Fire System Safety Score via Data Mining Analytics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing fire systems lack the ability to quantify and visually communicate the safety level of monitored regions, failing to provide predictive insights for enhancing occupation safety, and do not effectively identify the impact of system failures on safety.
Innovation Solution
A data analytics engine retrieves and analyzes data from fire system components to compute a fire system safety score, using weighted attributes and variables, and provides predictive analysis through a logical analytical engine, enabling visual representation of safety scores and identifying areas for improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If known fire systems monitor detection and notification components, then system operation data is collected, but the occupation safety level remains unknown and cannot be communicated to users
Solution Approach 1:
A data analytics engine is introduced as an intermediary component between the fire system's detection/notification components and the user interface. This engine retrieves operational data, analyzes it using weighted attributes, and generates a comprehensible safety score that communicates system status to users without requiring them to interpret raw operational data directly
Solution Approach 2:
The fire system performs self-assessment by automatically analyzing its own operational data through the data analytics engine. The system retrieves its own operational data from detection and notification components, analyzes this data using predefined weighted attributes, and generates its own safety score without requiring external evaluation
2Reliability
If fire systems collect operational data from components, then system performance information is available, but predictive analysis for enhancing safety is not provided
Solution Approach 1:
The logical analytical engine performs preliminary predictive analysis on operational data to forecast future system behaviors and identify potential safety issues before they occur. By analyzing trends in operational data using weighted attributes, the system can predict future safety levels and recommend preventive actions before actual safety degradation occurs
Solution Approach 2:
The system implements a feedback mechanism where the data analytics engine continuously analyzes operational data, generates safety scores, and provides actionable insights back to the fire system. This feedback loop enables continuous improvement of safety by identifying areas needing enhancement and tracking the impact of implemented changes over time
3Ease of operation
If fire systems monitor component status, then operational data is gathered, but visual communication of safety levels to users is not achieved
Solution Approach 1:
A user interface component serves as an intermediary that translates complex operational data and safety scores into visually comprehensible representations. The interface displays the safety score generated by the data analytics engine in a format that is easily interpreted by users, such as visual indicators or graded scales, without requiring users to understand the underlying complex data analysis processes
Solution Approach 2:
The user interface utilizes visual indicators such as color-coded representations to communicate safety levels to users. Different color ranges correspond to different safety score levels, allowing users to quickly comprehend the current safety status of the fire system at a glance without needing to interpret numerical data or complex operational parameters
Data Source
AI summary
Systems and methods for assigning or computing a fire system safety score via data mining and for predictive analysis via data mining are provided. Some methods can include identifying one or more pieces of data information from an ambient condition monitoring system installed in a region, quantifying the one or more pieces of the data information, and identifying a safety score of the region based on the quantified one or more pieces of the data information.