210 results about "Data compression" patented technology

This invention provides a data storage method and system for tuberculosis detection, relating to the field of tuberculosis detection technology. The data storage method for tuberculosis detection includes the following steps: S1. Collecting whole-genome sequencing data of Mycobacterium tuberculosis, host serum IgG titer, and drug sensitivity test results; S2. Calculating genetic distance D based on a reverse evolution model to generate four-dimensional spatiotemporal coordinates (t, x, y); S3. Performing data partitioning and storage based on the drug target barrier value β; S4. Generating dynamic metadata using a host-pathogen dynamics model and compressing and storing it. This invention implements a dynamic storage entropy adjustment algorithm at the hardware and software collaborative level, continuously optimizing the matching efficiency of data compression and physical storage. This results in an intelligent data hub that can perceive the evolutionary pulse of pathogens and autonomously optimize resources, providing support for clinical tuberculosis prevention and control decisions with temporal depth, spatial correlation, and risk evolution.

This invention discloses a method, system, and medium for storing degree-of-freedom numbers in a numerical computation grid, relating to the field of numerical computation technology. By improving the storage structure of degree-of-freedom numbers, the storage and access efficiency of degree-of-freedom numbers is optimized, and different numbering methods can be selected according to actual needs, thereby improving the flexibility and overall performance of numerical computation. Two data sets are designed for each variable group to uniformly store the degree-of-freedom numbers of variables within the group, and the storage space occupied is effectively reduced through data compression.

Embodiments of the present application disclose a touch data transmission and acquisition method and device, equipment and a storage medium. In the case of obtaining an initial data stream, a first target data in the initial data stream is replaced by a preset value to obtain a target data stream. The initial data stream is one frame of data generated according to a touch operation on a touch screen of an electronic device, and the first target data is data whose absolute value in the target data stream is within a first value interval. The target data stream is compressed to obtain compressed data, and the compressed data includes first compressed data for indicating a plurality of continuous data in the target data stream whose values are the preset value. The compressed data is sent to an operating system of the electronic device. According to the embodiments of the present application, invalid data is discarded and valid data is retained by means of lossy data compression, so as to reduce the data amount of touch screen change data to be transmitted, and further reduce data transmission time and transmission power consumption.

This invention discloses a method for compressing and reconstructing distribution network data based on graph transform compressed sensing, belonging to the field of distribution network measurement data technology. The method includes the following steps: inputting the network topology and historical operating data of the distribution network; constructing a power-voltage relationship parameter matrix; determining the number of principal components and obtaining the projected principal component coefficient matrix; calculating the graph Fourier transform basis; using the graph Fourier transform basis as the sparse basis after projecting the principal component coefficient matrix, calculating the sparsity and initial compression of each principal component under the sparse basis, and obtaining spatially compressed observations; reconstructing the voltage amplitude measurement data of distribution network nodes, and further reconstructing the active power measurement data and reactive power measurement data of branches. This invention employs the above-mentioned method for compressing and reconstructing distribution network data based on graph transform compressed sensing, achieving high data reconstruction accuracy while maintaining a high compression ratio, reducing computational load, and making it more suitable for applications on edge-side devices.

A data compression method and a related device, used for reducing I / O overhead. The data compression method comprises: acquiring attribute information of a logic block, wherein the logic block comprises a plurality of data pages, and the attribute information indicates data sizes of data contained in the plurality of data pages across a plurality of time periods; if the attribute information indicates that the total data size of the data contained in the plurality of data pages during a target time period is greater than or equal to a first threshold, compressing the data contained in the logic block, and storing the result obtained by compression, wherein the target time period includes at least time period among the plurality of time periods, and the time interval between the target time period and the current moment is greater than or equal to a time threshold; and if the attribute information indicates that the total data size of the data contained in the plurality of data pages during the target time period is less than the first threshold, determining not to compress the data contained in the logic block.

