3 results about "Data series" patented technology

A method (200) for assessing the condition of a road (120) comprises steps of periodically acquiring (205) vertical velocity data on board a vehicle (105) traveling on the road (120); generating (215) several overlapping data series (405-450) of predetermined length from the acquired data; recreating (220) each data series (405-450) using a capsule autoencoder (138); determining (225) a mean absolute error between each data series (405-450) and its corresponding newly created data series (405-450); and detecting (230) an anomaly on the road (120) when the mean absolute error exceeds a predetermined threshold.

This invention discloses a method for assessing the safety status of a material yard based on multi-source sensing data, belonging to the field of data processing technology. The method includes acquiring multi-source sensing data from various location units within a continuous sampling time and constructing a data time series; establishing adjacency relationships between location units based on spatial coordinates; correcting the data based on the differences between adjacent time points and the spatial differences between adjacent location units to obtain a target data series; calculating spatial difference indices for each location unit under various data types based on the target data series; determining weight values ​​based on the magnitude of the spatial difference index differences; weighted fusion of the spatial difference indices to obtain the overall spatial status value of the material yard; and performing spatial difference concentration analysis based on this spatial status value to determine the safety status level. This invention, through time and spatial consistency correction and multi-source fusion analysis, achieves a quantitative assessment and graded output of the overall safety status of the material yard, improving the ability to identify local spatial anomalies and the overall risk characterization capability.

This document describes a method (400) and a system (101) for training a generative model to model operational profiles. The method (400) comprises training an initial model based on a plurality of input data samples and processing an input training data sample from a plurality of input training data samples to generate a data series profile corresponding to the input training data sample. Furthermore, the method comprises processing the data series profile using one or more pairs of models to generate profiles of one or more parameters. Finally, the method comprises determining the initial loss value by comparing the generated profiles of one or more parameters with the corresponding profiles of one or more ideal parameters.Finally, the procedure involves training the generative model by repeating the above steps for the remaining input training data samples until the determined first loss value is less than a first threshold loss value, in order to train the second, third, and fourth models.