7 results about "Affinity propagation clustering" patented technology

The invention discloses a method, a system, a medium and equipment for detecting abnormal data of electric charge, and belongs to the field of machine learning, and the method comprises the steps: collecting a historical power utilization data set of a user; based on a preset Affinity Propagation clustering algorithm, clustering division is carried out on the historical electricity consumption data set, and clustering clusters representing different electricity consumption behavior modes and clustering center parameters of the clustering clusters are obtained; recursively constructing an isolated tree for each cluster through a preset isolated forest algorithm, and obtaining a segmentation rule parameter of each isolated tree; integrating each clustering center parameter and a segmentation rule parameter of the isolated tree, and constructing an anomaly detection model; and detecting the current power consumption data according to the anomaly detection model, and outputting a detection result. Therefore, by implementing the method and the device, the problem of low abnormal electricity charge detection precision in a complex electricity consumption scene in the prior art can be solved.

The invention discloses a method and a device for establishing a short-term probability prediction model of a comprehensive energy load polymer. The method comprises the following steps: carrying out preprocessing and dimension reduction on collected comprehensive energy load polymer historical data; grouping the dimensionality-reduced data by adopting an affinity propagation clustering algorithm based on comprehensive similarity, extracting centroid features of each group as representative load features, and inputting the representative load features into a prediction model; and constructing a comprehensive energy load polymer short-term probability prediction model fusing Copula adaptive correlation analysis, a cross feature-time graph neural network and a mixed density network. According to the method, the precision of short-term probability prediction of the comprehensive energy load polymer is effectively improved through a dynamic coupling relation mining and spatial-temporal feature joint learning mechanism.

The application discloses a visual detection method for early warning of icing environment, and relates to the technical field of aviation icing detection, comprising the following steps: constructing a two-dimensional joint feature residual space based on real-time contour vectors and real-time gray field vectors, generating a candidate region set, constructing a three-dimensional feature vector for maximum points in the candidate region set, clustering by using an affinity propagation clustering algorithm, and obtaining structured ice phase units; combining the affinity propagation clustering algorithm with an effective rank quantization method based on singular value entropy of a trajectory matrix to construct an ice layer analysis mechanism with multiple singular structure decoupling capabilities and continuous morphological phase state quantization capabilities; and constructing a spatial distribution sequence of internal deformation variables of each ice phase unit into a trajectory matrix, introducing singular value decomposition and Shannon entropy calculation to obtain an effective rank continuous scalar, and improving the perception granularity of the icing visual detection system on the spatial non-uniformity of the ice layer.

The application discloses a kind of comprehensive energy load aggregate short-term probability prediction model establishing method and device.The method includes: the historical data of comprehensive energy load aggregate collected is preprocessed and dimensionality reduction;Grouping is carried out to the data after dimensionality reduction using affinity propagation clustering algorithm based on comprehensive similarity, and the centroid feature of each group is extracted as representative load feature input prediction model;The short-term probability prediction model of comprehensive energy load aggregate that fusion Copula self-adapting correlation analysis, cross feature-time graph neural network and mixed density network is constructed.The precision of short-term probability prediction of comprehensive energy load aggregate is effectively improved by dynamic coupling relationship mining and spatiotemporal feature joint learning mechanism.

The invention discloses a non-cooperative OFDM user number estimation method, and belongs to the field of communication. According to the method, the orthogonality between OFDM subcarriers is utilized, the signal is decomposed into a plurality of subcarriers through FFT, the power feature, the high-order cumulant feature and the high-power spectrum line feature of the signal are calculated according to the characteristics of different signal modulation modes and power distribution between users, feature extraction is conducted on the signal, and blind recognition of the signal can be achieved without priori conditions. According to the method, OFDM user number estimation is carried out based on AP affinity propagation clustering, and the number of clusters does not need to be specified in advance. According to the method, the dimension reduction operation is utilized, and the high-dimension data is subjected to visualization processing. When the dimension of a classification result is relatively high, a principal component analysis (PCA) algorithm is utilized to carry out linear dimension reduction, original data is projected to a new coordinate system, a direction with the maximum variance is selected as a principal component, and an important component is selected to convert high-dimensional data into two-dimensional data or three-dimensional data, so that visualization is realized, and cluster targets in the data can be found.

The present invention relates to the field of agricultural environment, and in particular to a method for analyzing microbial community clustering. The method for analyzing microbial community clustering of the present invention comprises the following steps: first, based on the absolute abundance of different individuals in the microbial community that changes over time, the relative change rate of the individual is calculated to describe the change in the growth trend of the microorganism; second, considering the similarity of the growth trends between different individuals, the correlation coefficient of the relative change rate is calculated, and a similarity matrix is ​​constructed based on the correlation coefficient of the relative change rate; finally, affinity propagation clustering analysis is performed based on the obtained similarity matrix to obtain cluster centers. By constructing a similarity matrix based on the relative change rate correlation coefficient and using affinity propagation clustering to calculate the cluster centers, while taking into account the competition and symbiotic relationships between individuals, more effective cluster centers and cluster clusters for the study of microbial community relationships can be obtained.

The invention provides a method for judging a cell time delay problem based on an XGBoost decision tree. The method comprises the following steps: acquiring a detailed call record of a user plane XDR and network management KPI index data; analyzing and screening characteristic indexes strongly correlated with the downlink time delay of the base station through a Pearson correlation coefficient; carrying out dynamic clustering on the key characteristic indexes by using an affinity propagation clustering method; an XGBoost regression model is constructed, and model training is carried out; hyper-parameter tuning is carried out on the XGBoost model; according to the trained model, identifying a key root cause influencing the time delay; outputting a delimiting conclusion and an optimization suggestion of the cell delay problem in combination with the clustering result and the feature importance; according to the method, a large-scale data set formed by massive user plane XDR call tickets and network side KPI indexes can be efficiently processed, and the characteristic engineering and the strong computing power of the XGBoost model are learned through a machine.