To solve traffic congestion problems, a new road condition judgment model was proposed. Firstly, the model was based on YOLOv3 target detection algorithm. Then, according to the eigenvalue matrix corresponding to the picture, the difference between adjacent frames was made by the eigenvalue matrix, and the difference value was compared with preset value to determine whether the current road was in a congested state or a normal traffic state. Secondly, the current calculated road state was compared with previous two calculated road states. Finally, the state statistics method in the model was used to calculate the duration of a state (congestion or patency) of road. The proposed model could analyze the states of three lanes of a road at the same time. Through experiments, the average accuracy of model to judge the state of single lane could reach 80% or more, and it was applicable to both day and night roads.

In recent years, the amount of data in various industries grows rapidly, which results in the increasing of optimization demands in database storage system. Relational databases are I/O-intensive, take use of relatively free CPU time, data compression technology could save data storage space and I/O bandwidth. However, the compression features of current database systems were designed for traditional storage and computing environments, without considering the impact of virtualized environments or the use of Solid State Drive (SSD) on system performance. To optimize the cache performance of database compression system, a database compression system performance model was proposed, and the impact on the I/O performance of various system environments was analyzed. Take the open source database MySQL as an example, the corresponding cache optimization methods were given based on analysis. Evaluation results on Kernel-based Virtual Machine (KVM) and MySQL database show that the optimized version has an increase of more than 40% in performance under some configurations, even close to superior physical machine performance.