Considering the problem of out-of-stock and inventory overstock caused by dual-channel supply chain inventory system, three dynamic optimization models of three modes: single control, centralized control and cross-replenishment control of dual-channel inventory were established under the condition that both online and offline channels are facing stochastic demand. Firstly, based on the dynamic differential equation of inventory, guided by the control theory creatively, and by means of Taylor expansion and Laplace transformation, the feedback transfer function of dual-channel inventory system was obtained. Secondly, considering the periodic interactions, upstream and downstream interactions and inter-channel interactions in the process of cross-replenishment’s purchase-sale-stock, delay control, feedback control and Proportion-Integral-Derivative (PID) control were used to construct a complex interactive system with two inputs and two outputs, so as to explore the dynamic balance between supply and demand of the dual-channel inventory system itself and among channels, optimize the dual-channel inventory holdings, reduce the out-of-stock times and amount and keep them to a dynamic equilibrium. Finally, through numerical simulation experiments, three dual-channel inventory control strategies were compared. The simulation results show that when on online and offline channels were facing different distributions of stochastic demand, the residual stock of cross-replenishment control decreased by 4.9% compared with that of single control, and the out-of-stock rate of cross-replenishment control decreased by 66.7% and 60% respectively compared those of single control and centralized control. The experimental results show that when online and offline channels are facing different distributions of stochastic demand, the use of cross-replenishment strategy can effectively reduce inventory holdings, reduce the times and amount of out-of-stock, and thus save the inventory costs.

With the popularity of mobile smart devices, Mobile Crowd Sensing (MCS) has been widely used while facing serious privacy leaks. Focusing on the issue that the existing original data privacy protection scheme is unable to resist collusion attacks and reduce the perception data availability, a Data Privacy Protection algorithm based on Mobile Node (DPPMN) was proposed. Firstly, the node manager in DPPMN was used to establish an online node list and send it to the source node. An anonymous path for data transmission was built by the source node through the list. Then, the data was encrypted by using paillier encryption scheme, and the ciphertext was uploaded to the application server along the path. Finally, the required perception data was obtained by the server using ciphertext decryption. The data was encrypted and decrypted during transmission, making sure that the attacker was not able to wiretap the content of the perception data and trace the source of the data along the path. The DPPMN ensures that the application server can access the original data without the privacy invasion of the nodes. Theoretical analysis and experimental results show that DPPMN has higher data security with increasing appropriate communication, and can resist collusion attacks without affecting the availability of data.

Focusing on the low accuracy of person re-identification caused by that the similarity between different pedestrians' images is more than that between the same pedestrians' images in reality, a person re-identification method based on Siamese network combined with identification loss and bidirectional max margin ranking loss was proposed. Firstly, a neural network model which was pre-trained on a huge dataset, especially its final full-connected layer was structurally modified so that it can output correct results on the person re-identification dataset. Secondly, training of the network on the training set was supervised by the combination of identification loss and ranking loss. And according to that the difference between the similarity of the positive and negative sample pairs is greater than the predetermined value, the distance between negative sample pair was made to be larger than that of positive sample pair. Finally, a trained neural network model was used to test on the test set, extracting features and comparing the cosine similarity between the features. Experimental result on the open datasets Market-1501, CUHK03 and DukeMTMC-reID show that rank-1 recognition rates of the proposed method reach 89.4%, 86.7%, and 77.2% respectively, which are higher than those of other classical methods. Moreover, the proposed method can achieve a rank-1 rate improvement of up to 10.04% under baseline network structure.