3264results about "Code conversion" patented technology

A system and method for adaptive real-time multi-modal compression with dynamic resource allocation provides intelligent compression optimization based on continuously monitored device conditions. The system monitors battery level, CPU utilization, and memory availability while classifying incoming multi-modal data streams comprising image, audio, text, and sensor data to determine processing priorities. Multi-objective optimization balances compression efficiency, reconstruction quality, and energy consumption using evolutionary algorithms that generate optimal parameters for an adaptive variational autoencoder. The autoencoder features dynamically selectable processing complexity, adjustable latent space dimensionality, and modality-specific processing layers. The system automatically switches between operational modes including emergency mode triggered by resource constraints, which applies maximum compression settings and intelligent data triage. Continuous learning adapts compression parameters based on observed performance outcomes, improving future optimization decisions. The system enables homomorphic operations on compressed data and provides enhanced compression performance under varying resource constraints across diverse edge computing applications.

A system for adaptively caching network communication protocols enhances efficiency across heterogeneous device environments through a multi-level cache architecture with device-capability-based tiers. The system collects endpoint telemetry data including device capabilities and operational constraints to classify endpoints and generate context-aware protocol variants optimized for specific device types. Protocol optimization opportunities are determined through structural analysis of message patterns and state transitions. The system performs protocol deduplication by identifying functionally equivalent variants and maintaining canonical representations to reduce cache redundancy. Cache synchronization across distributed nodes uses enhanced Merkle tree structures with protocol normalization processing. The system predicts communication needs based on historical patterns, network context, and endpoint constraints, enabling proactive cache management tailored to device capabilities. Integration with event-driven data communication systems enables seamless protocol selection and translation while maintaining compatibility between diverse endpoint types, from high-performance servers to resource-constrained IoT devices.

The invention discloses a self-adaptive threshold SAMP reconstruction method for a power quality disturbance signal. According to the method, a compression observation value is obtained by constructing a random Gaussian observation matrix, and sparse representation is carried out on an original signal by using discrete Fourier transform. In the iterative reconstruction process, the spectrum amplitude difference is introduced for the first time to serve as an adaptive termination basis, and automatic adaptation of different noise levels and different disturbance characteristics is achieved in combination with a dynamic threshold update function. According to the method, the problems of traditional SAMP sparseness overestimation and redundant iteration are effectively avoided, and the calculation load is remarkably reduced. Compared with an OMP method, an original SAMP method and the like, the method has the advantages that the number of iterations can be reduced by 30%-60%, the reconstruction signal-to-noise ratio is increased by 2-5 dB, the root-mean-square error is reduced by 10%-25%, higher robustness and real-time performance are achieved in power quality disturbance signal reconstruction, and the method is quite suitable for scenes such as compressed sampling, edge calculation and high-speed signal reconstruction in a power quality monitoring system.

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.

A correlation-aware adaptive codebook compaction system for multi-modal data compression that preserves cross-modal relationships while providing enhanced reconstruction quality. The system analyzes temporal and spatial relationships between different data modalities to generate correlation maps that guide compression decisions. A virtual management layer performs stream characterization and adaptive routing, while a processing pipeline implements primary codebook compression with mismatch handling for novel data blocks. High-entropy data segments receive pre-compression processing before codebook compression. Sequential registration data is processed through matrix factorization and dedicated matrix codebooks. The system continuously monitors data distribution characteristics and automatically retrains codebooks when drift thresholds are exceeded. A neural upsampling subsystem uses correlation information to guide cross-modal enhancement processes through modality-specific networks and attention mechanisms. The unified output includes compressed data streams, correlation maps, synchronization metadata, neural model parameters, and updated codebooks, enabling synchronized reconstruction with preserved cross-modal relationships and enhanced quality through correlation-guided neural upsampling.

The invention discloses an efficient storage and transmission method for monitoring data of a low-voltage power distribution network based on lossy / lossless hybrid compression, and relates to the technical field of data storage and transmission, comprising the following steps: completing data denoising correction at an edge node, and setting a plurality of compression strategies and layering mechanisms; judging whether the data is abnormal or not based on the mahalanobis distance, and selecting a proper compression mode through reconstruction error and bandwidth adaptation; the compressed data is subjected to importance labeling and FEC optimization and then sent to a receiving end, the receiving end evaluates the decoding quality and the packet loss rate, and finally a feedback result is used for online updating of the auto-encoder. According to the method, the differential FEC redundancy rate is allocated, so that the lossless fidelity of a key fault waveform and the high compression ratio of a common periodic signal are considered; and meanwhile, closed-loop self-adaption of compression discrimination, coding and model optimization is realized by utilizing online updating of the variable auto-encoder, so that the storage and transmission efficiency is improved, and the reliability and the real-time performance of the system in sudden failure and network fluctuation scenes are enhanced.

