8359results about "Code conversion" patented technology

A system and method for adaptive edge-cloud data processing dynamically distributes computational tasks between edge devices and cloud infrastructure in response to changing conditions. The system continuously monitors resource availability, network parameters, and workload characteristics while predicting future conditions using hierarchical forecasting models. A multi-objective optimization approach determines optimal task distribution, balancing processing latency, energy consumption, bandwidth utilization, and result quality. The system implements a partitionable processing pipeline that enables seamless task migration through state synchronization protocols and checkpoint mechanisms. During migration, the system preserves processing continuity by establishing dependencies, creating execution checkpoints, and verifying successful state transfer. Performance metrics may be continuously collected and analyzed to improve future decision-making. The system maintains operational resilience during connectivity disruptions through local decision-making capabilities and eventual consistency protocols, making it suitable for diverse applications including industrial IoT, connected vehicles, healthcare wearables, and smart city infrastructure.

An AI-enhanced distributed system for neural network-based data compression leverages reinforcement learning optimization and privacy-preserving computation across edge and central computing devices to autonomously optimize efficiency and quality. The system includes a lightweight compression subsystem at edge devices that applies privacy-preserving preprocessing and partially compresses input data before securely transmitting it to central computing devices. A reinforcement learning agent continuously monitors system performance and automatically optimizes compression parameters, model selection, and task allocation based on multi-objective rewards. The central compression subsystem processes data using AI-optimized parameters and temporal modeling components. The system incorporates hardware detection capabilities that automatically select optimal compression models based on available processing resources and implements homomorphic encryption for computation on encrypted data while coordinating federated learning across distributed devices. This AI-enhanced distributed approach improves bandwidth efficiency, energy consumption, and adaptability while ensuring data privacy and security.

A nonvolatile memory device includes a plurality of latch groups, an address controller, an encoder, and a buffer. The address controller controls an input address and an output address to indicate one of the plurality of latch groups. The encoder receives sector data from a latch group corresponding to the output address among the plurality of latch groups and also compresses the received sector data. The buffer stores the compressed sector data. Among the plurality of latch groups, the compressed sector data stored in the buffer is overwritten in a latch group corresponding to the input address.

Apparatuses, systems, and methods for scalable 1-pass error correction code operations. A memory device includes an error correction code (ECC) circuit which generates a number of parity bits based on a plurality of data bits during a write operation. The number of parity bits may be selected based on a setting in a mode register. The data and parity are written to the memory array as part of a single access pass. The data may be written to a selected portion of the data column planes, while the parity is written to one or more column planes of the extra column plane or a non-selected portion of the data column planes.

A system and method for multi-scale temporal attention processing in multimodal technology deep learning systems. This system processes time-series, textual, sentiment, and structured tabular data across three hierarchically-organized temporal streams—quarterly, weekly, and intraday levels—with bidirectional cross-temporal information flow. Scale-specific attention mechanisms are optimized for respective temporal granularities, while an adaptive controller dynamically weights each temporal level based on real-time market volatility indicators. A multi-scale fusion processor integrates attention-weighted representations to generate temporally unified representations preserving both short-term market dynamics and long-term trends. This approach enables superior forecasting and risk assessment by leveraging temporal correlations across multiple time scales while automatically adapting to changing market conditions. The system facilitates interpretable AI analysis through attention visualization and enables synthetic scenario generation for model testing.

Apparatuses, systems, and methods for scalable 1-pass error correction code operations. A memory device includes an error correction code (ECC) circuit which generates a number of parity bits based on a plurality of data bits during a write operation. The number of parity bits may be selected based on a setting in a mode register. The data and parity are written to the memory array as part of a single access pass. The data may be written to a selected portion of the data column planes, while the parity is written to one or more column planes of the extra column plane or a non-selected portion of the data column planes.

