7 results about "Data separation" patented technology

The application discloses a power grid voltage long-time sequence dynamic prediction method and system, and the method comprises the following steps: obtaining historical voltage effective value data and cleaning; using a signal decomposition algorithm to separate the data into a trend component and a fluctuation component; extracting global long-time sequence features and local mutation features through a double-flow network; using an adaptive gating unit to fuse the features and inputting the decoder to output voltage prediction values and fluctuation intervals; and generating a stability evaluation index based on the fluctuation interval. Through multi-scale feature decoupling and physical constraint loss function optimization, the application can accurately capture the long-time evolution and instantaneous disturbance of the voltage, solve the problems of lagging prediction of traditional models for mutation working conditions and lack of physical consistency, and provide a reliable basis for power grid safety scheduling.

The application provides a pipeline system-based pre-breakage leakage automatic analysis method and system. The pipeline system-based pre-breakage leakage automatic analysis method can realize accurate identification of the safety state of all positions of a pipeline, constructs a whole-process automatic pre-breakage leakage analysis system from three-dimensional model input to criterion determination, effectively solves the technical pain points of data separation, multiple manual intervention, redundant calculation, low determination efficiency and insufficient accuracy in traditional analysis, and greatly improves the automation, intelligent level and result reliability of pipeline pre-breakage leakage analysis.

This invention discloses a method and system for isolating microgrids using a grid-connected energy storage converter, belonging to the field of microgrid state identification technology. The method includes: collecting and cleaning multi-source monitoring data of the microgrid; using a disturbance baseline separation algorithm based on dynamic time window integration and threshold filtering to separate the data into steady-state and disturbance components; inputting the two components into a pre-trained state identification deep learning model, which is processed sequentially through a disturbance feature decoupling module, a dual-channel spatiotemporal feature fusion module, and a spatiotemporal feature collaborative extraction module to extract spatial feature vectors containing spatiotemporal correlations; and finally, calculating the state probability distribution using an adaptive state classification head based on Mahalanobis distance to determine the operating state. This invention significantly improves the identification accuracy and stability under noisy, transient, and transitional states by effectively separating disturbances, decoupling features, and collaboratively modeling spatiotemporal information.

This application relates to the field of navigation planning technology, and particularly to a method and system for rapid navigation path planning in stochastic flow fields. The method includes: based on flow field ensemble forecast data, separating the deterministic and uncertain components of the stochastic flow field, constructing a median flow field and a perturbation flow field set for the stochastic flow field; using the median flow field to characterize the deterministic component of the stochastic flow field, and using the perturbation flow field set to characterize the uncertain component of the stochastic flow field; proposing a conservative adjustment strategy for navigation speed based on the median flow field and the perturbation flow field set; under the condition of conservative navigation speed, using an improved level set equation to evolve the reachable frontier clockwise from the navigation starting point until the reachable frontier reaches the navigation target point and stops evolving; after reaching the target point, performing a counter-clockwise backtracking of the reachable frontier to calculate the time-optimal trajectory under the conservative navigation speed adjustment strategy, and using this time-optimal trajectory as the navigation planning path in the stochastic flow field.

The application provides a log function lookup table method based on a float data format, which utilizes the storage format of floating point data to quickly calculate the value of log2 through lookup table; the method comprises the following steps: S1, a data separation stage: separating the input floating point base and exponent; only the base is subjected to lookup table; S2, a lookup table construction: constructing a lookup table, wherein each entry comprises a floating point number and a corresponding pre-computed result; S3, a quick retrieval: when the result of a certain floating point number needs to be calculated, the corresponding pre-computed result is directly retrieved in the lookup table, thereby avoiding complex floating point number calculation. Through the method, the precision loss can be effectively overcome, the accuracy of the calculation result is avoided to be affected, and the precision is higher than that of general direct lookup table. The calculation speed of projects such as image processing, scientific calculation and financial analysis can be effectively improved, and the system efficiency is improved.

The application belongs to the technical field of hub motor bearing fault detection, and provides a hub motor bearing fault detection method based on multi-source fusion features, which comprises multi-source physical signal acquisition, suspected fault effective data separation and model detection; the application realizes accurate elimination of noise in the fault signal and screening of suspected fault effective data through CEEMDAN-PE-WTD denoising and multi-dimensional abnormality judgment mechanism, significantly reduces the misjudgment caused by noise; the application realizes accurate representation of fault related features through multi-modal fault feature extraction and capsule network deep fusion; the application provides sufficient and high-quality training data for the fault detection model by integrating real data and simulation data, and ensures the adaptability under complex and variable working conditions; the application realizes accurate evaluation of bearing fault types and fault probabilities through multi-source physical signal dynamic weight fusion and Gaussian mixture clustering analysis.

A method for generating a machine learning (ML) predictive model for a monitored target utility asset is disclosed. The method includes obtaining historical sensor reading data associated with the target utility asset from one or more historical sensor reading databases and filtering the historical sensor reading data to separate non-relevant data that is anomalous with respect to operation of the target utility asset from relevant historical sensor reading data. The method further includes clustering the relevant historical sensor reading data to identify historical sensor reading data that is relevant to other utility assets that have been identified as being similar to the target utility asset and generating at least one ML predictive model using the clustered relevant historical sensor reading data, the at least one ML predictive model configured to determine at least one predicted future condition of the target utility asset accordingly.