33 results about "Pattern clustering" patented technology

The invention provides a log data-oriented system fault mining method, which belongs to the technical field of information, and comprises the following steps: collecting original log data of a system, and preprocessing original system logs, including analysis, key information extraction, vectorization and generation of a structured log event sequence; processing the log event sequence by adopting a two-stage model fusing anomaly detection and mode clustering so as to distinguish accidental anomaly and a real fault mode; and for each candidate fault mode cluster, analyzing a time sequence causal relationship between internal events, and positioning a root cause event according to the causal flow score of the node. The method has the advantages that dependence on artificial experience is reduced through full-process automatic modeling and analysis; according to the method, log analysis, feature extraction, anomaly detection and root cause positioning are all automatically completed by adopting a preset algorithm process, and the detection effect is maintained through self-adjustment of the model, so that the flexibility and sustainability of fault mining are improved.

The invention relates to the technical field of assisted reproduction health management, and discloses an assisted reproduction management method and system. The method comprises the steps that physiological data of multi-source monitoring equipment is received and subjected to standardization processing, and a multi-dimensional feature matrix is formed through time sequence alignment and feature fusion; identifying a dynamic evolution track of the key physiological mode by utilizing a mode clustering and sliding window technology; performing state deduction on the mode sequence based on the personalized model, and predicting a future physiological state; generating a regulation and control strategy sequence according to the difference between the prediction and the target, and executing the instruction; physiological response data is collected in real time and compared with an expected model, a follow-up strategy is dynamically adjusted, and closed-loop optimization is formed; and finally evaluating path validity and updating model parameters according to complete cycle data. According to the method, continuous accurate prediction and individualized self-adaptive regulation and control of the reproductive physiological state are realized, and the management accuracy and efficiency are improved.

The application provides a resource scheduling method and system based on campus user behavior analysis, and relates to the technical field of resource management. The method comprises the following steps: collecting target behavior information of a target campus user in a preset period, and analyzing a target behavior time sequence; acquiring an arbitrary time zone and matching arbitrary behavior data; forming a campus user set, and analyzing first behavior information of a first user; if a first mode index of the first behavior time sequence and the target behavior time sequence reaches a predetermined index limit value, a mode clustering cluster is formed; acquiring a number of user individuals in the mode clustering cluster, and combining the arbitrary behavior data to obtain arbitrary resource demand of the arbitrary time zone; and performing campus resource scheduling processing. The application solves the technical problem that, in the prior art, inaccurate prediction of user demand leads to unreasonable resource allocation, thereby affecting resource scheduling efficiency. By analyzing user behavior mode and determining resource demand, the efficiency of resource scheduling is improved.

The invention provides a multi-type railway track inspection data mining and state evaluation method based on knowledge embedding and cross attention. The method aims at solving the key technical problems of adaptive feature extraction, high-dimensional spatial pattern clustering, online real-time evaluation and the like of multi-source heterogeneous data. By constructing a technical system fusing domain knowledge embedding, multi-modal feature alignment and dynamic model optimization, full-process intelligentization from data preprocessing, feature extraction to state evaluation is realized, and an efficient and accurate solution is provided for intelligent operation and maintenance of rail transit.

The invention discloses an enterprise data intelligent storage optimization method and system. The method comprises the steps of enterprise data collection, data access mode preliminary screening, data access mode clustering, alternating refinement and enterprise data storage optimization. The invention belongs to the field of data processing, and particularly relates to an enterprise data intelligent storage optimization method and system.According to the scheme, a dispersion popularity membership degree is introduced, a weakening coefficient is added into a target function, historical access influences are attenuated, the weight of burst high-frequency access is reduced, and then misclassification caused by accidental access is reduced; business association driven local self-adaptive approaching clustering is performed on enterprise data, so that a high-benefit storage scheme can be formulated in a unified manner; a common precision objective function is constructed, and bidirectional matching of the mode and enterprise data association degree is ensured through an alternate precision framework; and the enterprise data intelligent storage optimization effect is improved.

