444 results about "Lossless compression" patented technology

The invention discloses a multi-source three-dimensional model lightweight processing and lossless compression supporting method, system and device and a storage medium, and relates to the three-dimensional technical field, and the method comprises the following steps: carrying out format compatibility check on an imported three-dimensional model, and analyzing the geometric structure, texture characteristics and material attributes of the model; the geometric structure of the three-dimensional model is simplified by adopting a quadratic error measurement algorithm and edge folding, the simplified model is optimized through edge segmentation operation, and a lightweight model is obtained; and compressing geometric data of the lightweight model by using a data compression algorithm, and compressing texture data through predictive coding to complete lossless compression of the three-dimensional model. According to the method, the loading speed and the real-time rendering efficiency are remarkably improved by reducing the size of the three-dimensional model file and optimizing the texture compression algorithm. The lossless compression technique ensures that a high-quality visual effect is kept in the lightweight process, the lightweight model reduces the storage space requirement, and the energy consumption of data transmission and processing is reduced.

The invention discloses a data compression transmission method and system applied to a ferry inspection image, and the method comprises the steps: collecting and obtaining the ferry inspection image in real time, recognizing a key inspection target region in the image, and carrying out the segmentation and partitioning of the image; compressing the key inspection target area based on lossless compression coding; the quantization step size is dynamically adjusted by comparing statistical variances of background pixels between continuous frames, and lossy compression coding is carried out on a background area; based on the boundary distance between the key inspection target area and the background area, adaptive compression coding is carried out on the transition area; constructing a hierarchical data packet; and constructing a data transmission optimization model, dynamically allocating data transmission links, and obtaining a transmission scheme with the highest total transmission. The method has the advantages that efficient data compression transmission is realized by accurately segmenting the image area and adopting a lossless, lossy and adaptive compression technology, the overall transmission efficiency is improved through intelligent transmission optimization, and the definition and real-time performance of the inspection image are ensured.

The invention relates to the technical field of computer vision and image recognition, in particular to an image recognition method based on edge computing, which comprises the following steps: dynamically capturing an original image through a plurality of edge nodes, rejecting redundant regions through a multi-modal perception triggering mechanism, and establishing a cooperative processing group. Illumination equalization, noise filtering and resolution self-adaptive compression tasks are distributed according to dynamic role election, a standardized preprocessed image is generated, a lightweight convolutional neural network is operated in parallel to extract a dual-channel feature vector, and after entropy coding lossless compression and equipment identity tag and time sequence stamp attachment, the dual-channel feature vector is transmitted to a cloud end by adopting a lightweight encryption protocol. The cloud end analyzes the data packet, reconstructs a feature topological graph based on space-time relevance, loads a depth residual error recognition model to execute feature fusion and classification decision, feeds back and updates the weight of an edge node model, solves the problems of low collaborative efficiency and feature distortion, and improves the efficiency and precision of image recognition.

The invention relates to the technical field of electric power management and control, and discloses an electric power BIM type data GIS discount classification method and system, and the method comprises the following steps: 1, constructing an electric power industry exclusive classification system and semantic mapping, and building a three-stage classification frame based on a GIS and an electric power industry standard; 2, unifying a space coordinate reference and a topological relation, and verifying topological consistency between components and between the components and topography and roads through a GIS (Geographic Information System) tool; 3, multi-scale classification and dynamic LOD model generation are carried out, and LOD model details are automatically switched according to a GIS scale; 4, performing data format conversion and intelligent lightweight processing, performing lossless compression on key components, and performing simplified compression on secondary components; and step 5, performing classification verification and dynamic updating mechanism, and comparing whether BIM and GIS base map position classification is correct or not, thereby realizing data synchronization. According to a dynamic classification framework and an intelligent lightweight strategy in the power industry, the BIM model efficiently adapts to the spatial analysis requirement of the GIS, and the data fusion and application efficiency is improved.

