Detection cabin fire-fighting monitoring and fire-fighting processing method and system based on multiple sensors

By combining multi-sensor deep learning and distributed edge computing with mobile fire extinguishing devices, the problems of response delay and low fire extinguishing efficiency in the fire monitoring system of the detection cabin were solved, achieving efficient and accurate fire handling and model optimization.

CN120815312AActive Publication Date: 2025-10-21YUANXINSHE TECHNOLOGY (JIANGSU) CO LTD
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Patent Information

Application Number
CN202511324122.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-09-17
Publication Date
2025-10-21
Estimated Expiration
2045-09-17

AI Technical Summary

Technical Problem

Existing fire monitoring systems for testing chambers suffer from response delays due to centralized computing resources, low fire extinguishing efficiency due to fixed locations of fire extinguishing devices, and lack of real-time monitoring and feedback mechanisms, making them ill-suited to handle complex and ever-changing fire scenarios.

Method used

A deep learning model based on multiple sensors is used for real-time fire assessment, and distributed edge computing is used for fire response decision-making. Mobile fire extinguishing devices are used to autonomously adjust the fire extinguishing location and parameters, and the fire handling effect is monitored and fed back in real time to optimize the model.

Benefits of technology

It improves the accuracy and timeliness of fire detection, realizes the efficient use of computing resources, enhances the efficiency and success rate of fire fighting, and forms a self-improving closed-loop system.

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Abstract

The invention provides a multi-sensor-based detection cabin fire-fighting monitoring and fire-fighting processing method and system, and relates to the technical field of monitoring processing, and the method comprises the steps: obtaining multi-sensor real-time monitoring data, employing a deep learning model to evaluate a fire behavior state, employing a distributed edge calculation mode to carry out a fire behavior response decision based on a fire behavior evaluation result, and the movable fire extinguishing device is controlled to autonomously move to the optimal fire extinguishing position, spraying parameters are adaptively adjusted, the execution effect is monitored in real time, and the deep learning model is fed back and optimized. The fire behavior evaluation accuracy and the fire-fighting processing efficiency are improved, and the reliability and the adaptability of the system are enhanced.
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