This invention relates to an intelligent deformation discrimination method based on InSAR temporal deformation patterns and building semantic information, comprising: acquiring InSAR temporal monitoring data and building vector data of the target monitoring area; performing temporal deformation pattern clustering on discrete deformation scattering points to determine the deformation pattern category label of each discrete deformation scattering point; identifying discrete deformation scattering points falling within the polygonal area of ​​the building outline as the internal point set of the building, and identifying discrete deformation scattering points falling within the surrounding foundation ring zone as the surrounding foundation point set; extracting a building-level comprehensive feature vector, including basic statistical feature components, temporal pattern distribution features, and internal and external difference features, using a single building as a unit; inputting the building-level comprehensive feature vector into an unsupervised anomaly detection model to filter out anomaly building sets; inputting the features of the anomaly building sets into an anomaly attribution clustering model, and determining the anomaly cause category of each anomaly building based on the clustering results.

This invention discloses a method and system for battery aging assessment of battery swapping cabinets based on charging pattern clustering, belonging to the field of battery testing technology for battery swapping cabinets. The method includes: analyzing battery charging cycles according to the charging cycle of the swapping cabinet to construct multi-dimensional charging features; performing clustering learning based on these features to construct multiple charging pattern clusters; performing health analysis on the battery based on these clusters to obtain battery health status parameters; retrieving real-time charging data and matching the charging pattern clusters with the health status parameters to determine the target charging pattern cluster; and performing battery aging assessment based on the target charging pattern cluster to generate aging assessment results. This invention solves the technical problems of low assessment accuracy and poor real-time performance caused by the diversity of charging patterns in existing battery aging assessment methods. It achieves refined analysis and dynamic matching of battery health status based on charging pattern clustering, improving the accuracy, scenario adaptability, and real-time responsiveness of battery aging assessment results.

The invention relates to the technical field of cloud data processing, and discloses a cloud financial data analysis and early warning device which comprises a cross-domain event aggregation module, a dynamic graph construction module, a causal flow inference module, a pattern convergence analysis module and an interactive presentation module. The method comprises the following steps: aggregating business events from a plurality of business systems, and constructing a dynamic business process map; then, identifying an abnormal disturbance event in the map, and generating one or more abnormal causal chains connected to the quantitative financial influence based on the event tracing so as to generate tactical early warning; the device performs pattern clustering and root cause analysis on the plurality of abnormal causal chains to generate strategic insights. According to the method, the dynamic graph is constructed, and causal inference and mode convergence are executed, so that the problems that traditional financial analysis is lagged and a deep causal relationship between business and finance is difficult to reveal are solved, and prospective and penetrating analysis and early warning from a single business event to a systematic risk root cause are realized.

The invention relates to the field of multi-section lightning arrester state monitoring, in particular to a multi-section lightning arrester state synchronous monitoring system. According to the system, firstly, historical operation state vectors formed by all types of historical operation data of all node lightning arresters at each time point are clustered, and a plurality of normal mode clusters are obtained; according to the distance between a real-time operation state vector formed by all types of real-time operation data of each node lightning arrester at the current time point and the clustering center of the normal mode clustering cluster, the operation deviation degree of each node lightning arrester at the current time point is obtained; and according to the operation deviation degree of each node lightning arrester and the adjacent node lightning arrester at the current time point and the distribution of the historical operation state vectors in the normal mode cluster, carrying out whole group fault early warning on the multi-section lightning arrester group. The method can adapt to the dynamic change of cooperative operation of multiple sections of lightning arresters, so that the fault condition of the whole group can be accurately monitored.

The invention discloses a supply chain demand prediction method based on pattern clustering, and the method comprises the following steps: S1, collecting multi-source data, and constructing a multi-source original data set; s2, preprocessing the original data set to generate a standard data set; s3, based on improved HDBSCAN clustering analysis, identifying a potential demand trend and dividing an output demand mode group; s4, extracting a key feature vector of each group, inputting the key feature vector into a DeepAR model for autoregressive training and cross validation, and outputting a prediction model; s5, loading the trained model, and combining real-time sales and market data to generate a current key feature vector; and S6, inputting the current feature vector into a DeepAR model, predicting a future demand, and outputting the future demand to a supply chain scheduling system. According to the invention, automatic identification and high-precision prediction of the demand trend under the driving of multi-source data are realized, and the supply chain response, efficiency and inventory decision intelligent level are improved.

