604 results about "Clustered data" patented technology

The invention relates to the field of energy storage system management and battery performance evaluation, in particular to an energy storage system electrical performance evaluation method and system.The method comprises the steps that battery cluster data and environment data are collected in real time, the charging and discharging process of a battery is accurately simulated in combination with simulation data and a digital twin model, and the future running state of the battery is predicted. The reinforcement learning algorithm adaptively optimizes the charging and discharging strategy according to simulation data and feedback results, and adjusts the charging power, the discharging rate and the temperature control strategy in real time through model prediction control. The method effectively evaluates the charging and discharging efficiency, the internal resistance change, the temperature fluctuation and the remaining service life of the battery, and provides decision support for operation optimization and maintenance of the system. According to the invention, the energy storage system can significantly improve the charging and discharging efficiency, prolong the service life of the battery, and ensure the stable and efficient operation of the system under different load and environmental conditions.

The invention relates to the technical field of coal mine informatization and data management, in particular to a data governance method and system for a coal mine and a storage medium, and provides a layered architecture from edge node preprocessing to central platform deep fusion in order to solve the problem of coal mine multi-source heterogeneous data governance. The edge nodes realize real-time anomaly judgment and output clustering data through an auto-encoder, a generative adversarial network and deep reinforcement learning; the central platform generates global collaborative model parameters and logic semantic data by using federated learning and a self-attention mechanism, then constructs a coal mine knowledge graph through unified semantic mapping and a graph convolutional network, and synchronously updates the digital twinborn platform with real-time verification data to generate real-time digital twinborn data. And finally, outputting an early warning decision under the action of a long short-term memory network and a self-attention mechanism, and performing traceability management in combination with a block chain technology, thereby realizing efficient and safe coal mine data management.

The invention discloses a multi-modal offshore wind power ultra-short-term prediction method in the field of offshore wind power plant cluster power prediction, and aims to solve the technical problems of spatial-temporal feature splitting and insufficient dynamic dependency relationship modeling. The method comprises the steps of performing anomaly detection and restoration on fan data, and generating a corrected wind power cluster data set; extracting a mean value, a standard deviation and a latest value of core operation data of each fan through a dynamic time window, and constructing a multi-dimensional node feature; a static geographic similarity matrix is generated based on geographic coordinates, a basic wake effect matrix is generated in combination with real-time wind direction data, correction is carried out through the maximum mutual information quantization time-delay effect, and then a dynamic adjacency matrix is obtained through self-adaptive fusion; and integrating the multi-dimensional node features and the dynamic adjacency matrix into a space-time diagram sequence data architecture, inputting the space-time diagram sequence data architecture into a multi-scale wake flow perception diagram space-time prediction model, and outputting a multi-fan power prediction value. According to the invention, high-precision multi-fan power prediction can be realized.

The invention relates to the technical field of electrical variable measurement, in particular to a power equipment system real-time dynamic monitoring method and system based on the Internet of Things. The method comprises the following steps: acquiring different data types; forming vibration sequences for the vibration signals, and obtaining fitting curve functions of all the vibration sequences; determining a trend sequence based on the variation coefficient of the vibration sequence; combining the trend sequence and the vibration sequence with a fitting curve function to determine a vibration trend coefficient; clustering the period based on the vibration trend coefficient, respectively obtaining a local change trend and an overall change trend based on cluster data and all data, and obtaining an equipment disturbance coefficient based on the local change trend and the overall change trend; and determining a prediction function based on the equipment disturbance coefficient, and obtaining a corrected vibration signal in combination with the vibration trend coefficient difference of adjacent periods, thereby completing dynamic monitoring. According to the invention, the monitoring accuracy of the power equipment fault is enhanced.

The invention relates to the technical field of data processing and analysis, and discloses a cost consultation data arrangement and analysis system based on a cloud platform, and the system comprises a data collection module which is used for obtaining and standardizing original cost data; the data clustering module is used for clustering data according to specific dimensions and extracting key feature vectors; the strategy generation module is used for constructing candidate analysis strategies; the strategy optimization module is used for screening an optimization strategy; and the execution engine module processes the data according to the optimization strategy to generate an analysis result. In addition, a data verification module is arranged to verify results, a dynamic adjustment module is arranged to monitor data changes, and a knowledge graph module is arranged to construct an entity relation graph. The system can solve the problems of data source heterogeneity, single analysis strategy and the like in cost data processing, realizes efficient data processing and accurate analysis, ensures that the result is accurate and reliable, and improves the data processing and analysis level of the cost consultation industry.

