16 results about "Adaptive interpolation" patented technology

This invention belongs to the field of fault prediction and health management technology, specifically involving a machine learning-based method and system for monitoring the operating status of vehicle-mounted brushless fans. The method includes: acquiring multi-source time-series data of the vehicle-mounted brushless fan drive circuit and constructing a non-stationary distortion index; dynamically triggering an adaptive equal-angle synchronous resampling mechanism using the non-stationary distortion index, combining virtual electrical angles and introducing an adaptive interpolation density function to determine the interpolation accuracy at each angle position, thereby mapping the three-phase stator current into a stationary sequence in the angular domain; performing order analysis on the stationary sequence in the angular domain to obtain the order spectrum, introducing the average value of the non-stationary distortion index combined with a logarithmic function for weighting to obtain fault judgment feature values; inputting the fault judgment feature values ​​along with the effective current value into a vector machine classifier to identify and output the operating status label of the vehicle-mounted brushless fan and execute online closed-loop early warning. This invention improves the fault diagnosis accuracy of vehicle-mounted brushless fans under varying operating conditions.

This invention relates to an intelligent processing method for wind profiler data, which solves the problems of existing wind profiler data processing methods, such as reliance on manual intervention for identifying missing height layers and the lack of structural consistency guarantees in interpolation methods. This method achieves automatic structural analysis and intelligent completion of missing height layers in wind profiler data through multi-step collaborative processes. The steps sequentially execute data access and format standardization, height layer structure parsing and index construction, intelligent identification of missing height layers, interpolation condition judgment, adaptive interpolation calculation, data structure reconstruction, and database storage. It automatically identifies missing height layers and adaptively selects interpolation algorithms to generate interpolation results based on data change trends, ensuring the integrity and continuity of the data structure and providing a reliable data foundation for wind field analysis models. This invention can be widely applied to wind profiler radar data platforms, meteorological data operational processing systems, and other scenarios.

The application discloses a kind of chlorophyll relative content prediction method and system based on multispectral image, the method includes: obtaining multi-source data;According to environmental data, determine phenological period category and environmental stress category, and determine comprehensive scene mode accordingly;Environment data and multispectral image are extracted, and vegetation index feature, texture feature and environmental feature are obtained;According to comprehensive scene mode, scene self-adapting interpolation is carried out;According to environmental feature, vegetation index feature correction parameter is generated, and vegetation index feature is dynamically corrected;The corrected feature is constructed as multi-source time series feature sequence, multi-source time series feature sequence is weighted and fused, and input prediction model, and the chlorophyll relative content prediction value is obtained.The system includes data acquisition module, scene mode determination module, feature extraction module, feature processing module and prediction module.The application can be widely applied in the field of agricultural information technology.

This invention provides a time-domain correction method based on local adaptive thin-plate spline interpolation, comprising the following steps: S10, separating the pilot signal from the mixed measurement signal, performing fine phase tracking on the pilot signal, and extracting the phase noise sequence introduced by clock jitter; S20, converting the phase noise sequence into jitter at the sampling time; S30, determining the ideal sampling time, selecting several original sampling points adjacent to each ideal sampling time, obtaining a local interpolation subset, evaluating the jitter intensity of the local interpolation subset, and performing adaptive interpolation calculation accordingly to obtain the signal value at the ideal sampling time; S40, outputting the uniformly sampled signal sequence after interpolation reconstruction; this invention also provides a time-domain correction system based on local adaptive thin-plate spline interpolation. This invention solves the problems of poor real-time performance and weak adaptability of traditional TPS interpolation, achieving high-precision suppression of clock jitter noise and meeting the stringent requirements of phase measurement for space gravitational wave detection.

The application discloses a structure surface grid automatic generation method based on topological filling, and relates to the technical field of aerospace engineering simulation and grid generation. The method comprises the following steps: intelligently dividing a three-dimensional CAD model into parts to obtain parts with semantic labels and geometric contours; sequentially performing ordered topological filling on each part according to a preset order, adjusting the number of intersection edge points of adjacent parts to ensure grid connection, and generating topological lines; projecting the topological lines onto the model surface for calibration; adaptively interpolating the projected topological lines based on model curvature and simulation conditions to generate point cloud data; generating an initial quadrilateral grid based on the point cloud; projecting the initial grid nodes onto the model surface again and optimizing the quality, and outputting the final grid. The application realizes end-to-end automatic generation from a CAD model to a high-quality surface grid, eliminates manual intervention, ensures the natural matching of grids between parts, and significantly improves the grid generation efficiency and quality.

