527 results about "Clustered data" patented technology

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.

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.

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 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 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.

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 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.

The invention discloses an aerial target velocity vector remote sensing method. The method comprises the following steps: transmitting an annular light beam with a linear divergence characteristic through a laser radar system; the receiving system is divided into two paths, one path obtains the distance of a target through a distance measuring detector, and the other path obtains the azimuth angle of the target through an area array sensor; establishing a three-dimensional coordinate system, resolving to obtain a spatial three-dimensional coordinate of the target relative to the laser radar system based on the divergence characteristic, the distance and the azimuth angle of the annular beam, and recording a timestamp; when the target passes through the annular light beam for multiple times, clustering data points based on spatial-temporal characteristics: data continuously collected at the same position belong to the same cluster; and averaging data belonging to the same cluster, and when two clusters of effective data exist and azimuth angles are different, calculating a velocity vector of the target relative to the laser radar system. By utilizing the method, the spatial position and the velocity vector of the target can be quickly and accurately obtained under a simple background.

The invention provides a brain function dynamic prediction device and equipment based on rTMS and a medium, and relates to the technical field of brain data processing.The device is used for obtaining multi-mode magnetic resonance imaging data and conducting preprocessing to generate a BOLD time sequence; carrying out Hilbert transform on the BOLD time sequence, and determining a phase coherence matrix of different brain regions at the same time point; performing weighted eigenvector dynamic analysis on the phase coherence matrix to generate a weighted eigenvector; performing clustering analysis on the weighted feature vectors to generate functional connection brain state clustering data; determining the similarity between the function connection brain state clustering data and the Yeo resting state network, and generating a function connection brain network state; state change data between functional connection brain network states is determined, and a state transition probability matrix is generated; and generating an rTMS prediction result according to the transition probability matrix and the brain index. The prediction efficiency can be improved.

The invention relates to the technical field of data synchronization, in particular to a distributed database system disaster recovery method and system based on cross-cluster data synchronization. The method comprises the steps that a target database cluster pulls source cluster metadata from a source database cluster based on a synchronization period, the target database cluster is any slave database cluster, and the source cluster metadata comprises total data information and total node information of the source database cluster; the target database cluster compares the target cluster metadata of the target database cluster with the source cluster metadata to determine difference data; the target database cluster determines a plurality of transactions based on the difference data and executes a data version synchronization task corresponding to each transaction, and the data version synchronization tasks are used for synchronizing data from the source database cluster. According to the invention, efficient and flexible cross-cluster data synchronization can be realized.

The invention provides a distributed cluster security backup method and system oriented to a cloud-side environment, and relates to the technical field of distributed storage systems, and the method comprises the steps: obtaining cloud-side cluster data, and adaptively selecting a backup mode based on an erasure code and a multi-copy hybrid redundancy strategy; establishing a workload-aware incremental replication mechanism, and optimizing fault recovery by using idle resources; the problem of lock competition is solved by adopting a multi-copy consistency guarantee mechanism based on two-stage submission, and lock-free reading is realized based on MVCC. According to the invention, the data backup flexibility, the fault recovery efficiency and the system consistency guarantee capability are improved.

Systems and methods are disclosed for identifying relationships between complex datasets and / or high-dimensional datasets for predictive modeling. The method includes clustering data associated with one or more entities in a first dataset based on distance data; clustering the data associated with the one or more entities in a second dataset based on longitudinal data; consolidating the first dataset and the second dataset into a third dataset based on weights assigned to one or more edges between one or more nodes in the first dataset and the second dataset; generating a diagnosis space indicating a condition of the one or more entities based on the third dataset; and determining, via input of the diagnosis space into a machine learning model, an optimization of a weighting scheme for assigning weights to one or more features within the longitudinal data.

The invention relates to the technical field of point cloud registration, in particular to a multi-view point cloud data registration method and system. The method comprises the following steps: acquiring single-view point cloud data to perform adjacent field configuration construction to obtain local structure chart data; performing resonance mode analysis on the local structure diagram data to obtain diagram embedded data; performing geometric potential energy mapping according to the graph embedded data to obtain morphological stability distribution graph data; performing spectral domain supporting point family extraction according to the morphological stability distribution diagram data to obtain a skeleton topology node set; constructing cross-view structure chart data according to the skeleton topology node set corresponding to the multiple pieces of single-view point cloud data; performing semantic perception fingerprint generation on the cross-vision-field structure chart data to obtain structure recognition cluster data; obtaining different-source structure coupling graph data according to the structure identification cluster data; and performing low-degree-of-freedom rigid body calculation according to the heterogeneous structure coupling graph data to obtain point cloud registration data. According to the invention, the stability and precision of multi-view point cloud registration are improved.

The invention relates to the technical field of semiconductor packaging detection and repair, in particular to an FCWB copper column bump in-situ optical detection and laser repair integrated method and system, comprising: constructing a repair parameter database which comprises a plurality of repair parameter nodes, and the repair parameter nodes comprise fault repair parameters and reference clustering data; fault diagnosis is carried out on the initial chip to obtain a to-be-repaired chip or a first fault chip, if the initial chip is the to-be-repaired chip, fault diagnosis is carried out on the to-be-repaired chip to obtain diagnosis detection data, and after target detection parameters are retrieved in a parameter database by utilizing the diagnosis detection data, the to-be-repaired chip is subjected to fault diagnosis; and if yes, repairing the to-be-repaired chip by using a fault repairing parameter corresponding to the target detection parameter and a laser repairing unit to obtain a first repaired chip, and performing secondary diagnosis on the first repaired chip. According to the invention, the accuracy and the intelligent degree of parameter setting required by laser repair can be improved.

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.

The invention discloses a multi-voltage-level collaborative operation optimization method and system considering new energy consumption. The method comprises the following steps: clustering data of a distributed power supply and a load demand in a new energy output operation scene; establishing a single-voltage level operation optimization model considering new energy consumption according to a clustering result, and analyzing an interactive influence between different voltage levels and a weight relationship between multi-dimensional economic indexes; and constructing a double-layer model of multi-voltage-level operation optimization generated based on target function constraint, and carrying out iterative solution on an optimization problem of the double-layer model to realize multi-voltage-level collaborative operation optimization. According to the method, the interactive influence between different voltage level operation modes is fully considered, and the weight of the multi-dimensional new energy economic consumption index is considered, so that the optimization of each voltage level operation mode on the premise of ensuring the safe operation of the power grid is effectively realized, and the safety and economy of new energy consumption are improved.