276 results about "Spectral clustering" patented technology

The invention relates to the technical field of data protection, and discloses a privacy protection type data joint modeling method based on federated learning, which comprises the following steps: acquiring local data to perform meta-feature extraction, calculating key statistics to characterize data characteristics, collecting meta-features, grouping the meta-features into similar feature clusters through spectral clusters, and carrying out feature clustering on the similar feature clusters; dynamically allocating and calculating resource weights according to the similar characteristic cluster scale and the equipment computing power; distributing a basic privacy budget according to the client type, calculating a local model accuracy rate and an intra-cluster level difference, dynamically adjusting the privacy budget, adding adaptive Gaussian noise based on the privacy budget, and adjusting gradient sensitivity of gradient calculation; verifying gradient compliance through zero knowledge, carrying out safe aggregation on gradients passing verification, optimizing a meta-model through a knowledge distillation loss function, and generating confrontation sample analysis to obtain a leakage risk value to identify knowledge leakage risks; sensitive neurons in the neuron sensitivity positioning element model are analyzed and calculated, directional noise is injected, and initial parameters are adjusted for initialization training.

The invention discloses a voiceprint clustering method guided by a speaker recognition pre-training model based on short voices, and the method comprises the following steps: obtaining an original voice signal, and randomly combining a plurality of enhancement strategies to achieve data enhancement; performing voice segmentation on the voice signal after data enhancement based on a uniform segmentation mode; extracting voiceprint features of the segmented voice based on an attention mechanism of global time-frequency domain context modeling; and obtaining a clustering result based on K-means clustering and spectral clustering, matching the clustering result with a real speaker tag, performing reverse transmission based on angle-dependent AAM-Softmax loss, and outputting a voiceprint clustering result. According to the method, the influence of environmental interference on feature extraction can be overcome, feature dimensions which are more effective for identity identification can be screened, a clustering output effect which is superior to that of a mainstream algorithm can be obtained with a relatively low parameter quantity, and the robustness under a noise interference condition is improved.

The invention discloses an electricity larceny identification method based on graph calculation, and particularly relates to the technical field of electricity utilization anomaly detection of an electric power system. Historical power consumption data and a power supply topological relation of power consumers are collected, and a multi-dimensional behavior graph model fusing behavior characteristics and structural information is constructed; performing structure disturbance analysis on each node in the graph, calculating information entropy change before and after node removal, performing attention fusion on a time sequence behavior feature of the node and a structure disturbance vector, constructing a joint feature vector, and enhancing feature expression through spectral clustering and linear reconstruction; a behavior propagation field and a disturbance adjustment mechanism are introduced into the graph to form a disturbance response graph, and an abnormal gathering area is identified through path energy analysis and focusing area fitting; calculating confidence scores of the nodes and outputting a suspicious user list; the method can realize efficient identification of electricity stealing behaviors with strong concealment and complex transmissibility, and has the advantages of high precision, strong interpretability and wide application scene adaptability.

The invention discloses a high-precision distributed photovoltaic output prediction method and system based on micrometeorology and feature hierarchical clustering, and relates to the technical field of distributed photovoltaic output prediction.The method comprises the steps that numerical weather forecast data are collected, an initial micrometeorological field is generated through space-time alignment and self-adaptive KNN interpolation, and the initial micrometeorological field is subjected to feature clustering; a WRF-LES system and a bidirectional LSTM are combined to establish cross-scale mapping, a dynamic residual correction field is fused to generate hectometer-level high-resolution micrometeorological data, and the problem of insufficient resolution of traditional numerical forecasting is solved. MIC and PA-DTW are used for jointly analyzing the characteristics of the power station, and dynamic clustering is achieved through a sliding time window and incremental spectral clustering. According to the method, a physical information graph network and causal expansion convolution are coupled to extract features, federal learning cross-power-station cooperative training is combined, the distributed photovoltaic output prediction precision and robustness are improved, and privacy security is considered.

