To address the issue of insufficient accuracy in the embedded design of traditional sports performance verification devices, this study innovatively proposes an embedded design scheme for a sports performance accuracy verification system based on intelligent vision technology. In terms of hardware configuration, a high-efficiency Advanced RISC Machines (ARM) processor is selected to replace the original bulky computing unit, outdated embedded controllers, and complex independent power modules. This significantly enhances the system’s integration and energy efficiency. On the software level, advanced intelligent vision technology is ingeniously integrated. Through the meticulously constructed reverse vision model design, the system not only achieves fine classification and evaluation of sports performance but also optimizes the execution efficiency of the underlying code, ensuring the standardized and regulated operation of the model. This model is customized for high-precision performance determination and can adapt to the scoring rules of various sports events. To verify the practical effectiveness of this system, simulated application scenarios were constructed, and detailed simulation tests were conducted. The analysis of the experimental data shows that the embedded sports performance verification system based on intelligent vision significantly improves the accuracy and efficiency of performance evaluation. It also demonstrates excellent stability and versatility, thus strongly confirming the effectiveness and innovative value of this design.
In recent years, intelligent question answering system has been widely used in various interactive services. Intelligent question answering system is to arrange the accumulated disordered corpus information in an orderly and scientific way, and establish a classification model based on knowledge to support the question answering of various forms. In this paper, vertical medical website data and electronic medical record data are fused to solve the mapping problem of colloquial medical vocabulary and medical professional vocabulary. PLSA (probabilistic latent semantic analysis) theme architecture model solves the hidden theme content crawling problem of web page crawler. The application of vector space model and knowledge graph in medical knowledge question answering system solves the problem of low efficiency in traditional interactive question answering system. After testing, it can accurately understand the user’s intention, and the accuracy of question and answer is high.
KEYWORDS: Internet, Mining, Knowledge management, Artificial intelligence, Data storage, Data storage servers, Data communications, Data analysis, Telemedicine, Visualization
An AI-based clinical thinking mining discovery system that can be used on PCs and mobile terminals (cell phones, pads, etc.). The system utilizes artificial intelligence technology to obtain massive medical case data and case discussions from the Internet for data extraction and organization, and intelligently classifies them by disease type and clinical presentation. Using natural language processing technology and intelligent mapping mechanism of medical terms, it mines the information of clinical features, tests, inspections, disposal measures and reasons (drugs, surgeries, etc.) of the extracted cases, abstracts and visualizes the diagnostic rules and clinical treatment channels of the cases. The medical case data processed on the Internet will be combined with the typical case data in the HIS system to form a clinical knowledge repository of diseases, guide junior doctors and students to conduct clinical thinking training and consolidate medical knowledge.
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