With the rapid development of information technology, great changes have taken place in the way of managing, analyzing, and using data in all walks of life. Using deep learning algorithm for data analysis in the field of medicine can improve the accuracy of disease recognition. The purpose is to realize the intelligent medical service mode of sharing medical resources among many people under the dilemma of limited medical resources. Firstly, the Digital Twins module in the Deep Learning algorithm is used to establish the medical care and disease auxiliary diagnosis model. With the help of the digital visualization model of Internet of Things technology, data is collected at the client and server. Based on the improved Random Forest algorithm, the demand analysis and target function design of the medical and health care system are carried out. Based on data analysis, the medical and health care system is designed using the improved algorithm. The results show that the intelligent medical service platform can collect and analyze the clinical trial data of patients. The accuracy of improved ReliefF & Wrapper Random Forest (RW-RF) for sepsis disease recognition can reach about 98%, and the accuracy of algorithm for disease recognition is also more than 80%, which can provide better technical support for disease recognition and medical care services. It provides a solution and experimental reference for the practical problem of scarce medical resources.

Currently, people are actively seeking new and clean energy sources to replace traditional fossil fuels. As a widely distributed, abundant, and easily accessible renewable energy source, wind energy has multiple applications both domestically and internationally, and has a promising future. In wind power forecasting, this paper proposes a new algorithm LFTformer that combines the Transformer model with linear fuzzy information granulation (LFIG).The model uses the improved LFIG algorithm to extract the semantic information of time series, and divides the original time series into multiple information granules, which are then used as inputs to the Transformer model. Through comparative experiments for wind power prediction over 72 hours, 120 hours, and 168 hours, this research demonstrates that the LFTformer can improve the accuracy of wind power prediction. Finally, a Digital Twins system for wind power prediction is constructed using three-dimensional visualization techniques, displaying the prediction results and key data indicators, providing reliable data support for wind power systems.

In this article, the proposed work aims to further optimize the fault diagnosis effect of manufacturing equipment, explore the application of digital twins technology in intelligent manufacturing. The equipment failures in Poka Yoke technology are adopted, and a fault identification and chopping algorithm is designed based on the active learning—deep neural network (AL-DNN) and domain adversarial neural networks (DANN). In addition, a digital twins workshop management and control system is designed for intelligent manufacturing management. The experimental exploration reveals that the accuracy of the AL-DNN algorithm is as high as 99.248%, which is more in line with practical applications. The DANN algorithm can realize fault identification and diagnosis under different working conditions. Compared with other deep learning algorithms, the accuracy of the DANN can be increased by up to 20.256%, showing higher accuracy in contrast to the traditional algorithm, so the effect is more stable. In addition, the digital twins manufacturing management system designed shows good performances, which can intuitively display the specific conditions of workshop and realize basic operating functions. The concept of digital twins is innovatively introduced into equipment fault diagnosis and trend prediction, which can provide scientific and effective reference data for subsequent research on intelligent manufacturing.

This study aims to explore the security issues and computational intelligence of drone information system based on deep learning. Targeting at the security issues of the drone system when it is attacked, this study adopts the improved long short-term memory (LSTM) network to analyze the cyber physical system (CPS) data for prediction from the perspective of predicting the control signal data of the system before the attack occurs. At the same time, the differential privacy frequent subgraph (DPFS) is introduced to keep data privacy confidential, and the digital twins technology is used to map the operating environment of the drone in the physical space, and an attack prediction model for drone digital twins CPS is constructed based on differential privacy-improved LSTM. Finally, the tennessee eastman (TE) process is undertaken as a simulation platform to simulate the constructed model so as to verify its performance. In addition, the proposed model is compared with the Bidirectional LSTM (BiLSTM) and Attention-BiLSTM models proposed by other scholars. It was found that the root mean square error (RMSE) of the proposed model is the smallest (0.20) when the number of hidden layer nodes is 26. Comparison with the actual flow value shows that the proposed algorithm is more accurate with better fitting. Therefore, the constructed drone attack prediction model can achieve higher prediction accuracy and obvious better robustness under the premise of ensuring errors, which can provide experimental basis for the later security and intelligent development of drone system.

With the development of science and technology, the high-tech industry is developing rapidly, and various new-age technologies continue to appear, and Digital Twins (DT) is one of them. As a brand-new interactive technology, DT technology can handle the interaction between the real world and the virtual world well. It has become a hot spot in the academic circles of all countries in the world. DT have developed rapidly in recent years result from centrality, integrity and dynamics. It is integrated with other technologies and has been applied in many fields, such as smart factory in industrial production, digital model of life in medical field, construction of smart city, security guarantee in aerospace field, immersive shopping in commercial field and so on. The introduction of DT is mostly a summary of concepts, and few practical applications of Digital Twins are introduced. The purpose of this paper is to enable people to understand the application status of DT technology. At the same time, the introduction of core technologies related to DT is interspersed in the application introduction. Finally, combined with the current development status of DT, predict the future development trend of DT and make a summary.

This paper focuses on traffic sign spotting (TSS which automatically recognizes not only the conventional traffic signs but also information, facility and service signs, and traffic lights. TSS is divided into two sequential tasks: detecting traffic sign candidate regions in an image and recognizing the traffic signs in the regions. It is a very challenging task. We make the following contributions: 1) we create a traffic sign collection from the driverless car. The traffic signs are shot under the natural environment which covers large variation in illuminance and weather conditions. It not only contains the common traffic signs but also contains the information, facility and service signs which are called signposts, as well as traffic lights. 2) we proposed a systematic solution. We construct an Inception convolutional neural network. We use Faster-RCNN for traffic sign detection and make it suitable to detect small targets. 3) We adopt three schemes for the common traffic signs, the signposts and the traffic lights, respectively. The experimental results demonstrate the effectiveness and efficiency of our methods. Our methods won the first place in the traffic sign recognition task of Intelligent Vehicle Future Challenge 2017, China.