A method for using machine learning models to automatically cluster data into meaningful populations and groups and to identify hidden patterns and structures in such data is provided. The method includes: receiving first information that relates to a group of entities; analyzing the first information with respect to a predetermined set of parameters and a predetermined set of data types; selecting, based on a result of the analysis, a first machine learning (ML) model from among a predetermined set of ML models; and using the selected first ML model to generate a report that includes second information that relates to at least one data cluster identified by the first ML model from the first information.

Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support automated source code review using sentiment analysis with magnitude of entities. Known compliant and non-compliant source code may be used to generate dictionaries for evaluating lines of code using AI and ML techniques, such as by clustering data entities (lines of software code) and performing sentiment analysis on the data entities (lines of software code) which accounts for a magnitude of the data entities in the software code. The dictionaries enable automated review and correction of non-compliant code, such as vulnerable or insecure code, during the coding process. For example, sentiment analysis may be performed using the dictionaries on in-development code to determine a polarity and magnitude score for each line of code. The scores for each line can be compared to one or more conditions to determine a remediation action for individual lines of code.

The invention relates to the technical field of digital data processing, in particular to a circuit breaker fault detection method based on electrical parameter analysis, and the method comprises the steps: obtaining monitoring data, and constructing a parameter matrix; screening suspicious elements based on prediction deviation of elements in the parameter matrix and a mutation point detection result, and determining a first characteristic value according to a starting moment determined by the suspicious elements and a rule degree of data fluctuation in a fluctuation duration of the suspicious elements, and determining a fault interval by combining a consistency analysis result of irregular data fluctuation in parameter matrixes of different types of monitoring data, screening target data from residual terms, obtaining a plurality of clustering trees based on the target data by using an agglomerated hierarchical clustering algorithm, and determining a fault detection result of the circuit breaker based on differences among different clustering trees. According to the method, the residual component is used for replacing the initial data, so that the clustering effect of the clustering number on the monitoring data with different fault sensitivity degrees is improved while the clustering data size is reduced.

Hierarchical modelling and advanced feature engineering discover abnormalities in time series data with irregular trends. Data is collected in real time to ensure temporal integrity in the invention. Extraction filters and isolates useful data. Data cleansing removes noise and extraneous data after preliminary analysis identifies patterns and abnormalities. Feature engineering organizes cleansed data for machine learning algorithms. Primary storage stores this data for fast retrieval and extensive trend analysis. Holidays and weekends provide unique patterns in trend analysis. These trends are used to cluster data and create hierarchical predictive models, starting with a first-order model for general trends and increasing in order to refine residuals. Serializing these models improves storage and retrieval. Trend clusters are created from new data points, and algorithms detect pattern deviations. Statistical tests and machine learning classifiers identify anomalies and create alerts and remedial measures. The system monitors and analyzes incoming data to detect anomalies.

The invention provides a ship traffic flow prediction method and device, electronic equipment and a storage medium, and belongs to the field of marine traffic management. The method comprises the following steps: acquiring ship trajectory data of a to-be-detected port, and performing bidirectional trajectory prediction restoration on the ship trajectory data to obtain restored trajectory data; performing direction identification trajectory clustering on the repaired trajectory data to obtain trajectory clustering data, and performing density adaptive parameter node clustering on the trajectory clustering data to obtain node clustering data; and performing spatial-temporal feature extraction and prediction output on the node clustering data to obtain a ship traffic flow prediction result. According to the method, the integrity of ship trajectory data is ensured through bidirectional trajectory prediction and restoration, trajectory direction information is mined while clustering is performed through direction recognition trajectory clustering, and node comprehensive information is mined while clustering is performed through density adaptive parameter node clustering; dynamic transformation information and a deep relationship can be reserved, data can be effectively integrated, and the accuracy of ship traffic flow prediction is improved.

