Intelligent Safety Management System for Hazard Prediction
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Solution Overview
Problem
Current safety management control systems in risky industries like steel mills rely on user notification for dangerous situations, which is inadequate for preventing accidents and tracking individuals or objects over large areas, leading to inefficiencies and increased costs.
Innovation Solution
An RFID and USN-based intelligent integrated safety management control system that uses integrated tag devices, reader devices, and worker terminals to track movement patterns, predict potential hazards, and notify workers of risks through a centralized server, minimizing the need for direct user notification and enhancing location tracking precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If user notification-based safety management is used, then system simplicity is maintained, but accident prevention capability and tracking precision deteriorate
Solution Approach 1:
The system performs preliminary actions by automatically detecting dangerous situations and predicting potential accidents before they occur. The RFID tags continuously transmit location data, and the server analyzes movement patterns to identify risky behaviors and predict potential hazards, enabling preventive intervention rather than reactive response.
Solution Approach 2:
The server acts as an intermediary between RFID tags and the safety management system. It receives location information from multiple tags, analyzes movement patterns, predicts potential accidents, and generates notifications. This intermediary processing layer enables sophisticated safety management without requiring complex equipment at each user endpoint.
2Measurement precision
If RFID and USN-based tracking system is implemented, then location tracking precision and accident prediction capability are improved, but system establishment cost increases
Solution Approach 1:
The RFID tags serve multiple functions: location identification, movement pattern analysis, and potential accident prediction. The same RFID infrastructure supports both tracking individual workers and monitoring objects, enabling the system to achieve high measurement precision while avoiding the need for separate specialized tracking equipment.
Solution Approach 2:
The system uses RFID technology to create information copies of physical objects and persons. Instead of physically monitoring each worker and object, the system uses RFID tags to generate digital representations that can be tracked, analyzed, and used for prediction, significantly reducing the cost and complexity of the physical monitoring infrastructure.
3Loss of time
If automatic risk signal generation is implemented, then response time to dangerous situations is reduced, but false alarm rate may increase
Solution Approach 1:
The server continuously receives location information from RFID tags and provides feedback by analyzing movement patterns against safety rules. The system adjusts its monitoring based on the analyzed patterns, generating notifications only when actual risky behaviors are detected. This feedback mechanism enables rapid response to genuine dangers while filtering out false alarms through intelligent pattern recognition.
Solution Approach 2:
The system performs preliminary analysis of movement patterns to establish baseline behavior for each worker and object. By understanding normal movement patterns in advance, the system can quickly identify deviations that indicate real dangers without generating false alarms from normal variations in behavior.
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
The system effectively predicts and prevents workplace accidents, reduces system establishment costs, and enables precise tracking of individuals and objects across indoor and outdoor areas, enhancing overall safety management in risky industries.
Implementation Method 1
a plurality of integrated tag devices configured to each include an active tag attached to a moving entity and transmit a tag signal; a plurality of reader devices configured to recognize tag device identification information from a tag signal received from each tag device
Data Source
AI summary
An integrated tag device includes an active tag and transmits a tag signal; a reader device recognizes tag device identification information, generate and transmit a tag device recognition signal, receive a risk notification signal and inform a worker; a worker terminal checks location information thereof, includes the checked location information in a map information request signal, and transmits the map information request signal, receives and displays factory facility map information; an intelligent integrated safety management control server checks reader device identification information and tag device identification information, checks installation location information, tracks a location and predicts a movement path of each moving entity, classifies and stores a movement pattern and a movement path, checks reader device identification information, generates and transmits a risk notification signal, checks worker terminal location information and identification information, checks reader device identification information, reads and transmits factory facility map information to the worker terminal.