A system and method for deep learning using a large codeword model with hierarchical caching is disclosed. The system processes input prompts into tokens, maps them to codewords using a codebook, and processes these through a machine learning core to generate responses. A sophisticated caching architecture stores and retrieves responses across both local and global cache tiers. The local cache maintains frequently accessed responses on edge devices through short-term and persistent storage components, while the global cache enables knowledge sharing across multiple devices. A context aggregator identifies relationships between cached responses to form comprehensive contextual representations. This hierarchical caching system significantly reduces computational requirements by reusing previously generated responses for similar prompts, while continuously optimizing cache contents based on relevance scoring and usage patterns. The approach enables efficient scaling across distributed environments while maintaining response quality.

A computer system for compacting video data. The system acquires a video stream, reduces redundancy through pre-processing, and analyzes the stream to identify patterns and irregularities. It detects spatial or temporal anomalies in the video and produces three outputs: a conditioned video stream based on statistical analysis, an error stream reflecting adjustments made during conditioning, and an anomaly meta-stream containing metadata about detected anomalies. The system communicates with one or more remote systems to synchronize and negotiate a compatible compression codebook, optionally exchanging compact updates that represent differences between local and remote codebooks. The conditioned video stream is then compressed using the agreed codebook. The system outputs a compacted representation of the video that includes the compressed stream, the error stream, and the anomaly metadata, supporting efficient storage or transmission while maintaining the ability to detect, trace, and reconstruct key information within the video.

A federated system and method for data compression optimization in distributed device networks. The system comprises multiple edge devices that analyze local data patterns to generate device characteristic profiles while performing local compression optimization and maintaining data privacy. Edge devices contribute to collaborative learning by generating privacy-preserved updates without transmitting raw data. A central coordination system aggregates encrypted contributions using secure multi-party computation protocols, identifies device groups based on data pattern similarities, and generates optimized compression parameters for each group. The system coordinates collaborative training of data reconstruction models across device groups and deploys group-optimized reconstruction capabilities. Device grouping is performed by calculating similarity scores between device characteristic profiles and clustering devices with scores above predetermined thresholds. The system dynamically adapts compression and reconstruction parameters through federated learning while preserving individual device data privacy, enabling efficient data compression and near-lossless recovery across heterogeneous Internet-of-Things networks.

A federated byte latent transformer platform utilizing homomorphically-compressed and encrypted byte-level data. The system integrates dynamic entropy-based patching into federated learning to enable efficient, robust, privacy-preserving collaborative learning across distributed nodes. Client devices convert local data into dynamically sized patches based on entropy thresholds, encrypt these patches, and send them to a central server that processes them without decryption. The system offers improved robustness to input noise, enhanced character-level understanding, and better adaptation to low-resource languages compared to token-based approaches. It enables simultaneous scaling of both patch size and model size while maintaining fixed inference budgets, allowing efficient deployment on resource-constrained devices. These innovations address critical challenges in federated learning: efficiency, robustness to data heterogeneity, and privacy preservation.

The invention relates to the technical field of data compression, in particular to a ZSTD compression algorithm optimization method based on joint coding and ternary decomposition, and the method comprises the following steps: S1, building a joint frequency table based on the joint distribution of the literal length and the matching length of a previous section, setting a frequency deviation criterion, and dynamically adjusting a low-frequency combination proportion; s2, decomposing the offset value structure into a leading segment, a trailing segment and a middle segment, performing bit width-entropy ratio analysis on the middle segment, and selecting a symbol index or bit stream coding; and S3, entropy discriminant coding is performed on the joint symbol and the offset symbol respectively, and a structure position is mapped synchronously to generate a structured compressed output stream. According to the method, collaborative optimization of frequency self-adaption, redundancy minimization and structured mapping is realized through joint symbol dynamic modeling and offset ternary structure entropy discriminant coding, so that the ZSTD compression efficiency and the coding stability are remarkably improved.