Disclosed are a method and system for an inference task, and a computing device cluster and a related product, which belong to the technical field of AI. The method comprises: inputting tokens of an inference task and tokens of first text data into a first LLM, acquiring an attention score of each token in the first text data during a prefill phase of the first LLM, and on the basis of the attention score of each token, acquiring tokens, the attention scores of which meet a compression condition, so as to obtain tokens of compressed first text data; and inputting the tokens of the compressed first text data and the tokens of the inference task into a second LLM for inference, so as to obtain an inference result of the inference task. Since it is only necessary to calculate an attention score of each token in first text data on the basis of a prefill phase of a first LLM, during the compression of the first text data, there is no need for the first LLM to execute phases following the prefill phase, thereby shortening the duration for compressing the first text data, and thus improving the compression efficiency of the first text data.

The embodiment of the application discloses a lossy compression method, a decompression method and equipment for time sequence floating point data, which are used for improving the data compression rate and the decompression speed of the time sequence floating point data. The method comprises the following steps: acquiring a floating point array, the floating point array comprising a plurality of floating point data arranged in time sequence; sequentially determining other floating point data in the floating point array as target floating point data, and determining a previous floating point data of the target floating point data from the floating point array; determining a difference between the target floating point data and the previous floating point data as a difference data of the target floating point data; performing quantization processing on the difference data based on a preset error boundary to obtain quantized difference data corresponding to the target floating point data, wherein a difference between lossy data corresponding to the target floating point data and the target floating point data is not greater than the preset error boundary, and the lossy data corresponding to the target floating point data is obtained by performing inverse quantization processing on the quantized difference data corresponding to the target floating point data; and performing compression processing on each quantized difference data.

A method, apparatus, device, and storage medium for compressing distributed acoustic wave sensing data are disclosed. The method includes: inputting the original beat frequency signal into a pre-trained encoder module, which directly outputs a low-dimensional compressed feature vector, thus achieving efficient compression of distributed acoustic wave sensing data. The encoder module, through end-to-end training, learns how to map the high-dimensional original signal into a low-dimensional feature vector, preserving the key physical features of the signal in the process. Compared to traditional fixed downsampling or threshold compression, this application can adapt to the dynamic characteristics of the signal, effectively maintaining signal fidelity while compressing the data volume, thereby reducing the burden of subsequent storage, transmission, and computational processing.

The present application provides data compression and decompression methods, an apparatus, and a storage system. The data compression method comprises: acquiring FastQ data and BAM data, the FastQ data comprising a plurality of sequences, the BAM data comprising a plurality of alignment items corresponding to the plurality of sequences, and the plurality of alignment items being obtained by aligning the plurality of sequences separately with a reference genome; determining a first alignment item corresponding to a first sequence among the plurality of sequences, the first alignment item being one of the plurality of alignment items, the first alignment item comprising an alignment result of the first sequence, and the alignment result of the first sequence comprising position information of the first sequence in the reference genome; and on the basis of the alignment result of the first sequence comprised in the first alignment item, performing reference compression on the first sequence, so as to obtain compressed data of the FastQ data. The method can improve the compression rate of data and also improve the compression performance of the data.

This specification provides a data compression method and apparatus. The data compression method is applied to an intelligent storage device and includes: acquiring at least one piece of business data to be processed; compressing each piece of business data to be processed according to a preset storage sector size to obtain a storage block to be compressed corresponding to each piece of business data to be processed, wherein the storage block to be compressed includes at least one storage sector; compressing the fill data in each storage block to be compressed to obtain a target storage block, wherein the target storage block includes compressed business data corresponding to each piece of business data to be processed, and the fill data is used to fill the free space in the storage sector. This method compresses the original business data at a preset storage sector size granularity, aligns the compressed business data at the storage sector granularity, and fills the empty space with fill data. In the hardware compression stage, the fill data in each storage sector is compressed, improving the data compression ratio.

The present application belongs to the technical field of distributed speech enhancement, and particularly relates to a distributed speech enhancement system based on maximum likelihood. In order to expand the diversity of speech enhancement technology in WASN and complete good noise elimination performance, the system comprises a discrete Fourier transform module, a speech activity detection module, a steering vector estimation module, a data compression module, a result output module, a signal construction module, a weighted correlation matrix estimation module, a filter update module, and a discrete inverse Fourier transform module. The present application is a distributed speech enhancement technology which can be applied to a wireless acoustic sensor network without a data processing center. The technology estimates a weighted correlation matrix through a local signal constructed by a node and a variance of an output result, and updates a filter by combining the estimated weighted correlation matrix with a constructed local steering vector, so as to complete distributed speech enhancement.

