介绍了基于CCD成像的多角度激光散射的火灾探测技术。本技术采用数字图象、神经网络等处理技术，对不同的燃烧方式设定相应的灵敏度，能准确识别灰尘、飞虫和水蒸汽等非火灾的干扰因素，实现了在复杂环境中稳定可靠的超早期的火灾探测和报警，改变了现有探测技术在提高灵敏度后却只能用于洁净环境、对恶劣环境只能通过提高报警阈值等手段来解决的被动局面。

采用多波长激光同时与火灾烟雾和非火灾气溶胶相作用，研究了火灾烟雾对多波长激光的衰减特性，得到了熏香烟雾和柴油明火烟雾等5种典型火灾烟雾和非火灾气溶胶的3对波长消光系数比的数值。结果表明，消光系数比反映了气溶胶粒子对两个波长激光衰减的相对程度，仅与气溶胶的本质特征有关，不同烟雾气溶胶的消光系数比的数值有明显差别。

In this work, by carefully calculating the different Knudsen numbers, the free molecular and continuum regimes of flaming combustion were first explicitly defined as the soot particle growth zone low in the flame and the upper zone near the ambient air in the plume respectively, and the fractal coagulation theory was applied to analyze the soot particle agglomeration in the two cluster-cluster aggregation-dominating regions. The accurate analytical simplicity solution obtained proved that self-preserving size distribution (SPSD) exists in the two regions. Additionally we got the relation between the normalized number concentration of the total particle aggregates and the dimensionless time as well as the actual lag-time from monodisperse distribution to SPSD. The results indicate that the fractal morphology of the soot particles affects the particle coagulation process to a large extent, especially has a great influence on soot particle SPSD, total aggregates number concentration in the growth region and the time needed from monodisperse distribution to SPSD above the flame region near the air conditions. The validation of our theoretical results was verified in comparison with previous work and our experimental results.

本文在深入研究早期火灾一般燃烧火焰在彩色影像上所呈现的光谱及运动特性基础上，首次提出利用彩色影像实时探测室内火灾的新方法，通过物方坐标与像方坐标的直接线性变换，实现了早期火灾的自动探测与空间定位，实验证明了该方法的有效性与稳健性，且具有巨大的应用前景。

An automatic fire searching and suppression system with remote-controlled fire monitors for large spaces is developed. The fire searching method is realized based on computer vision theory via one CCD camera fixed at the end of a fire monitor chamber. While the fire monitor is pivoting, continuous images are taken from the CCD camera and transmitted into a computer. Then the images are processed with an image fire detection method to determine whether a fire occurs in the vision field. Once a fire is detected, it will be automatically located through an image processing algorithm. Displacement and pivot angle of the CCD camera in searching process are the essential parameters to calculate the space coordinates of a fire. When the coordinates of the fire are obtained, the fire monitor can be adjusted to the appropriate direction and elevation to spray according to water pressure.