The invention is suitable for the technical field of power transmission line alarm, and provides a power transmission line alarm method based on fault data flow dynamic analysis and lightweight compression, and the method comprises the steps: a terminal collects line state data in real time through a multi-mode sensor disposed on a power transmission line; the terminal carries out lightweight compression processing on the line state data, lossless compression is carried out on steady state data by using improved Huffman coding, and lossy compression is carried out on transient fault data by using combination of wavelet transform and sparse coding; the terminal uploads the compressed data to the cloud according to a preset priority scheduling rule; the cloud end automatically identifies a data format and decompresses the data format through a protocol non-inductive analysis engine, and reconstructs a transient waveform; performing space-time correlation analysis on the analyzed line data, meteorological data and a historical fault library, and calculating a fault confidence coefficient; and according to the fault diagnosis result, generating a grading alarm instruction, and transmitting the grading alarm instruction to the terminal for visualization, thereby improving the power grid fault response speed and the operation and maintenance intelligent level.

The invention discloses a rolling bearing fault diagnosis method based on a WSETCNNLBKALSSVM, and the method comprises the steps: S1, carrying out the modal decomposition of a rolling bearing vibration signal collected by a sensor through employing the high time-frequency resolution characteristic of wavelet synchronous extraction transformation, obtaining a decomposed time-frequency image, and carrying out the lossless compression processing of the time-frequency image; s2, dividing the time-frequency image data after lossless compression processing into a training set and a test set according to a set proportion; s3, inputting the generated time-frequency image data in the training set into a two-dimensional convolutional neural network, and performing adaptive fault feature extraction; and S4, taking an output result of a full connection layer of the two-dimensional convolutional neural network as an input of a least square support vector machine, optimizing hyper-parameters of the LSSVM by adopting an improved black-wing optimization algorithm, and completing classification of fault types through the LSSVM. The rolling bearing fault diagnosis method has higher classification precision and diagnosis accuracy in rolling bearing fault diagnosis.

The invention discloses a time series data lossless compression method and system based on dynamic context awareness, and relates to the field of data lossless compression, and the method comprises the steps: extracting target data from a device port, and carrying out the preprocessing of the target data through a data processing technology according to the business dimension corresponding to the target data, constructing a structured data set based on a preprocessing result; capturing local time sequence features based on the structured data set, distributing attention weights in combination with a hidden state, generating a feature vector used for sensing a context, and outputting a dimension vector by using a three-layer full-connection network; and according to the dimension vector, analyzing the compression performance score value of each compression algorithm in the resource library, sorting the compression performance score values, and selecting the compression algorithm meeting the target requirement to compress the target data. According to the method, the global periodic features and the local mutation features of the time series data are analyzed through the space-time attention mechanism, and real-time driving algorithm switching is achieved.

Lossless compression for HARQ-ACK codebooks with different BLER is described. An apparatus is configured to receive downlink transmission(s) from a network node. The apparatus is configured to transmit two-part HARQ-ACK feedback for the downlink transmission(s) based on: a first compression data set for a first HARQ-ACK codebook and a second compression data set for a second HARQ-ACK codebook, and / or a third compression data set based on a combination of the first and second HARQ-ACK codebooks. Another apparatus is configured to configure a UE with a first configuration for a first compression data set, a second compression data set, or a third compression data set, or a second configuration for a first rate, a second rate, or a third rate. The apparatus is configured to receive two-part HARQ-ACK feedback for a downlink transmission(s) based on the first and second compression data sets or the third compression data set.

A method for streaming dynamically changing user interface data from a server to a client, including resizing user interface data in accordance with a client display, repeatedly transmitting in the form of IP packets, the user interface data in a lossy compression format, for each transmission, if an acknowledgement is received that all packets have been received, then increase the number of IP packets in the next transmission, and if the increased number of IP packets is sufficient to transmit the user interface data in a lossless compression format, then retransmit previously transmitted user interface data in the lossless compression format, including partitioning previously transmitted user interface data into display strips, and for each strip, if the user interface data is unchanged, transmit the strip in the lossless compression format, and if the user interface data has changed, return to the repeatedly transmitting for the changed user interface data.

