Social network data has a certain degree of aggregation, namely the similar users are more prone to the same behavior. According to the conventional horizontal database shard method, a large amount of time and connection loss were consumed in order to access a plurality of databases in turn when performing the information query of these events. In order to solve this problem, the database shard strategy based on clustering analysis was proposed. Through clustering the characteristic scalars of social network subjects, the main body with the high aggregation was divided into one or as possible libraries to improve the query efficiency of the events, and to give consideration to load balancing, large data migration and other issues. The experimental results show that for the mainstream social networking events, the performance improvement of the proposed strategy is up to 23.4% at most, and local optimal load balance and zero data migration are realized. In general, the database shard strategy based on clustering analysis of social network, has a considerable advantage on improving query efficiency, balance load balancing and large data migration feasibility over the traditional conventional horizontal database shard method of cutting library.

Against at the problems of oscillation and poor adaptability of robot motion state in the path planning of traditional unstructured environment, a new path planning strategy based on Glowworm Swarm Optimization algorithm of Scene understanding (SGSO) was proposed. The initialization was realized based on the regularity, randomness and generalization of chaotic systems, and golden section method was used for later optimization, which improved the diversity of the population, suppressed the premature and local convergence of the algorithm. And, by introducing scene understanding of glowworm "natural enemy", the selection mechanism of glowworm swarm was optimized to solve the grounding phenomenon of glowworm in the process of tracing under unstructured environment, which enhanced the adaptability and robustness of the algorithm. The simulation results of four test functions show that, the proposed algorithm is superior to the basic Glowworm Swarm Optimization (GSO) algorithm in solving precision and convergence efficiency. The proposed algorithm was applied to the path planning of mobile robots in unstructured environment, the test results show that the planning path based on SGSO was shorter and the corner was more smooth, which could effectively avoid the additional load on power system caused by large angle steering of robot, verifying the feasibility and effectiveness of the proposed algorithm.

The construction of digital fingerprint embedding and acquisition scheme for anti-rotation, anti-scaling and anti-distortion attack can improve the anti-attack capability of the digital fingerprint. The designed spatial-DCT (Discrete Cosine Transform) domain combinational embedding scheme of digital fingerprint provided the construction of differential characteristic point, on which the digital fingerprint embedding and acquisition algorithm was proposed. And an attack parameter recognition algorithm with high accuracy was presented. The simulation results show that the accuracy of attack recognition algorithm can be the order of sub-pixel and can resist the scaling attack with parameter larger than 0.5, any rotation attack with angle less than 45° and any distortion attack with angle less than 25°. In addition, the effect of the proposed scheme would not decrease with the increase of the rotation and distortion angle. The proposed scheme improves the robustness of the digital fingerprint and enables the digital fingerprint system to resist the removal and CTP (cutting, trimming, pasting) attack as well as RSD (rotation, scaling, distoration) attack.