AI Glasses used in construction sites

　　I. Typical Product Case: Functional Innovation Adapted to Construction Site Scenarios

　　Safety Monitoring and Early Warning Type

　　Equipped with a high-definition wide-angle camera and AI visual recognition module, it can detect safety hazards at construction sites in real time: when it detects personnel not wearing safety helmets or safety belts, the temples immediately emit a 3-level adjustable vibration warning, while a bone conduction speaker plays a voice prompt (such as "Please remember to wear your safety helmet"); integrating a gas sensor and temperature and humidity module, it can monitor formaldehyde, dust concentration, and ambient temperature. When levels exceed the limits, it automatically pops up a warning window and pushes an evacuation route, suitable for enclosed work scenarios such as foundation pits and tunnels.

　　**Work Guidance and Assistance Type:** Supports AR technology overlaying BIM (Building Information Modeling) drawings, projecting data such as rebar spacing and pipeline routing 1:1 onto the real-world scene, allowing workers to intuitively compare against construction standards. Built-in AI material recognition functionality quickly reads specifications, models, and certification information by scanning building materials such as rebar and pipes, achieving an accuracy rate exceeding 99% and improving acceptance efficiency by more than 4 times compared to manual methods. For equipment maintenance scenarios, it can automatically match equipment fault databases and use AR markers to indicate fault locations and repair steps.

　　**Remote Collaborative Management Type:** Utilizes 5G networks for real-time first-person perspective transmission. Back-end management personnel can mark construction deviation areas (e.g., "Wall verticality exceeds standard here"), with the marked content directly displayed on the camera lens. Supports remote guidance from experts across regions; experts can send text, images, and operation demonstration videos, overlaid on the on-site view, reducing response time for complex technical problems by 60%. Integrated personnel positioning functionality tracks the location of workers in real time, triggering immediate two-way warnings when workers enter dangerous areas.

　　**Remote Collaborative Management Type:** Leveraging 5G networks for real-time first-person perspective transmission, back-end management personnel can mark construction deviation areas (e.g., "Wall verticality exceeds standard here"), with the markings directly displayed on the camera lens. Supports remote guidance from experts across regions; experts can send text, images, and operation demonstration videos, overlaid on the on-site view, reducing response time for complex technical problems by 60%. II. Core Technology Architecture: A Four-Layer Support System for Complex Working Conditions

　　Perception Layer: Employing a TOF sensor and an infrared depth camera, it achieves accurate identification of people, equipment, and building materials within a range of 0.3-10m. Even in strong light and dusty environments, the error in joint movement and object contour recognition remains ≤3%. Combined with a gas sensor (detection accuracy 0.01mg/m³) and an inertial measurement unit, it simultaneously captures environmental data and personnel posture, comprehensively perceiving the on-site situation.

　　Interaction Layer: Based on bone conduction technology (maintaining over 85% sound clarity in noisy environments), it avoids hearing obstruction caused by earbuds. It supports both voice (offline recognition accuracy 92%) and gesture control. While wearing gloves, functions can be switched via waving or clenching a fist. The operation response latency is ≤150ms, without interrupting the work process.

　　Intelligent Analysis Layer: Equipped with a lightweight industrial AI model, it features a database of over 1000 construction site safety hazard characteristics (such as violations of operating procedures and abnormal equipment vibrations) and a database of over 500 building material parameters. It can compare collected data in real time and generate early warnings. It integrates with enterprise ERP and MES systems, automatically synchronizing work orders, tasks, and asset information, ensuring full traceability of operational data.

　　Protection and Battery Life Layer: Utilizing an IP68 waterproof and dustproof shell and impact-resistant polycarbonate lenses, it is explosion-proof certified (Ex ib IIB T4 Ga) and can withstand a 1.5-meter drop impact. Equipped with a 3000mAh flexible battery, it provides up to 8 hours of continuous operation and supports fast charging (20 minutes for 4 hours of work), making it suitable for long-term continuous construction scenarios.

　　III. Market Landscape: Professional Growth Driven by Smart Construction Sites

　　Scale and Growth Rate

　　The global market size for smart wearable devices for smart construction sites is projected to reach RMB 12.6 billion by 2025, with AI glasses experiencing a growth rate exceeding 55%, becoming the core growth driver in this segment. The Chinese market accounts for approximately 38%, with penetration rates reaching 22% in densely populated construction areas such as the Yangtze River Delta and Pearl River Delta. Large construction companies have seen a 70% year-on-year increase in procurement volume.

　　Price and User Segmentation

　　Basic Type (RMB 2000-4000): Accounts for 51%, focusing on safety warnings and basic recognition functions. Core users are frontline construction workers (72%, concentrated in the 25-45 age group).

　　Advanced Type (RMB 4000-8000): Accounts for 34%, adding AR drawing and remote collaboration functions, primarily serving construction technicians and team leaders.

　　Professional Type (RMB 8000 and above): Accounts for 15%, supporting digital twins and multi-system integration. Users are mainly project management and equipment maintenance experts, with B2B centralized procurement accounting for over 80%. Channel and Consumption Characteristics: Core channels are construction enterprise group procurement (65%), industrial equipment service providers (20%), and offline experience stores (15%), with online channels accounting for less than 5% (due to the need for on-site adaptation and debugging). The core factors for user decision-making are, in descending order: "Protection level and durability" (78%), "AI recognition accuracy" (71%), and "System compatibility" (63%).

　　IV. Future Trends: Deep Integration of Smart Construction

　　Technological Precision: By 2026, early warning signs of equipment failure (such as predicting abnormal motor vibration) will be identified, with an accuracy rate exceeding 95%. A new muscle fatigue monitoring function will be added, using posture data analysis to suggest that "prolonged bending over can easily lead to strain; alternating postures is recommended."

　　Scenario-Based Integration: A dedicated mode for underground engineering will be developed, integrating low-light night vision and underground positioning technology (to compensate for GPS signal deficiencies), adapting to enclosed scenarios such as tunnels and pipe corridors. A new high-altitude operation assistance function will be added, using AR guidance to avoid blind spots in tower crane operations.

　　Data Collaboration: Achieve data interoperability with drones, smart safety helmets, and construction machinery to build a comprehensive monitoring network covering "personnel, equipment, and environment." For example, after a drone patrol detects a potential hazard, it automatically synchronizes its location to the AI glasses of nearby workers and pushes a response plan.

　　Ecosystem Integration: Deeply integrate with smart construction site platforms to automatically generate "daily safety reports" and "material acceptance lists," reducing manual data entry; support cross-enterprise data sharing to assist government regulatory departments in achieving remote safety supervision.