The invention discloses a multi-edge-node collaborative industrial energy consumption data compression and storage method and system, particularly relates to the technical field of data compression and storage, and realizes hierarchical compression processing by layering industrial energy consumption data acquisition equipment into a main node, a core slave node and a terminal slave node. According to the method, various compression algorithms are used for carrying out lossy or lossless compression on different types of data, and cooperative compression and data redundancy elimination between edge nodes are realized under the coordination of a main node by introducing a distance calculation mechanism of'node hierarchy + auxiliary parameters'. The system constructs a node correlation graph through data abstracts, optimizes a data transmission path based on reinforcement learning, and adopts a cold and hot data classification strategy to improve the storage efficiency. The data compression efficiency and reliability are improved, and the method is suitable for real-time processing and long-term storage of large-scale industrial energy consumption data.

This disclosure relates generally to encoding and decoding of 3-dimensional (3D) mesh and specifically to encoding and decoding order and dependency between 3D positions of 3D vertices and 2D positions of 2D texture coordinates of a 3D mesh. In particular, the various embodiments in this disclosure provide a novel method and device to enhance lossless compression of 3D mesh models. The disclosed method and device prioritize encoding texture coordinates before encoding 3D positions. The disclosed method and device further employ angle-preserving position prediction. The disclosed approaches aim to optimize the compression process of 3D meshes by leveraging an inherent correlation between 3D structures and their corresponding 2D texture mappings.

The invention relates to the technical field of Internet of Things, in particular to an Internet of Things intelligent linkage control method and an Internet of Things intelligent linkage control device, and the method comprises the steps: S1, obtaining the working state of home equipment and the real-time data of environment information, and transmitting the real-time data to a central control platform; s2, preprocessing the real-time data to obtain preprocessed data; s3, analyzing the preprocessed data, and generating a corresponding linkage control instruction; s4, encoding the linkage control instruction to generate encoded data, and performing lossless compression on the encoded data by adopting a ZSTD compression algorithm to obtain a compressed code; s5, searching an optimal sending communication network by adopting a flora optimization algorithm, and sending the compressed code to the home equipment; and S6, the home equipment decompresses the compressed code by using an LZMA compression algorithm, and executes the linkage control instruction. According to the invention, the ZSTD compression algorithm and the flora optimization algorithm are adopted, the delay of data transmission and processing is significantly reduced, the overall response speed of the smart home system is improved, and the user experience is better.

The invention relates to the technical field of robot navigation and autonomous positioning, and discloses a real-time multi-modal map generation method and system, and the method comprises the steps: synchronously obtaining the position and posture of equipment and a spatial point cloud through a tight coupling mechanism of a laser radar and an inertial measurement unit, and forming original data input; on the basis, an incremental point cloud registration technology is adopted to process a continuous point cloud flow, real-time organization and updating of the point cloud are realized through a dynamic spatial index architecture, and an accurate three-dimensional point cloud map is constructed. And inputting the three-dimensional point cloud into the probability octree model, analyzing a space occupation state by using a logarithm domain Bayesian updating mechanism, and generating a three-dimensional probability occupation map. And finally, performing spatial dimension conversion on the three-dimensional occupation map: screening an effective height interval along the vertical direction, and fusing a multi-level occupation probability to generate a two-dimensional grid map, thereby realizing lossless compression from three-dimensional environment information to two-dimensional navigation information.

The present invention discloses a weather forecast data compression method based on video compression technology, belonging to the field of data compression technology. The steps include: S1, obtaining original weather forecast data, extracting time slices and weather element data; S2, preprocessing weather forecast data: mapping the same weather element data of each station at the same time to the interval [0-1023*n], n is the number of mapped channels; S3, three-dimensional grid point reconstruction of weather data: storing weather forecast data according to three-dimensional grid point data, and forming a weather data map in the data format of three color channels of the corresponding picture; S4, using a video compression tool to perform lossless compression encoding of weather forecast data with a bit depth of 10 bits and a chrominance-luminance ratio of 4:4:4; S5, using a video compression tool to decode the weather data of the encoded file generated by S4, and perform inverse transformation of weather forecast data. The present invention reduces the storage space of weather forecast data, and the final compression ratio can reach 10:1.

