Glass Surface Thermocouple Localization for High-Rise Fire Detection
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Solution Overview
Problem
Existing fire source localization technologies in high-rise buildings rely on fixed-position sensors, which lack flexibility and fail during fires, and are time-consuming, making them unsuitable for real-time firefighting needs.
Innovation Solution
An indoor fire source localization method using wireless thermocouples on glass surfaces, combined with a multilayer perceptron (MLP) and k-nearest neighbors (KNN) algorithm, to accurately determine fire source location based on temperature data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fixed-position sensors are installed during construction, then localization can be achieved, but deployment flexibility is reduced and the system is highly likely to fail during fire
Solution Approach 1:
The patent transitions from fixed-position sensors to mobile wireless thermocouples that can be dynamically deployed and repositioned. The wireless thermocouples are carried by firefighters or deployed via drones, allowing the sensing system to adapt its position based on real-time fire conditions and operational needs, thereby resolving the contradiction between fixed installation reliability and deployment flexibility.
Solution Approach 2:
The patent replaces the mechanical fixed-sensor installation system with a wireless sensing system using wireless thermocouples. This substitution eliminates the need for physical installation during construction and allows the sensing system to be deployed flexibly in various positions, including on glass surfaces, without being constrained by the building's structural installation phases.
2Measurement precision
If fixed-position sensors are used, then localization can be performed, but time consumption increases and real-time requirements cannot be met
Solution Approach 1:
The patent implements continuous temperature monitoring through wireless thermocouples that operate in real-time during fire conditions. The system continuously acquires temperature data from multiple points and processes this data to provide ongoing localization updates, eliminating the time delays associated with fixed sensor installation and initial calibration, thereby meeting real-time firefighting requirements.
Solution Approach 2:
The patent employs preliminary temperature field simulation and machine learning model training before actual fire incidents. The system pre-processes data from fire simulations to establish patterns and relationships between temperature distributions and fire source locations, enabling rapid and accurate localization during actual fires without time-consuming real-time analysis.
3Adaptability or versatility
If wireless thermocouples are deployed on glass surfaces, then deployment flexibility is improved, but temperature measurement reliability may be affected by glass thermal properties
Solution Approach 1:
The patent combines multiple wireless thermocouple measurements from different locations on the glass surface into a unified temperature field analysis. By integrating data from multiple sensing points and using machine learning algorithms to analyze the spatial temperature distribution patterns, the system compensates for the thermal insulation effects of glass and maintains reliable fire source localization despite the challenging glass surface deployment.
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 flexible, real-time, and reliable fire source localization in high-rise buildings, enhancing firefighting efficiency and reducing casualties and property losses.
Implementation Method 1
arranging an array of m wireless thermocouples on an outer surface of glass in a fire room
Data Source
AI summary
Provided is an indoor fire source localization method for high-rise buildings based on glass surface temperature, including the following steps: arranging an array of m wireless thermocouples on an outer surface of glass; measuring horizontal distances from a centrally positioned wireless thermocouple in the arranged array of wireless thermocouples to both side walls of a fire room; acquiring temperature data signals at preset time intervals t and transmitting the acquired temperature data to a data processing terminal for averaging; using averaged real-time glass temperature data and the horizontal distances from the centrally positioned wireless thermocouple to the two side walls as input data to form an input dataset, and feeding the dataset into a trained prediction program integrating a multilayer perceptron (MLP) and a k-nearest neighbors (KNN) algorithm; and determining a predicted fire source location block through backpropagation and category voting.


