Emergency fire-fighting monitoring method and system based on big data

By processing multimodal data from fire scenes using big data-based methods, and combining target detection, LSTM networks, and genetic algorithms, safe evacuation route planning at fire scenes was achieved. This solved the problem of the impact of multimodal data at fire scenes and improved the flexibility and reliability of evacuation routes.

CN121963375AInactive Publication Date: 2026-05-01XINZHIYUN (JIANGSU) TECH CULTURE CO LTD
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Patent Information

Application Number
CN202610248361.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-02
Publication Date
2026-05-01
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

Existing emergency fire monitoring methods are affected by multimodal data at the fire scene, making it difficult to accurately determine safe evacuation routes.

Method used

A big data-based approach is adopted, using a target detection model to detect smoke, open flames, and obstacles. An LSTM network is used to analyze the degree of smoke diffusion, and a sound detection model is used to extract flame sound information. Multimodal information fusion is performed to calculate the fire intensity index, and a genetic algorithm is used for dynamic evacuation path planning, combined with Dijkstra's algorithm for local path adjustment.

Benefits of technology

It improves the efficiency of utilizing affected data during a fire and enhances the flexibility and reliability of evacuation route planning, ensuring dynamic adjustment and safety of evacuation routes.

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Abstract

The invention discloses an emergency fire-fighting monitoring method and system based on big data, relates to the field of fire-fighting monitoring, and specifically comprises the following steps: collecting real-time multi-modal data of a fire scene, carrying out multi-scale processing, and extracting smoke, open fire and obstacle information from image data by using a target detection model; performing time sequence analysis on the continuous frames of smoke region images by using an LSTM network, and predicting the smoke diffusion degree; extracting flame sound information from the sound data by using a sound detection model; according to the method, regional fire behavior information is obtained through fusion of various kinds of information, the danger coefficient of each region is calculated, dynamic planning of an evacuation path is carried out based on the regional danger coefficient by using a genetic algorithm, a weight dynamic adjustment mechanism is designed, and a path planning scheme is flexibly adjusted according to different needs according to actual conditions; introducing a Dijkstra algorithm to dynamically adjust a local path according to the real-time information of the fire scene; the utilization efficiency of affected data and the flexibility and reliability of evacuation path planning in the case of a fire are improved.
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