15 results about "Affinity propagation clustering" patented technology

The invention discloses a network construction energy storage locating and sizing method for supporting transient stability of a high-proportion new energy power grid, and the method comprises the steps: obtaining power grid measurement data, extracting spatial-temporal features through segmented Hankel matrix recursive decomposition and dynamic weighted graph decomposition, and constructing a feature tensor through multi-scale tensor fusion; transient response features are analyzed based on hierarchical sparse principal component mapping, stability indexes are constructed through multi-scale coupling evaluation, and node scores are obtained through adaptive weight fusion; candidate nodes are screened through affinity propagation clustering, the supporting capacity is evaluated through multi-time-scale influence propagation, and capacity configuration is determined through fractional programming constraint optimization; real-time evaluation is realized based on recursive state estimation, a response strategy is generated through multi-time scale hierarchical decision, and cooperative control is realized by adopting distributed consistency optimization. The transient stability of the high-proportion new energy power grid is effectively improved.

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 an agricultural irrigation load-containing intelligent farm virtual power plant model and an optimal scheduling method, and belongs to the field of power systems, and the model comprises the steps of constructing a typical scene based on an affinity propagation clustering algorithm, constructing an agricultural-power coupling relation model, and constructing a farm virtual power plant operation cost calculation model. The optimal scheduling method can achieve the operation effects of single-field stabilization, multi-field peak shifting, production guaranteeing and overall optimization, calculates the optimal regulation and scheduling scheme of the flexible resources of the intelligent farm virtual power plant according to the obtained typical scene operation random optimization strategy by using the regulation and control total cost model of the flexible resources of the intelligent farm virtual power plant, and achieves the optimal regulation and scheduling of the flexible resources of the intelligent farm virtual power plant. And obtaining a day-ahead scheduling strategy. According to the method, the regulation and control level of the intelligent farm virtual power plant can be effectively improved, distributed energy resources are effectively integrated and optimized, the energy utilization efficiency is improved, and the farm virtual power plant is promoted to develop in a more environment-friendly, efficient and flexible direction, so that the sustainable development target of an electric power-agriculture coupling system is achieved.

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 invention provides a sewage fault diagnosis method and device based on adaptive dynamic slow feature analysis, and the method comprises the steps: carrying out the classification of online obtained sewage treatment data based on an affinity propagation clustering algorithm, and obtaining multi-cluster online classification data; inputting the multi-cluster online classification data into a pre-constructed slow feature analysis model to obtain online advantage slow features and online disadvantage slow features output by the slow feature analysis model; according to the on-line advantage slow features and the on-line disadvantage slow features, determining corresponding on-line advantage statistics and on-line disadvantage statistics respectively; and comparing the online advantage statistic with the advantage self-adaptive control limit, comparing the online disadvantage statistic with the disadvantage self-adaptive control limit, and determining that the system has a fault based on the fact that the corresponding statistic exceeds the corresponding self-adaptive control limit, otherwise, determining that the running state of the sewage treatment process is normal and no fault exists. According to the invention, timely and accurate fault detection is realized, and loss caused by faults is reduced.

The invention discloses an affinity propagation clustering high-voltage cable partial discharge positioning method and system, and relates to the technical field of high-voltage cable partial discharge detection, and the method comprises the following steps: installing an ultrahigh frequency sensor, and measuring the electromagnetic wave of partial discharge; a plurality of partial discharge power sources are arranged to simulate a discharge source; positioning calibration points are selected, two ultrahigh-frequency sensors collect ultrahigh-frequency electromagnetic wave amplitudes of the calibration points in the discharging process, and a calibration database is established; when partial discharge occurs, collecting a group of signal amplitudes, inputting the group of signal amplitudes into a calibration database, and analyzing by adopting an affinity propagation clustering algorithm; and the acquired data is compared with the coordinates of the calibration points of the same category in the database, and the obtained result is the positioning result of the partial discharge. According to the positioning algorithm, the influence of the discharge type on the positioning result is overcome, the robustness is high, the positioning precision is good, and the effectiveness of the positioning algorithm is verified.

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 provides a diagenesis phase sample automatic labeling method and system fusing affinity propagation clustering and a graph convolutional neural network, and belongs to the field of diagenesis phase intelligent identification. The method aims to solve the problems of small sample size and low accuracy of diagenetic phase labeling work. The method comprises the following steps: preprocessing logging curve data, labeling a small amount of labels, and constructing an automatic labeling data set; establishing an incidence relation between logging curve depth nodes by utilizing an affinity propagation clustering structure map structure; node features are aggregated through a graph convolution layer, and rapid and accurate marking of lithogenous phases is achieved. Compared with a traditional method, the method has the advantages that complicated correlation and spatial distribution characteristics between lithogenous phases can be better processed, and the graph structure and the logging curve characteristics are combined, so that the marking accuracy and stability are improved.

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 provides a method and system for automatically annotating diagenetic facies samples by integrating affinity propagation clustering and graph convolutional neural network, belonging to the field of intelligent identification of diagenetic facies. In order to solve the problems of small sample size and low accuracy in the annotation work of diagenetic facies. The present invention preprocesses the logging curve data and performs a small amount of label annotation to construct an automatic annotation data set; uses affinity propagation clustering to construct a graph structure and establish the association relationship between logging curve depth nodes; aggregates node features through a graph convolutional layer to achieve rapid and accurate annotation of diagenetic facies. Compared with traditional methods, the present invention can better handle the complex associations and spatial distribution characteristics between diagenetic facies, combine the graph structure and logging curve features, thereby improving the annotation accuracy and stability.

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.

The invention discloses a method and a device for generating a long-term random scene in new energy output, and belongs to the field of power grid operation scheduling. The new energy output medium-and-long-term random scene generation method comprises the steps of performing dimension reduction on a historical day scene to extract significant features of the historical day scene; clustering the salient feature set of the historical day scene by adopting an affinity propagation clustering algorithm, and dividing the generated day scene into a corresponding cluster; then simulating evolution characteristics among different categories of day scenes based on a Markov chain; and generating a medium and long term random scene through Monte Carlo simulation. According to the method, probability distribution, time sequence characteristics and spatial correlation of medium and long-term scenes of new energy output can be accurately fitted, and effective data support can be provided for medium and long-term power transaction and operation mode formulation.

The invention discloses a radar signal sorting method based on improved affinity propagation clustering. The method comprises the following steps: S1, detecting pulse description word parameter data of an original signal; s2, segmenting the pulse description word parameter data into a plurality of frames; s3, pre-sorting each frame of pulse data by using an improved affinity propagation algorithm; s4, extracting candidate batch centroids by using an adaptive density peak clustering algorithm; s5, completing the blending detection of the candidate blending cluster corresponding to each candidate blending mass center; and S6, completing blending and outputting a final sorting result. The method comprises the following steps: pre-sorting pulse data by using an IAP algorithm introducing an attenuation factor, extracting candidate batch centroids based on density, and finishing batch detection in combination with statistical characteristics of a pulse cluster arrival direction; geometric characteristics, distribution characteristics and statistical characteristics of radar pulse data are comprehensively utilized, potential relevance and mode information between signals are fully mined, and high sorting accuracy is guaranteed while batch increasing phenomena are effectively reduced.