The present application provides a kind of alternative video data compression method, video specification conversion integrated circuit and operating method.Video specification conversion integrated circuit includes input interface, specification conversion engine, internal memory, decompression circuit and output interface.Specification conversion engine receives input video signal from input interface.In specification conversion mode, specification conversion engine converts input video signal meeting first video specification into converted video signal meeting second video specification, and output interface selects converted video signal as output video signal and outputs to display.In standby mode, decompression circuit decompresses the alternative video data of internal memory to generate alternative video signal, and output interface selects alternative video signal as output video signal and outputs to display.

A data compression device (100) that compresses discrete time series data including a plurality of continuous values, the continuous values being constituted by one or more data points representing the same value that are consecutive in the time series, includes a noise determination section (11). When each continuous value included in the time series data is taken as a target continuous value, the noise determination section (11) determines the target continuous value to be noise in a case where the duration of the target continuous value is less than a specified duration and the difference between the value of the target continuous value and the value of the data point immediately preceding the start point of the target continuous value in the time series is equal to or less than a difference threshold. The noise determination section (11) determines the target continuous value to be not noise in a case where the duration of the target continuous value is equal to or more than the specified duration and the difference between the value of the target continuous value and the value of the data point immediately preceding the start point of the target continuous value in the time series is equal to or less than the difference threshold. The noise determination section (11) generates compressed data that is data in which each continuous value determined to be noise is not represented, each continuous value determined to be not noise is represented, and the time series data is compressed.

Disclosed are methods and apparatuses for image data encoding / decoding. A method for decoding a 360-degree image includes the steps of: receiving a bitstream obtained by encoding a 360-degree image; generating a prediction image by making reference to syntax information obtained from the received bitstream; adding the generated prediction image to a residual image obtained by dequantizing and inverse-transforming the bitstream, so as to obtain a decoded image; and reconstructing the decoded image into a 360-degree image according to a projection format. Therefore, the performance of image data compression can be improved.

This invention discloses a method for encrypted transmission of IoT sensor data for NB-IoT, relating to the fields of IoT communication and information security technology. The invention constructs an association mapping relationship between wireless channel coverage enhancement level, transport block size index, transport block bit length, and number of repeated transmissions, and matches the optimal transport block configuration by combining transmission resource overhead budget. Based on channel state and transmission capacity, it performs adaptive encryption and data compression processing, generates implicit random numbers, and completes data encapsulation. Stable transmission of the encapsulated data is achieved through the narrow bandwidth channel of NB-IoT, and consistency verification is performed at the receiving end. This effectively solves the problems of poor adaptability and high transmission overhead of traditional encryption schemes, improving the security, efficiency, and reliability of IoT sensor data transmission.

The invention relates to the technical field of data compression, and provides an optimization method and device for PCIE bearing AXI write data. The method comprises the steps of determining at least one cavity position in to-be-carried data; generating a hole identification bit according to the at least one hole position; generating an original TLP packet according to the to-be-carried data; adding the hole identification bit in the original TLP packet to obtain a target TLP packet; and transmitting the target TLP packet to a target device so as to write the to-be-carried data into the target device, thereby solving the problem of low transmission efficiency caused by splitting into a plurality of TLPs for transmission when holes exist in AXI burst data writing.

Embodiments of an implantable system and apparatus for providing electrical and optical deep brain stimulation are disclosed. Through the biological introduction of light-responsive opsins to neurons, the system is operable to transform neurons into optogenetically programmable state and history-dependent logical transformers. Embedded processors of the system are operable to stimulate individual neurons using phase-shifted electrical and / or optical signals through a neural interface comprising a plurality of carbon nanotubes, then electrically and / or optically measure the neurons. Due to the parallelism and chemical superposition of the human brain, the implantable system is operable to establish a quantum-like processing ability using its neural interface.