The invention discloses a high and cold arid region slope soil stability safety risk evaluation system and method, and relates to the technical field of slope engineering. The state of each side slope under multi-dimensional indexes such as freeze-thaw cycle frequency, dry-wet alternation index, wind erosion strength, shear strength, water content, porosity and fracture density is quantified, the feature similarity between any two side slopes is calculated, and then a side slope feature similarity matrix is formed. Based on the matrix, an unsupervised clustering algorithm (such as spectral clustering, similarity propagation and the like) can be adopted to divide a plurality of side slopes into similar subsets with structural characteristics similar to environmental response, and category attribution of risks is achieved. On the basis, structural variation analysis, historical instability statistics and central risk difference extraction are performed on similar slope samples, so that the internal instability tendency of the slope can be identified, and a risk prediction model suitable for the type of slope can be constructed through feature training.

The invention relates to the technical field of cloud platform computer control, and discloses a computer control system and method based on a cloud platform. The system comprises a multi-source data acquisition module which acquires heterogeneous data in real time and divides the heterogeneous data into time sequence segments; the data fusion preprocessing module is used for removing noise and screening key features to generate a standardized data stream; the dynamic decision control module outputs a control instruction set based on a deep reinforcement learning algorithm; the abnormal mode recognition module is used for recognizing anomalies by adopting spectral clustering and an isolated forest model; the resource flexible scheduling module is used for optimizing resource scheduling by using an improved genetic algorithm; and a safety audit module is also arranged to guarantee instruction safety. According to the system and the method, heterogeneous data of the cloud platform can be effectively acquired and processed, accurate decision control is realized, abnormity is accurately identified, resources are reasonably scheduled, system security, stability and resource utilization rate are improved, and complex service requirements of the cloud platform are met.

The invention relates to the technical field of tunnel defect identification, in particular to a tunnel defect identification method based on a dielectric distribution diagram. According to the method, on the basis of AI rock-soil perspective radar field monitoring, gprMax simulation and laboratory electromagnetic data, a two-dimensional dielectric distribution diagram is generated through variational Bayesian inversion fusion. Through multi-scale spectral clustering and expert knowledge, a potential abnormal region is automatically identified, and then accurate classification and identification of a defect region are realized by adopting graph form constraint propagation and integrating a Transform-graph neural network model. And finally, projecting an identification result to an original image, and generating a visual defect labeling layer and a structured report. According to the invention, high-precision, automatic, visual and structured detection of tunnel structure defects is realized, the intelligent level and risk early warning capability of tunnel safety operation and maintenance are significantly improved, and the digitization and intelligent process of tunnel operation and maintenance management is promoted.

The invention discloses a joint optimization clustering distributed photovoltaic power prediction method and system, and relates to the related field of distributed energy management, and the method comprises the steps: carrying out the local calling of data, carrying out the time sequence alignment, and constructing a multi-dimensional feature association number table; extracting geographic position information and historical output data to perform similarity analysis, performing spectral clustering, and outputting a photovoltaic cluster; collecting meteorological time series data according to the power prediction scale, inputting the meteorological time series data into the cluster power prediction model, and performing cluster power prediction; grid-connected space-time joint optimization is carried out according to the cluster-level power prediction value, and an energy storage charging and discharging scheduling strategy is output; after single-station fitting conversion is carried out and a distributed power prediction value is output, output deviation identification is carried out, and a fault node is positioned; and dynamically updating the energy storage charging and discharging scheduling strategy. The technical problems that existing power prediction is not high in precision, cross-cluster power optimization scheduling is difficult to achieve, and the power grid operation stability is poor are solved, and the technical effect of improving prediction accuracy and the power grid operation stability is achieved.

The invention discloses a method for constructing an evaluation model for the influence of climate change on biodiversity, and particularly relates to the technical field of ecological modeling and climate response analysis. The method comprises the following steps: generating a high-resolution characteristic grid based on microclimate disturbance data and a remote sensing image, extracting ecological plaques and establishing a heterogeneity spatial index model, constructing a connectivity map and a coupling response path library, extracting a typical species response mode by using nonlinear dimension reduction and spectral clustering, and performing dynamic fitting in combination with biological survey data. And finally, generating a diversity attenuation trend prediction curve and a risk thermodynamic diagram, identifying a biodiversity collapse critical point and outputting an intervention priority, and the method can be widely applied to regional ecological early warning and protection planning.