A latent transformer architecture with latent attention mechanisms and expert processing systems for federated deep learning is disclosed. The system operates entirely within latent space, eliminating traditional embedding and positional encoding layers while maintaining full attention capabilities. Input data is compressed into latent vectors via variational autoencoder encoding, then processed by a latent attention module that computes query, key, and value matrices directly from latent representations. The architecture incorporates expert processing systems including gated latent expert networks for sparse computation and latent mixture of experts for collaborative processing. In the gated approach, a routing network selectively activates specialized expert modules based on latent vector characteristics. The mixture approach enables all experts to contribute through weighted combination, facilitating distributed computation and enhanced model expressiveness.

A system and method of decoding a bitstream to produce tensors for use by a neural network second portion. The method comprises decoding a network abstraction layer (NAL) unit from the bitstream having a predetermined length, wherein the NAL unit of the predetermined length indicates a network abstraction layer (NAL) unit format of one inner codec of a plurality of inner codecs, each other inner codec having NAL unit lengths different to the predetermined length; selecting an inner codec from the plurality of inner codecs based on the decoded NAL unit of the predetermined length; and decoding the bitstream using the selected inner codec to produce the tensors.

The invention belongs to the technical field of data analysis and processing, and particularly relates to an equipment state monitoring method based on a time sequence synchronous compression mechanism, and the method comprises the steps: 1, a monitoring terminal collects vibration type, acoustic type and acceleration type signals on a unified time baseline of Beidou time service signals, and registers a second sequence number and a frame sequence number in a unified fragment index table; 2, inputting an original time sequence data packet, constructing a phase grid frame by a phase locking ring according to a reference phase rail, extracting road-level flag event bits frame by frame, and fusing the road-level flag event bits into frame-level flag event bits; and step 3, outputting an equipment state label according to a mutual exclusion priority rule only on the basis of the synchronous compression time sequence packet and the coupling mark, the mode instruction and the abnormal fragment mark in the unified fragment index table in the identification window. The method is obviously superior to the prior art in the aspects of time sequence alignment precision, pattern recognition certainty, data compression efficiency and health state judgment reliability.

A collaborative transformation matrix learning system extends adaptive compression and encryption architectures through federated, privacy-preserving optimization. Each node analyzes local data distributions to generate anonymized distribution profiles using differential-privacy mechanisms, securely exchanging profiles and validated transformation matrices across a collaborative network. A trust and validation engine verifies mathematical properties and evaluates claimed performance metrics. Validated matrices are integrated into local optimization when trust and performance thresholds are satisfied. The system employs secure multi-party computation, homomorphic encryption, and conflict-resolution logic to ensure integrity of shared insights while preventing exposure of sensitive information. By combining collective learning with local adaptation, the invention accelerates convergence to optimal matrix configurations, mitigates cold-start inefficiencies, and improves compression-encryption efficiency and cryptographic strength across distributed deployments.

A data processing method and apparatus are disclosed, which may be applied to communication systems such as 5G systems and 6G systems. The method includes: obtaining an extended first codebook based on a second codebook by increasing a quantity of codewords or lengths of codewords, and performing first network coding or decoding based on the first codebook. Linear independence between codewords in the extended first codebook may be ensured as much as possible, so as to generate more valid redundant packets or check packets, thereby improving system reliability. Alternatively, flexible block lengths are supported, so as to perform efficient network coding or decoding on more original data packets, thereby improving system spectral efficiency. This application may be applied to an extended reality (XR) service or a low-delay service.

The invention discloses a memory management method and related equipment, and relates to the technical field of data storage, the method comprises the following steps: based on access information of memory pages of a target operating system, determining a dynamic popularity score of each memory page; on the basis of the current memory pressure level and the dynamic popularity score of the target operating system, executing a corresponding hierarchical data migration strategy so as to migrate different memory pages to the corresponding main Swap storage space or auxiliary Swap storage space; adjusting a compression algorithm applied to the auxiliary Swap storage space based on the current central processing unit utilization rate of the target operating system; and detecting real-time states of the main Swap storage space, the auxiliary Swap storage space and the physical memory, and adjusting a hierarchical data migration strategy and a compression algorithm based on a detection result. Through dynamic popularity perception, hierarchical data migration, adaptive compression regulation and cross-layer collaborative optimization, the memory utilization rate, the exchange efficiency and the overall system stability can be improved under different system loads.

Compressing files is disclosed. An input file to be compressed is first aligned. Aligning the file includes splitting the file into sequences that can be aligned. When splitting the file into sequences or when performing subsequent recursive splitting, the splitting is based on a longest sequence match. The result is a compression matrix, where each row of the matrix corresponds to part of the file. A consensus sequence is determined from the compression matrix. Using the consensus sequence, pointer pairs are generated. Each pointer pair identifies a subsequence of the consensus matrix. The compressed file includes the pointer pairs and the consensus sequence.

