6results about How to "Great contribution" patented technology

The application relates to the technical field of file processing, and discloses a file processing method suitable for structured and unstructured data, which comprises the following steps: S1, identifying the type of an input file and classifying the input file, including structured data, semi-structured data and unstructured data; S2, pre-processing and feature extraction are performed on different types of data; S3, the correlation between different data modalities is quantified based on multi-modal mutual information optimization; S4, the interaction consistency between data modalities is verified, and the fusion weight is dynamically adjusted. Through classification and feature extraction of structured data, semi-structured data and unstructured data, efficient fusion of multi-modal data is realized by using mutual information optimization and a dynamic weighting strategy. Compared with a traditional method, the application can significantly improve the precision and robustness of data processing, and provides an efficient solution for multi-modal data fusion in a complex scene.

This invention belongs to the field of power system technology and provides a method and system for state estimation of distribution systems that considers measurement communication reliability. First, a stochastic state estimation model for distribution systems considering measurement loss is established, introducing a series of random 0-1 variables to represent the random loss of measurements. To calculate the values ​​of each random variable, a method for calculating measurement communication reliability considering the operating state of communication equipment is proposed. A depth-first search is used to find the communication path between the measurement and the estimator, and the reliability of each path is calculated based on the current operating time of the equipment. Then, all paths are orthogonalized, and the reliability of all orthogonal paths is summed to obtain the measurement communication reliability. Finally, an extended Kalman filter algorithm for the established stochastic state estimation model is derived based on projective theory. By incorporating the communication reliability of each measurement into the estimation process, the reasonable allocation of measurement weights is achieved, improving the accuracy of state estimation.

The invention discloses a carbon cloth three-dimensional structure loaded zinc sulfide cobalt composite material and a preparation method and application thereof, and belongs to the technical field of composite material preparation, the composite material is composed of carbon cloth, zinc sulfide cobalt nanoparticles and spiny structure TiO2; the carbon cloth is a carbon fiber substrate of a composite material, carbon fiber rods of the carbon cloth are interwoven to form the carbon cloth, the zinc sulfide cobalt nanoparticles uniformly cover the carbon fiber rods of the carbon cloth, and the titanium dioxide with the thorn-shaped structure is grown on the zinc sulfide cobalt nanoparticles in a wrapping manner. The prepared composite material has a relatively high specific surface area, and the carbon cloth provides excellent conductivity for the composite material. The binder-free composite material shows long-term cycling stability and excellent rate capability.

This invention relates to a radar micro-motion target classification method combined with a reliable XGBoost classifier, comprising: acquiring radar echo data of the micro-motion target to be tested; performing clutter suppression preprocessing on the radar echo data; performing feature extraction processing on the preprocessed radar echo data to obtain a feature matrix to be tested; classifying the feature matrix to be tested using a trained reliable XGBoost classifier to obtain a corresponding preliminary classification result; and performing inter-frame decision fusion processing on the preliminary classification result based on the temporal relationship of the radar echo data of the micro-motion target to obtain a classification result for the micro-motion target to be tested. The preliminary classification result output by the reliable XGBoost classifier includes predicted class probabilities and uncertainty results. The predicted class probabilities are used to determine the classification decision result, and the uncertainty results are used to evaluate the reliability of the classification decision result. This invention reduces the uncertainty of the overall classification system and improves the accuracy of the classification results.

The invention discloses a cable fault abnormal data self-checking method and system based on multi-source traveling wave comparison, and relates to the field of power system protection and fault diagnosis. Under the condition that sampling clocks are inconsistent, overlapped data segments which can be mutually correlated are formed through same-window triggering collection and caching; performing wave head transient feature extraction on the multi-phase sheath traveling wave, obtaining the relative time difference of each channel by using cross correlation, and constructing a relative time difference matrix and an energy ratio matrix in combination with a wave head neighborhood energy ratio; generating a normalized reliability weight according to the signal-to-noise ratio and the energy consistency, jointly estimating the arrival time of each channel wave head in a weighted iteration time estimation model, identifying and correcting outlier abnormal data, and obtaining a self-checked arrival time sequence; and the position / segment length of the grounding box and traveling wave propagation velocity mapping are combined to output the position of a fault point. As a result, reliable positioning without high-precision synchronization is achieved.