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 and methods for processing diverse financial data types using a jointly trained Vector-quantized Variational Autoencoder (VQ-VAE) and neural upsampler. This system efficiently handles time-series, textual, sentiment, and structured tabular data through specialized encoding modules. A novel fusion module integrates these encodings, capturing cross-modal relationships via attention mechanisms and gated fusion units. The fused representation is compressed into a discrete latent space by the VQ-VAE encoder, then reconstructed and enhanced by the VQ-VAE decoder and neural upsampler, respectively. Joint training optimizes all components simultaneously, using a comprehensive loss function that balances reconstruction quality across modalities with upsampling performance. This approach enables superior data compression, reconstruction, and analysis, leveraging inter-modal correlations to improve financial forecasting, risk assessment, and decision-making. The system's ability to explore the latent space facilitates generation of new, synthetic financial scenarios for robust model testing and strategy development.

A system and methods for implementing adversarial-robust compression and reconstruction using a vector quantized variational autoencoder (VQ-VAE) with secure latent space management. The system provides comprehensive protection against adversarial attacks through multi-channel threat detection, adaptive defensive parameters, and coordinated response mechanisms. Input data is continuously monitored for potential threats, and defensive parameters are dynamically adjusted based on detected threat levels. The system implements bounded constraints and hierarchical projections to maintain latent space security while preserving compression efficiency. Multi-stage reconstruction with progressive validation ensures reliable data recovery even under adversarial conditions. The system coordinates defensive responses across all compression and reconstruction processes, implementing various recovery mechanisms when security violations are detected. This approach enables robust compression and reconstruction of time-series data while maintaining protection against various forms of adversarial manipulation.

The invention relates to the technical field of wind power generation equipment data processing. The invention provides a time sequence database data compression method, system and device and a storage medium. The method comprises the steps of dynamically partitioning time sequence data based on a preset time window; hybrid coding compression is performed on each data block, and a composite strategy of difference coding and run length coding is adopted for numerical data; a metadata index of the compression blocks is established, the time range, the data feature statistics and the compression parameters of each data block are recorded, and the metadata index comprises extreme value distribution, variance features and data fluctuation frequency indexes; and dynamically adjusting a compression strategy according to a historical data feature analysis result, predicting data fluctuation modes of different equipment sensors through a machine learning model, and automatically selecting an optimal coding combination and compression granularity for subsequent data blocks. The problems that when wind power plant time sequence data are processed through an existing method, time sequence characteristics are difficult to adapt, the storage cost is high and the query efficiency is low are solved.

The invention relates to the field of data backup, in particular to an intelligent data backup method and system based on an AI large model. The method comprises the following steps: acquiring an enterprise global data list, performing intelligent data structure deconstruction and dynamic attribute mapping modeling, and constructing a holographic data semantic perception model; performing real-time transient risk mutation detection on the holographic data semantic perception model, and constructing an intelligent backup triggering mechanism; carrying out storage resource demand prediction based on an intelligent backup trigger mechanism, carrying out multi-storage cloud environment resource dynamic scheduling, and constructing an elastic backup storage resource pool; carrying out incremental backup analysis and self-adaptive compression coding to obtain an incremental backup coding packet; and performing dynamic backup sequence adjustment and intelligent incremental backup decision on the incremental backup coding packet based on the elastic backup storage resource pool, and constructing an intelligent incremental backup execution engine. According to the method, the reliability, the accuracy and the traceability of a backup result are improved through self-adaptive intelligent incremental backup.

A system for adaptively caching network communication protocols provides improved efficiency and performance through a multi-level cache architecture. The system monitors performance metrics and synchronizes cache contents across distributed nodes using a hierarchical structure. Protocol cache requirements are predicted based on usage patterns, enabling proactive cache management. The system compresses cached protocols using existing codebooks to optimize storage and transmission efficiency. Integration with event-driven data communication systems enables seamless protocol selection and translation. The caching system works in conjunction with transaction managers and protocol predictors to enhance network communication performance. The system maintains cache coherency across local, regional, and global cache levels while minimizing network overhead and optimizing protocol availability. By predicting and pre-caching likely needed protocols, the system reduces protocol negotiation latency and improves overall network communication efficiency.

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.

Methods, systems, and devices for programmable metadata and related operations are described. A method may include receiving signaling that indicates a set of rules for transitions of states of metadata at a memory device storing the metadata. The memory device may receive a command from a host device associated with a set of data after receiving the set of rules. The memory device may transition metadata associated with the set of data stored at the memory device from a first state to a second state based in part on the set of rules and the command. The memory device may execute the command received from the host device.