The present application relates to the technical field of unbalanced data processing, and particularly relates to a kind of oversampling method and system for processing class unbalanced data, method includes: feature standardization processing is carried out to unbalanced data set, and at least the training set containing minority class sample is obtained by division;Select multiple base kernel functions, map and combine to construct composite kernel feature space to minority class sample;Local linear reconstruction weight for depicting the local structure of minority class sample is calculated;Joint optimization objective is constructed, and adaptive interpolation coefficient for the generation of minority class sample and the kernel weight of composite kernel feature space are jointly solved;Adaptive interpolation coefficient is used to generate minority class synthetic sample, and kernel weight is transferred to classifier as prior parameter.By the present application, the problems of sample distortion, loss of local structure and insufficient performance of classifier in existing methods are effectively solved, and the recognition ability and classification accuracy of classifier on class unbalanced data set are improved.

The application discloses an image scaling method based on edge perception and content adaptive interpolation. Firstly, edge detection is performed on an input original image to obtain edge strength representing the richness of image details. Secondly, according to the edge strength, the distribution of the two values with the highest weights in an interpolation function is adaptively adjusted. For a position with higher edge strength, a larger weight distribution is adopted to better preserve edge and detail features. For a position with lower edge strength, a smaller weight distribution is adopted. Finally, pixel resampling of the whole image is completed by using the interpolation kernel after adaptive adjustment, and an output image with a target resolution is generated.

The invention discloses a structure surface grid automatic generation method based on topology filling, and relates to the technical field of aerospace engineering simulation and grid generation. The method comprises the following steps: carrying out intelligent component subdivision on a three-dimensional CAD model to obtain components with semantic tags and geometric contours of the components; sequentially executing ordered topology filling on each component according to a preset sequence, cooperatively adjusting intersection edge points of adjacent components to ensure grid connection, and generating a topological line; projecting the topological line to the surface of the model for calibration; performing adaptive interpolation on the projected topological line based on the model curvature and the simulation working condition to generate point cloud data; generating an initial quadrilateral grid based on the point cloud; the initial grid nodes are projected to the surface of the model again, quality optimization is carried out, and a final grid is output. According to the method, the end-to-end automatic generation from the CAD model to the high-quality surface grid is realized, the manual intervention is eliminated, the natural matching of the grids between the parts is ensured, and the grid generation efficiency and quality are remarkably improved.

This invention provides a method, system, and medium for multi-source data processing in industrial production lines based on asynchronous communication. The method includes: receiving multi-source data from events at different workstations on the industrial production line via an asynchronous event queue; sorting events according to their priority and timestamps; performing multi-threaded parallel processing of the data using a backpressure mechanism and a persistence mechanism; performing coarse alignment of the data; performing time-series matching using a dynamic event warping algorithm; and introducing Kalman filtering and adaptive interpolation algorithms to correct lost or delayed data; performing anomaly detection and data cleaning using statistical methods, machine learning methods, and spatial consistency constraints; correcting the scan numbers of different workstations and establishing a mapping relationship between the corrected numbers and the data; performing data compression and storage; and performing parameter data visualization, trend warning, and anomaly prediction. This invention achieves accurate collection, integration, and traceability of data from different workstations, using different protocols, and with different timestamps.

This invention relates to the field of data processing technology, and in particular to an adaptive interpolation method for reconstructing land spatial survey data. The method includes: acquiring the locations of sampling points, feature vectors, and interpolation location sets for each dimension; calculating the adaptive neighbor number for any interpolation location in the current dimension, combining the spatial distance within a preset detection range and globally optimized constraint parameters; obtaining the comprehensive reliability of the interpolation location by analyzing the spatial correlation between the current dimension and other dimensions, and the data stability of other dimensions of the interpolation location; calculating the reference degree of each nearest neighbor point determined by the adaptive neighbor number to the interpolation location; and normalizing to obtain the adaptive interpolation weight of each nearest neighbor point, thereby obtaining the feature value of the interpolation location and achieving multi-dimensional data spatial alignment reconstruction. This invention improves the fidelity and analytical reliability of heterogeneous geospatial boundary reconstruction through dynamic evaluation and multi-source collaborative constraints.

This invention provides a wafer yield prediction method and system based on data interpolation and incremental learning. The method includes: acquiring the spatial coordinates, electrical parameters, and missing markers of the wafer; identifying abnormal wafers based on the missing markers, and constructing a feature set containing electrical parameters and spatial distance information based on normal wafers with complete parameters within the spatial neighborhood of the abnormal wafers; performing distance-aware adaptive interpolation using a multilayer perceptron model based on the feature set to generate complete data; constructing a preliminary prediction model using a teacher model and a student model based on the interpolated complete data; continuously distilling the student model on a new batch of samples based on the preliminary prediction model to obtain a yield prediction model; and predicting the target wafer yield using the yield prediction model to obtain the prediction result. This invention effectively reduces prediction errors and enhances generalization ability while maintaining a lightweight model, and has significant engineering application value.

