19 results about "Data reduction" patented technology

A method of conducting operations for an IPG adapted to provide electrical pulses to tissue of a patient for medical therapy is provided. The IPG provides the medical therapy according to pulse parameter data executed according to an execution path. The method can include identifying a root node repeated within the execution path of a first set of the pulse parameter data, identifying a stimulation pulse pattern repeated in the first set between occurrences of the root node, replacing the repeated pattern with (a) a single instance and a recurrence count or (b) a pointer to a reference pattern to generate a second set of the pulse parameter data, and communicating the second set between the external controller and the IPG and / or storing the second set within a memory of the IPG.

This invention discloses a fine-grained data reduction method and system for Tar archive files, relating to the field of data storage and version management technology. The method includes the following steps: S1: Initialize performance statistics variables; set fingerprint index, feature index, pathname index, and incremental index; S2: Receive multiple backup versions of Tar archive files in the input directory, and process each backup version in ascending order of backup time; S3: Perform semantic-aware block segmentation on the current Tar archive file backup version; S4: Calculate SHA-256 fingerprints for all generated header blocks, file blocks, and CDC blocks, and identify duplicate and unique blocks using the fingerprint index; S5: Perform a hybrid metadata and feature value deduplication method on unique blocks; S6: Perform incremental compression or local compression on selected blocks, and update performance statistics variables; S7: Save the compressed blocks to a container for persistent storage, and synchronously update the index information. This invention achieves efficient data reduction.

A system and method for simultaneous compression and encryption of data. The system analyzes input data to determine its properties and creates a transformation matrix based on these properties. Using this matrix, the input data is transformed into a modified distribution, generating a main data stream of transformed data and a secondary stream of transformation information. The main data stream is compressed, and both streams are combined into a single output. The system implements security measures to protect against various attacks, including side-channel vulnerabilities. By using a dyadic distribution algorithm, the system achieves both compression and encryption in a single pass over the data, offering significant efficiency gains. The system can operate in both lossless and lossy modes, providing flexibility for different application requirements. This approach offers a unique solution for data transmission and storage scenarios where both data reduction and security are critical concerns.

The present disclosure provides a data processing method and device, electronic equipment and computer readable storage medium, the method comprising: sending reduction request information to a reduction processing unit by a computing unit, the reduction request information comprising reduction mark information and reduction data attribute information; determining a target workgroup from the computing unit according to the reduction mark information by the reduction processing unit, reading reduction data from the to-be-processed data of the data storage unit corresponding to the target workgroup according to the reduction data attribute information, and performing reduction processing on the read reduction data to obtain a reduction result. Through the above technical solution, the data can be read from different types of storage during the reduction operation, resulting in low efficiency of data reduction.

This application is directed to intelligent video data management and processing optimization. A method includes receiving video streams and analyzing individual frames to extract comprehensive object metadata including physical characteristics, contextual information, and spatial relationships. The method also includes calculating movement scores for detected objects based on frame-to-frame variations and classifying objects into active or passive categories using these scores. The system dynamically predicts object movement probabilities by analyzing historical patterns, object characteristics, and inter-object relationships. The system selectively processes video content based on object importance and predicted behaviors, implementing adaptive storage mechanisms where active objects receive higher processing priority. The method manages video data through intelligent compression and selective processing, improving storage efficiency while maintaining critical information quality. The system addresses challenges of large-scale video management by implementing predictive mechanisms that anticipate and adapt to object behavior patterns.

This application provides a data reduction method and a graphics processor. The method includes: adding a validity flag to each thread in a target thread bundle and storing it in a thread buffer queue, with each thread corresponding to a row of data in the thread buffer queue; retrieving two rows of data with a validity flag in a first state from the thread buffer queue; updating the two validity flags corresponding to the two rows of data from the first state to a second state; performing calculations based on the two rows of data to obtain the thread calculation result; returning to the previous step of retrieving two rows of data with a validity flag in the first state from the thread buffer queue until all validity flags in the thread buffer queue are in the second state; storing the thread calculation result in the position of the data row with a validity flag in the second state in the thread buffer queue, and updating the validity flag corresponding to the data row from the second state to the first state. Using this method, redundant calculations can be reduced, the use of computing resources can be reduced, and the energy efficiency ratio of reduction calculations can be improved.

