Aiming at the problem that it is difficult to fully utilize the sequence contextual semantic information and the implicit correlation information among variables in multivariate time-series prediction， a model based on decoupled attention mechanism — Decformer was proposed for multivariate time-series prediction. Firstly， a novel decoupled attention mechanism was proposed to fully utilize the embedded semantic information， thereby improving the accuracy of attention weight allocation. Secondly， a pattern correlation mining method without relying on explicit variable relationships was proposed to mine and utilize implicit pattern correlation information among variables. On three different types of real datasets （TTV， ECL and PeMS-Bay）， including traffic volume of call， electricity consumption and traffic， Decformer achieves the highest prediction accuracy over all prediction time lengths compared with excellent open-source multivariate time-series prediction models such as Long- and Short-term Time-series Network （LSTNet）， Transformer and FEDformer. Compared with LSTNet， Decformer has the Mean Absolute Error （MAE） reduced by 17.73%-27.32%， 10.89%-17.01%， and 13.03%-19.64% on TTV， ECL and PeMS-Bay datasets， respectively， and the Mean Squared Error （MSE） reduced by 23.53%-58.96%， 16.36%-23.56% and 15.91%-26.30% on TTV， ECL and PeMS-Bay datasets， respectively. Experimental results indicate that Decformer can enhance the accuracy of multivariate time series prediction significantly.

Concerning the trade-off between convergence and diversity in solving the multi-trip pickup and delivery Vehicle Routing Problem （VRP）， a hybrid Non-dominated Sorting Genetic Algorithm Ⅱ （NSGA-Ⅱ） combining Adaptive Large Neighborhood Search （ALNS） algorithm and Adaptive Neighborhood Selection （ANS）， called NSGA-Ⅱ-ALNS-ANS， was proposed. Firstly， considering the influence of the initial population on the convergence speed of the algorithm， an improved regret insertion method was employed to obtain high-quality initial population. Secondly， to improve global and local search capabilities of the algorithm， various destroy-repair operators and neighborhood structures were designed， according to the characteristics of the pickup and delivery problem. Finally， a Best Fit Decreasing （BFD） algorithm based on random sampling and an efficient feasible solution evaluation criterion were proposed to generate vehicle routing schemes. The simulation experiments were conducted on public benchmark instances of different scales， in the comparison experiments with the MA （Memetic Algorithm）， the optimal solution quality of the proposed algorithm increased by 27%. The experimental results show that the proposed algorithm can rapidly generate high-quality vehicle routing schemes that satisfy multiple constraints， and outperform the existing algorithms in terms of both convergence and diversity.

The precision of the common network time protocol is low when being realized in the application layer, while high in physical layer yet a definite hardware is in need. With reference to the WinPcap time stamps management method of the famous network packet pawing software, a new high precision relative time synchronization algorithm was implemented with pure software in data link layer. Besides, the software was tested under Windows XP circumstance in an abbreviated LAN. The result indicates that the proposed algorithm can attain synchronization precision within 100μs.

Currently, many computers are required to cooperate frequently in the network application. Therefore, a uniform clock is in need. A new mechanism to realize clock synchronization in LAN by using CPU internal high-precision time stamp was presented; besides, the server and client software was also developed in VC++, and tested under Windows XP and Windows 2000 circumstance in an abbreviated LAN. The result indicates that the algorithm can attain much high synchronization precision, and the system runs steadily.