7 results about "Pattern sequence" patented technology

The application discloses a kind of both can guarantee calculation real-time, and can realize the high-fidelity restoration of missing link working condition flow power consumption settlement method, device, equipment and medium, wherein method includes: receiving the electric meter data message sent by edge computing node;According to the time zone of electric meter, the timestamp of energy consumption cumulative indication value is uniformly converted into logical local time, and maximum tolerance disorder time is determined based on network delay fluctuation in data message upload process to construct flow water level line, and flow water level line is used to drive the flow settlement of statistical window;Based on instantaneous power feature vector, load fingerprint library of electric meter is generated using clustering algorithm, and the load fingerprint library includes various load fingerprint patterns and their corresponding reference values;After detecting that electric meter is missing and recovered, extract the instantaneous power feature vector before and after missing, deduce the load fingerprint pattern sequence during missing period in combination with load fingerprint library, and restore the power increment generated during missing period to the corresponding each statistical window in missing period.

A method for constructing a large prediction model of coal burst based on multimodal data is provided. The method includes: constructing a multimodal data set by collecting data from different modalities; preprocessing the multimodal data set to construct precursor pattern sequences; and converting, according to features of different mining areas, each precursor pattern sequence into a corresponding grade form to assign a corresponding risk grade label of coal burst; processing graded precursor pattern sequences by using Transformer to predict an occurrence probability of risk grade of coal burst; evaluating, by using a comprehensive index method, risk degrees of mining information data and geological structure data, and evaluating an overall risk grade of coal burst comprehensively by combining the prediction result output by the coal burst prediction module. The method can improve applicability and prediction accuracy of the model under different mining conditions, and achieve accurate prediction of coal burst risks.

This application relates to the field of printing equipment condition monitoring technology, and discloses a method and system for monitoring and fault early warning of the rotational state of printing rollers. By collecting vibration and temperature data of the printing rollers, a time series decomposition algorithm is used to separate short-term fluctuations and long-term trends to obtain a trend sequence; the slope change of the trend sequence is calculated, and deterioration signs are captured by combining signal strength and duration, generating a slope growth pattern sequence; positive growth portions are identified and potential deterioration tendencies exceeding duration thresholds are marked, forming a labeling sequence; the deviation between cluster centers and normal ranges is calculated through cluster analysis to obtain a deviation degree sequence; the deviation values ​​are compared with thresholds to generate a candidate fault signal set; finally, a fault progression feature model is matched to determine and confirm the fault signal set. This application can effectively capture early abnormal trends of the rollers, improve the accuracy of fault judgment, and is suitable for online condition monitoring and intelligent early warning of printing equipment, realizing fault prediction and health management of the equipment.

The application belongs to the technical field of land change degree analysis methods, and particularly relates to a land change degree analysis method and system based on space-time data, which comprises the following steps: acquiring multi-temporal remote sensing data of a space monitoring unit and extracting time sequence characteristics; constructing a preliminary time similarity pair set based on the similarity between time sequence characteristic analysis units; constructing a space connection relationship graph by using a graph theory method, and identifying a candidate change group; performing spatial clustering analysis on the candidate group, and screening a group with strong internal connectivity; performing time sequence consistency analysis on the screened group, and fusing interannual change characteristics to generate a space-time comprehensive homogeneity score to determine a homogenous change region; identifying a dominant change mode sequence of each homogenous region and classifying the evolution law thereof; and finally performing grouping optimization based on the evolution law type. The application solves the problem of time-space fragmentation and difficulty in accurately identifying homogenous change regions in traditional methods, and realizes high-precision automatic identification and law interpretation of change regions.

The present application belongs to the technical field of computational biology, and particularly relates to a protein surface multi-level modular representation method and an interaction interface prediction method. First, a protein manifold triangle mesh containing multi-modal physical and chemical characteristics is constructed. Then, based on the geodesic distance and local density field, the continuous surface is discretized into geometrically compact virtual residues using a restricted region growing algorithm. Next, an improved soft subspace clustering is used to mine atom patterns with specific functions, and a graph-guided manifold autoencoder is introduced to map them into high-dimensional embedding vectors. On this basis, a compatibility network is constructed by fusing statistical, semantic and physicochemical characteristics, frequent sub-patterns are mined, and the surface is resolved into a structured sub-pattern sequence. Finally, a graph neural network model is constructed for PPI prediction, and the prediction area is resolved into core modules and variable modules. The present application uses a unique four-level hierarchical structure and manifold geometry calculation to solve the problems of weak geometric perception and lack of interpretability in the prior art.

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.