The present application relates to spaceflight measurement and control technical field, especially relate to a kind of generalized data detection and simplification method, system and equipment for improving the precision of space target positioning, comprising: obtaining the observation data of space target, construct extended Gaussian-Markov model containing potential modeling error, generate multiple hypothesis set by different combination of modeling error parameter;Utilize likelihood ratio test criterion to construct the test statistic between different hypotheses, according to the observation environment requirement self-adaptive selection executes generalized data detection process or generalized data simplification process;Determine the optimal model hypothesis of current observation by multiple hypothesis testing, based on the optimal model hypothesis parameter adaptive estimation is carried out, and the position parameter of space target is solved.This application guarantees high-precision requirement by forward selection strategy, enhances robustness under complex environment by backward elimination strategy, realizes the effective identification and elimination of multiple modeling error, significantly improves the precision and reliability of space target positioning.

The present application belongs to the technical field of hydrological data processing, and particularly relates to a water level data collating method and system and a computer readable storage medium, which comprises the following steps: S1, collecting original water level data of different sources, and performing data preprocessing on the original water level data to obtain standard water level data; S2, judging whether there is an anomaly based on the standard water level data; if yes, proceeding to step S3; if no, proceeding to step S4; S3, performing interpolation repair, and then proceeding to step S4; S4, performing data reduction through dynamic feature point screening threshold control and a feature point screening algorithm, retaining key feature points in the water level data, and obtaining reduced water level data; S5, performing line setting and flow pushing based on the reduced water level data, and obtaining collating results; wherein the collating results comprise water level collating data and flow collating data. Through the whole-process processing of data preprocessing, anomaly identification, interpolation repair, data reduction and line setting and flow pushing, efficient and accurate collation of water level data is realized.

The time required for processing measurement data is reduced. The data processing device, according to the present disclosure, acquires reception intensity data representing a reception intensity of signals reflected at measurement points located at distances from the target vehicle for each of multiple distances. It identifies a short-range measurement point that is within a first predetermined distance from the target vehicle. If the short-range measurement point meets predetermined data reduction conditions, it specifies the data corresponding to the distance within a second predetermined distance from the target vehicle, among data indicated in the reception intensity data, as the data to be analyzed.

A data processing method and apparatus relates to the field of data storage technology. The method includes: determining feature data of several data blocks, the feature data including gradient fingerprints, the gradient fingerprints being used to characterize the content features of the data blocks in multiple dimensions; generating a hierarchical graph index structure of the several data blocks based on the gradient fingerprint matching degree of the data block pairs formed by the several data blocks, the hierarchical graph index structure including multiple layers with high and low levels, the multiple layers being used to distribute data block pairs, the gradient fingerprint matching degree of the data block pairs corresponding to the level of the distributed layers; traversing each second data block that is a data block pair with a first data block from the hierarchical graph index structure according to the hierarchical order of the multiple layers, the first data block being one of several data blocks; and performing data reduction processing on the first and second data blocks based on the traversal results.

A data processing method and apparatus, relating to the technical field of data storage. The method comprises: determining feature data of a plurality of data blocks, the feature data comprising gradient fingerprints, and the gradient fingerprints being used to characterize content features of multiple dimensions of the data blocks to which the gradient fingerprints belong; on the basis of the degree of gradient fingerprint matching of data block pairs formed by the plurality of data blocks, generating a hierarchical graph index structure of the plurality of data blocks, the hierarchical graph index structure comprising a plurality of graph layers having different hierarchical levels, the plurality of graph layers being used to distribute the data block pairs, and the degree of gradient fingerprint matching of the data block pairs corresponding to the levels of the graph layers in which the data block pairs are distributed; according to a hierarchical order of the plurality of graph layers, traversing, from the hierarchical graph index structure, second data blocks each forming a data block pair with a first data block, the first data block being one of the plurality of data blocks; and, on the basis of a traversal result, performing data reduction processing on the first data block and the second data blocks.

This invention discloses a method for measuring the overhead of MPI broadcast operations based on system bias elimination, comprising the following steps: traversing all processes and sequentially selecting a root node; all processes other than the root node are considered non-root nodes; performing synchronization operations on all processes starting from the root node to ensure that the root node is the last to exit the synchronization barrier; the root node broadcasting MPI to other processes and recording the broadcast start time using a local timer; all processes recording the broadcast end time using a local timer and calculating the difference between the broadcast end time and the broadcast start time; repeating the step of traversing all processes and sequentially selecting a root node until all processes have been traversed; accumulating the differences corresponding to all processes to obtain the total measurement time; and performing global data reduction on the total measurement time to obtain statistical data for all processes. This invention eliminates the need for hardware synchronization and completely cancels out process clock differences during the accumulation calculation, providing a reliable basis for MPI library performance optimization.

