8 results about "Time scaling" patented technology

This invention proposes a multi-energy safety assessment method and system considering multiple time scales and coupled faults. The method includes: establishing an intermediate time-scale model and a long-time-scale model of the integrated energy system; conducting a safety assessment for each fault scenario in a fault scenario set; wherein, using the long-time-scale model to perform an N-1 safety scan of the fault scenarios considering coupled faults, calculating system risk assessment indicators; when the system risk assessment indicators reflect that the fault scenario may lead to water hammer consequences, using the intermediate time-scale model to calculate the overpressure value generated during the water hammer process; and generating the final safety assessment result of the integrated energy system based on the safety assessment results of each fault scenario. This invention can capture the water hammer effect of the heating network and the cascading faults caused by the coupling of the logical and geographical layers, which are often overlooked in traditional assessments, achieving a more comprehensive and accurate safety assessment of the integrated energy system and helping to improve the operational safety of the system.

This paper relates to a generator set control method and device based on multi-timescale features. The higher-level scale provides guidance information to the lower-level scale; the weekly plan provides the unit start-up and shutdown status for the day-ahead plan, and the day-ahead plan provides the basic output points of the units for the intraday plan. At the weekly scale, the constraints of unit timing coupling can be ignored, and a dataset is generated by feeding back from the lower-level scale to the higher-level scale. This results in smaller data dimensionality and lower computational cost for data generation. Then, based on a neural network, the start-up and shutdown status of the units is predicted, and reliable start-up and shutdown statuses are selected and passed down, eliminating the need for feasibility remedial measures for start-up and shutdown statuses and reducing computational complexity. At the day-ahead and intraday scales, the start-up and shutdown statuses of the units determined by the higher-level time scale are used to identify more redundant constraints and accelerate the solution speed. This paper utilizes the features of different time scales to solve the unit combination problem, which can accelerate the solution speed while ensuring solution quality, and the solution speed is more stable.

The application belongs to the technical field of intelligent education, and discloses a knowledge tracking method and system based on a cascaded time sequence feature processing architecture, which comprises the following steps: mapping historical answering interaction sequence data of students into a high-dimensional feature vector; performing deep processing on the high-dimensional feature vector to capture data features of different time scales; and predicting the performance of students based on the data features of different time scales. Through the collaborative design of the heterogeneous cascaded architecture and the selective state space model, the application improves the training stability and prediction accuracy of the knowledge tracking model, and realizes efficient use of linear time complexity and computing resources.

The application provides a fault monitoring method and system for a numerical control machine tool, and particularly relates to the field of numerical control machine tool fault monitoring; by uniformly collecting and time window slicing processing of multi-channel running state time sequence data, a machine tool fault monitoring training set and a test set are constructed, and the dynamic correlation relationship of each monitoring channel under the same time scale is ensured; a spectral response value is constructed based on the ratio of the change amplitude of adjacent moments in the sliding window to the channel mean amplitude value, and a disturbance response value is formed by normalization compression; combined with the disturbance deviation intensity of the driving channel and the change direction sign of the response channel, a directional dependence structure between channels is analyzed and formed; further, a reverse interference reconstruction value and a strong residual quantity are constructed to form a channel-level fusion indicator, and a path tension indicator is constructed by sample difference extraction; on this basis, a path abnormality indicator is generated and weighted, and a machine tool fault detection value is output, so that stable detection and time sequence modeling of the fault state in the running process of the numerical control machine tool are realized.