8 results about "Normalized Time" patented technology

The invention provides a kinematics equation linearization method of normalized time, which converts a time variable under an absolute scale into a relative scale, thereby facilitating derivation and numerical calculation of an optimal control problem. Converting free time into time with boundary conditions by using normalized time, and converting time serving as a differential term in the motion equation into a multiplication term, so as to realize transformation of the motion equation; and according to the motion equation after time normalization, the time after normalization is optimized, and equivalent solution of an optimal control problem is realized. Equivalent solution of an original optimal control problem is realized, and derivation and numerical calculation of the optimal control problem are facilitated.

The invention provides a multi-dimensional feature extraction and attention fusion edge resource calculation method and device, and the method comprises the steps: obtaining current scheduling data and time sequence data under a current historical time window when a current scheduling moment arrives; performing data preprocessing on the current scheduling data and the time sequence data under the current historical time window to obtain normalized time sequence data and scheduling data tensor; inputting the normalized time sequence data and the scheduling data tensor into a resource scheduling model, so that the resource scheduling model outputs a resource state prediction result of each host at the next moment and a task scheduling scheme at the next moment according to the normalized time sequence data and the scheduling data tensor; and updating a resource scheduling strategy according to the resource state prediction result of each host at the next moment and the task scheduling scheme at the next moment. According to the scheme of the invention, efficient fault prediction and fault-tolerant scheduling optimization are realized, and an efficient and adaptive solution is provided for dynamic fault tolerance in an edge computing environment.

The invention relates to the technical field of image processing, in particular to a compensation dispersion transfer entropy-based causal analysis method, and aims to accurately capture an instantaneous causal relationship and improve the accuracy of a constructed brain network. The method comprises the following steps: acquiring an original time sequence, and normalizing the original time sequence by adopting a normal cumulative distribution function to obtain a normalized time sequence; and performing dispersion processing on the normalized time sequence, and endowing each normalized data point with an integer in a preset integer range through a linear algorithm to obtain a dispersion time sequence. And performing information transmission quantitative analysis on the dispersive time sequence based on the information entropy, and evaluating the uncertainty degree of information flow transmission from the source system to the target system and the uncertainty degree of information flow transmission from the source system to the target system under the constraint of a preset condition. And introducing a compensation mechanism to optimize the uncertainty evaluation result.

The application discloses a multi-bridge monitoring data anomaly identification method and system based on multi-modal feature fusion, relates to the field of data anomaly identification, and comprises the following steps: one, collecting SHM time series data of multiple bridges, performing normalization processing and segmentation; two, visualizing the normalized time series data, simultaneously extracting statistical features in the data, and manually marking labels for the image and the statistical features to construct a data set; three, establishing a multi-modal convolutional neural network model based on the input of the gray image and the statistical features, using the data set from the multiple bridges, simultaneously inputting the gray image and the corresponding statistical features into the network model for training, four, using the trained convolutional neural network model to perform anomaly identification on target bridge SHM data. The application realizes automatic identification of bridge SHM data anomalies, improves the identification precision of abnormal data under the condition that the categories of the bridge monitoring data set are unbalanced and the data volume is limited, and provides a basis for bridge operation state evaluation and early warning.

The invention discloses a fire-fighting system alarm method based on multi-sensor coupling data support utility index prediction, and relates to the technical field of fire-fighting system alarm, and the method comprises the steps: synchronously collecting real-time measurement data, and forming respective time sequences; carrying out normalization processing on various measurement data by adopting a minimum-maximum normalization method to obtain a normalized time sequence; an autoregressive integrated moving average model algorithm is adopted for the normalized time sequence, and normalized values of various parameters at the next moment in the future are predicted; and calculating a multi-sensor coupling data support utility index according to the predicted normalized values of various parameters at the next moment in the future, evaluating the coupling utility of the sensors, and triggering a fire behavior judgment strategy. According to the method, comprehensive identification of early characteristics of the fire is realized, so that the system can process abnormal data in a complex environment more stably, technical support is provided for upgrading of an intelligent fire-fighting system, and the method has engineering application value and popularization significance.

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.