The complexity and variability of traffic scenarios challenge existing human-vehicle target detection algorithms， especially when dealing with occlusion， illumination changes and multi-scale targets， existing algorithms tend to have insufficient accuracy and low computational efficiency. To solve the above problems， an improved detection model， CDC-DETR （CPPA-DWRC-CGNET-DETR）， was developed based on the RT-DETR （Real-Time DEtection TRansformer） architecture. Firstly， a Context Pre-activation Pooling Attention （CPPA） module was designed to enhance long-range dependencies and optimize feature extraction. Secondly， a Dilation-Wise Residual Connection （DWRC） module was introduced to improve multi-scale feature representation. Thirdly， a lightweight Context Guided Block （CG Block） was proposed to fuse local， surrounding， and global information and reduce computational cost. Finally， these modules were integrated to construct a high-accuracy and efficient real-time human-vehicle detection model suitable for complex traffic scenarios. Experimental results on the BDD100K dataset show that compared to RT-DETR， when the Intersection over Union （IoU） is 0.5， CDC-DETR improves the mean Average Precision （mAP） by 6.12%， increases the recall by 4.35%， and decrease the number of floating-point operations by 11.23%， enhancing computational efficiency significantly and providing an effective solution for deployment on edge devices.

In the simulation of Software Defined Network (SDN), the existing network simulation tools usually do not consider the processing delay of SDN switchs. To make the simulation result more realistic and accurate, a scheme to simulate the processing delay was proposed. First, the scheme divided the process of the switch forwarding into two aspects: inquiry operations on flow table and execution of various actions, and then transferred the two aspects into processing delay by using processor frequency and memory cycle. Measurement and comparison were conducted on the processing delay of switches with different configuration in real and simulation environments. The results show that the simulated processing delay of the proposed method is almost close to that in real environment, it can accurately estimate the processing delay of switches.

Concerning the random prediction results of the neural network model, a compact wavelet neural network was constructed. The method transferred the wavelet function into the hidden layer of the Back-Propagation (BP) network and made use of a random certain state command to obtain the definite prediction results. Compared with the wavelet neural network realized by programming and BP network, this method is suitable for mass data training and has such advantages as strong adaptability and robustness for data samples, especially has better adaptability for high frequency stochastic time series, and has characteristics of determined predicted results, powerful practicability and so on. It can obviously improve the training speed, prediction accuracy and prediction efficiency of the model. Its efficiency has been proved by the gas emission prediction experiment of wavelet packet transformation and wavelet neural network.