The invention relates to the technical field of driving control, in particular to a new energy automobile safe driving control method and system. The method comprises the following steps that a historical urgent danger avoiding data set and a corresponding user misoperation braking state are obtained through a new energy automobile control center, sample undersampling processing is conducted firstly, and an urgent danger avoiding balance sample is obtained; thirdly, analyzing user misoperation braking abnormity by using a balance sample, and performing disordered vector intensity calculation under a time dimension based on braking abnormity data to generate disordered vector segmentation clustering data; then, braking time sequence intervention correction sensing is carried out according to the clustering data, optimized braking intervention correction data are generated, finally, the optimized data are transmitted to a new energy automobile control terminal, and safe driving control is executed. According to the invention, the driving control technology is optimized, so that the driving control technology is more perfect.

The invention discloses a blockchain technology-based switch security management and data integrity method, which comprises the following steps of S1, deploying blockchain nodes in a network, and accessing each switch as a node to the blockchain network; s2, using a chain structure to store configuration data and operation log information of the switch, and generating a digital signature; s3, configuring an adaptive consensus mechanism; s4, training the threat prediction model by using a support vector machine algorithm, and detecting and identifying known abnormal behaviors and potential threats; s5, performing clustering analysis on the data in the switch network in combination with a density-based clustering algorithm and an improved isolated forest algorithm, identifying unknown abnormal behaviors and potential security threats, and triggering an early warning mechanism; s6, deploying a smart contract for switch authority management and configuration recovery operation; and S7, providing a user visual interface. According to the invention, a self-adaptive consensus mechanism and an intelligent contract are utilized, so that switch security management and data integrity are realized.

The invention relates to the technical field of urban fire safety rescue, in particular to a low-altitude fire fighting system and method based on urban CIM and Beidou positioning. According to the system and the method, full space-time accurate positioning is realized through the Beidou differential base station and the UWB indoor positioning equipment; integrating the urban information model system and unmanned aerial vehicle cluster data, and constructing a multi-source heterogeneous data set; a coordinate system is unified based on a space-time reference calibration module, and fire behavior prediction, path planning and resource scheduling are realized through AI computing nodes; the unmanned aerial vehicle task distribution module is used for realizing multi-vehicle cooperative rescue; and iteratively optimizing system parameters through a closed-loop adaptive optimization module. The method is suitable for fire emergency rescue in an urban complex building environment, and the rescue response speed, the decision-making precision and the cooperative combat capability are remarkably improved.

The invention discloses a 3D printing process defect monitoring method and system based on a multi-modal large model. The method comprises the steps that S1, before related operation is carried out, corresponding preposition work needs to be completed, wherein the preposition work comprises knowledge graph creation, retrieval enhancement generation, reasoning and action framework and fine adjustment of the large model; s2, collecting and preprocessing industrial camera cluster data; s3, according to the preprocessed image, segmenting the image, constructing a region adjacency graph, finding an optimal region combination by applying a random algorithm, and combining some regions to obtain image representation; s4, calling the multi-mode large model subjected to pre-training and specific field fine adjustment to realize real-time defect monitoring in the 3D printing process; s5, a detection report is given according to a defect detection result, user interaction intelligent consultation is opened, and the model is updated and adjusted according to printing data; according to the method, the problems of standard, efficiency, trust and supervision of defect monitoring in the 3D printing process can be effectively solved.

The invention discloses a low-altitude economy-oriented decentralized unmanned aerial vehicle (UAV) task allocation method and system, and the method comprises the steps: firstly registering a UAV by a smart contract, starting an anti-Sybil attack mechanism, uploading task information and paying guarantee deposit by a publisher, verifying a task by the smart contract, and carrying out the delay decision. Secondly, the UAV obfuscates own geographic position coordinates and encrypts the geographic position coordinates, and the task publisher clusters the encrypted coordinates to obtain encrypted clustering data; finally, the intelligent contract calculates the probability that each UAV arrives at the task location according to the encrypted clustering data, the intelligent contract evaluates the matching degree of the UAVs, and a candidate UAV set is screened out; and according to a delay decision result, the smart contract performs task allocation on the candidate UAV set. According to the method, the motion states of the tasks and the UAV are considered, the reasonability and the performability of task allocation are ensured, and the accuracy and the real-time performance of task matching are effectively enhanced.