The invention discloses a winter wheat LAI inversion method based on scenarized parameter calibration and ensemble learning. The method comprises the following steps: fusing a remote sensing image with auxiliary geographic information such as terrain and soil, and performing typical planting scene division on a research area by adopting a spectral clustering method; then scene optimization is carried out on highly sensitive parameters (such as LAI, Cab, Cm and ALA) in the radiation transfer model PROSAIL based on a Markov chain Monte Carlo (MCMC) algorithm, and parameter posterior distribution more consistent with actual observation is obtained; further utilizing optimization parameters to generate a high-quality and homogenized simulation sample set, and mapping the simulation sample set to a Sentinel-2 waveband to construct a training data set; and finally, performing training by adopting various machine learning models (such as LightGBM, XGBoost, RF and KNN), and constructing a fusion model through a stacking integration strategy to realize high-precision inversion of LAI. Experimental results show that the method has higher fitting precision and stronger generalization ability in a plurality of typical planting scenes.

The invention discloses a decision scheme generation method and device based on complex multi-modal management data, equipment and a medium, and relates to the technical field of data processing, and the method comprises the steps: receiving enterprise management data of texts, numerical values and images, extracting a unified feature vector, carrying out the clustering analysis through the dynamic updating of torque clustering and spectral clustering, and obtaining a clustering result; and a causal knowledge graph is constructed according to the result, and a decision scheme is generated by using a fourth-order inference engine, so that efficient processing and real-time response of an enterprise to dynamic multi-modal management data are realized, the cross-modal data analysis capability is enhanced, and the decision accuracy is improved.

The invention discloses a wireless communication resource allocation method based on dynamic spectrum sensing. The method comprises the following steps: acquiring equipment node data and network environment parameters, and performing preprocessing to generate feature data; based on the preprocessed feature data and the time window, generating a spatio-temporal feature map by adopting dynamic spatio-temporal feature coding so as to capture spatio-temporal association between the devices; a hyperedge set is constructed through a hyperedge dynamic generation mechanism by using equipment position information, and network topology changes are reflected; based on the hyperedge set, the transmitting power and the channel gain, a high-order interference tensor is constructed and decomposed, and the interference relation is accurately quantified; according to the interference tensor, the number of resource blocks and the service quality requirement, generating an optimal resource allocation strategy by adopting spectral clustering and an optimization algorithm; according to the method, through the space-time attention mechanism and the high-order interference tensor modeling, the dynamic adaptability and optimization performance of resource allocation can be improved, and an efficient solution is provided for a modern wireless communication network.

The invention relates to a multi-time scale scene analysis-based micro-grid day-ahead and intra-day optimal scheduling method, which belongs to the field of micro-grid scheduling, and is characterized in that a variational mode decomposition (VMD)-long short-term memory network (LSTM) multi-scale prediction framework is constructed, ultra-short-term precision is improved through variable mode decomposition and frequency division prediction, a Canopy-spectral clustering-K-means hybrid algorithm is designed, and the optimal scheduling of a micro-grid is realized. A typical scene is generated based on Latin hypercube sampling (LHS), the scene coverage capability is enhanced, a day-ahead and intra-day two-stage optimization model is finally constructed, a high-dimensional problem is rapidly solved by adopting a mixed integer programming algorithm, and theoretical support is provided for high-proportion renewable energy consumption and micro-grid refined scheduling.

