The lack of computational power and limited storage of the mobile terminals lead to the low accuracy and slow speed of vehicle information detection models. Therefore, an improved vehicle information detection algorithm based on RetinaNet was proposed to solve this problem. Firstly, a new vehicle information detection framework was developed, and the deep feature information of the FPN （Feature Pyramid Network） module was merged into the shallow feature layer, and MobileNet V3 was used as the basic feature extraction network. Secondly, the direct evaluation index of target detection task——GIoU (Generalized Intersection over Union) was introduced to guide the positioning task. Finally, the dimension clustering algorithm was used to find the better size of Anchors and match them to the corresponding feature layers. Compared with the original RetinaNet target detection algorithm, the proposed algorithm has the accuracy improved by 10.2 percentage points on the vehicle information detection dataset. When using MobileNet V3 as the basic network, the mAP （mean Average Precision） can reach 97.2% and the forward inference time of single frame can reach 100 ms on ARM v7 devices. The experimental results show that the proposed method can effectively improve the performance of mobile vehicle information detection algorithms.

Aiming at the problem of scattered initial labels and large randomness of label propagation, an overlapping community detection algorithm fusing label preprocessing and node influence was proposed. Firstly, the influence value of each node was calculated, and the node with the largest influence value was selected as the central node gradually. Secondly, the label of the central node was used to preprocess the labels of the homogeneous neighbor nodes, so as to reduce the number of initial labels as well as the randomness of subsequent label propagation, and preliminarily identify the overlapping nodes. Thirdly, the overlapping nodes were identified by the label belonging coefficient, and the labels of non-overlapping nodes were selected by the node influence values, improving the stability and accuracy of the proposed algorithm. Finally, in order to maximize the increment of the adaptive function, the communities with weak cohesion were merged together to improve the quality of communities. The simulation experimental results show that the proposed algorithm has the largest extended modularity value on 50% datasets of the six real networks, and has the best performance in Normalized Mutual Information (NMI) index on the artificial benchmark networks with different mixing degrees, overlapping degrees of node and the maximum numbers of communities to which the node belongs. In conclusion, the algorithm has good adaptability to all kinds of networks, and has nearly linear time complexity.

The current network relationship analysis mainly studies the association relationship or group relations between users. Due to the variety of characteristic relation between users in mobile communication network, the relationship between the users and the groups are also diverse. On the basis of the specific groups with certain communication correlation and location similarity in the mobile communication network, the position prediction was introduced to the correlation measurement of position item, the location trajectory correlation measurement criterion was established, and an association user mining algorithm was proposed. The experimental result indicates that the proposed method can achieve the measuring of the relationship between users and the groups, and discover potential users associated with specific groups.

The real-time property of the serial communication program for UAV(Unmanned Aerial Vehicle) flight controller was studied. The hard real-time serial receiving program which dealt with totally four different data frames was designed with four techniques: data frame picking-up in interrupt serve program, finite state machine theory, buffer sharing and code optimizing. Test results show that the program has reasonable time consumption and ideal hard real-time property. Meanwhile, it has an outstanding data frame picking-up ratio with high reliability and satisfies the requirement to control the UAV craft by serial communication.