The invention relates to the technical field of bioinformatics, in particular to a mining method and system for synthetic biology functional elements and a storage medium. The method comprises the following steps: acquiring functional demand data of a target metabolic pathway; performing evolutionary distance analysis and function prediction on homologous genes of a plurality of species according to the function demand data, and performing protein structural domain conservative classification on preset genome sequence data to obtain candidate element cluster data with function relevance; obtaining expression system data of the host cells; and according to the candidate element cluster data and the expression system data, scoring each functional element based on codon optimization degree, protein folding stability and metabolic load prediction to obtain hierarchical element suitability data. According to the method, factors such as sequence characteristics, expression efficiency and regulation and control network interaction are considered at the same time, multi-level accurate screening is achieved, and therefore the low efficiency and limitation of a traditional screening mode are overcome.

The invention provides a fast recovery method for hadoop cluster data, which relates to the technical field of erasure codes, and comprises the following steps of: calculating an importance score of a data block to be recovered, determining a target copy number based on a nonlinear copy number adjustment function, calculating a load weight in combination with network topology information, and selecting candidate nodes; and predicting a node fault probability by using a long short-term memory network, calculating a migration priority based on the importance score and the fault probability, and selecting an optimal target node to execute data recovery. According to the invention, the intelligence and differentiation of the data recovery process are realized, and the data recovery efficiency and reliability are improved.

The invention relates to the technical field of large language models, in particular to a technology trend prediction method and system based on multi-layer fine tuning of a large language model. Performing clustering processing on the data after the time sequence preprocessing based on a clustering algorithm to divide the result data into n categories, and taking three technical keywords with highest frequency in each category as technical tags to obtain clustering data; performing knowledge graph processing on the data subjected to the time sequence preprocessing to obtain an association relationship among the technical points, and obtaining text description content according to the association relationship among the technical points to obtain knowledge graph data; and according to the data after the time sequence preprocessing, the clustering data and the knowledge graph data, carrying out fine tuning on the pre-trained large language model to obtain a technology prediction large model, and carrying out technology trend prediction through the technology prediction large model. The prediction accuracy of the large language model can be effectively improved.

The invention discloses an Internet of Things cluster data analysis method based on big data and artificial intelligence, and relates to the technical field of data analysis, and the method comprises the steps: carrying out the initialization and parameter synchronization processing of an edge side feature coding sub-model and a cloud space-time depth model through a space-time diagram sequence, obtaining a collaborative modeling parameter set, and obtaining a collaborative modeling parameter set; carrying out self-supervised reconstruction and prediction task training on the cloud space-time depth model based on the collaborative modeling parameter set, obtaining a depth representation model, learning abnormal mode parameters by using the depth representation model, constructing an abnormal scoring function, obtaining an abnormal detection model, and after receiving real-time multi-source heterogeneous data at an edge node, carrying out real-time reconstruction and prediction task training on the cloud space-time depth model; performing state prediction and anomaly score calculation by using the anomaly detection model to obtain a real-time anomaly score result; through the intelligent data analysis method, the accuracy, the real-time performance and the interpretability of Internet of Things cluster data analysis are improved.

The invention discloses a mine water inflow prediction method and system based on adaptive graph convolution, and relates to the field of coal mine safety. Comprising the steps of making a clustering data set based on historical data; training a water burst scene classifier by using the clustering data set; using the trained water burst scene classifier to identify historical data to obtain different water burst types corresponding to mine water burst; based on different water inflow types and the clustering data set, respectively training to obtain a water inflow prediction model; identifying the new water inflow association index data by using the trained water inflow scene classifier to obtain a target water inflow type; and predicting the new water inflow data by using the target water inflow prediction model to obtain the water inflow. According to the method, a unique adaptive graph convolution formula and a hydrogeological attention mechanism are introduced, the prediction capability and scene adaptability of the prediction model are improved, working face water inflow prediction under different hydrogeological conditions is considered, and important technical support is expected to be brought to coal mine water disaster prevention and control.