Methods, systems, and devices for programmable metadata and related operations are described. A method may include receiving signaling that indicates a set of rules for transitions of states of metadata at a memory device storing the metadata. The memory device may receive a command from a host device associated with a set of data after receiving the set of rules. The memory device may transition metadata associated with the set of data stored at the memory device from a first state to a second state based in part on the set of rules and the command. The memory device may execute the command received from the host device.

A system and method for neural network weight compression with intrusion detection capabilities that optimizes model storage and transmission while providing security. The system analyzes weight characteristics to identify statistical properties within different neural network layers, generates optimized encoding schemes based on the analysis, and creates reference distributions for security verification. The compression process employs a multi-resolution approach that produces a progressive representation with base and enhancement layers, enabling flexible deployment across diverse computing environments. Security markers and statistical fingerprints can be embedded throughout the encoded representation, allowing for detection of unauthorized modifications during transmission or deployment. The system monitors encoded weight streams, measures distribution divergence against reference baselines, and generates alerts when statistical anomalies indicate potential tampering. This approach achieves superior compression ratios while maintaining model performance and providing robust protection against increasingly sophisticated attacks targeting neural network weights.

The invention discloses a data processing method for network-provincial cooperative monitoring of a power dispatching system, and the method comprises the steps: obtaining a change trend of a data stream, and employing an incremental learning and forgetting mechanism to adaptively adjust a compression dictionary; for non-stationary power data, realizing self-adaptive compression by continuously updating the compression dictionary; constructing a multi-scale space-time compression model by adopting autocorrelation analysis, mutual information calculation, principal component analysis and a deep learning method; combining the multi-scale space-time compression model with a graph model to realize fusion representation of a topological structure and data features of the power system; fusing the graph structure features into predictive coding and differential coding in a compression process; a unified compression algorithm selection framework is constructed to realize high-adaptability compression of the complex power data; adopting a reinforcement learning method to autonomously learn and optimize parameter configuration of each compression algorithm; and adaptively adjusting a compression ratio threshold and a reconstruction quality target according to the precision tolerance of the power application.

The invention relates to the technical field of data compression and transmission, in particular to a dynamic data compression and transmission system and method based on big data. The method comprises the following steps: a data acquisition and processing unit acquires original data from various data sources and processes the original data; the data compression execution unit compresses original data through an LZ4 compression algorithm, and introduces a layered matching DEW-LZ4 technology based on dynamic entropy weight into the LZ4 compression algorithm to optimize the compression process; the data transmission unit performs network transmission on the compressed data through a transmission protocol HTTPS, and dynamically adjusts transmission parameters by adopting a machine learning Q-Learning model according to a network state; and a receiving end decompression unit obtains the transmitted compressed data from the network, decompresses the compressed data by using an LZ4 decompression algorithm, and recovers the compressed data to an original data format. According to the design of the invention, by introducing a layered matching DEW-LZ4 technology based on dynamic entropy weight, the system can intelligently select an optimal compression strategy according to different characteristics (Shannon entropy values) of data.

The invention provides a multi-feature information hidden document integrity verification and tampering positioning method, and relates to the field of network security and digital document protection. The multi-feature information hidden document integrity verification and tampering positioning method comprises the following steps: S1, document multi-dimensional feature extraction and encryption: based on an original Word document, adopting an SHA-3 algorithm to calculate a full-text hash value as a content feature, and extracting document object model features such as paragraph hierarchy, table structure and the like through a DOM tree analyzer; after the two types of features are combined into a feature matrix, a dynamic secret key is generated through Logistic chaotic mapping for XOR encryption, and an encrypted document feature matrix is generated. A dynamic key is generated by combining Logistic chaotic mapping encryption through a multi-dimensional extraction technology fusing document content hash values and structural features, so that the anti-cracking capability of a feature matrix is remarkably improved, the confidentiality of document features in open network transmission is ensured, the block information retention rate is simulated and predicted by adopting Monte Carlo, and the watermark embedding weight is dynamically allocated.

