Context. Photospheric bright points (BPs), as the smallest magnetic element of the photosphere and the footpoint tracer of the magnetic flux tube, are of great significance to the study of BPs. Compared with the study of the characteristics and evolution of a few specific BPs, the study of BPs groups can provide us with a better understanding of the characteristics and overall activities of BPs groups. Aims. We aim to find out the evolution characteristics of the brightness and number of BPs groups at different brightness levels, and how these characteristics differ between quiet and active regions. Methods. We propose a hybrid BPs detection model (HBD Model) combining traditional technology and neural network. The Model is used to detect and calculate the BPs brightness characteristics of each frame of continuous high resolution image sequences of active and quiet regions in TiO-band of a pair of BBSO. Using machine learning clustering method, the PBs of each frame was divided into four levels groups (level1-level4) according to the brightness from low to high. Finally, Fourier transform and inverse Fourier transform are used to analyze the evolution of BPs brightness and quantity in these four levels groups. Results. The activities of BPs groups are not random and disorderly. In different levels of brightness, their quantity and brightness evolution show complex changes. Among the four levels of brightness, BPs in the active region were more active and intense than those in the quiet region. However, the quantity and brightness evolution of BPs groups in the quiet region showed the characteristics of large periodic changes and small periodic changes in the medium and high brightness levels (level3 and level4). The brightness evolution of PBs group in the quiet region has obvious periodic changes, but the active region is in a completely random and violent fluctuation state.

翻译：背景：光球亮点是光球层最小的磁结构单元，也是磁通量管的足点示踪物，对研究磁流管具有重要意义。相较于对少数特定亮点的特征及演化研究，针对亮点群的研究能更全面地揭示其整体活动特性。目的：我们旨在探明不同亮度层级下亮点群亮度与数量的演化特征，以及这些特征在宁静区和活动区之间的差异。方法：本文提出一种融合传统技术与神经网络的混合亮点检测模型（HBD模型），利用该模型对BBSO望远镜TiO波段一对连续高分辨率图像序列中活动区和宁静区的每帧亮点进行检测及亮度特征计算。采用机器学习聚类方法，将每帧的亮点按亮度从低到高划分为四个层级（等级1-等级4）。最后，运用傅里叶变换与逆傅里叶变换分析这四个层级中亮点亮度与数量的演化规律。结果：亮点群的活动并非随机无序。在不同亮度层级下，其数量与亮度演化呈现复杂变化。在四个亮度层级中，活动区亮点的活跃度和强度均高于宁静区。然而，宁静区亮度群在中高亮度层级（等级3和等级4）表现出大周期和小周期交替变化的特征。宁静区亮点群的亮度演化具有明显周期性，而活动区则完全处于随机剧烈波动状态。