This invention relates to a learning-based lossless lightweight compression, decompression, random access, and query processing method and system for GPUs. The method includes: processing input data using the GPU's native learning compression pipeline and directly generating learning-based lossless lightweight compressed SLAP layout data in GPU memory; decompressing and reading the target SLAP layout data using a warp collaborative learning decompression module to obtain decompressed data; selectively accessing the target SLAP layout data on the GPU side based on target point or range requests, and calling the warp collaborative learning decompression module to decompress a portion of the target SLAP layout data to obtain decompressed data; and outputting the decompressed data to a query operator for further processing or directly returning the query result, depending on actual needs. This invention can be widely applied in the fields of data compression, columnar storage, GPU parallel computing, and database acceleration.

This invention relates to an efficient particle sphere generation method based on principal component analysis (PCA), belonging to the field of quantum computing. It solves the problem of low data representation efficiency caused by excessive segmentation and noise sensitivity in existing particle sphere algorithms. The technical solution includes: initializing the particle sphere set using a parallel sampling strategy; calculating the splitting gain and setting a threshold; calculating the optimal splitting direction based on the PCA core sample set; splitting the particle spheres along the principal direction and iteratively updating the set. This method achieves efficient data compression, reduces computational complexity, enhances the representation ability of complex geometric structures, and has strong robustness.

This invention belongs to the field of communication technology, and specifically relates to an adaptive data compression and transmission method and apparatus based on SSD thermal behavior in an edge network environment. Its main features include the following steps: S1, deploying an edge box device between a mobile terminal device and a local network; the mobile terminal device connects to the edge box device via a wired connection; the edge box device connects to a 5G base station via a 5G CPE (Network Premises Equipment); S2, the mobile terminal device uploads the data to be transmitted to the edge box; the edge box performs security verification on the user's identity and the data to be transmitted; if the verification is successful, proceed to step S3; otherwise, terminate the transmission and display an illegal operation message; S3, the data to be transmitted is fragmented, and each data fragment is compressed and stored using a multi-threaded parallel approach; simultaneously, the temperature of the SSD storage medium is monitored to dynamically adjust the number of parallel compression threads and the SSD storage strategy; S4, the compressed data fragments are distributed to multiple communication paths and transmitted to a private cloud according to a multi-path load balancing strategy; the integrity of the data fragment transmission is monitored synchronously, and retransmission is performed for data that fails to transmit. While ensuring the long-term safe operation of SSDs, this invention can balance system throughput and hardware lifespan, achieving the goals of high throughput, high reliability, and low latency in edge data transmission systems.

This application discloses a state diagnosis method, system, device, and medium based on UAV operational data, mainly relating to the field of state diagnosis technology. It addresses the problems of single-channel statistical features failing to capture cross-channel gradient correlations and dynamic coupling relationships between sensor signals, and unidirectional LSTMs only utilizing historical information and neglecting the inverse constraints of future temporal context on the current state. The method includes: inputting training samples into a forward LSTM and a backward LSTM respectively to obtain forward and backward outputs; concatenating the forward and backward outputs; compressing the concatenated data into a fixed-length semantic vector; obtaining the labeled state of the semantic vector; and obtaining a trained classification function based on the semantic vector and the labeled state; when new multi-channel UAV operational data is obtained, obtaining the corresponding new semantic vector; and inputting the new semantic vector into the trained classification function to obtain the predicted state.

The DAS seismic noise suppression method based on the conditional latent diffusion model belongs to the fields of machine learning and seismic data processing. This invention utilizes a latent signal representation module encoder to compress high-dimensional seismic data into a low-dimensional latent space, improving computational efficiency while preserving signal characteristics. In the inference phase, noise components are estimated through a noise prediction network, and clean latent variable estimation and state transitions are iteratively performed according to a deterministic sampling strategy. The decoder then reconstructs the denoised seismic data. In the training phase, self-supervised pre-training of the latent signal representation module, supervised training of the noise prediction network, and joint fine-tuning of all network parameters are performed sequentially, using KL divergence loss, reconstruction loss, and mean square error loss to simultaneously optimize network weights. This invention significantly reduces the computational overhead of the diffusion model while suppressing various complex noises.

