Enterprise AI Employees
AI EHS Intelligent Assistant
Shift from reactive response to proactive prevention, achieving intelligent oversight across all elements of environment, health, and safety.
Four Key Pain Points in Traditional EHS Management
Enterprise EHS management has long struggled with slow response times, efficiency bottlenecks, and compliance risks. It urgently needs AI-driven transformation to achieve fundamental change.
Passive Risk Response
Intervention occurs only after an incident, lacking a proactive warning system to eliminate risks at their source.
High reliance on manual intervention
Manual inspections, logging, and reporting are time-consuming and labor-intensive, prone to human error, and difficult to cover all hours.
Severe data silos
Business systems are siloed, preventing comprehensive analysis and intelligent decision support.
High compliance costs
Manual inspections are inefficient, compliance risks are hard to control, and enterprises face immense regulatory pressure.
From reactive response to proactive prevention
Build a comprehensive intelligent supervision system across Environment, Health, and Safety (EHS) dimensions to enable real-time risk visibility and prevention.
ENVIRONMENT
Environment Management
Monitor corporate activities' impact on the natural environment, including wastewater, exhaust gas, and solid waste emissions, noise pollution, energy consumption, and resource recycling.
Comply with environmental regulations, reduce environmental impact, and achieve sustainable development.
HEALTH
Occupational Health Management
Occupational health monitoring (dust, chemical hazards, radiation), employee health checkups, mental health counseling, and ergonomic optimization of work posture and physical workload.
Goal: Prevent occupational diseases and safeguard employee health and well-being
SAFETY
Safety Production Management
Manage device safety, operational procedures, fire safety, and emergency plans to prevent workplace injuries and other accidents.
Safeguard employee safety and protect company assets
Meets diverse needs for intelligent security and lean production
Three core capabilities work in synergy to cover all scenarios of security compliance, quality control, and computing power networks.
CAPABILITY01
AI EHS Safety Requirements
- Real-time detection of missing PPE across the site: hard hats (white/yellow/red), protective clothing, gloves, safety glasses, and workwear/high-visibility vests.
- Regional Security Control: Intrusion detection and alerts for hazardous areas, attendance monitoring (on/off-duty), AGV obstacle avoidance, and detection in mixed pedestrian-vehicle zones.
- Drone Inspection: 7x24 hours of coverage for high-risk points, automatic hazard detection, replacing manual inspections to reduce safety risks.
CAPABILITY02
AI SOP Quality Requirements
- Operational Compliance Management: Detects improper actions such as threading before insertion or pulling out prematurely, and verifies correct usage of tools and materials.
- Step Sequence Validation: Verify operation order against predefined SOPs and confirm completion of critical steps (e.g., screw tightening, label application).
- Quick changeover support: Deploy models with few samples (1-3 images) in under 1 minutes.
CAPABILITY03
Intelligent Computing Network Requirements
- Business Model Support: Meets application needs for AI EHS Smart Supervision and AI SOP Quality Inspection.
- Core Technical Metrics: Microsecond-level latency, high bandwidth, high reliability, and unified compute-network architecture design.
- Edge Computing Collaboration: ECS-powered compute orchestration enables seamless edge-cloud synergy with dynamic resource allocation.
Four-Layer Collaborative Architecture
Operates on a four-layer architecture to build an intelligent safety system with data collection, smart analytics, real-time response, and closed-loop control, fully leveraging the collaborative advantages of large-scale integrated computing and networking.
MANAGEMENT · Management
Video Management + Video AI Large Model
AI Video Management Platform: Orchestrate algorithms, video streams, business logic, and alerts to enable AI-driven insights and decision support.
Compute · Compute Layer
Computing-Network Integrated Appliance
Configure GPU/NPU (e.g., NVIDIA Jetson AGX Orin with 170TOPS TOPS) to support multi-stream parallel execution of complex models and containerized deployment.