The invention discloses an intelligent substation virtual loop online transmission verification method based on GOOSE / SV closed loop test, and relates to the technical field of intelligent substation operation and maintenance. Through precise clock synchronization and an improved cross-correlation algorithm, in combination with wavelet noise reduction and spectral clustering analysis, nanosecond synchronization quality evaluation of GOOSE / SV signals is realized, and the hidden transmission risk discovery time is shortened from regular maintenance to real-time monitoring; a hierarchical hidden Markov model is adopted to analyze equipment-level to system-level behavior modes, real-time probabilistic reasoning is realized in combination with a dynamic Bayesian network and particle filtering, a multi-dimensional evaluation system is constructed, the reliability evaluation capability under complex working conditions is remarkably improved, an optimization scheme is generated based on network path characteristic analysis and bottleneck identification, and the reliability of the system is improved. Multi-scene closed-loop verification is carried out by means of a digital twin technology, safety and reliability of parameter optimization are ensured, and full-process intelligent operation and maintenance of the virtual circuit of the intelligent substation from state perception to optimization verification are realized.

The invention relates to the technical field of air tightness detection, in particular to a diaphragm type energy accumulator air tightness detection method which is used for solving the problems that in the prior art, equipment operation characteristics cannot be accurately described, typical defect modes cannot be recognized in combination with spectral clustering, and a quantitative basis cannot be provided for equipment fault trend analysis and intelligent maintenance. The method comprises the following steps: constructing a low-dimensional state map to describe equipment operation characteristics, identifying typical defect modes in combination with spectral clustering, establishing a nonlinear correlation model to reveal a defect evolution relationship, optimizing classification model parameters by adopting an evolutionary algorithm, improving the identification accuracy, mining a most probable defect evolution path based on a transition probability matrix, and improving the identification efficiency. And a quantitative basis is provided for equipment fault trend analysis and intelligent maintenance.

The invention discloses a multi-view clustering method based on tensor feature extraction, and the method comprises the steps: inputting a multi-view data matrix, constructing a similarity matrix of each view through a K-NN algorithm and a Gaussian kernel function, and carrying out the spectral clustering to obtain a sample embedding matrix; performing singular value decomposition on an original data matrix of each view, taking first c left singular vectors to construct a feature embedding matrix, applying 2, 1 norm group sparse constraint on the feature embedding matrix, and connecting a sample embedding matrix through a bigraph to extract features; and normalizing the sample embedded matrix, and reconstructing a block diagonal matrix into a third-order tensor. And integrating the sample embedding matrix, the feature embedding matrix and global tensor learning to construct a target function, and optimizing through an alternating direction multiplier method until convergence. And finally, the normalized samples are embedded into the matrix to form block diagonals to form a consistent similarity graph, and a clustering result is obtained by using an N-Cut or k-means algorithm.

The invention discloses an active fluctuation collaborative stabilizing method and system for a high-proportion distributed new energy power grid, and relates to the technical field of power grid dispatching. According to the method, a physically consistent weather prediction model is established through multi-source meteorological data fusion, and a regional fluctuation propagation rule is accurately captured; identifying a high-risk fluctuation cluster based on dynamic time warping and spectral clustering, simulating a fluctuation propagation path by using a digital twin platform, and quantifying resource requirements; energy storage resource configuration is optimized by adopting mixed integer programming and a column generation algorithm, and multi-dimensional stability verification is carried out through a digital twin environment; a self-adaptive optimization mechanism based on reinforcement learning is established, continuous evolution of the system is realized, the technical bottlenecks of a traditional method in the aspects of fluctuation perception, resource allocation, system self-adaption and the like are solved, a collaborative stabilization mechanism with accurate prediction, intelligent recognition and decision optimization is formed, and a complete solution is provided for safe and stable operation of a high-proportion new energy power grid.

The invention discloses a historical building digital monitoring protection system based on multi-scale features. The system comprises a building data acquisition module, an original data optimization module, a feature extraction model construction module, a state recognition model construction module and a building digital monitoring module. The invention relates to the technical field of historical building digital monitoring, in particular to a historical building digital monitoring protection system based on multi-scale features, and the method comprises the steps: obtaining original data through building data; a data optimization method of data alignment, data cleaning, data standardization and data set segmentation is adopted; a deep learning model is adopted as a feature extraction model, and deep correlation features of building time-varying behaviors and structural damage are learned through feature decoupling driven by time sequence modeling and physical laws; an improved clustering model is adopted as a state recognition model, and robust recognition of the building state is achieved by introducing physical threshold constraint spectral clustering, a dynamic time kernel function and a multi-scale probability fusion mechanism.

