Xiuhua Lin, Wei Chen
Intelligent fire protection, weighted fusion, multiple sensors, intelligent fire identification, fire prediction
The vast expanse of large spaces allows for the accommodation of many individuals, and incidents involving fire often present complex and diverse rescue challenges. However, the current fire protection system lacks the ability to predict fire condition changes effectively. This research introduces an intelligent fire recognition plan for large areas and a novel adaptive weighted fusion algorithm for combining multi-sensor data. By combining data from three types of detectors, temperature, smoke concentration, and carbon monoxide content, the study provides a novel and comprehensive method of acquiring fire information to improve the accuracy of fire warnings. Simulation tests on similar detectors demonstrated the algorithm’s efficiency in reducing external white noise interference on detection data. In a medium-sized rehearsal space, it successfully integrated data from over six detectors, enhancing fire recognition efficiency by approximately 80 s. The studio experiment indicated that the recognition scheme significantly improved fire index judgment, with regional monitoring aiding in enhancing the prediction of fire changes. Compared to the traditional identification methods, the identification efficiency of the proposed method increased by an average of 55.78%. The results show that the proposed fusion algorithm resists external noise interference, enhancing system robustness and reliability. This intelligent fire identification scheme is effective for large spaces, improves fire information utilisation, and holds value for indoor fire emergencies like in broadcasting halls.
Important Links:
Go Back