The invention relates to the field of traffic storage optimization and traffic data compression, in particular to a multithreading-combined lossless compression method for fine-grained traffic, which comprises the following steps of: acquiring a traffic data file, extracting a header triple based on the traffic data file, generating an identifier according to the header triple, and transmitting the identifier to a server; aggregating the traffic data files with the same identifier to obtain data streams, and generating a sorted data stream size table according to the data volume of the data streams; creating threads according to system computing power, distributing the data flow to the thread with the minimum thread load according to the data flow size table and the thread load condition, and outputting a data flow distribution scheme; and performing fine-grained characterization and serialization on each data stream to obtain an integer sequence and a byte sequence, selecting a compression processing method according to redundancy characteristics of the integer sequence and the byte sequence, obtaining a redundancy-eliminated sequence, and writing the redundancy-eliminated sequence into a compressed file. The invention aims to provide high compression efficiency to reduce the storage cost while ensuring the data precision.

The invention discloses a parallelizable self-adaptive matrix lossless compression method, which comprises the following steps of: partitioning a first matrix with a larger scale into second matrixes with smaller scales, distributing a splitting process for the second matrixes, splitting all the second matrixes into a plurality of bit matrixes in parallel by the splitting process, and distributing compression threads for the bit matrixes. And adaptively selecting an optimal compression strategy from the compression strategy set by the compression thread, and finally, completing compression of all bit matrixes by the compression thread according to the determined compression strategy in parallel to form a compression matrix and a flag array as final compression data of the first matrix. Through compression process grading and step-by-step parallelization, the memory overhead in the compression process is effectively reduced, the compression speed is improved, an optimal compression strategy is selected through trial compression, self-adaptive compression is achieved, the data compression rate is effectively improved, and lossless compression of data is achieved by constructing a flag array.

The invention provides a lossless compression method and decompression method for power grid waveform data, equipment, a medium and a program product, and the method comprises the steps: carrying out the periodic feature analysis of the original sampling data of a power grid waveform, so as to determine the periodic structure of the original sampling data, and carrying out the segmentation processing of the original sampling data based on the periodic structure; for the sampling data in each segment, extracting differential information between adjacent sampling points to form a differential data sequence; dynamically determining a corresponding quantization parameter based on the statistical distribution characteristic of the differential data sequence, and performing adaptive quantization processing on the differential data sequence to generate quantized differential data; and entropy coding processing is carried out on the quantized differential data to generate compressed data used for representing the original sampling data, and characteristic parameters used for reconstructing the original sampling data are stored in the compressed data in an associated mode. Unification of high compression ratio, low power consumption and strict lossless reconstruction is realized, and the application requirements of long-term monitoring and remote transmission of the edge side can be met.

The invention provides a lossless compression and decompression method and system based on a probability model, and belongs to the technical field of data compression. The method comprises the processes of segmentation of input data, probability model training, probabilistic reasoning, entropy coding compression / decompression of an asymmetric digital system and bisection reconstruction input. The system comprises a software module and a hardware module, the software module comprises probabilistic model training, probabilistic reasoning and asymmetric digital system entropy coding compression / decompression, and the hardware module comprises a probabilistic model operation module and a compression / decompression module. According to the method, lossless, high-speed and high-compression-ratio lossless compression can be carried out, and input sequences of different sizes and different types can be compressed through segmentation and parameterization on the input sequences, such as pictures, audios and videos; lossless compression with a high compression ratio is realized through a joint function of probability model reasoning and asymmetric digital system entropy coding; through independent selection or design of hardware modules, high-speed lossless compression is realized.

The invention discloses an aurora spectral image lossless compression method based on time-space spectrum combination of Transform. A neural network based on the Transform can improve the modeling precision and the prediction accuracy; time correlation is introduced, and meanwhile, reference pixels in time, space and spectrum directions are used for prediction, so that the prediction accuracy can be improved, and redundancy can be further eliminated; and by introducing exception processing, the code rate can be further reduced.