The application provides a verification method and device for compression function in an SOC system, and relates to the chip detection technical field. The method comprises the following steps: determining a master device and a corresponding slave device in the SOC system; calling a test case of the SOC system; performing multiple access operations on the master device and the slave device based on a write / read data path between the master device and the slave device to perform compression / decompression test and obtain bandwidth data corresponding to each access operation, wherein the access operation comprises a write operation and a read operation; determining a compression efficiency corresponding to each write data path based on first bandwidth data corresponding to the write operation on the write data path; and determining a decompression efficiency corresponding to each read data path based on second bandwidth data corresponding to the read operation on the read data path. The application has the advantages of high working principle efficiency, fast simulation speed, and accurate SOC system compression efficiency and decompression efficiency.

This application provides an intra-frame prediction method and a color component prediction method. The intra-frame prediction method includes: obtaining color component samples from a template region; applying a first sample-side filter (SSF) to the color component samples; obtaining a linear function based on the filtered color component samples of the template region, the linear function being used to predict the chrominance component based on the luminance component; obtaining a luminance sample of the current block; and obtaining a chrominance prediction of the current block based on the linear function and the luminance sample of the current block. Implementation of these embodiments can improve data compression efficiency.

The application relates to the technical field of CAE preprocessing and cloud cooperation, and discloses a lightweight CAE geometry engine and a cloud real-time cooperation method and system. A cloud server acquires an original CAE geometry model, performs topological simplification and data compression on the original CAE geometry model, and generates a lightweight geometry data file containing multiple resolution levels; the cloud server pushes the received difference data packet to other online cooperation terminals; the other cooperation terminals parse the unique version number carried by the difference data packet through a locally loaded WebAssembly geometry engine module, and perform operation replay updating on the local geometry model according to the differential data contained in the difference data packet. According to the scheme, the original CAE model data volume is reduced by 90%-95%, the WebAssembly engine realizes a 100,000-face model rendering frame rate of 30-60 fps, and the operation response delay is less than or equal to 100 ms, thereby breaking through the performance bottleneck of browser-side geometry processing.

This invention discloses a multimodal perception-based intelligent integration system and method for duty information, specifically relating to the field of public security duty information processing technology. It addresses the problem of mismatch between existing front-end data compression methods and the specific task requirements of back-end big data analysis platforms, leading to the loss of key feature information and affecting analysis reliability. By parsing task instructions to form a feature requirement set, and based on this, evaluating the task contribution and cross-modal collaborative gain of the original perception data, it intelligently generates a compression control strategy tailored to the current analysis task. Finally, it performs differentiated compression and reorganization of the original data, forming a task-optimized data stream that is fed back to the back-end platform. This improves the value density and analytical relevance of the returned data, effectively ensuring the efficiency and accuracy of the back-end big data analysis platform under resource-constrained conditions.

The application relates to the technical field of computing data processing, and discloses a multi-dimensional power transmission and distribution equipment operation data acquisition and edge computing processing system, which comprises a data compression module, the data compression module comprises an edge node configured at an equipment side, carries out filtering analysis and compression on collected data of power transmission and distribution equipment, and establishes a ring-shaped original buffer area used for storing uncompressed original data for a short time before the data is compressed. The application realizes the pre-simulation of the decompression data result of the data center at the edge node by decompressing the compression package in the data simulation area in a consistent logical manner with the data center, compares the original sample in the ring-shaped original buffer area, can pre-quantify the data distortion caused by filtering and compression in the edge node without affecting the front-end collection and uploading, and does not need to wait until the data is used in the data center to expose the problem.

The application relates to the technical field of electrocardio data processing, in particular to an electrocardio data compression method and system and a decompression method and system, which comprises the following steps: converting collected original data to obtain an electrocardio sequence with a preset bit width; framing the electrocardio sequence to obtain a plurality of frames; performing difference calculation on all sampling points in each frame except the first sampling point of the frame to obtain a difference value, and selecting a corresponding encoding rule for compression encoding according to the difference value; wherein the plurality of encoding rules comprise a first encoding rule corresponding to a first value range and L second encoding rules corresponding to a plurality of value ranges exceeding the first value range, the length of the encoding result corresponding to the first encoding rule comprises a compression bit width, and the length of the encoding result corresponding to the second encoding rule comprises the compression bit width and a feature value bit width. The application has the effect of achieving a good balance among compression efficiency, calculation complexity and implementation difficulty on an embedded device.