The invention provides a product full life cycle management method based on digital twinning, a medium and a product full life cycle management system based on digital twinning, and belongs to the technical field of digital twinning. Based on a super-sparse pre-training model of a liquid neural network architecture, in combination with spectral clustering and an attention mechanism, precise prediction of product performance degradation is realized, and intelligent maintenance suggestion generation and dynamic optimization of operation parameters are realized through a Hilbert matrix state evaluation and decision adjustment mechanism. The balance between calculation efficiency and prediction precision is realized by using a multi-precision simulation analysis and incremental updating technology, a backtracking decision matrix is established to support the optimal decision of product decommissioning and recovery, and the technical problem that the real-time perception and dynamic prediction optimization management of the full-life-cycle multi-dimensional state of the product cannot be realized in the prior art is solved.

The invention belongs to the technical field of computer big data, and discloses a relation graph construction and layout method, device and system based on spectral clustering and a storage medium, a clustering center is initialized through a genetic algorithm, the clustering center serves as genetic information and is coded into a character string, the operation time can be shortened, and the classification precision can be improved; furthermore, a weighted Euclidean distance is constructed as a distance function of a K-means algorithm, mutual relation weighting between the features can be reflected, features of different weights are counted into the distance, the classification precision can be effectively improved, the loss is reduced, and the classification efficiency is improved. According to the method, an initial similarity matrix, obtained through a traditional similarity calculation method, between XML documents is corrected through an affinity propagation algorithm, the similarity between the hidden similar XML documents can be reflected, on the basis, the correct clustering number and the correct clustering result are obtained by applying a multi-path spectral clustering method NJW, the method is irrelevant to the sequence of the XML documents, and the method has the advantages of being high in practicability and easy to popularize. The method is suitable for clustering the retrieval results of the XML documents arranged in any sequence.

The invention designs an image classification method based on machine learning and granular ball spectral clustering, and the method comprises the steps: obtaining an image data set, and extracting a feature vector of each image in the image data set through a feature extraction model; taking the feature vectors of all the images in the image data set as a whole to create an initial granular ball, and calculating a variable sparseness measure VSM value of the initial granular ball; if the VSM value is lower than a preset threshold value, segmenting the granular ball into two smaller granular balls by using a 2-means clustering algorithm; if the VSM value is greater than or equal to the preset threshold value, subdivision is not performed any more; repeating the operations of VSM value calculation and segmentation on the newly generated pellets until the VSM values of all pellets meet the conditions; calculating the similarity between the generated pellets according to the center feature vectors of the pellets and the radiuses of the pellets; the generated pellets are divided into a plurality of clusters through spectral clustering according to the similarity between the pellets, and each cluster represents one category; and calculating the similarity between the feature vector of the to-be-detected image sample and the center feature vector of each particle ball, and taking the category corresponding to the cluster to which the most similar particle ball belongs as the classification result of the to-be-detected image sample. According to the invention, more reliable and efficient technical support can be provided for the field of image classification.

The invention provides a non-technical line loss state evaluation method based on multi-scale spatial-temporal feature fusion in a low-voltage distribution network environment, and relates to the technical field of electric power big data analysis and intelligent operation and maintenance of a distribution network. According to the invention, a fusion architecture based on a multi-scale time convolution network and a long short-term memory network is constructed; extracting multi-scale spatio-temporal characteristics of a user from instantaneous electricity utilization abrupt change to a periodic load rule through an MSTBlock unit; designing a cluster balance constraint mechanism to ensure that rare and key non-technical line loss abnormal early warning signals are not covered by mass normal power utilization data; according to the data scale, adaptively selecting a graph segmentation or spectral clustering integration strategy to output a clustering label, and mapping the clustering label into a user power consumption behavior evolution track; according to the method, the power utilization abnormal level can be identified from the original load signal with random fluctuation interference, and the troubleshooting priority is calculated in combination with the transformer area correlation analysis, so that the accuracy and interpretability of the non-technical line loss unsupervised evaluation decision of the power distribution network are remarkably improved.

