基于CHHO优化LSTM的火场环境预测模型研究

准确预测火场环境变化有助于精准掌握火情的发展趋势，保障人员的安全。由于火场环境多参数并存、耦合关系复杂，且具有时序性和非线性，难以建立准确的预测模型，因此提出了一种基于改进哈里斯鹰算法的自注意机制长短期记忆网络模型，实现了对火场环境数据的精准预测。首先，将Logistic映射策略、余弦权重因子、高斯扰动策略引入哈里斯鹰优化算法，丰富算法的种群多样性、平衡其全局探索和局部开发能力、提高算法的收敛精度。然后，利用改进后的哈里斯鹰优化算法对自注意机制长短期记忆网络模型中的超参数进行优化，基于优化后的参数对火场环境进行预测。仿真结果表明，基于改进后的哈里斯鹰优化算法的自注意机制长短期记忆网络模型拟合效果更好，具有更高的预测精度。

Research on fire environment prediction model based on CHHO optimized LSTM

Accurately predicting changes in the fire environment helps to accurately grasp the development trend of the fire and ensure the safety of personnel. Due to the coexistence of multiple parameters of the fire scene environment, the complex coupling relationship, and the time series and nonlinearity, it is difficult to establish an accurate prediction model. Therefore, this paper proposes a long-term and short-term memory network model of self-attention mechanism based on the improved Harris Hawk algorithm, which realizes the accurate prediction of the fire scene environment data. Firstly, the logistic mapping strategy, cosine weighting factor, and Gaussian perturbation strategy are introduced into the Harris Hawk optimization algorithm to enrich its population diversity, balance its global exploration and local development capabilities, and improve its convergence accuracy. Then, the improved Harris Hawk optimization algorithm is used to optimize the hyperparameter in the self-attention mechanism short-term memory network model, and the fire environment is predicted based on the optimized parameters. The simulation results show that the self-attention mechanism based on the improved Harris Hawk optimization algorithm has better long-term memory network model fitting effect and higher prediction accuracy.