The computational costs of traditional deep neural network-based person search algorithms are very high， so that these algorithms are difficult to deploy on devices with limited hardware resources and budgets because of high cost and low speed. Aiming at the above problems， a person detection and person re-identification algorithm based on the high-performance inference chip Sophon SC5+ was proposed to optimize the efficiency of deep learning from the algorithm end to the hardware end in a top-down approach. Firstly， by using the lightweight Ghost module to replace the backbone network of YOLOv5s， the parameters and computational cost of the model were greatly reduced. Secondly， Convolutional Block Attention Module （CBAM） attention mechanism was integrated to enhance the feature learning capability and improve the detection precision of the algorithm. Thirdly， the central loss constraint and Non-local attention mechanism were added to the person re-identification module， and the central constrained triple loss and the additional interval cross-entropy loss were combined to optimize the model and improve the performance of the person re-identification algorithm. Finally， based on Sophon SC+， person detection model and person re-identification model were quantized and the final inference model was generated. Experimental results on Market-1501 and DukeMTMC-ReID datasets show that， the mean Average Precisions （mAPs） of the person detection and person re-identification algorithms were improved by at least 43.8 and 25.7 percentage points compared with YOLOv4-tiny， Attribute-Complementary Re-ID Net （ACRN）， Singular Vector Decomposition Net （SVDNet） and other mainstream algorithms. After the implementation of int8 quantization based on Sophon SC5+ chip， although the proposed algorithm has the mAP decreased by 1.7 percentage points， it has the model size reduced by 74.4%. It can be seen that the proposed algorithm can be used in large-scale， city-level person search systems.

Aiming at the problem of poor recognition precision and serious missed detection of small traffic signs in bad weather such as haze， rain and snow， a traffic sign recognition model in haze weather based on YOLOv5 （You Only Look Once version 5） was proposed. Firstly， the structure of YOLOv5 was optimized. By using contrary thinking， the problem of small object recognition difficulty was solved by reducing the depth of feature pyramid and limiting the maximum down sampling multiple. By adjusting the depth of residual module， the repeated overlapping of background features was suppressed. Secondly， the mechanisms such as data augmentation， K-means anchor and Global Non-Maximum Suppression （GNMS） were introduced into the model. Finally， the detection ability of the improved YOLOv5 facing the bad weather was verified on the Chinese traffic sign dataset TT100K， and the study on traffic sign recognition in the haze weather with the most obvious precision decline was focused on. Experimental results show that the F1-score， mean Average Precision @0.5 （mAP@0.5）， mean Average Precision @0.5：0.95 （mAP@0.5：0.95） of the improved YOLOv5 model reach 0.921 50， 95.3% and 75.2%， respectively. The proposed model can maintain high-precision recognition of traffic sign in bad weather， and has Frames Per Second （FPS） up to 50， meeting the requirement of real-time detection.

In the Case-Based Reasoning (CBR) case retrieving and matching, different cases are usually composed by different features. But most of the traditional CBR engines adopt fixed feature weights mode, which makes matching rate of whole system very low. To solve this problem, this paper proposed a CBR engine model with variable feature weights and brought interactive mode into feature weights calculating module. It calculated subjective weight based on group decisionmaking theory and proposed an adjustment method which used differences between a single expert and his group. It used similarity rough set theory to calculate objective weight in order to make results calculating more objective and accurate. At last, it designed composite weights adjustment algorithm which calculated the distance between the subjective weight and objective weight, considered the deviation degree of those two weights, then deduced weights adjustment coefficient, and get the final weight adjustment results. The calculation example and simulation analysis of network attack cases validate the effectiveness of the proposed method and prove this method has much better performance in different performance indexes.