The traditional person re-identification methods are difficult to independently cope with the complex and diverse recognition tasks in the security scenario of smart city in practice. In order to meet the needs of multi-level person re-identification, the deep integration of person re-identification and multi-level urban information units is proposed. Existing models and attentions for person re-identification tasks only focus on learning the robust features while neglecting the difference between features of pairs. Diff attention module is proposed to guide the network to learn a more discriminative attention map based on the difference of feature vectors. Taking the diff attention module, diff attention framework which matches many backbone models is developed. Two training strategies: joint training and separate training are proposed. Compared with other person re-identification methods, these framework and strategies have achieved excellent performance on Market-1501, CUHK03, and MSMT17 datasets.