Systems and methods for simultaneously analyzing time-series and dominant frequency data from both a process domain and an electrical domain of a facility, such as an industrial plant, to detect instances of deviation from optimal, normal, or other predefined process conditions or electrical trips. Detecting when changes in frequencies occur in the time-series data creates a time-series of the changes in dominant frequencies. A data analysis server detects instances of deviation from the predefined process conditions or electrical trips by detecting one or more of correlations, patterns, clusters, and the like in the rates of change. The data analysis server employs one or more of statistical analyses, data mining, machine learning, deep neural networks, parallel coordinate analyses, etc. to identify the deviations and predict or detect onset of an undesired event such as a process perturbation or electrical trip.

The invention provides a CFRP reinforced ancient building wood structure flexural capacity calculation method based on finite element analysis, relates to the technical field of model simulation, and aims at providing a calculation framework for converting historical text big data into finite element analysis prior guide information. According to the framework, qualitative historical vulnerability evaluation and quantitative physical and mechanical simulation are seamlessly integrated through an innovative probability distribution weighting mechanism. Simulation sampling is guided through historical information, the number of finite element analysis times needed for achieving the same confidence degree is reduced, and the calculation cost and time are reduced. The simulation process focuses on a weak link which is more likely to exist in history, the recognition capability of a real and specific risk mode of the structure is effectively improved, and the misjudgment rate caused by disconnection of model hypothesis and historical reality is reduced. Finally, a reinforcement intervention priority list fusing failure mode clustering analysis and component importance evaluation is output, and direct, quantitative and data-driven decision support is provided for engineers.

The invention discloses an enhanced fingerprint-based DDoS attack recognition, pattern clustering and tracing method and system, and the method comprises the steps: extracting multi-dimensional features, including basic features, application layer behavior features, load entropy features and time sequence statistical features, from network traffic, and calculating derivative features, including time sequence features and rate features; carrying out normalization processing on the multi-dimensional features and the derivative features, and carrying out weighted fusion to form an enhanced multi-dimensional traffic fingerprint vector; performing DDoS attack traffic preliminary screening on the enhanced multi-dimensional traffic fingerprint vector through a dual-threshold preliminary screening logic, performing secondary verification through a trained Bi-LSTM, and performing classification; clustering is carried out on the identified DDoS attack traffic; and tracing the identified DDoS attack traffic according to the network topology information and the traffic log. According to the method and the system, accurate identification, classification and traceability of the DDoS attack are realized.

A method of evaluating an integrated circuit, a method of emulating an integrated circuit, and a system of evaluating an integrated circuit are provided. The method of evaluating an integrated circuit includes: obtaining a plurality of patterns representing a layout of the integrated circuit; clustering the plurality of patterns into a plurality of clusters based on geometric features of the plurality of patterns and simulation results obtained by simulating properties of the plurality of patterns; selecting a representative pattern for each of at least one cluster of the plurality of clusters; and validating the representative pattern for each of the at least one cluster and evaluating performance of the integrated circuit based thereon.

The invention discloses a runoff annual distribution mode clustering method based on threshold guidance and average connection, and aims to solve the problems that an existing clustering method needs to manually preset parameters, is sensitive to abnormal values and is not suitable for runoff percentage data, and the method comprises the following steps: converting daily runoff data into monthly / ten-day runoff percentage to construct a sample matrix; detecting and removing abnormal values by adopting a mahalanobis distance; a distance threshold is calculated adaptively, and automatic cluster allocation of samples is realized in combination with an average connection criterion without presetting the number of clusters; and outputting a clustering label, a cluster center and a clustering quality index, and supporting dimension reduction visualization. Through adaptive threshold and cluster number determination, targeted abnormal value processing and percentage data adaptive design, the method has the advantages of high automation degree, strong robustness and high precision, can be directly applied to hydraulic engineering scheduling, water resource configuration and ecological flow management and control, and provides reliable technical support for runoff time distribution characteristic analysis.

