4results about How to "Improve clustering quality" patented technology

The application relates to the field of intelligent electric meter detection, in particular to an intelligent electric meter power consumption anomaly detection method and system based on mode classification, which comprises the following steps: obtaining historical current sequences and to-be-detected current sequences of each user, performing multi-scale equal-length adaptive segmentation and frequency band division on the historical current sequences, combining user intra and inter power consumption mode differences to calculate power consumption tolerance of each frequency band, performing EMD decomposition and feature extraction on the historical current sequences, calculating mode classification distances based on power consumption tolerance and clustering to obtain power consumption mode clusters, and finally comparing distances between the to-be-detected sequences and the mode clusters, calculating anomaly scores and determining power consumption anomaly states. Through multi-scale equal-length adaptive segmentation, frequency band differential analysis on the historical current sequences, and combination of user intra and inter power consumption mode differences, tolerance and anomaly scores are constructed, precise and personalized anomaly detection of user power consumption is realized, and detection accuracy and efficiency are improved.

The application discloses a power system load curve clustering analysis method, determines the target and requirement of power system load curve clustering analysis, carries out data preprocessing, uses a self-organizing feature mapping neural network to construct a feature extraction model, uses an MAML algorithm to carry out multi-task learning, carries out rapid adjustment through meta learning, applies a Gaussian mixture model to cluster the extracted features, uses indexes to evaluate the clustering effect, carries out clustering result analysis, determines whether the predetermined performance standard is met, and further carries out iterative optimization, so that the self-organizing feature mapping neural network, meta learning and the Gaussian mixture model are combined, and greater value is brought to power system operation and energy management.

The application provides a power user power consumption behavior portrait method considering power consumption sensitivity, and belongs to the technical field of industrial data processing. The method solves the problem that in most power user portrait methods, sensitive loads and basic loads influenced by other factors are mixed for analysis, which makes the portrait result not fine enough and unable to accurately grasp the power consumption behavior characteristics of users. The method comprises the following steps: obtaining a power load data set, drawing a load curve, and performing preliminary clustering; labeling the preliminary clustering cluster; inputting the preliminary clustered load data set into an STL model for decomposition and division into basic loads and sensitive loads; performing clustering analysis on the basic loads and the sensitive loads respectively, defining a basic load label library and an external factor sensitive load label library according to the clustering results; and labeling each user with two types of labels according to the basic load label library and the external factor sensitive load label library, combining the labels of the initial clustering results, and generating a precise portrait of the power user.

The application provides a brain function connection map construction method based on non-negative matrix three-factor orthogonal decomposition (ONMTF). The method first establishes a group level function similarity matrix between two brain region voxels of each subject in a resting state functional magnetic resonance imaging data set according to a function connection measurement between the two brain regions, wherein the rows and columns of the matrix represent the voxels of the two brain regions. Then, the group level function similarity matrix X is decomposed into the product of three non-negative matrices, namely ASY, through ONMTF. According to the two posterior probability label matrices A and Y obtained through the decomposition, a refined function connection network between the two brain regions is obtained. The function of the unknown subregion network extracted by the method can be inferred through the function of the subregion network which has been studied thoroughly. Moreover, the brain function connection network extracted by the method has better clustering quality, namely better function consistency.