The invention belongs to the technical field of image acquisition, and discloses a field image acquisition method and system. The method comprises the following steps: acquiring real-time field images and videos by using an edge computing gateway, and extracting a plurality of real-time key frame images; image segmentation is carried out, and the real-time attention of the real-time image area is obtained; lossless compression and dynamic encryption are carried out; collecting real-time network quality data of the transmission network, and generating a real-time data transmission strategy; uploading the plurality of encrypted real-time image compression packets to a cloud data center according to a real-time data transmission strategy; and restoring the plurality of encrypted real-time image compression packets of each real-time foreground image by using the cloud data center to obtain a restored real-time field image video. According to the invention, the problems of low data transmission efficiency, insufficient security, centralized processing bottleneck, high energy consumption and poor transmission reliability in the prior art are solved.

The invention relates to the technical field of signal processing, in particular to an underwater acoustic signal lossless compression method suitable for ocean monitoring, which comprises the following steps: after ocean underwater acoustic signals are subjected to front-end amplification and filtering conditioning, the signals are acquired to form a data stream, and the acquired signal data stream is subjected to primary packaging in a segmented manner according to a time sequence; the load data in the packet is compressed, secondary packaging is carried out after compression is completed, and the secondary packaging comprises the steps of carrying out updating packaging on primary packaging information, updating packet header load length information, mean value information of the packet header added into the whole data segment, information of the occupied space of single data after compression and load data CRC verification information; and storing the secondarily packaged data packet into a signal storage module. By applying the method, the ocean monitoring data can be effectively compressed, and the data storage capacity and the communication bandwidth of the ocean monitoring equipment are reduced, so that the power consumption, the size and the cost of the ocean monitoring equipment are reduced, and the ocean monitoring equipment tends to be more practical.

The invention provides a large model KV precision lossless compression method and system, and relates to the field of data processing, and the method comprises the steps: obtaining key value cache data generated in a large language model reasoning process; performing high-dimensional feature extraction and similarity analysis on the key value cache data to generate a corresponding hash signature; based on the Hash signature, performing physical reordering on the key value cache data, so that the key value cache data with similar characteristics are adjacently arranged in a physical storage space; transmitting the reordered key value cache data to an intelligent network card through a high-speed interconnection interface; and calling an integrated hardware lossless compression module through the intelligent network card, and performing streaming compression on the reordered key value cache data. According to the technical scheme, the hardware unloading and assembly line technology is utilized, transmission bandwidth occupation and system overall delay are remarkably reduced, and key technical support is provided for efficient reasoning of a large model in a resource-limited edge environment.

The invention discloses a lossless compression storage method and system based on heterogeneous data in a credential environment, and the method comprises the steps: firstly, extracting a mapping relation between a file type and a structural feature through scanning a target file, carrying out the preliminary lossless compression after classification and grouping, and further employing a sliding window matching and sequence matching algorithm to optimize compressed data for a redundancy mode, and then generating a metadata structure containing a check code and a compression parameter, packaging the metadata structure into a data packet supporting cross-platform transmission, finally transmitting the data packet to a target storage device through a secure write-in protocol, and verifying data consistency and recovering path feasibility by using the check code. According to the method, the data compression efficiency is remarkably improved, the compatibility of cross-platform transmission and the integrity of stored data are ensured, and efficient and reliable technical support is provided for complex electronic file processing.

The invention provides an electroencephalogram data transmission method combining lossless and lossy compression, belongs to the field of electroencephalogram data transmission and compression processing, and is used for solving the problems of low transmission efficiency of pure lossless compression and easy loss of key diagnosis information of pure lossy compression in related technologies. According to the method, multi-dimensional features of electroencephalogram data are extracted, a key area and a non-key area are divided, lossless compression and lossy compression processing are adopted respectively, the area division proportion is dynamically adjusted in combination with transmission bandwidth, signal features and application scenes, and corresponding data are transmitted through dual protocols. The transmission efficiency can be improved while the clinical diagnosis marker and the core characteristics are reserved, and the scene adaptability and the transmission reliability are achieved at the same time.