This application is directed to intelligent video data management and processing optimization. A method includes receiving video streams and analyzing individual frames to extract comprehensive object metadata including physical characteristics, contextual information, and spatial relationships. The method also includes calculating movement scores for detected objects based on frame-to-frame variations and classifying objects into active or passive categories using these scores. The system dynamically predicts object movement probabilities by analyzing historical patterns, object characteristics, and inter-object relationships. The system selectively processes video content based on object importance and predicted behaviors, implementing adaptive storage mechanisms where active objects receive higher processing priority. The method manages video data through intelligent compression and selective processing, improving storage efficiency while maintaining critical information quality. The system addresses challenges of large-scale video management by implementing predictive mechanisms that anticipate and adapt to object behavior patterns.

A transformer chromatogram baseline anomaly identification method based on similarity and Wasserstein distance. The method comprises: first, performing preprocessing steps such as denoising by means of Gaussian filtering, baseline correction, data cleaning, data integration, data conversion and data reduction, so as to ensure data quality; then, establishing an anomaly-free standard chromatogram library, and extracting a baseline template; using a Pearson correlation coefficient to calculate the overall similarities between chromatograms to be measured and a standard chromatogram, and setting a similarity threshold value to preliminarily screen abnormal chromatograms; in addition, calculating the Wasserstein distances between baselines of the chromatograms to be measured and the standard baseline template, and setting a distance threshold value to accurately identify tiny anomalies such as baseline drift; by comprehensively determining the results of the similarities and the Wasserstein distances, determining whether there is a baseline anomaly in the chromatograms; and finally, developing a graphical user interface, outputting an anomaly identification result comprising the similarities, the Wasserstein distance values and anomaly types, and providing an anomaly alarm function. The present invention improves the accuracy and reliability of anomaly detection, and is suitable for maintenance and fault diagnosis of transformers.

This invention discloses an intelligent operation and maintenance management system for data center monitoring equipment based on the Internet of Things (IoT), specifically relating to the field of monitoring equipment operation and maintenance management technology. It aims to solve the problems of data silos, alarm storms, and difficulties in locating the root cause of faults in heterogeneous devices. The system includes a device semantic access module, which transforms multi-source device data into a unified semantic stream through protocol parsing and an adaptive fingerprint database; an edge health assessment module, which performs feature extraction and health calculation at edge nodes, achieving local intelligence and simplified data upload; and a root cause analysis module, which constructs a dynamic Bayesian network based on device topology to perform temporal reasoning on multi-source abnormal events, locate the root cause of faults, and predict risk propagation. This invention achieves plug-and-play functionality, intelligent root cause diagnosis, and proactive early warning for devices, significantly improving the automation level and reliability of data center operation and maintenance.

Reduce the time required to process measurement data. [Solution] The data processing device according to this disclosure acquires received intensity data representing the received intensity of a signal reflected at a measurement point located at a distance from the target vehicle for each of a plurality of distances, identifies a nearby measurement point which is a measurement point located within a first predetermined distance from the target vehicle, and if the nearby measurement point satisfies predetermined data reduction conditions, identifies the data shown in the received intensity data that corresponds to a distance within a second predetermined distance from the target vehicle as the data to be analyzed.

A network-on-chip, a data reduction method, and an electronic device are provided. The network-on-chip includes a plurality of routing nodes that are coupled, where each of a plurality of object routing nodes among the plurality of routing nodes includes a streaming reduction engine, each object routing node is coupled with a processing unit. The streaming reduction engine is configured to perform a reduction operation to obtain a reduction result on first data provided by a previous stage with respect to the streaming reduction engine and second data provided by an object processing unit, where the object processing unit is a processing unit among the plurality of processing units that is coupled to an object routing node where the streaming reduction engine is located; and provide the reduction result to a next stage with respect to the streaming reduction engine.

The application provides a mixed load-oriented storage data reduction method, comprising: type identification on an input data block; different types of data blocks are processed in a corresponding manner; wherein the type identification on the data block comprises sampling to obtain a sample set representing the characteristics of the data block, analyzing the numerical characteristics, and judging the data type based on a preset threshold rule; for model data blocks, the redundant high-order indices are separated and compressed; for non-model data blocks, the redundant area and the non-redundant area are identified through fine-grained feature comparison, and are respectively processed by reference or compression. The application solves the technical problems of invalidity and low efficiency of the existing technology in dealing with AI model data and traditional file mixed load scenarios.