3 results about "Normalized Time" patented technology

This invention belongs to the field of satellite orbit prediction technology and provides an artificial intelligence-based method for predicting the orbital state of low-Earth orbit satellites. The method includes: dividing the predicted orbit into time windows, calculating the composite acceleration for each step, inputting the initial position and velocity with forces to obtain a nominal orbital state sequence, and constructing an augmented state vector with aerodynamic composite parameters; inputting the vector into a time-series neural network model, and updating the augmented state based on the output; obtaining the original standard deviation through mapping using an uncertainty metric, calculating the standardized residual, obtaining the right-hand weighted quantile value according to normalized time weights, comparing it with a preset threshold, and performing adaptive bias adjustment; using the acquired data to calculate a risk metric, comparing it with a set threshold and priority rules, automatically performing a judgment action on the risk metric, recording the triggering cause and judgment action, and updating the parameters in the time-series neural network model.

This application discloses a statistical method for the delivery timeliness of a remote sensing satellite ground system. The method includes: identifying each stage of the ground system delivery process and constructing a ground system delivery timeliness model. The ground system delivery timeliness model characterizes the delivery time value of the ground system, which is obtained by weighting the time consumption and stability coefficient of each stage. A time sequence is obtained based on the recorded time values ​​for processing each task at each stage, and the time sequence for each stage is normalized to obtain a normalized time sequence. The time consumption and stability coefficient of each stage are calculated based on the normalized time sequence, and the time consumption and stability coefficient of each stage are substituted into the ground system delivery timeliness model to calculate the delivery time value of the ground system. This application solves the technical problem of the lack of a statistical scheme for ground system delivery timeliness capability in the prior art.

This application provides a time-sensitive diffusion model weight calibration method and content generation method, including: calculating the sensitivity index of each layer of the diffusion model at different time steps using several calibration data; normalizing the sensitivity of all time steps to obtain a normalized time-series importance score; modeling the weight quantization problem as a weighted least squares problem based on the normalized time-series importance score; and solving the weighted least squares problem to obtain the calibrated quantized weights. This application employs time-series importance assessment and normalization techniques, enabling accurate positioning and weighting of key stages in the generated trajectory, solving the problem in traditional mean calibration methods where the fitting requirements of key time steps are diluted by non-key time steps. The quantized diffusion model calibrated using this method, when deployed on resource-constrained devices, can ensure the generation of semantically accurate and structurally stable virtual scenes at key stages, effectively improving the reliability and real-time performance of the device.