Mobile Safety Platforms for Dynamic Industrial Hazard Detection
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Solution Overview
Problem
Conventional safety systems are inadequate for dynamically changing environments, such as construction sites and entertainment venues, due to their fixed nature and limited accuracy in sensor technologies like computer vision, real-time locating systems, and LIDAR, which suffer from computational power constraints, resolution issues, and obstructions.
Innovation Solution
A mobile safety system combining computer vision, real-time locating, and LIDAR technologies with edge computing and machine learning algorithms to provide real-time, accurate safety management, using a multimodal sensor suite and mobile platforms for flexible deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fixed facilities (e.g., surveillance camera) are used for safety monitoring, then safety coverage is provided, but the system cannot adapt to dynamically changing environments
Solution Approach 1:
The patent employs mobile platforms (vehicles, robots, drones) that can dynamically reposition themselves to different locations within the environment, transforming the static monitoring approach into a dynamic one. The system continuously moves and adjusts its monitoring zones to track changing workspaces, construction sites, or event venues, enabling adaptability without requiring complex reconfiguration of fixed infrastructure
Solution Approach 2:
The mobile safety system integrates multiple sensor types (computer vision cameras, LIDAR, RTLS) and multiple functions (detection, tracking, alerting, reporting) into a single multi-functional platform. This universal system can monitor various environments (construction sites, entertainment venues, workplaces) and perform multiple safety functions simultaneously, reducing the need for separate specialized systems for different scenarios
2Measurement precision
If conventional sensor technologies (computer vision, RTLS, LIDAR) are used, then safety detection is provided, but accuracy is limited due to computational power constraints and obstructions
Solution Approach 1:
The patent segments the computational workload by distributing processing tasks across multiple components: edge computing devices on the mobile platform handle real-time processing of sensor data, while cloud-based systems perform more intensive analytics and model training. This segmentation allows accurate safety detection without requiring all computational power to be concentrated in a single device, managing complexity through distributed architecture
Solution Approach 2:
The system introduces edge computing devices as intermediaries between the mobile sensors and the central processing system. These edge devices pre-process sensor data, filter relevant information, and transmit only critical data to cloud systems, reducing computational burden while maintaining detection accuracy. The edge devices act as mediators that bridge the gap between limited on-platform computing resources and comprehensive safety analysis requirements
3Productivity
If fixed safety facilities are deployed, then safety monitoring is established, but deployment and reconfiguration are time-consuming
Solution Approach 1:
The mobile safety system replaces static, permanently installed facilities with dynamic mobile platforms that can be rapidly deployed and repositioned. Vehicles, robots, or drones carrying safety monitoring equipment can quickly arrive at a location, begin monitoring, and relocate as needed, dramatically reducing deployment time while maintaining continuous reliable monitoring through their mobile nature
Solution Approach 2:
The system combines multiple safety monitoring functions (computer vision, LIDAR, RTLS, alerting, reporting) into integrated mobile platforms. By merging these previously separate fixed facilities into unified mobile systems, the patent achieves both rapid deployment (bringing all functions in one mobile unit) and reliable monitoring (maintaining all safety functions simultaneously), resolving the contradiction between deployment speed and monitoring reliability
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables precise, proactive safety management by detecting unsafe situations and alerting individuals and managers, improving situational awareness and compliance with safety protocols in dynamic environments.
Implementation Method 1
a computer vision component for generating a computer vision output data
Implementation Method 2
a real-time locating component for generating location data about an object within the industrial environment
Implementation Method 3
a LIDAR component for generating 3D point cloud data of the industrial environment
Data Source
AI summary
A mobile system is provided for managing safety in a dynamic environment. The system comprises: a computer vision component for generating a computer vision output data; a real-time locating component for generating location data about an object within the industrial environment; a LIDAR component for generating 3D point cloud data of the industrial environment; and one or more processors coupled to the computer vision component, the real-time locating component and the LIDAR component and configured to: (i) process the data stream with aid of a machine learning algorithm trained model to generate a safety related result and feedback data, and (ii) deliver the feedback data to the object via the mobile tag device, and a mobile platform configured to move the mobile system in the environment.