The invention discloses a data deduplication method and system based on incremental calculation and feature clustering and electronic equipment, and the method comprises the steps: receiving multi-source heterogeneous text, image, audio and video data streams in real time, adding a timestamp to each data unit, and generating an input data set with a time attribute; performing multi-modal feature extraction on the input data set, and dynamically adjusting feature weights of the extracted multi-modal features through a sliding window model and a time decay factor to obtain an increment feature vector set with the weights; executing two-stage clustering based on the feature vector set to generate a plurality of fine-grained data clusters; and comparing the intra-cluster data of each fine-grained data cluster in pairs by adopting a composite similarity model to determine intra-cluster repeated data, and performing data deduplication on the input data set according to the intra-cluster repeated data to obtain a deduplicated input data set. According to the method, the processing efficiency of the real-time data is improved, and the storage and calculation cost is remarkably reduced.

The invention provides a model training method and device, a data cleaning method and electronic equipment, and the method comprises the steps: obtaining a first data set; performing multi-scale clustering processing on the first data set to obtain a clustered data set; performing anomaly detection on the first data set to obtain an anomaly feature set; performing multi-feature extraction on the first data set according to a plurality of preset feature types to obtain a target feature set corresponding to each feature type; performing feature fusion on the clustering data set, the abnormal feature set and each target feature set to obtain an enhanced feature set; and training the integrated model by using the enhanced feature set to obtain a trained integrated model. By applying the model training method provided by the invention, the integrated module with higher precision can be obtained, and when the integrated model is applied to large-scale data cleaning, the data cleaning efficiency can be improved.

Because Kubernetes clusters can be ephemeral, backing up in-cluster data to storage outside the cluster is important. Prior art solutions used the cluster's API server, which facilitates communications with the cluster control plane, to transfer backup data through the API server. However, the API server as a data transfer node has resiliency weaknesses and can slow down backup job performance. The present solution provides a more streamlined and scalable approach, which circumvents the API server and additionally includes more robust error checking, log capture, and realtime job monitoring to provide improved data protection resilience. The disclosed approach employs a “sponsor” data agent outside the cluster and temporarily deploys a specialized backup resource within the cluster during a backup job, such as a lightweight Kubernetes File Client and / or an enhanced File System Data Agent, both of which present substantial performance and resiliency advantages over the API server.

The invention provides a carrier-based aircraft intelligent re-flight decision-making method based on a Bagging algorithm, and relates to the technical field of carrier-based aircraft re-flight decision-making. According to the method, a carrier-based aircraft is simulated in different flight states to obtain carrier-based aircraft re-flight track clusters, a re-flight boundary criterion is used for carrying out flag bit definition on the re-flight tracks, and the carrier-based aircraft re-flight track clusters are obtained; therefore, a re-flight decision-making system is converted into a dichotomy problem of re-flight track cluster data, and in a carrier landing process, real-time flight state quantity of a shipboard aircraft is input into a re-flight decision-making model, and the model calculates whether current carrier landing is dangerous or not in real time. In the process, the shipboard aircraft can predict whether re-flight operation needs to be executed or not in real time and whether re-flight has risks or not. Therefore, the safety of carrier landing and re-flying of the shipboard aircraft is ensured. Finally, the method can effectively improve the safety of the shipboard aircraft during carrier landing and re-flight, ensures the carrier landing recovery efficiency, and lays a technical foundation for the carrier landing and re-flight decision of the shipboard aircraft.

The invention discloses a parameter identification method for an electrochemical-thermal-micro short circuit coupling model. The method comprises the following steps: constructing the electrochemical-thermal-micro short circuit coupling model; collecting multi-working-condition data of battery operation, and clustering the multi-working-condition data; performing feature extraction on the clustered data to obtain a feature data set; performing sensitivity analysis on parameters in the electrochemical-thermal-micro short circuit coupling model, and estimating the influence degree of the parameters on the characteristics; and parameters with high influence degrees are preferentially considered, probability influence evaluation is carried out on the parameters, and a parameter identification result is obtained. According to the method, on the premise of accurately judging the potential influence of the parameters, the accurate identification of the operation characteristic parameters of the battery system when the micro short circuit occurs can be realized.

