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The power grid is an important connection between power sources and users, responsible for supplying and distributing electric energy to users. Modern power grids are widely distributed and large in scale, and their security faces new problems and challenges. Information entropy theory is an objective weighting method that compares the information order of each evaluation index to judge the weight value. With the wide application of entropy theory in various disciplines, the subject of introducing entropy into the power system has been gradually concerned. This article aims to study the smart terminal security technology of the power grid perception layer based on information entropy data mining. This article analyzes its related methods and designs a smart terminal for the power grid. On this basis, a data analysis platform is built and a safety plan is designed. The result is that the average absolute error, root mean square error, average absolute percentage error, and mean square error of the platform's power load forecast are 1.58, 1.96, 8.2%, and 3.93, respectively. These error values are within the ideal range, and the data processing ability is strong. The packet loss rate of the adversary's eavesdropping was tested, and the average packet loss rates at locations a, b, c, and d were 1.05, 1.2, 1.81, and 2.2%, respectively. Data packets will be definitely lost, so the platform is highly secure.

Casualties caused by people trapped in cars have been common in recent years. Despite a variety of solutions, complex detection devices need to be arranged, or privacy is poor. Since device-free Wi-Fi sensing has attracted much attention due to its simplicity, low cost, and no need for additional hardware, this paper proposes a contactless wireless Wi-Fi sensing-based method for detecting people left in cars: Car-Sense. The method uses ESP32 devices in the vehicle to build a wireless Wi-Fi network for low-cost, real-time, and accurate personnel awareness. By capturing and analyzing the CSI (Channel State Information) signal, extracting features, and building a machine-learning correlation model, the number and location of occupants can be estimated and further inferred in combination with sensing data such as vehicle temperature. Even better, with the computing power of the edge-side devices to process data in collaboration with the cloud, the computing process is partially localized to reduce the computing pressure and latency in the cloud. The approach has been experimentally verified to have more than 85% accuracy.

Cyber-physical system (CPS) can intelligently feel the interactive information between data and terminal. The combination of CPS data and intelligent terminal can make terminal devices communicate with each other, coordinate operation, and share information in a complex environment. At present, the demand for smart terminal technology in university libraries and national cultural museums is also increasing. However, the data for smart terminal display and development of shared services face the problem that the original data is difficult to collect and the incomplete images are difficult to repair. In order to display the ancient books in a smart terminal, we have established a model for image inpainting through deep learning. In this paper, we set up a smart terminal display solution to display the digital protection effect of incomplete character images. Then, we propose a model based on Conditional Generative Adversarial Network (CGAN) to solve the problem of repairing incomplete character images. We put this model into the computing module and deployed it to the cloud computing platform of the smart terminal. Moreover, we construct a handwritten Yi character image data set in the cloud. To verify the CGAN calculation model, we design and construct two quality detection tests. According to the experimental results, the image inpainting quality of the CGAN is better than the traditional image restoration technology. Based on the experimental analysis and evaluation criteria, we find that the CGAN calculation model is of great significance for ancient book inpainting.

Demand response (DR) is widely accepted as a feasible and potential solution to improve the operation of the power system. In this paper, an economical and practical DR system architecture based on internet and Internet of things (IoT) communication technologies is discussed to achieve wide-area DR control without using an expensive metering infrastructure. Multi agents are introduced with respective control strategies to implement multi-time-scale control in a power system. In order to support quick DR strategies, a novel smart terminal design for the proposed DR system is described with functions of local parameter detection and action. The practicality of the proposed system was validated on a developed hardware-in-loop co-simulation platform.

Nowadays operators are focusing on developing the video communication services based on smart terminals.The characteristics and key affecting factors of video communication quality were discussed.Furthermore，according to the analysis of the methods of subjective and objective video quality assessment，an assessment model of video communication service quality was proposed and verified by experimental data，which can provide test reference and evaluation methods for operators to improve the quality of video communication service.