Systems, methods, and instrumentalities are disclosed for priority-based channel coding for control information. A wireless transmit / receive unit (WTRU) may sort control information associated with a first control information type into a first control information group and the control information associated with a second control information type into a second control information group, for example, based on respective priorities associated with the first and second control information types. The WTRU may group one or more bits of the first control information group into a first bit level control information group and a second bit level control information group based on priority. The WTRU may selectively apply a cyclic redundancy check (CRC) to the first control information group, the second control information group, the first bit level control information group, and / or the second bit level control information group.

The invention provides a decoder, a decoding method, a memory system, an operation method of the memory system and a controller. The decoder comprises a first processing circuit, a second processing circuit, a processor and a bit flipping circuit, the first processing circuit is configured to obtain a check formula and a check formula weight based on a to-be-decoded code word and a check matrix in a current iteration; the second processing circuit is configured to obtain the energy of the to-be-decoded codeword in the current iteration based on the check matrix, the check formula and the overturning state of the to-be-decoded codeword in the current iteration; the processor is configured to: determine a flipping threshold in a current iteration based on a change state of the checkout weight; and the bit flipping circuit is configured to output the to-be-decoded code word in the next round of iteration based on a comparison result of the energy of the to-be-decoded code word in the current iteration and the flipping threshold value in the current iteration determined by the processor.

The invention discloses a multi-source heterogeneous time series data compression and motor fault prediction method and system, and the compression method achieves the intelligent compression and feature enhancement of time series data through the cooperation of multi-scale feature extraction, SE weighted fusion, mixed pooling and aggregator collapse. According to the architecture, reasonable reduction of the sequence length and effective improvement of the feature depth can be completed at the same time, on the premise that key fault features are reserved, the technical problem of multi-source heterogeneous sensor data fusion in an industrial scene is solved, and the limitation of serious information loss of a traditional dimension reduction method is overcome.

The invention discloses a method and equipment for compressing multi-dimensional data based on column storage and self-adaption, and the method comprises the steps: recombining original multi-dimensional data into a column data set structure, and organizing data block storage for the column data set structure according to a hierarchical structure; each data block stores observation grid data of a single variable of multi-dimensional data at a certain moment so as to support column type storage and self-adaptive compression; and carrying out real-time statistical analysis on characteristics or parameters of variables in the data blocks, and dynamically selecting and configuring a filter and parameters thereof to compress the multi-dimensional data. The data of the same variable are stored together through column storage, so that the data access efficiency is greatly improved, especially during variable-level analysis. Meanwhile, the self-adaptive compression technology can dynamically select a compression mode according to the distribution characteristics of the data, so that the compression rate of the data is effectively improved, and the occupied storage space is reduced. The processing speed of the meteorological grid point data can be obviously improved, and the storage cost is reduced.

The invention relates to the technical field of ecological environment monitoring, in particular to a grassland environment resource database construction method, which comprises the following steps of: arranging a multi-modal sensor in a fractal multi-scale grid and synchronously collecting; obtaining a standard frame by adopting quantum random walk compression and space-time registration; extracting a topological bar code through neuromorphic pulse coding and persistent coherent topology analysis, and generating a unified potential space tensor and a missing mask in combination with weighted mutual attention and optimal transmission; constructing an energy function containing carbon and nitrogen conservation and topological deviation, and reconstructing a complete ecological submerged space tensor through Hamiltonian Monte Carlo-diffusion combined sampling; ecological indexes and abnormal events are reasoned in the Shenchang differential graph database, sampling scheduling is driven by prediction errors, closed-loop updating of data, models and sampling is achieved, and therefore the grassland monitoring precision and early warning timeliness are improved.

A system and method for data compaction and encryption of anonymized data records. A dataset may be pre-processed by dividing into sourceblocks at reasonable intervals and tallying each sourceblock's frequency, creating a tally record of tokens and count values. This tally record may then be anonymized and transmitted to a data deconstruction engine which combined with a library manager creates a codebook and performs optimization techniques on the codebook. The data deconstruction engine and library manager may be distributed across multiple nodes or devices. The received anonymized tally record may be parsed into individual tokens by identifying the tokens with the highest count value. The tokens may then be sent descending order of count value to the library manger where each token may be assigned a codeword. A half-backed codebook is then created using the tokens and each token's unique codeword, before sending the half-backed codebook to a system user.