The invention discloses a data compression method and system based on a hybrid compression algorithm, and relates to the field of time sequence measurement data compression, and the method comprises the steps: obtaining an actual measurement monitoring parameter sequence and historical actual measurement monitoring seed data, calling a prediction model to determine a prediction monitoring parameter sequence, and calculating a residual sequence; dividing the residual error sequence into a low variance section and a high variance section according to the standard deviation of the residual error sequence in each sliding window; clustering processing is carried out on the residual values of the low-variance segments to generate corresponding dictionary indexes, and frequency domain transformation, quantization processing and entropy coding are carried out on the residual values of the high-variance segments to generate corresponding frequency domain compression codes; and performing lossless coding on the historical actually measured and monitored seed data to generate corresponding seed data compression codes, and performing packaging and combination to obtain actually measured and monitored compressed data for the actually measured and monitored parameter sequence. Therefore, the hybrid compression algorithm is introduced into the edge device coding end, and the dependence on real-time data total analysis is reduced through priori knowledge modeling.

There is provided methods and apparatus of encoding / decoding a point cloud representing a physical object. Points are captured by a spinning sensors head and are represented by sensor indices associated with sensors that captured the points, azimuthal angles representing capture angles of said sensors, and radius values of spherical coordinates of the point. Points are ordered based order indices obtained from the azimuthal angles and the sensor indices. Order index differences are encoded. An order index difference represents a difference between order indices associated with two consecutive ordered points. Optionally, the method encodes radius values, residual azimuthal angles associated with ordered points and residuals of three-dimensional cartesian coordinates of ordered points based on their three-dimensional cartesian coordinates, decoded azimuthal angles based on azimuthal angles, decoded radius values and sensor indices.

A system and method for accelerating applications in closed networks through intelligent compression and routing. The system includes a flow analysis system that classifies network traffic as latency-critical or bandwidth-critical, enabling optimized handling of different flow types. Latency-critical flows are transmitted directly to minimize delay, while bandwidth-critical flows undergo compression using dynamically selected methods including Huffman, alphabetic, and Tunstall coding. The system maintains synchronized codebooks across network nodes while enabling node-specific optimizations based on local traffic patterns. A network topology manager maintains comprehensive network state awareness, enabling intelligent route selection based on flow classification and current conditions. The system continuously monitors performance and adapts compression and routing strategies in real-time. This approach enables significant performance improvements in closed network environments where all compression-accelerated applications are developed by the same team.

The invention relates to the technical field of data processing, in particular to an artificial intelligence data aggregation method based on big data, and the method comprises the following steps: collecting traffic monitoring original data, removing repeated data and correcting wrong data through data cleaning and preprocessing, and generating preliminary cleaning data; dynamic index adjustment is applied to the preliminarily cleaned data, the depth and branches of an index tree are dynamically adjusted according to the access frequency and types of the data items, and optimized index data are obtained. In the dynamic index adjusting process, the index tree structure is dynamically optimized according to the data access frequency and type, the retrieval efficiency of high-frequency data is improved, and the storage overhead of low-frequency data is reduced. The hierarchical data compression performs hierarchical processing on the data, the use of storage space is greatly reduced through a mode of combining lightweight compression and an advanced compression algorithm, and a foundation is laid for large-scale data processing.

Methods and apparatus for leveraging transfer learning of one Wireless Transmit / Receive Unit (WTRU) to benefit another WTRU are provided. One method may include the WTRU receiving AI / ML model configuration information indicating one or more AI / ML models available from the network node, a profile associated with the AI / ML models, and a training convergence threshold. Based at least on the profile(s), the WTRU determining that the one or more AI / ML models are not suitable for use by the WTRU, and sending first information indicating that the one or more AI / ML models are not suitable for the WTRU and / or that the WTRU will be training a local AI / ML model. The method may then include training the local AI / ML model according to the convergence threshold, receiving a request to transfer AI / ML model parameters, and sending an indication of the AI / ML model parameters associated with the trained local AI / ML model to the network node.

