Contactless Medical Equipment AI Big Data Risk Control and Quasi Thinking Iterative Planning

Abstract Contactless medical equipment AI big data risk control and quasi thinking iterative planning，The tanh equilibrium state of heavy core clustering based on hierarchical fuzzy clustering system based on differential incremental equilibrium theory is adopted. Successfully control the parameter group of CT / MR machine internal data, big data AI mathematical model risk. The polar graph of high-dimensional heavy core clustering processing data is regular and scientific. Compared with the discrete characteristics of the polar graph of the original data. So as to correctly detect and control the dynamic change process of CT / MR in the whole life cycle. It provides help for the predictive maintenance of early pre inspection and orderly maintenance of the medical system. It also puts forward and designs the big data depth statistics of AI risk control medical equipment, and establishes the standardized model software. Scientifically evaluated the exposure time and heat capacity MHU% of CT tubes, as well as the internal law of MR (nuclear magnetic resonance ), and processed big data twice and three times in heavy nuclear clustering. After optimizing the algorithm, hundreds of thousands of nonlinear random vibrations are carried out in the operation and maintenance database every second, and at least 30 concurrent operations are formed, which greatly improves and shortens the operation time. Finally, after adding micro vibration quasi thinking iterative planning to the uncertain structure of AI operation, we can successfully obtain the scientific and correct results required by high-dimensional information and images. This kind of AI big data risk control improves the intelligent management ability of medical institutions, establishes the software for predictable maintenance of AI big data, which is cross platform and embedded into the web system.