The application provides a laser radar networking monitoring method based on intelligent cooperation and closed-loop optimization, and relates to the technical field of data processing.The method comprises the following steps: constructing a deep neural network, taking a laser radar equation as a physical constraint, combining consistency of multiple radar overlapping areas, inverting extinction and backscattering coefficients and quantifying confidence; using uncertainty dynamic optimization scanning parameters to encrypt scanning in a high-uncertainty area and pre-scanning a high-information-gain area; embedding self-supervised calibration into inversion loss, based on physical residual to reverse repair abnormal points; fusing inversion, calibration and uncertainty confidence to construct an adaptive interpolation kernel and a space-time physical constraint, and reconstructing a four-dimensional data field; and fusing comprehensive confidence to map to transparency to construct an interactive four-dimensional visualization platform.The application realizes closed-loop self-optimization of inversion, calibration, scanning, interpolation, visualization and tracing, improves inversion precision and data reliability, and reduces operation and maintenance cost and energy consumption.

This disclosure provides an adaptive interpolation method and electronic device for carbon emission power density at the city block level, relating to the field of data processing technology. The implementation scheme is as follows: Based on the differences in the gradient vectors of building development volume at various sampling points of a first urban plot, the location of a first interpolation sampling point in the sampling network of the first urban plot is determined, where the building development volume of the sampling point is the ratio of the building area to the base area of ​​the sampling point; based on the carbon emission power density of multiple first neighboring sampling points and the slope of the change in carbon emission power density of the multiple first neighboring sampling points, the carbon emission power density of the first interpolation sampling point is determined; based on the location and carbon emission power density of the first interpolation sampling point, interpolation is performed on the sampling network of the first urban plot. Using the scheme of this disclosure, the accuracy of carbon emission data interpolation can be improved, and the sampling cost of carbon emission data can be reduced.

The application discloses a medical image super-resolution system based on kernel prior and adaptive interpolation Transformer, which comprises a data processing module, a feature processing module, a kernel prior dynamic weighting module, a cross-modal alignment module and an adaptive interpolation module; the data processing module is used for acquiring low-quality medical images to be processed and a multi-modal pathology-degradation joint annotation dataset; the feature processing module extracts pathological semantic features and generates modal-specific degradation kernel priors through double parallel sub-networks; then, the kernel prior dynamic weighting module is sequentially used for completing pathology risk-driven kernel prior weighting, the cross-modal alignment module is used for completing cross-modal feature alignment, a spatial adaptive interpolation kernel is generated through a Transformer module, and finally, a super-resolution high-definition image is output through closed-loop optimization. The application significantly improves the reconstruction accuracy of pathological microstructure, realizes cross-modal fine-tuning adaptation, and greatly reduces the deployment algorithm power threshold of the grassroots scene.

This invention proposes a method for identifying feature points and detecting forming dimensions of cladding passes in arc additive manufacturing based on 3D visual full-morphology point cloud information. The method includes the following steps: using a high-precision 3D scanner to perform omnidirectional scanning of each cladding pass layer, and preprocessing the point cloud data such as filtering, 3D surface reconstruction, point cloud refinement, and feature fitting alignment to obtain effective point cloud data; performing dimensionality reduction on the effective point cloud, and using the DW-GCT method to bidirectionally approximate and search for weld toe feature points of the first cladding pass layer; employing a heterogeneous region adaptive interpolation method to search for edge convex and non-edge convex regions and obtain cladding pass interpolation contours that retain small surface features; and combining this with a layer intersection point dynamic positioning method to bidirectionally search for bite points in the incomplete fusion zone between adjacent layers. The method proposed in this invention can effectively solve the problems of lack of cladding pass side point cloud information, depth of field error, and breakpoint error in existing technologies, and provides a method for extracting the top-layer cladding pass dimension data using the full-morphology 3D point cloud contour information of the cladding pass.

The application provides a strong convective weather recognition method and device based on GNSS occultation observation data, which comprises the following steps: collecting original observation data, summarizing and arranging the original observation data according to an established real-time data pool, and performing quality control on the arranged original observation data to obtain quality-controlled observation data; gridizing the quality-controlled observation data mode, and interpolating the quality-controlled observation data to numerical mode grid points through a multi-source constraint adaptive interpolation algorithm to form strong convective cell mosaic data; performing data assimilation on the strong convective cell mosaic data based on a Bayesian-Gaussian hybrid assimilation model to generate an analysis field; and driving a global weather forecast model to perform numerical prediction hour by hour according to the analysis field to recognize the strong convective cell. The application significantly improves the precision and efficiency of meteorological data processing, fusion, prediction and strong convective weather monitoring and early warning, and improves the precise prediction capability of the strong convective weather.