The application relates to the technical field of signal processing, data representation and classification, and discloses a radar signal spectrum clustering sorting method based on SOM anchor point extraction and graph fusion, which comprises the following steps: configuring a radar signal sorting cluster number and a radar pulse parameter, obtaining a normalized radar pulse data set, taking each normalized radar pulse in the radar pulse data set as a node, and constructing a KNN graph of the radar pulse; extracting an anchor point of the normalized radar pulse data set based on SOM, calculating the similarity between the extracted SOM anchor point and all nodes, obtaining a similarity matrix, and constructing an anchor graph adjacency matrix based on the similarity matrix, namely an adaptive anchor graph; weightedly fusing the KNN graph and the adaptive anchor graph to obtain a fusion graph; and performing spectrum clustering sorting based on the radar signal sorting cluster number and the fusion graph to obtain a sorting result. The application breaks through the limitation that a classical radar sorting clustering algorithm can only utilize distance information and density information, and improves the sorting performance under complex radar pulse distribution conditions.

The invention discloses a multi-view self-representation and clustering joint optimization deep clustering method, which comprises the following steps of: firstly, acquiring data samples of a plurality of views, and selecting a most representative sample from each view as an anchor point by adopting a VDA algorithm; constructing and pre-training an auto-encoder network; on this basis, a self-representation module is introduced, shared self-representation and view unique self-representation are learned at the same time, and self-representation of each view is constructed through the similarity between an anchor point and a sample; constructing comprehensive self-representation based on sharing and unique self-representation, constructing a bipartite graph affinity matrix, and obtaining an initial clustering result of samples and anchor points by adopting a bipartite graph clustering algorithm; a clustering result is fed back to the self-representation module, and the representation is iteratively corrected; and finally, sharing and view unique self-representation are fused, comprehensive self-representation is obtained and used for spectral clustering, and a final clustering result is obtained. According to the method, the consistency and diversity characteristics between the views are effectively combined, and the problems that a traditional method is insufficient in structure modeling and low in optimization efficiency are solved.

The invention provides a bacterial colony heterogeneous data separation method and device, equipment and a storage medium. Relates to the technical field of microbial colony heterogeneous data separation. The method comprises the following steps: performing hierarchical clustering on microbe single-cell Raman spectrum data to identify outliers; based on the outliers, performing de-noising processing on the single-cell Raman spectrum data of the microorganisms to obtain de-noised data; performing spectral clustering based on the de-noised data to obtain a spectral clustering result; wherein the spectral clustering result is used for identifying or marking bacterial colony cells in different growth periods; and evaluating the spectral clustering result by using a contour coefficient and a CH index to determine an optimal clustering number, and performing spectral clustering on the de-noised data based on optimal clustering data to obtain a final clustering result. According to the method, outlier detection, pruning optimization, spectral clustering and quantitative evaluation are combined, so that the precision and stability of microbe heterogeneity analysis are remarkably improved.

A distribution transformer low-voltage prediction method based on spectral clustering and SDTCNet comprises the following steps: step 1, taking historical voltage data of a low-voltage side of a pole-mounted transformer in a power distribution area and meteorological data of the area, checking whether abnormal values and missing values exist in the data or not, and performing corresponding processing; 2, extracting a daily minimum voltage characteristic according to the historical voltage data of each distribution transformer, carrying out spectral clustering analysis based on the daily minimum voltage characteristic, dividing the distribution transformers into k types, and obtaining distribution transformers with different daily minimum voltage characteristics; 3, performing normalization processing on historical voltage data and meteorological characteristic data of different types of distribution transformers, constructing a characteristic data set, and dividing the characteristic data set into a training set, a verification set and a test set; 4, constructing an SDTCNet neural network model, and inputting data of the training set and the verification set into the model for training to obtain a trained model; and 5, inputting test set data into the trained SDTCNet network model for prediction to obtain prediction results of low voltages of different types of distribution transformers.

