30% higher main road traffic efficiency).

Urban Event Perception: AI cameras detect anomalies (garbage accumulation, displaced manhole covers, illegal parking) in real time, triggering rapid urban management responses and shortening processing time to 1 hour.

Community Security Upgrade: Infrared and visual sensors enable nighttime intrusion detection; behavior analysis algorithms reduce false alarms by 80% (a residential community cut monthly false alerts from 200 to 20).

 

3. Smart Automotive

Driver Monitoring System (DMS): Infrared cameras analyze driver fatigue (blink frequency) and distraction (gaze deviation), issuing timely alerts and lowering accident rates by 35%.

In-Cabin Passenger Perception: Detects passenger count and position (e.g., rear-seat child left-behind alerts), automatically adjusting air conditioning and entertainment systems to enhance user experience.

Blind Spot Perception Enhancement: Millimeter-wave radar and AI algorithms warn of approaching vehicles, reducing lane-change accidents by 40% for a vehicle model.

 

4. Smart Healthcare

Chronic Disease Monitoring: Wearable devices and AI algorithms analyze heart rate and blood oxygen levels in real time, sending alerts within 5 seconds of abnormalities and improving emergency rescue success rates.

Remote Diagnosis Assistance: Edge terminals process medical images (CT, MRI) locally, ensuring diagnostic latency <100ms while protecting patient privacy.

Operating Room Intelligent Perception: Sensor networks monitor sterile zone compliance (glove integrity, mask wearing), issuing real-time alerts and reducing infection risks by 60%.

 

5. Smart Home

Whole-House Voice Control: Microphone arrays enable 5-meter far-field wake-up, with >98% accuracy in executing complex commands (e.g., "Turn off all lights and play soft music").

Fall Detection & Emergency Call: Millimeter-wave radar detects elderly posture changes, triggering emergency calls within 3 seconds of a fall with a false alarm rate <5%.

Intelligent Energy Consumption Management: Environmental sensors link with household appliances, automatically adjusting air conditioners and curtains based on temperature and light—cutting household energy use by 15%.

 

R&D and Ecosystem Layout: Continuously Breaking Through Technical Boundaries

Guided by the vision of "perception intelligence and computing power democratization", Dishan Technology focuses R&D investment on three core areas:

1.  Multi-Modal Fusion Perception: Developing cross-modal data correlation algorithms (e.g., vision + voiceprint fusion recognition) to achieve 99.9% identity recognition accuracy in noisy environments; exploring bioelectrical signal sensing for emerging scenarios like emotion monitoring. The team is currently researching applications in smart homes, integrating vision, temperature, and humidity data to enable more intelligent environmental control.

 

2.  Ultra-Low Power AI Architecture: Collaborating with the Chinese Academy of Sciences to develop Compute-in-Memory (CIM) technology, reducing inference power consumption to one-fifth of existing solutions and overcoming edge device battery life bottlenecks. Plans are underway to deploy this technology in wearables, delivering longer battery life and stronger computing power for smart watches and health monitors.

 

3.  Edge Autonomous Evolution: Building an edge federated learning framework to enable local collaborative model training across device clusters (e.g., multi-camera joint optimization of traffic flow algorithms). This avoids privacy risks from centralized data while doubling algorithm iteration efficiency. The team is exploring expansions into smart city projects, using multi-sensor data analysis to optimize urban traffic and public service layouts.

 

Through these forward-looking R&D initiatives, Dishan Technology is committed to leading the industry’s technological frontier and creating greater value for partners and customers.

 

#Customer Value and Service System: From Delivery to Empowerment

Full-Process Service Matrix

Demand Insight: Industry expert teams conduct scenario-specific demand analysis (e.g., smart manufacturing production line pain point diagnosis).

Joint Development: Co-establishing laboratories with customers to customize hardware specifications (e.g., explosion-proof sensors) and algorithm models (e.g., specialized defect recognition).

Rapid Implementation: Standardized modules and reference designs shorten R&D cycles by 6 months; automated production line testing solutions ensure >99.5% yield rates.

Long-Term Operation and Maintenance: 24/7 technical support and OTA upgrade services with <2-hour fault response time.

 

Ecosystem Win-Win Plan

Dishan Technology has established a RMB 100 million ecosystem fund to support partners in developing industry applications and sharing revenue; it also hosts annual developer competitions to incubate innovative projects.