Construction and Optimization of Multi-Scenario Autonomous Call Rule Models in Emergency Command Scenarios

In response to the slow processing speed, weak anti-interference, and low accuracy of autonomous call models in current emergency command scenarios, the research focuses on the fire scenario, aiming to improve the emergency response efficiency through technological innovation. The research innovativ...

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Published in:International journal of advanced computer science & applications Vol. 15; no. 12
Main Authors: Zheng, Weiyan, Zhu, Chaoyue, Huang, Di, Zhou, Bin, Yan, Xingping, Chen, Panxia
Format: Journal Article
Language:English
Published: West Yorkshire Science and Information (SAI) Organization Limited 2024
Subjects:
Accuracy
Algorithms
Autonomous vehicles
Communication
Computer science
Digital signal processing
Disasters
Efficiency
Emergency response
Energy consumption
Flexibility
Innovations
Machine learning
Processing speed
Signal detection
Signal processing
ISSN:2158-107X, 2156-5570
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Abstract In response to the slow processing speed, weak anti-interference, and low accuracy of autonomous call models in current emergency command scenarios, the research focuses on the fire scenario, aiming to improve the emergency response efficiency through technological innovation. The research innovatively integrates digital signal processing algorithm and two-tone multi-frequency signal detection algorithm to develop a hybrid algorithm. Then, a novel autonomous call model based on the hybrid algorithm is constructed. The comparative experimental results indicated that the accuracy of the hybrid algorithm was 0.9 and the error rate was 0.05, which was better than other comparison models. The average accuracy and comprehensive performance score of the model were 0.95 and 97 points, respectively, both of which were better than comparison models. The results confirm that the autonomous call model proposed in this study can accurately and quickly judge emergency scenarios and handle calls, and provide new ideas and theoretical basis for emergency command and rescue of fire and other disasters, with broad application prospects.
AbstractList In response to the slow processing speed, weak anti-interference, and low accuracy of autonomous call models in current emergency command scenarios, the research focuses on the fire scenario, aiming to improve the emergency response efficiency through technological innovation. The research innovatively integrates digital signal processing algorithm and two-tone multi-frequency signal detection algorithm to develop a hybrid algorithm. Then, a novel autonomous call model based on the hybrid algorithm is constructed. The comparative experimental results indicated that the accuracy of the hybrid algorithm was 0.9 and the error rate was 0.05, which was better than other comparison models. The average accuracy and comprehensive performance score of the model were 0.95 and 97 points, respectively, both of which were better than comparison models. The results confirm that the autonomous call model proposed in this study can accurately and quickly judge emergency scenarios and handle calls, and provide new ideas and theoretical basis for emergency command and rescue of fire and other disasters, with broad application prospects.
Author Zhou, Bin
Zheng, Weiyan
Zhu, Chaoyue
Chen, Panxia
Huang, Di
Yan, Xingping
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SubjectTerms Accuracy
Algorithms
Autonomous vehicles
Communication
Computer science
Digital signal processing
Disasters
Efficiency
Emergency response
Energy consumption
Flexibility
Innovations
Machine learning
Processing speed
Signal detection
Signal processing
Title Construction and Optimization of Multi-Scenario Autonomous Call Rule Models in Emergency Command Scenarios
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