针对燃煤锅炉传统优化方法存在的系统性不足与炉内燃烧状态无法精确感知等问题，提出了一套融合智能感知与智能控制的锅炉整体优化系统。首先，通过部署红外测温阵列，结合梯度定位算法实现炉膛三维温度场的在线重构与可视化；其次，基于连续时间贝叶斯网络建立锅炉燃烧动态过程模型；最后，采用惯性权重动态调整的粒子群多目标优化算法进行在线寻优，构建了实时闭环的自适应智能燃烧控制系统。工程应用结果表明：该系统能有效感知燃烧状态，精准识别并预警结焦、偏烧等异常工况；系统投运后，锅炉效率提升不低于0.3%，氮氧化物生成量降低不低于12%。结论认为，该系统为解决电站锅炉运行优化难题，实现安全、经济与环保协同发展提供了有力的技术支撑。

To address the systematic limitations of traditional optimization methods for coal-fired boilers and the inability to accurately perceive in-furnace combustion states, this paper proposes a comprehensive boiler optimization system integrating intelligent sensing and control. First, an infrared temperature measurement array is deployed, combined with a gradient positioning algorithm, to achieve online reconstruction and visualization of the three-dimensional temperature field within the furnace. Second, a dynamic model of the boiler combustion process is established based on a continuous-time Bayesian network. Finally, a multi-objective particle swarm optimization algorithm with dynamically adjusted inertia weights is employed for online optimization, thereby constructing a real-time closed-loop adaptive intelligent combustion control system. Engineering application results demonstrate that the proposed system can effectively perceive the combustion state and accurately identify and provide early warnings for abnormal conditions, such as slagging and uneven combustion. After the system was put into operation, the boiler efficiency increased by no less than 0.3%, and NOx emissions were reduced by no less than 12%. In conclusion, this system provides robust technical support for resolving operational optimization challenges in utility boilers and achieving the synergistic development of safety, economic efficiency, and environmental protection.