The invention discloses an eddy current data compression transmission optimization method, relates to the technical field of data compression transmission, and solves the technical problems that a compression algorithm lacks scenario adaptation and data transmission lacks priority and link adaptation. Scene demand-algorithm characteristic accurate matching is achieved, compressed data overall reconstruction errors and defect area reconstruction errors are reduced, delay and compression ratio requirements of different scenes are met, data priorities are judged in a multi-dimensional mode, it is ensured that defect data acquisition resources are inclined, key information is prevented from being lost, and the method is suitable for large-scale popularization and application. A transmission protocol is optimized for a short / medium / long distance scene, the protocol adaptability and the transmission efficiency are improved, a transmission scene-link state-data priority three-dimensional linkage transmission strategy is constructed, the bandwidth occupation proportion is dynamically adjusted, core data are preferentially transmitted when links are congested, and resources are fully utilized when the links are idle.

The invention discloses a dynamic compression coding method, which is applied to a sparse matrix, and comprises the following steps: S1, obtaining the sparse matrix, and generating a bitmap with the same dimension as the sparse matrix, a value in the bitmap being used for indicating whether an element at a corresponding position in the sparse matrix is zero; s2, a mark sequence is generated according to the bitmap, and each mark corresponds to multiple continuous elements in the sparse matrix and is used for representing whether each element in the multiple elements is zero or not; s3, extracting non-zero elements in the sparse matrix according to the mark sequence; and S4, combining the flag sequence with the non-zero elements to form a data stream after compression coding. The invention also discloses a dynamic compression coding system, a storage medium and a device. According to the dynamic compression coding method, the index storage cost can be reduced, the data access efficiency can be improved, the storage and bandwidth occupation can be reduced by supporting parallel coding and decoding, and finally the calculation throughput can be improved.

A system and method for cross-stream asymmetric enhancement combines machine learning-driven asymmetric codebook generation with dyadic distribution algorithms to enable simultaneous optimization of compression efficiency, cryptographic security, and error correction capability. The system analyzes input data characteristics and initializes multiple specialized ML models to generate stream-specific asymmetric codebooks optimized for different objectives. Enhanced dyadic distribution processing creates three pre-conditioned data streams that are processed through parallel asymmetric transformation pipelines: compression-optimized for maximum data reduction, security-optimized for cryptographic strength, and error-correction-optimized for robust recovery capability. Cross-stream optimization coordinates the multiple processing paths to ensure overall system coherence while maintaining individual stream objectives. The system supports multiple operating modes including ultra-high compression using only the primary stream, broadcast quality using primary and secondary streams, and archival mode using all streams for lossless reconstruction. The system supports graduated access control that enables different reconstruction quality levels based on available stream combinations.

Data compression with quantum-resistant intrusion detection, that measures in real-time the probability distribution of an encoded data stream and analyzes entropy characteristics across multiple bit-scale windows to detect both classical and quantum-generated intrusions. The system compares the probability distribution to a reference probability distribution and uses statistical algorithms to determine divergence between distributions while simultaneously analyzing entropy cascade patterns characteristic of quantum computing sources. When divergence exceeds configured thresholds or quantum-generated characteristics are detected, the system generates intrusion alerts identifying the threat type. The system comprises encoding and decoding machines, an intrusion detection engine that performs multi-scale entropy analysis, a codebook training engine that creates quantum-resistant codebooks using entropy-stratified training algorithms, and databases including a quantum signature database storing compression patterns of known quantum algorithms. The codebook training engine adaptively retrains encoding algorithms upon detecting new quantum patterns, maintaining system effectiveness against evolving quantum threats.

The invention discloses an entropy coding compression method for high-dimensional sparse data, which relates to the technical field of data compression, and comprises the following steps: reading an original high-dimensional sparse matrix, extracting a position index set and a corresponding non-zero value set of all non-zero elements, extracting active samples from the non-zero value set, and compressing the active samples. After an active sample matrix and an optimal mean value centralization matrix are constructed, principal component projection and a self-expression structure are introduced for joint modeling, a low-rank robust optimization objective function is formed, and a principal component feature matrix is finally output by alternately optimizing mean values, projection, weights and residual errors. According to the method, the compression efficiency and the processing pertinence of high-dimensional sparse data are effectively improved, self-expression structure modeling and residual regular optimization between samples are further combined, the structure consistency is kept in the dimension reduction process, a key information structure is kept while the compression ratio is guaranteed, and the high-fidelity and low-redundancy entropy coding compression effect is achieved.

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.