Advanced prediction method for tunnel fire

Abstract

The invention discloses a tunnel fire advanced prediction method, which comprises the following steps: establishing a fire model, and obtaining fire data under different working conditions according to the fire model; processing the fire data under different working conditions, and establishing a fire database according to the processed fire data under different working conditions; establishing a fire prediction model, and dividing the fire data in the fire database under different working conditions into a training set, a verification set and a test set, so as to train the fire prediction model according to the training set and the verification set to obtain a trained fire prediction model; inputting the test set into a trained fire prediction module to obtain prediction data; establishing a data assimilation model, and performing data fusion on the prediction data and the real-time observation data according to the data assimilation model so as to correct the prediction data and obtain optimal state estimation of the real-time fire; therefore, the tunnel fire can be quickly predicted in advance, and the real-time performance and accuracy of tunnel fire prediction are improved.

Description

technical field [0001] The invention relates to the technical field of fire detection, in particular to a method for predicting a tunnel fire in advance, a computer-readable storage medium and a computer device. Background technique [0002] In recent years, with the rapid development of cities in my country, the number and complexity of tunnels have been increasing. Due to the unique spatial characteristics of tunnels and transportation methods, tunnel fires are different from general industrial and civil building fires; tunnel fires are affected by transportation, transportation, transportation, etc. Influenced by factors such as on-board cargo, tunnel type, and traffic conditions during a fire, the mobility of the vehicle will cause the ignition point to change with the driving of the vehicle. The spread of fire smoke in tunnels and the control methods are more complicated, so that the safety evacuation of personnel and the difficulty of fire fighting and rescue in the even...

AI Technical Summary

Problems solved by technology

[0002] In recent years, with the rapid development of cities in my country, the number and complexity of tunnels have been increasing. Due to the unique spatial characteristics of tunnels and transportation methods, tunnel fires are different from general industrial and civil building fires; tunnel fires are affected by vehicles, Influenced by factors such as vehicle cargo, tunnel type, and traffic conditions during fire, the mobility of vehicles will cause the fire point to change with the driving of the vehicle; at the same time, with the gradual enrichment of tunnel structures, the number of branch tunnels increases, resulting in The spread of smoke and gas in a tunnel fire and the control methods are more complicated, which makes it more difficult for people to evacuate and fire rescue when a fire breaks out in the tunnel than ordinary ground buildings.

[0003] Tunnel fire prediction can not only provide technical support for fire risk assessment and fire fighting, but also has great significance for emergency rescue. However, the existing tunnel fire prediction methods are usually based on fire images, resulting in poor prediction results.

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