Aspect-based sentiment analysis based on pre-trained models generally uses end-to-end frameworks， has the problems of inconsistency between the upstream and downstream tasks， and is difficult to model the relationships between aspect words and context effectively. To address these problems， an aspect-based sentiment analysis method integrating prompt knowledge was proposed. First， in order to capture the semantic relation between aspect words and context effectively and enhance the model’s perception ability for sentiment analysis tasks， based on the Prompt mechanism， a prompt text was constructed and spliced with the original sentence and aspect words， and the obtained results were used as the input of the pre-trained model Bidirectional Encoder Representations from Transformers （BERT）. Then， a sentimental label vocabulary was built and integrated into the sentimental verbalizer layer， so as to reduce search space of the model， make the pre-trained model obtain rich semantic knowledge in the label vocabulary， and improve the learning ability of the model. Experimental results on Restaurant and Laptop field datasets of SemEval2014 Task4 dataset as well as ChnSentiCorp dataset show that the F1-score of the proposed method reaches 77.42%， 75.20% and 94.89% respectively， which is increased by 0.65 to 10.71， 1.02 to 9.58 and 0.83 to 6.40 percentage points compared with the mainstream aspect-based sentiment analysis methods such as Glove-TextCNN and P-tuning. The above verifies the effectiveness of the proposed method.

With the deepening of population aging， fall detection has become a key issue in the medical and health field. Concerning the low accuracy of fall detection algorithms in complex scenes， an improved fall detection model PDD-FCOS （PVT DRFPN DIoU-Fully Convolutional One-Stage object detection） was proposed. Pyramid Vision Transformer （PVT） was introduced into the backbone network of baseline FCOS algorithm to extract richer semantic information without increasing the amount of computation. In the feature information fusion stage， Double Refinement Feature Pyramid Networks （DRFPN） were inserted to learn the positions and other information of sampling points between feature maps more accurately， and more accurate semantic relationship between feature channels was captured by context information to improve the detection performance. In the training stage， the bounding box regression was carried out by the Distance Intersection Over Union （DIoU） loss. By optimizing the distance between the prediction box and the center point of the object box， the regression box was made to converge faster and more accurately， which improved the accuracy of the fall detection algorithm effectively. Experimental results show that on the open-source dataset Fall detection Database， the mean Average Precision （mAP） of the proposed model reaches 82.2%， which is improved by 6.4 percentage points compared with that of the baseline FCOS algorithm， and the proposed algorithm has accuracy improvement and better generalization ability compared with other state-of-the-art fall detection algorithms.

In order to overcome the difficulty of visual feature extraction and meet the real-time recognition demand of Beijing Opera characters, a Convolutional Neural Network based on HyperColumn Attention(HCA-CNN) was proposed to extract and recognize the fine-grained features of Beijing Opera characters. The idea of HyperColumn features used for image segmentation and fine-grained positioning were applied to the attention mechanism used for key area positioning in the network. The multi-layer superposition features was formed by concatenating the backbone classification network in the forms of pixel points through the HyperColumn set, so as to better take into account both the early shallow spatial features and the late depth category semantic features, and improve the accuracy of positioning task and backbone network classification task. At the same time, the lightweight MobileNetV2 was adopted as the backbone network of the network, which better met the real-time requirement of video application scenarios. In addition, the BeiJing Opera Role(BJOR) dataset was created and the ablation experiments were carried out on this dataset. Experimental results show that, compared with the traditional fine-grained Recurrent Attention Convolutional Neural Network(RA-CNN), HCA-CNN not only improves the accuracy index by 0.63 percentage points, but also reduces the Memory Usage and Params by 162.84 MB and 131.5 MB respectively, and reduces the times of multiplication and addition Mult-Adds and floating-point operations per second FLOPs by 39 885×10 ⁶ times and 51 886×10 ⁶ times respectively. It verifies that the proposed HCA-CNN can effectively improve the accuracy and efficiency of Beijing Opera character recognition, and can meet the requirements of practical applications.

In view of the fact that facial beautification has been widely studied,but the lack of effective beautification image quality evaluation methods limits the further development of beautification technology,a no-reference evaluation method for facial beautification image quality was proposed. In this method,the facial cognition and perception were combined with the facial beautification technologies to unearth the quality representation of beautified images. Firstly,a facial beautification image database was constructed,the facial image was decomposed to three areas:skin,eyes and mouth. Then,facial aesthetic features were extracted from five aspects:skin color,smoothness,illumination,grayscale difference and sharpness. Finally,Support Vector Regression(SVR)was used to train the facial beautification quality model and predict the quality of the beautified image. The experimental results show that the proposed method achieves 0. 920 5 and 0. 900 9 respectively in the Pearson linear correlation coefficient and Spearman RankOrder Correlation Coefficient(SROCC) on the proposed database,which are higher than those of image quality evaluation methods BIQI(Blind Image Quality Indices),and NIQE(Natural Image Quality Evaluation).

A smart and green home is a dynamic large-scale system with high complexity and a huge amount of information. In order to further improve coordination between subsystems and make the best of multi-source information for the smart home, a multi-Agent intelligent home system based on multi-source information fusion was designed. The framework and interaction mechanisms of Agent were introduced and a multi-source information fusion model based on Adaptive Neural-network-based Fuzzy Interference System (ANFIS) was put forward to conduct the feature extraction and learn occupant's personal behavior. A simulation platform using lightweight embedded Jade Agent on Android and Matlab on personal computer was developed to control the natural lighting system in smart home. The theoretical analysis and the simulation results show that the model can improve synergistic interaction of home systems, and finally enhance the efficiency of multi-source information fusion in decision making process.

Chaos is widely used in image encryption because of its high sensitivity to initial conditions and parameters and its stochastic series. A kind of chaos map whose parameters were randomly changed was brought forward. A chaos key stream with good randomcity and long cycle was made out, whose statistic character was proved strictly. The key stream has been used to encrypt image, and method was proved to work well.