2results about How to "Solve forecasting problems" patented technology

The application relates to the technical field of hardware equipment quality tracing, and discloses a hardware full-life-cycle quality tracing method and system based on IoT, which comprises the following steps: acquiring multi-dimensional real-time data flow and arranging the same into a real-time running data set; extracting distribution characteristics based on a historical data archive to obtain a benchmark reference space; projecting the real-time data to calculate a deviation quantitative value, correcting environmental deviation when the deviation quantitative value exceeds a threshold value to obtain a deviation result; extracting degradation characteristics to match historical trajectories to locate a degradation process; performing time sequence analysis to predict residual life to obtain a failure time window; and generating and arranging maintenance intervention instructions into a scheduling instruction set. The method realizes hardware full-life-cycle quality tracing and early abnormality early warning, improves maintenance accuracy, and reduces fault loss.

This invention discloses a method, modeling method, system, and application for predicting dynamic contact angle distribution, belonging to the technical field of predicting dynamic contact angle distribution of micro-protrusions. The method for predicting dynamic contact angle distribution provided by this invention constructs a joint probability density function of horizontal distance and height difference based on the normal distribution law of the horizontal distance and height difference between adjacent micro-protrusions on a rough surface; derives the probability density function of the static micro-protrusion contact angle through integral transformation and Leibniz's differentiation rule; derives the first and second relations through cyclic load experiments; and constructs a probability density function model of the dynamic contact angle based on the first and second relations and the probability density function of the static contact angle. By integrating the static model and the dynamic relations, it achieves the prediction of contact angle distribution under different cycles, adapting to the evolution of contact characteristics under varying loads, and effectively solving the problem that traditional static models cannot reflect the dynamic evolution of the contact angle under cyclic loads.