The invention discloses an Internet of Things panoramic perception data conversion method. The method comprises the following steps: acquiring a multi-source heterogeneous Internet of Things perception data set; performing space-time alignment preprocessing on the multi-source heterogeneous Internet of Things sensing data set to generate a standardized space-time data set; inputting the standardized spatio-temporal data set into a feature fusion model, and generating a panoramic perception feature vector set through a dynamic weight distribution algorithm; performing adaptive compressed encoding on the panoramic sensing feature vector set to generate a compressed sensing data stream; and inputting the compressed sensing data stream into the multi-modal decision model to generate a real-time abnormal decision instruction. The method has the following advantages and effects that the problem of panoramic feature expression distortion caused by dynamic value imbalance of the multi-source heterogeneous sensing data is effectively solved, and then the reliability of real-time decision making is improved.

The invention discloses a data asset classification method and system based on big data, and the method comprises the steps: carrying out the standardization processing of multi-source data assets in an enterprise business system, constructing a knowledge graph through correlation analysis, dynamically updating the graph according to the flow condition of the data assets, determining key data assets according to the use frequency of the data assets, and carrying out the classification of the key data assets. The method comprises the steps of obtaining a knowledge graph, obtaining an association path and association strength from the knowledge graph, carrying out foreground prediction, clustering prediction results according to data complexity, calculating a data asset evaluation average value of each cluster as a clustering center index, classifying all data assets according to the clustering center index, and forming a data asset portrait. And mutually isolated distributed storage is executed, and a minimum storage principle is followed to optimize life cycle management of data assets. According to the method, the utilization rate of data assets is increased, data security management is enhanced, an enterprise is supported to make a more intelligent data-driven decision, and meanwhile, the method has relatively good interpretability.

A system for recovering information lost during genomic data compression employs a quality-driven approach using neural networks. The system evaluates the importance of genomic regions through a quality analysis engine that assigns quality scores, while a rate control engine determines optimal compression rates based on these scores. A specialized neural network recovers lost information from correlated genomic datasets that have undergone lossy compression, utilizing recurrent layers for feature extraction and a channel-wise transformer with attention to capture complex relationships between data channels. The neural network architecture incorporates a deblocking network that combines these components to effectively reconstruct compressed data. A decoder receives and decompresses the data, then processes it through the neural network to recover information lost during compression. This adaptive system ensures critical genomic information is preserved while maximizing compression efficiency.

Apparatuses, systems, and methods for an enhanced ECC mode. The memory array includes a number of data column planes and an extra column plane. When the memory device is set in an Enhanced ECC mode, data is stored in a subset of the data column planes, and an error correction code circuit (ECC) stores corresponding parity data in one of a column plane other than one of the subset of data column planes or the extra column plane. In this manner, memory may be capable of performing single error correction or single error correction with double error detection (SECDED) depending on the mode selected.

The present invention belongs to the technical field of decoding algorithms, and specifically relates to normalized minimum sum decoding of BCH codes and post-processing decoding methods thereof, the steps of which include optimizing the standard form of the check matrix of the BCH code to obtain the best check matrix; inputting the received sequence into the improved normalized minimum sum decoding algorithm NMS based on the best check matrix for decoding; if the decoding is successful, outputting the best decoding estimate; if the decoding fails, inputting the sequence iteration trajectory after the NMS decoding failure into the DIA neural network model, analyzing and outputting the reliability metric of each bit, and then decoding again through the ordered statistical decoder OSD to obtain the best decoding estimate. The present invention successfully achieves the low density of the check matrix, the reduction of the number of short loops, and the uniform distribution of column weights through an innovative check matrix optimization algorithm, and provides a more efficient and better performance BCH code decoding solution.

The invention relates to the technical field of carbon footprint dynamics, in particular to a railway traffic carbon footprint dynamic tracking system and method based on a block chain and edge calculation. The data acquisition unit is used for acquiring train operation state, environment parameters and traction energy consumption data; the edge calculation unit fuses the multi-source data acquired by the data acquisition unit through adaptive Kalman filtering to obtain standardized data, converts a train operation state into carbon emission based on an instantaneous power model, and introduces a dynamic emission factor to calculate and obtain accumulated carbon emission; and the block chain network unit is used for compressing the accumulated carbon emission data and then generating a Merkle tree root hash upper chain, and storing the carbon emission data to a block chain. Incremental uplink and rapid verification of the data are realized, the on-chain data processing efficiency and the query traceability are improved, and the problems of high storage pressure and low verification efficiency of a traditional on-chain evidence storage mode in a high-frequency track data writing scene are solved.