In view of occlusion and lack of texture details of infrared targets in road scenes， which leads to false detection and missed detection， a lightweight infrared road scene detection YOLO （You Only Look Once） model based on Multi-Scale and weighted Coordinate attention （MSC-YOLO） was proposed. YOLOv7-tiny was taken as the baseline model. Firstly， a multi-scale pyramid module PSA （Pyramid Split Attention） was introduced in different intermediate feature layers of the MobileNetV3， and a lightweight backbone extraction network MSM-Net （Multi-Scale Mobile Network） for multi-scale feature extraction was designed to solve the problem of feature pollution caused by the fixed-size convolution kernel， improving the fine-grained extraction ability of targets of different scales. Secondly， Weighted Coordinate Attention （WCA） mechanism was integrated into the feature fusion network， and the target position information obtained from the vertical and horizontal spatial directions of the intermediate feature map was superimposed to enhance the fusion ability of target features in different dimensions. Finally， the positioning loss function was replaced to Efficient Intersection over Union （EIoU） to calculate the length and width influencing factors of the predicted frame and the real frame separately， accelerating the convergence. The verification experiment was carried out on the Flir dataset. Compared with the YOLOv7-tiny model， the number of parameters is reduced by 67.3%， the number of floating-point operations is reduced by 54.6%， and the model size is reduced by 60.5% under the premise that mAP（IoU=0.5） （mean Average Precision （IoU=0.5）） is only reduced by 0.7 percentage points. The Frames Per Second （FPS） reaches 101 on the RTA 2080Ti， achieving a balance between detection performance and lightweight， and meets the real-time detection requirements of infrared road scenes.

Aiming at the urgent requirement of rapid and efficient 3D model shape analysis and retrieval technology, a new method of 3D model shape correspondence analysis by combining the intrinsic heat kernel features and local volume features was proposed. Firstly, the intrinsic shape features of the model were extracted by using Laplacian Eigenmap and heat kernel signature. Then, the feature matching matrix was established by combining the stability of the model heat kernel feature and the significance of local space volume. Finally, the model registration and shape correspondence matching analysis was implemented through feature matrix similarity measurement and short path searching. The experimental results show that, the proposed shape correspondence analysis method with the combination of heat kernel distance and local volume constraint can not only effectively improve the efficiency of model shape matching, but also identify the structural features of the same class models. The proposed method can be applied to further realize the co-segmentation and shape retrieval of multigroup models.

To improve the utilization efficiency of heterogeneous resources in Multi-Cluster Grid （MCG） composed of many computer clusters, a data load balance model was proposed. According to computation power, storage power, and communication power of query node, the performance model of query node was studied. By using data saturation, matrix of data load balance and data migration technology, system's data load balance mechanism was described. Experiment results show:The model can be fit for querying massive data.