本文采用高速相机记录气体放电信息，获得高分辨率的空间与时间的视频数据. 通过分析其放电图案，发现其为树状分形结构，其单个放电点和多个放电点的分形维数分别为1.64和1.71. 利用课题组采用python编写的软件，对获取的大量的图像信息进行智能化的轮廓识别和质心标记，获取了每个放电条件下，5911张相邻时间间隔为0.5 ms的图像的放电点的位置分布信息. 结果显示，放电点数在空间内出现的位置概率接近高斯分布，其出现的时间间隔分布接近指数衰减.

In this paper, a high-speed camera is used to record the gas discharge information and obtain high-resolution spatial and temporal information. It is found that the discharge pattern is a tree fractal structure, and the fractal dimensions of a single discharge point and multiple discharge points are 1.64 and 1.71, respectively. Through the intelligent contour recognition and centroid marking of a large amount of image information obtained by self-written programs in Python, the location distribution information of discharge points of 5911 images with a time interval of 0.5 ms are obtained. The results show that the location probability of discharge points in space is close to Gaussian distribution, and the time interval distribution of its occurrence is close to exponential distribution.