The invention provides a fine-grained sentiment analysis-oriented sentiment data automatic labeling method and system, and belongs to the field of artificial intelligence and sentiment analysis. According to the method, coarse-grained emotion pre-classification and confidence weighted fusion are carried out by extracting text, voice and visual features; fine-grained emotion recognition is realized by combining large language model reasoning and spectral clustering; optimizing a result by utilizing a conflict resolution mechanism, and generating a final label through multi-level voting integration; and finally, through multiple dimensions of multi-modal consistency, feature space outlier degree and conflict resolution decision effect, evaluating the credibility of the final label, and identifying a low-credibility sample. According to the method, progressive analysis from coarse granularity to fine granularity is realized, the problems of modal isomerism, information conflict and low labeling credibility are effectively solved, the manual labeling cost is reduced, and the accuracy and reliability of sentiment analysis are improved.

The invention discloses a virtual power plant multi-dimensional resource dynamic aggregation and optimization regulation and control method based on cloud edge collaboration. The method comprises the steps of collecting multi-dimensional data and encrypting the multi-dimensional data to generate a data stream with a timestamp; extracting a global feature vector through cloud edge-end federal learning; dividing an aggregation unit by using Nystrom accelerated spectral clustering based on features, and constructing a virtual power plant model capable of being refreshed along with feature drift; designing a three-time-scale control closed loop and combining a digital twinning synchronization state; and realizing regulation and control result chaining, automatic settlement and model reverse optimization based on the alliance chain. According to the virtual power plant multi-dimensional resource dynamic aggregation and optimization regulation and control method based on cloud edge collaboration, data privacy is guaranteed, resource aggregation dynamics and regulation and control precision are improved, cross-subject credible collaboration is achieved, and the method is suitable for efficient operation of a virtual power plant.

The application discloses a kind of cluster distributed photovoltaic power prediction method and system of joint optimization, it is related to the field of distributed energy management, including: data local call is carried out, timing alignment is carried out, constructs multidimensional feature correlation number table;Extract geographical location information and historical output data to carry out similarity analysis, carry out spectral clustering, output photovoltaic cluster;According to power prediction scale collection meteorological time series data input cluster power prediction model, carry out cluster power prediction;According to cluster level power prediction value carries out grid-connected space-time joint optimization, outputs energy storage charge-discharge scheduling strategy;After carrying out single station fitting conversion, output distributed power prediction value, carry out output deviation identification, locate fault node;Dynamic update energy storage charge-discharge scheduling strategy.Solve the technical problems that existing power prediction has not high precision, it is difficult to realize cross-cluster power optimization scheduling, and grid operation stability is not good, to improve the technical effects of prediction accuracy and grid operation stability.

The invention relates to a consistent semantic enhanced single-step parameter-free multi-view clustering method, which comprises the following steps of: 1) automatically learning anchor points, and avoiding the problem that the quality of the anchor points is reduced due to the randomness of an anchor point selection strategy; (2) spectral clustering is converted into decoupling decomposition of a representation matrix, and single-step data processing is completed without depending on a subsequent clustering method; and 3) the method does not contain any parameter, so that the problem that the clustering quality depends on the parameter is eliminated. According to the method provided by the invention, the accuracy which can be achieved on a Dermatology data set is greatly improved on the same data set compared with the accuracy which can be achieved on the same data set through traditional anchor point-based multi-view subspace clustering. According to the method provided by the invention, the problem that parameters such as multi-view clustering anchor point selection are difficult to adjust can be effectively solved, the problem of optimizing flow splitting is solved, and the method does not need to depend on a subsequent clustering method, so that the multi-view data clustering precision is improved.