This invention discloses a liver fibrosis grading system based on serum index data. The invention relates to the field of liver fibrosis grading, and includes a standardized time-series acquisition module for liver fibrosis-related serum physiological signals, a gold-standard paired liver fibrosis grading pattern library construction and pattern clustering module, a subject physiological signal pattern matching and grading module, and a diagnostic result verification and pattern library dynamic optimization module. This liver fibrosis grading system based on serum index data utilizes a completely non-invasive serum detection method. Through standardized process design and intelligent module linkage, it achieves full automation from signal acquisition, preprocessing, feature extraction to judgment output, significantly shortening the diagnostic cycle and reducing reliance on the professional skills of clinical operators. It is suitable for large-scale population screening, dynamic follow-up, and promotion in primary healthcare institutions, possessing extremely high clinical application value and socio-economic benefits.

The invention provides an enterprise AI agent-oriented multi-dimensional behavior modeling and risk quantification method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: firstly collecting an agent behavior data flow which is generated by the operation of an enterprise AI agent and comprises a behavior event record and a time sequence association relation, inputting the agent behavior data flow into a multi-dimensional behavior decoupling network, and carrying out the multi-dimensional behavior modeling and risk quantification; the method comprises the following steps: extracting intention-oriented and environmental response type behavior characteristics, constructing a dynamic interaction mapping relation between the intention-oriented and environmental response type behavior characteristics, determining a driving weight and feedback correction parameters, carrying out iterative fusion according to the parameters to generate a behavior evolution path characteristic sequence, and finally carrying out behavior mode clustering on the behavior evolution path characteristic sequence. And matching with a preset risk behavior feature library to generate a behavior risk quantitative evaluation result. The enterprise AI agent behavior risk can be scientifically and accurately evaluated, and safe and stable operation of the enterprise AI agent is guaranteed.

The invention discloses an intelligent traffic regulation and control system and method based on traffic flow, and belongs to the technical field of intelligent traffic regulation and control. Comprising a real-time traffic flow sensing module, a traffic flow mode clustering analysis module, a multi-source context fusion prediction module, an intelligent signal lamp dynamic optimization module, a user feedback real-time integration module, a dynamic lane distribution module and an adaptive optimization module. Typical modes such as morning peak commuting flow and weekend purchase logistics are accurately identified, and a structured label is output; therefore, the system can implement differentiated strategies for different traffic scenes, the pattern recognition accuracy is improved, deep semantic support is provided for multi-source prediction, and the comprehensiveness and practicability of traffic state understanding are greatly enhanced.

The invention relates to the technical field of multi-point touch, in particular to a capacitive touch screen multi-point touch response optimization method based on an AI (artificial intelligence) algorithm, which comprises the following steps: acquiring touch point instantaneous capacitance signals, converting the touch point instantaneous capacitance signals into a two-dimensional coordinate sequence, calculating track displacement deviation and local curvature, generating basic track characteristic data, and calculating the basic track characteristic data; the method comprises the following steps: calculating a curvature change rate, identifying abnormal points, extracting an average curvature and a direction parameter, generating track segment characteristic data, inputting the data into a support vector machine to judge an action mode, carrying out clustering analysis to judge stability, generating an action classification stable identifier, and calculating a response weight to generate a multi-point touch response optimization signal. By dynamically analyzing the curvature and the change rate of a touch track, describing touch characteristics in real time and recognizing an action mode, stability is evaluated by utilizing curvature and direction clustering, touch weight is adaptively adjusted, multi-point consistent response is kept, high-precision feedback is provided in rapid sliding and complex interaction, the operation accuracy is improved, and the user experience is improved. The problems of response delay and trajectory drift are solved.

The invention discloses a pressure data clustering analysis method based on interactive text feature extraction, and particularly relates to the technical field of data clustering. The method comprises the following steps: acquiring multi-round interaction text data, extracting text features related to pressure, and extracting time sequence mode features representing a pressure evolution trend to obtain a time sequence mode feature sequence; performing feature coding on the time sequence mode feature sequence to generate a time sequence mode associated feature vector; clustering the time sequence mode feature sequence by adopting a serialized clustering method to generate a dynamic evolution mode cluster of a pressure state; based on the time sequence mode correlation feature vector and the pressure state dynamic evolution mode clustering cluster, performing comprehensive analysis of time sequence features and clustering tracks, generating pressure state dynamic evolution fusion features, and performing optimization adjustment on the clustering cluster to obtain a pressure data clustering result reflecting a pressure state evolution rule in an interaction process; effective recognition of the pressure state evolution rule of the user in the continuous interaction process is achieved.

