Abstract: In order to further improve the safety of the fire protection system of the oil depot, the fire information system has been improved, and a fire intelligent early warning algorithm based on the quantum particle swarm optimization optimized BP neural network is developed, with temperature, smoke concentration and CO concentration data as the input of the neural network, the probability of no fire, open fire and smoldering fire is used as the output of the neural network. The quantum particle swarm optimization algorithm is used to optimize the weights and thresholds randomly generated during the operationof the BP neural network, accelerate the convergence rate of the neural network to the expected error, and enhance the global search capability. The model of the intelligent fire early warning algorithm was simulated by MATLAB software. In the result, the fire probability output by the model was basically consistent with the actual value, and a multi-sensor data acquisition device was designed to obtain fire scene data to further verify the effectiveness of the algorithm. Entering the experimental data into the network model can effectively identify open flames, smoldering fires, and no- fire conditions, proving that the algorithm has achieved the purpose of improving the accuracy of the fire warning system.

Key words: fire fighting, fire warning, neural network, quantum particle swarm optimizati