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For the purpose of improving the efficiency of garment design, the computer-aided garment design virtual reality (VR) model for surplus fabric removal and reuse without segmentation of cutting pieces is analyzed in this paper to provide the architecture of the computer-aided garment design CAD system. The form of dividing the garment into multiple types of nonsegmented pieces is adopted so that each nonsegmented piece stands for a complete design element unit. Based on this structure, the computer analysis of garment design based on CAD can be connected at a deeper level, which will not only improve the design efficiency of new garments but also reduce the design time at the client terminal and enhance the quality of the design. Through the experimental operation of prototypes, it is verified that the intelligent system proposed in this paper can implement the design of prototypes quickly and effectively.

Road transport is an integral part of economic activity and is therefore essential for its development. On the downside, it accounts for 30% of the world’s GHG emissions, almost a third of which correspond to the transport of freight in heavy goods vehicles by road. Additionally, means of transport are still evolving technically and are subject to ever more demanding regulations, which aim to reduce their emissions. In order to analyse the sustainability of this activity, this study proposes the application of novel Artificial Intelligence techniques (more specifically, Machine Learning). In this research, the use of Hybrid Unsupervised Exploratory Plots is broadened with new Exploratory Projection Pursuit techniques. These, together with clustering techniques, form an intelligent visualisation tool that allows knowledge to be obtained from a previously unknown dataset. The proposal is tested with a large dataset from the official survey for road transport in Spain, which was conducted over a period of 7 years. The results obtained are interesting and provide encouraging evidence for the use of this tool as a means of intelligent analysis on the subject of developments in the sustainability of road transportation.

Based on big data and cloud computing technology, the development process of this system includes hardware cluster deployment of the delivery system, optimization of website delivery strategy, and development of the delivery management background system. The main functions in the following aspects are realized: first, we built the website back-end delivery subsystem and data collection and analysis subsystem to realize the control of website delivery and data collection; second, we designed and developed a process management subsystem for booking, management, and delivery of website resources and developed the contract management and order management subsystems to realize the accurate placement of user portraits on the website; then, the placement data monitoring and effect feedback subsystems, and the data inventory subsystem of the website system were designed and developed. Finally, based on the research of Android and based on the Eclipse platform, this article has completed the construction of the Android 4.0.3 version environment, successfully used the Java development language to develop a website information intelligent analysis and navigation system, and analyzed the various functional modules of the entire system. Experimental results show that the system not only realizes ordinary route and site query functions but also combines map API, integrates big data and cloud computing technology, and realizes congestion avoidance query, time optimal query, population heat map, and real-time viewing. The nearby use of this software’s personnel density distribution and other functions provides great convenience for personal travel, which can facilitate the real-time planning of travel plans, and has great practical and practical significance. Through the different levels of testing of various subsystems, the website delivery system meets the functional and nonfunctional requirements proposed by the network and on this basis realizes the use of group wisdom based on Pearson correlation coefficient, Cosine similarity, and Tanimoto coefficient for collaborative filtering website recommendation algorithm.

Based on the analysis of the causes of ship accidents, the development prospect and development direction of ship intelligent safe driving, the artificial intelligence safety prediction and intervention model is put forward. This model solves the problem of ship intelligent safety prediction by using intelligent analysis technology and network technology, and promotes the development of ship intelligence and ship safety navigation technology. Additionally, it expands the channels of obtaining information, connects the ship's mechanical and electrical equipment, collects, stores and analyzes the data reasonably, and constructs the intelligent analysis and processing platform of ship small-world data processing to implement intelligent intervention. What is impressive is that it makes ship navigation safer, more economical, more reasonable and optimized, and accelerates the development of ship artificial intelligence safe navigation.

Statistical intelligence formulates the analysis model and reveals the system that can be easily visible and understandable to mankind. On one hand, it will benefit the society to predict the nature or man-created virus environment, and on the other hand, it will solve the problems of intelligent agents' formation with their functionality. It's a well-known fact that the agents are visible and noticeable, and they perform their own assigned task, but their recognition process is delayed. The chapter will focus on the statistical intelligence analysis that includes the properties of the error tolerance, forecasting, and high reliability. The information is always the part of the memory, but the processing methodology that may lead to knowledge is lacking. This may include the logical induction, Bayesian statistics, functional decision theory, value learning, forecasting, etc. Statistics will assist in path selection to formulate the highly adaptive intelligent system with the said functionalities with reduction in the overall cost factors.

In order to improve the comprehensive nursing effect of the hospital emergency treatment, this paper analyzes the process of the hospital emergency treatment. In addition, this paper combines the possible risks to analyze the risk factors of the comprehensive nursing in the hospital emergency treatment and builds an intelligent analysis model based on the actual situation of the hospital emergency treatment. At the same time, this paper conducts a systematic survey of emergency services and gives the composition and structure of the system. In addition, this paper divides the business required by the system into modules, including registration module, doctor workstation, nurse workstation, query statistics module, decision-making module, and maintenance module. Finally, this paper suggests that in the process of the clinical triage, more ideas for improving the existing evaluation model should be proposed, and experience should be transformed into advantages, so as to improve emergency triage skills; establish an objective, quantitative, and scientific concept of emergency classification and triage; and fully realize scientific triage and precise triage.