An AI-based system and method for generating news articles in a blockchain ecosystem, is disclosed. The AI-based method includes obtaining data associated with the news articles from data sources on news publishing platforms; clustering the data associated with the news articles based on similarity of the data associated with the news articles, using an AI model; categorizing the clustered data associated with the news articles, into pre-defined categories, using the AI model; summarizing the categorized data associated with the news articles by rephrasing and generating concise and coherent summaries of the categorized data associated with the news articles, using the AI model; generating images corresponding to the summarized data associated with the news articles, using a stable diffusion model; and providing the summarized data associated with the news articles along with corresponding images, as an output, to users through user interfaces associated with communication devices of the users.

A method and apparatus for assessing the hazard of rainfall-induced landslide clusters, and a computer device. The method comprises: acquiring historical rainfall-induced landslide cluster data from past intense rainfall events to construct landslide sample data; performing analysis on the basis of landslide attribute parameters and rainfall-induced landslide cluster data distribution information of the landslide sample data to obtain landslide development characteristics; on the basis of landslide geographical features and geometric features, using the landslide sample data and a plurality of candidate landslide-influencing indexes to analyze underlying patterns of landslide occurrence, so as to establish a landslide hazard evaluation index system; and performing correlation analysis on the plurality of candidate landslide-influencing indexes to determine a target landslide-influencing index, and combining the target landslide-influencing index with the landslide hazard evaluation index system to obtain a landslide hazard assessment model constructed on the basis of an automated machine learning framework. The efficiency of rainfall-induced landslide cluster hazard assessment can be improved and the accuracy of rainfall-induced landslide cluster hazard assessment can be effectively enhanced.

The invention relates to the technical field of friction pair testing, in particular to a method and system for testing the lubricating performance of an engine friction pair. The method comprises the following steps: simulating reciprocating motion of a friction pair of an engine in a lubricating state by using a friction part of a friction tester, and acquiring data points of the friction part at different moments; according to the moments corresponding to the data points, the friction force data and the temperature data, the multiple data points of the friction part are clustered to obtain multiple clusters, and noise degree values of the clustered data points are determined; determining an abnormal degree value of the target data point according to the noise degree value of the data point in the data space; and taking the reciprocal of the average value of the abnormal degree values of all the correction data points as the lubricating performance parameter value of the friction pair in the lubricating state. According to the technical scheme, a more accurate test result of the lubricating performance of the friction pair can be obtained.

The invention relates to the field of three-dimensional data processing, and discloses a multi-modal point cloud quality evaluation method based on adaptive block projection, comprising the following steps: preprocessing point cloud data, dividing the point cloud data into a plurality of local clusters, each local cluster comprising a central point and neighborhood points thereof; generating a two-dimensional cluster projection drawing corresponding to the spatial position of each local cluster based on the local cluster; extracting features of the two-dimensional modal projection drawing and the three-dimensional local cluster data, and respectively obtaining two-dimensional modal features and three-dimensional modal features; decoupling the two-dimensional modal features and the three-dimensional modal features to generate modal-independent quality features and modal-related features; the modal-independent quality features are mapped to a shared unified feature representation space; fusing the features of the two-dimensional mode and the three-dimensional mode to generate a joint feature; and based on the joint features, predicting the quality score of the point cloud through a regression network. According to the invention, the accuracy, robustness and calculation efficiency of point cloud quality evaluation are improved.

The invention relates to the technical field of load prediction, and provides a power load prediction method and system based on time sequence decomposition and an attention mechanism, and the method comprises the steps: carrying out the adaptive time sequence decomposition of an obtained original load sequence, calculating the sample entropy of each decomposed component, and carrying out the clustering; constructing a group of encoder and decoder networks for each piece of clustered data, performing parallel encoding to extract features, performing serial decoding reconstruction on the features from low frequency to high frequency, and outputting prediction data from low frequency to high frequency step by step; the weight is initialized based on the sample entropy, the trained weight is obtained through optimization in the encoder and decoder network training process, and the predicted value of the power load is obtained through weighted fusion. According to the method, adaptive time sequence decomposition, a weight mechanism guided by sample entropy and an attention-enhanced encoder-decoder structure are introduced, so that multi-component collaborative modeling and cross-scale dynamic prediction are realized, and the prediction accuracy and stability in complex load data and small sample scenes are effectively improved.