This invention provides a method, apparatus, electronic device, medium, and product for constructing a target knowledge base, relating to the field of knowledge base construction technology. The method includes: obtaining a set of symbolic expressions; the set of symbolic expressions includes at least two symbolic expressions; converting the format of each symbolic expression in the set of symbolic expressions to determine a set of symbolic tree structures; the set of symbolic tree structures includes the symbolic tree structure corresponding to each symbolic expression, and each symbolic tree structure includes at least one symbolic subtree; calculating the subtree similarity between any two symbolic subtrees, and determining the symbolic expression pattern clustering result based on the subtree similarity; constructing a set of candidate decomposition patterns for symbolic expressions based on the symbolic expression pattern clustering result; and determining the target knowledge base based on the symbolic expression pattern clustering result and the set of candidate decomposition patterns for symbolic expressions. The technical solution of this invention improves the accuracy and efficiency of downstream symbolic regression by using a target knowledge base determined through hierarchical analysis.

This application discloses a method, apparatus, device, medium, and program for mining traffic behavior patterns, relating to the field of traffic data processing technology. The method constructs traffic scene samples by acquiring multi-dimensional traffic features of target objects, extracts and integrates target trajectories, maps, and interaction features to form a unified scene vector, and generates behavior reference labels by combining the real and predicted trajectories of future time windows after initial pattern clustering. It judges the consistency of cluster-level behavior and detects intra-cluster heterogeneity, and finally uses semantic auxiliary information to subclassify the initial pattern clusters to obtain more refined traffic behavior patterns. This solves the problems of intra-class mixing and insufficient semantic consistency in clustering results in the prior art, improves the purity and interpretability of pattern units, and is applicable to scenarios such as intelligent traffic analysis and autonomous driving assistance.

This invention belongs to the field of data management technology, specifically relating to a method and system for managing geographic information mapping data based on multi-source spatiotemporal features. The method includes: acquiring mapping data containing spatiotemporal coordinate attributes and generating time-granularity codes; selecting the optimal child node based on comprehensive cost when inserting data; when a node is overloaded, evaluating the spatiotemporal variability cost of candidate splitting strategies to select the optimal strategy; for newly generated leaf nodes, determining the reference object and differential accuracy level based on the spatiotemporal distribution of internal data objects, and storing the differential values ​​of the data relative to the reference object; extracting query feature vectors and matching historical query patterns to cluster centroids for data prefetching; and asynchronously updating the centroids and adjacency pointer transition probabilities in batches after the query. This invention improves the retrieval response performance of mapping data while effectively compressing data storage space by constructing an index structure and combining differential storage with predictive prefetching.

This invention relates to a traffic data prediction method based on a spatiotemporal graph network of sustainable learning, belonging to the field of spatiotemporal data mining technology. This invention models the generation process of spatiotemporal data based on representative traffic pattern clustering and matching theory, effectively improving the accuracy, robustness and generalization of traffic data prediction models. This invention captures the spatiotemporal characteristics of vehicle flow data while extracting representative patterns of vehicle flow data and forming a knowledge base, which better adapts to the dynamic changes in traffic patterns.

The invention discloses a WebAssessment type inference method and device based on an enhanced semantic map, and a storage medium. Firstly, deep memory access semantics are extracted from WebAssembly binary codes, then an enhanced semantic graph containing access mode clustering, dependency chain analysis and type propagation paths is constructed, finally, the semantic graph is converted into high-precision type definition and data structure layout, and accurate inference of a complex data structure is achieved. Specifically, deep semantic association can be found from a seemingly irrelevant memory access sequence, and a logic structure of an original program is reconstructed. According to the method, through the construction of the enhanced semantic map, a complete definition including a nested structure, a complex, an array and other complex data types can be accurately identified and reconstructed, and the reverse analysis precision of WebAssembly codes is remarkably improved.

