【摘要】 日光温室作为冬季节能型蔬菜生产设施，内部温度控制面临高热惯性、强非线性与外部扰动大的挑战。传统通风控制策略普遍存在响应滞后与精度不足的问题，难以满足作物稳定生长的环境要求。为提升温室调温系统的智能化与实时性，提出一种基于萤火虫算法（FA）-优化的长短期记忆网络（LSTM）-门控循环单元（GRU）混合模型（FALSTM-GRU），用于温室温度预测与通风控制。首先，结合LSTM与GRU结构，引入多头注意力机制（MHA）以增强时序特征提取能力，并通过FA优化模型超参数。其次，设计基于预测值的模型预测控制策略，利用近端策略优化（PPO）实现通风前瞻性调节。最后，搭建云服务器与Arduino平台的控制系统，实现闭环集成。试验结果表明，所构建的FALSTM-GRU模型在测试集上获得R~2=0.976 9、均方根误差0.770 8°C的预测性能，控制策略能在±0.6°C范围内稳定温度波动，具备良好的控制精度与系统鲁棒性。更多还原

【Abstract】 As energy-efficient vegetable production facilities in winter,solar greenhouses face significant challenges in internal temperature control due to high thermal inertia,strong nonlinearity,and large external disturbances.Traditional ventilation control strategies generally suffer from response delays and insufficient accuracy,making it difficult to meet environmental requirements for stable crop growth.To improve intelligence and real-time performance of greenhouse temperature regulation systems,a hybrid model combining long short-term memory(LSTM)and gated recurrent unit(GRU)optimized by firefly algorithm(FA)was proposed,referred to as FA-LSTM-GRU,for temperature prediction and ventilation control.First,model integrated LSTM and GRU structures,a multi-head attention(MHA)was incorporated to enhance temporal feature extraction,and FA was employed to optimize hyperparameters.Then,a model predictive control strategy based on predicted values was designed,in which ventilation behavior was proactively adjusted using proximal policy optimization(PPO).Finally,a control system was implemented on cloud server and Arduino platforms to achieve closed-loop integration.Experimental results showed that FA-LSTM-GRU model achieved R~2=0.976 9 and root mean square error of 0.770 8??°C.Control strategy stabilized temperature fluctuations within ±0.6??°C,demonstrating good accuracy and system robustness.更多还原