Alpine skiing beginners are easily injured if they fail make the right decisions when faced with unexpected situations. Excellent decision-making requires extensive training, while alpine skiing training is limited by time and space. To address these issues, this paper combines VR and machine learning techniques to develop a high fidelity VR skiing decision-making training system based on HCPS framework. The aim is to train beginner skiers in decision-making by creating automated and repeatable virtual scenarios. Firstly, physical world (ski resort environment, and user motions) was mapped into the VR world using the HCPS framework. Afterwards, the user’s decision motions were made based on the scenarios provided, while being identified through an improved SVM (94.5% accuracy). The motion recognition results were fed into the expert system to assess the feasibility of the decision-making. Finally, the feasibility of the system was assessed with a combination of subjective and objective approaches. The results showed that the VR system significantly improved the participants’ skiing decision-making ability and speed compared to traditional 2D video training. The high fidelity environment reduced participants’ motion sickness symptoms and resulted in a better sensory experience. In addition, participants were more focused in the high fidelity environment. Overall, these results demonstrated the construct validity of the virtual ski decision-making training system. This study changes the traditional training method to create an immersive training environment, and provides an effective avenue for the development of sports training.

In recent years, ski resorts have gradually become the first choice for people to travel and vacation in winter. But the current monitoring system of ski resorts is not mature and perfect. Therefore, this paper proposes a remote real-time monitoring system for ski resorts based on the CPS (information physical system) three-layer (physical layer, cyber layer and decision layer) theoretical model, combined with the B/S (browser/server) mode of computer technology. The physical layer uses embedded devices and sensors to gather information about the ski resorts. In the network layer, a web cloud server is built based on the ASP.NET Core framework to complete the data transmission. The decision-making layer uses the front-end development language to build a web human-computer interaction page for data display. This system can realize remote real-time monitoring of the ski resort meteorological data (temperature and humidity, wind speed and direction) and image information, which is the intelligent information of the ski resort monitoring system. Transformation provides actionable solutions.

Abstract This paper presents a virtual ski training system based on changes in environmental elements of ski resort. The aim of the system is to allow skiers to ski training without going to a ski resort, increase tactical training in the face of different environments and help skiers improve their performance. The system uses VR technology to construct a realistic ski training scenario. 5G cloud platform transmissions are connected to the actual ski resorts to obtain real-time environmental elements data to import into the VR system in order to construct a dynamic model of the environment. The effectiveness of the system was verified through a combination of indoor and outdoor tests to analyse participants' skiing speed in both real and virtual scenarios. In conjunction with questionnaires completed by the participants, differences in the effectiveness of the participants' experience of the different dimensions of the system were analysed. The results show that this VR system is effective in simulating actual ski resorts and is an effective aid for skiers, especially less experienced ski students.

With the improvement of people's living standards and the increasing maturity of wireless transmission technology, smart home is further promoted and applied, but due to the imperfect development of smart home bureau, there are still many security risks, mainly in the lack of effective authentication mechanism, the security of authentication is not high; smart home devices have loopholes, smart devices have memory loopholes, logic loopholes; data security is not high, in the transmission The data security is not high, and it is easy to be stolen and listened to during the transmission. In this paper, based on data security aspects, combining previous research experiences and combining asymmetric and symmetric encryption algorithms, we propose a smart home data encryption system based on RSA + AES hybrid encryption to improve the communication security of each smart home.

The 2022 Winter Olympics will be held in Beijing, and skiing is one of the sports. Athletes want to achieve excellent results, in addition to their own ability, they will also be affected by temperature, humidity, wind speed, wind direction, snow friction coefficient, snow angle and so on. In this paper, a set of snow field environment measurement device and BP neural network normalization algorithm based on snow surface friction coefficient are designed. The device is equipped with temperature and humidity sensor, wind speed and direction sensor, inclination sensor, tension sensor and IMU sensor to measure the environmental parameters of snow field. The tester uses this device to measure the environmental parameters of the snow field in the snow field and display them on the QT interface in real time, so as to provide effective scientific reference for coaches and athletes and improve the competitive performance of athletes.

The researchers designed an embedded system that can measure wind speed, wind direction, temperature and other environmental elements in real time and measure the in-run speed of athletes precisely. Information collection was completed by this system to build a BP neural network mode in which the environmental elements and in-run distance as iniput nodes and the in-run speed as the output node. The mode was established to predict the in-run speed and provide quantitative reference for coaches and athletes in training or competition.