The invention discloses an unmanned aerial vehicle multi-target tracking method and device based on motion mode clustering and model online self-adaption. The method comprises the steps that position information of a target is extracted based on a target detector, and a detection frame of the target is output; based on an attention module of a Transform, fully extracting motion characteristics and a long-range motion trend of the target in a time sequence frame; based on a motion mode classification module, multiple target motion modes are effectively classified according to cluster numbers; the model can be subjected to parameter fine adjustment according to a scene which changes at any time through self-adaption during motion constraint testing and self-adaption during multi-image disturbance testing. According to the invention, motion features and long-range motion trends are fully extracted, a motion mode classification module is designed to divide the motion of a target into a plurality of classes according to the number of clusters, and then parameter fine tuning is carried out through an adaptive fine tuning module during two tests; the multi-target tracking problem of motion blur, target motion mode diversity and scene real-time change in the unmanned aerial vehicle is effectively solved.

The invention provides an important personnel benefit risk intelligent early warning system based on clue association, and the system comprises a heterogeneous data integration module which is configured to be used for data desensitization and mode alignment; the dynamic behavior calculation module is configured to be used for track discretization and entropy evolution; the association graph generation module is configured to be used for quantum state coding and entanglement link analysis; the semantic conflict detection module is configured to be used for multi-modal embedding and contradiction point positioning; the time sequence abnormity deconstruction module is configured to be used for periodic base decomposition and residual mode clustering; the simulation verification module is configured to be used for scene reconstruction and pressure testing; the evaluation module is configured to be used for topological feature extraction and dynamic threshold adjustment; and the adaptive rule evolution module is configured to be used for regular gene coding and environmental adaptation. The adaptability is at least improved.

The invention relates to a cold storage load intelligent modeling method based on multi-operation-mode clustering analysis, and the method comprises the steps: collecting data of a physical layer, an equipment layer, a business layer and an external layer through LoRaWAN and 5G dual-mode sensors, after abnormal values are filtered and missing values are filled, extracting core features to construct a dynamic feature matrix, and carrying out the clustering analysis of the dynamic feature matrix; and identifying an abnormal mode by combining coarse and fine granularity clustering with an isolated forest, introducing a hidden Markov model learning mode transfer relationship and generating a probability matrix and a thermodynamic diagram, matching with a differentiation prediction model to improve the load prediction precision, and finally, based on a weighted optimization objective function containing energy consumption cost, temperature deviation and equipment loss, calculating the load prediction precision. A strategy engine generates an adaptive control strategy, local preprocessing, cloud global optimization and digital twinborn visualization are realized on the basis of an edge-cloud collaborative architecture, a closed loop of data acquisition-mode recognition-load prediction-optimization control is formed, the load modeling precision and dynamic regulation and control capability of the refrigeration house are effectively improved, and the modeling efficiency of the refrigeration house is improved. Energy conservation and cost reduction are facilitated; and intelligent management is realized.

This invention discloses a method and system for clustering analysis of test data patterns in smart terminals, belonging to the field of data analysis technology. The method includes: acquiring operational data of smart terminal components; identifying micro-deviation events based on the operational data and preset characteristic fluctuation patterns, wherein the preset characteristic fluctuation patterns reflect the fluctuation of system load under historical fault interference; constructing a directed acyclic graph (DAG) based on the micro-deviation events; extracting structural features from the DAG, wherein the structural features reflect the internal logical relationships of the DAG; and clustering the structural features using a preset clustering algorithm to identify fault patterns, wherein the fault patterns reflect the complete event chain of fault occurrence. This invention can combine micro-deviation events and structural features to identify fault patterns, thereby achieving data pattern clustering analysis and improving accuracy and reliability.

The application relates to the technical field of assisted reproductive health management, and discloses an assisted reproductive management method and system. The method comprises the following steps: receiving and standardizing physiological data of a plurality of monitoring devices, performing time sequence alignment and feature fusion to form a multi-dimensional feature matrix; utilizing pattern clustering and a sliding window technology to identify the dynamic evolution track of a key physiological pattern; performing state deduction on the pattern sequence based on an individualized model to predict a future physiological state; generating a regulation strategy sequence according to the difference between the prediction and a target, and executing an instruction; comparing real-time physiological response data with an expected model to dynamically adjust a subsequent strategy, thereby forming a closed-loop optimization; and finally evaluating the effectiveness of a path according to complete cycle data and updating model parameters. The application realizes continuous and accurate prediction of a reproductive physiological state and individualized adaptive regulation, and improves the accuracy and efficiency of management.