Multi-Computer Hazard Detection System with Proximity-Based Notification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional systems face challenges in efficiently detecting and communicating hazards in real-time, particularly in identifying users who need to receive hazard information, leading to inefficiencies in mitigating potential damage during wireless communication.
Innovation Solution
A multi-computer system that captures and aggregates data from vehicles and mobile devices to detect hazards, identifies user groups based on common characteristics or proximity, and transmits personalized notifications to relevant users using a hazard detection and broadcast computing platform, incorporating machine learning for data analysis and user grouping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional systems capture and evaluate data in real-time to identify hazards, then hazard detection capability is improved, but system complexity and processing difficulty increase
Solution Approach 1:
The system divides hazard detection into multiple specialized modules: sensor data acquisition module, wireless communication module for data transmission, machine learning model module for hazard identification, and notification module for alerting users. Each module handles a specific aspect of the detection process, reducing overall system complexity while maintaining real-time detection capability.
Solution Approach 2:
A centralized server acts as an intermediary between multiple mobile devices and the hazard detection system. The server aggregates data from multiple sources, processes information through machine learning models, and distributes notifications to relevant users. This intermediary approach simplifies individual device complexity while enabling comprehensive real-time hazard detection across the network.
2Reliability
If the system identifies and communicates hazards to all users, then safety coverage is improved, but communication efficiency decreases
Solution Approach 1:
The system determines user proximity to the hazard location and prioritizes notifications to users in closer proximity. The notification module uses location data to assess which users are most at risk and sends targeted alerts to those specific users first, rather than broadcasting to all users simultaneously. This approach maintains comprehensive safety coverage while significantly improving communication efficiency by focusing resources on the most vulnerable users.
3Loss of information
If the system transmits detailed hazard information to multiple users, then information completeness is improved, but network bandwidth consumption increases
Solution Approach 1:
The system transmits full hazard information details only to users who are in immediate proximity to the hazard and at highest risk. For users farther away or at lower risk, the system transmits summarized or partial information about the hazard. This differentiated communication approach maintains information completeness for those who need it most while reducing overall network bandwidth consumption by avoiding redundant transmission of identical detailed information to all users.
Data Source
AI summary
Systems, methods, computer-readable media, and apparatuses for providing hazard detection and broadcast functions are provided. In some examples, sensor data may be captured by a mobile device, vehicle, or the like. The data may be used to detect a hazard, identify a type of hazard, and the like. One or more users or groups of users for notification of the hazard may be identified and one or more notifications may be transmitted to users within the group.