NETWORK · Network Layer
All-Optical Network Terminal – Delivers Speed
High-bandwidth, low-latency interconnectivity designed to ensure end-to-end high bandwidth, high reliability, and intelligent operations.
PERCEPTION · Perception Layer
Camera + Environmental Sensor + Alarm Acquisition Device
Ensure clear visibility and capture on-site environmental data, supporting collaboration across multiple device types (cameras, smoke alarms, drones).
Multimodal Large Models Collaborating with Specialized Models
End-to-end video solution: collaborative deployment of video detection, multimodal large models, and specialized vision models within factory parks.
Video stream ingestion
RTSP / ONVIF Protocol, Terminal Access
Video Detection and Feature Extraction
Detect key behavioral events and analyze human pose keypoints
Guide professional models to focus
High-confidence large model outputs, professional models for precise recognition
Closed-loop response
Alert push, linked control, traceability, continuous optimization
Scenario 1: Comprehensive Inspection
Comprehensive monitoring for critical production role vacancies, unauthorized equipment guard displacement, non-compliant operations in key process steps, and unlicensed operation of specialized personnel.
Scenario 2: Defect Detection
For product surface defect detection, a large model performs initial screening followed by a specialized small model for precise confirmation, ensuring double-layered accuracy.
Local integrated deployment
Integrated on-site solution combining video acquisition, multimodal large models, and a professional vision model factory. Process massive video data locally for enhanced data security and control.
Edge vs. Cloud Deployment Comparison
Edge-side integrated deployment outperforms traditional edge solutions across compute, cost, maintenance, and scalability.
| Comparison Criteria | On-device deployment (within camera) | Edge Deployment (Appliance + Management) |
|---|---|---|
| Compute Intensity / Complex Algorithms | Limited: Only lightweight models (MobileNet, YOLOv5) are supported; complex scenarios are restricted. | Strong: GPU/NPU compute power reaches 170TOPS, supporting parallel execution of complex models such as YOLOv8x and ResNet-101. |
| Deployment Cost | High: Relies on expensive camera hardware | Low: Compatible with standard light source probes, flexible edge unit expansion, and cost control. |
| Maintenance & Upgrades | Challenge: Manual, camera-by-camera upgrades make large-scale deployment and operations extremely difficult. | Convenient: Unified version management, OTA remote updates, and batch updates completed in minutes. |
| Scalability and Flexibility | Poor: Rigid functionality that is difficult to modify or extend for new scenarios. | Strong: Supports containerized deployment (Docker) and dynamic scheduling (Kubernetes). |
| Multi-device collaboration | Not supported: Single-camera standalone operation; cross-device data integration unavailable. | Supports collaborative data from multiple devices, including multi-camera systems, inspection robots, and drones. |
Comprehensive intelligent supervision with visible and preventable risks
Validated the solution's effectiveness through real-world deployments, achieving closed-loop management from vessels to integrated government-enterprise industrial parks.
CASE01
Vessel Supervision
Intelligent Supervision of Freight Ships in Daishan
Consolidate comprehensive data on vessels, crews, and enterprises to build an intelligent supervision center. Integrate existing systems such as CCTV and AIS with new smart sensing devices to establish a three-tier risk control mechanism across ports, ships, and companies. Includes an intelligent blacklist module to automatically detect violations like expired certificates and insufficient crewing.
1000+
Annual Intelligent Work Order Disposal
30%
Reduction rate of maritime traffic accidents
CASE02
3D Inspection
Dai Shan's 3D Intelligent Inspection + Guarding the Peaceful Coastline
Drones equipped with AI vision algorithms build a sea-land-air collaborative system for intelligent port, waterway, and coastline management. Maritime signals and images are uploaded to the Daishan Port Integrated Control Cloud Platform, enabling joint enforcement with the Administrative Law Enforcement Bureau and multiple departments. This establishes a multi-agency drone inspection mechanism to ensure closed-loop task execution.
closed-loop
Discovery - Case Initiation - Verification
Multi-step
Cross-departmental Collaboration and Disposal