Raytracing and pathtracing systems use vertex compression including per-component shifts, unsigned arithmetic deltas, and base vertex shortening. Topology encodings include enhanced explicit vertex indexing and implicit triangle strip based indexing including left, right, start tokens, turn back, and degenerate tokens and including rotation. Substantial lossless compression ratio increases are realized, especially for highly quantized vertex data.

A computer-implemented method, according to one approach, includes: receiving one or more streams of system log information from one or more microservices, and comparing the received system log information to compressed templates. One or more blocks of the system log information that correspond to one or more of the compressed templates are identified. All static text is removed from the identified blocks. Moreover, runtime variables from the identified blocks are inserted into respective ones of the compressed templates. The streams of system log information are further updated by replacing the identified blocks with the compressed templates having the runtime variables therein.

The invention discloses a compression and decompression method for DNA sequencing data, and belongs to the technical field of biological information. The technical problems that in the prior art, when third-generation sequencing data are compressed, flexibility is poor, and the compression rate is low are solved. According to the compression method for the DNA sequencing data, global compression or block compression can be dynamically selected before compression, different compression methods are adopted for different types of data, and the flexibility of the compression method is improved; when the base sequence is compressed, the adopted self-indexing structure is a lossless compression method, the initial positions of all the sequences can be compressed and restored by using smaller data volume, and the compression rate is improved. Due to the fact that a special design is adopted in a run length coding structure, a character part is removed, an obtained run length segment is shorter, lossless compression of the position and the sequence is guaranteed, and meanwhile a better compression effect is provided. The method is mainly used for compression and decompression of DNA sequencing data.

The invention belongs to the technical field of testability design of super-large-scale integrated circuits, discloses a TSV test and diagnosis circuit and method based on BIST, and provides a hardware circuit architecture for TSV test and diagnosis. The device comprises a test access port controller, a test data register, a test vector generator, a shift register, a double-edge trigger, a TSV (Through Silicon Via) test controller, an improved loading chip packaging register and an improved capturing chip packaging register, and the test access port controller is responsible for configuring a test path, a test mode and a test enable signal; the TSV test controller is activated after the test enabling is synchronized by the double-edge trigger, and the improved chip packaging register based on the lossless compression structure is controlled to execute the test, so that efficient fault detection and diagnosis of different fault types are realized. Through cooperation of test vector generation and response compression, the test time is effectively shortened, and an innovative solution is provided for efficient and reliable TSV test and diagnosis.

The invention relates to the technical field of audio processing, in particular to an audio signal data management method and system of an audio chip. The method comprises the following steps: identifying a real-time multi-channel audio signal and a real-time network monitoring parameter of an audio chip; performing adaptive filtering noise reduction and detail enhancement processing on the real-time multi-channel audio signal to obtain a detail enhanced real-time audio signal; performing band-by-band analysis window definition on the detail enhanced real-time audio signal, performing dynamic spectrum feature mining, and generating a dynamic audio signal feature of each band; performing dynamic compression coding and lossless compression audio coding on the detail enhanced real-time audio signal based on the dynamic audio signal feature of each frequency band, and constructing a distributed stream storage framework; and performing audio channel identification on the real-time multi-channel audio signal, and performing phase alignment processing to obtain a channel phase alignment audio signal. According to the invention, high-efficiency and high-stability audio signal management is realized.

The present application provides a rapid evaluation method and system for generator vibration data of a wind turbine generator set. The method comprises: step 1, acquiring current-day operation data of a wind turbine generator set under test, wherein the current-day operation data comprises a time label, an operation power and a generator vibration signal; step 2, constructing a two-dimensional plane scatter plot on the basis of the current-day operation data; and step 3, combining the obtained two-dimensional plane scatter plot with a preset regression function, and evaluating the vibration state of said wind turbine generator set. The present application utilizes a lossless compression method to process data, so that the current-day vibration state of a generator can be accurately evaluated.