2550 results about "Data compression" patented technology

The invention discloses a cloud edge cooperative control method, which comprises the following steps: establishing a cloud edge cooperative architecture, collecting data by edge nodes, and selecting a corresponding data compression strategy to compress the data; generating an edge-cloud collaborative uploading data stream; the cooperative control layer executes adaptive load balancing based on a cloud-side node state, task characteristics and a network condition which are monitored in real time so as to realize task allocation and scheduling between the cloud and the side; the cloud receives the data stream transmitted by the edge node, decompresses and processes the compressed data according to the metadata information, completes the task and feeds back an execution result; a node performance prediction model is constructed by using system state monitoring information, a task allocation strategy and a resource allocation and data compression strategy are adjusted in real time according to a prediction result, and cloud-side collaborative optimization is realized. Efficient data transmission, flexible task scheduling and self-adaptive resource management are realized, and the performance of the system in a complex and changeable environment is comprehensively improved.

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 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.

The invention relates to the field of road traffic, in particular to a multi-feature fusion fatigue driving detection method and a fatigue driving detection system. The method comprises the following steps: extracting facial features from a face image of a driver; extracting vehicle features from the vehicle driving parameters of the vehicle driven by the driver; and fusing the facial features and the vehicle features to judge whether the driver is in fatigue driving. Extracting facial features by designing a CNN model; extracting basic convolution features; extracting local convolution features; extracting global convolution features; performing pooling operation; aggregating global features; and carrying out dimensionality reduction mapping. A self-encoder is designed to extract vehicle characteristics; a symmetric deep neural network structure is adopted, and high-dimensional time sequence data is compressed to a low-dimensional potential space through nonlinear mapping; through combination and matching of the CNN model and the auto-encoder, the technical defects of feature redundancy, noise interference, information loss and suboptimal decision existing in an existing multi-feature fusion fatigue driving detection system are thoroughly solved.

The invention discloses a multi-agent non-cooperative game driven high-concurrency task reasoning method and system, and the method comprises the steps: decomposing a heterogeneous task into standardized task units, and carrying out the quantitative modeling of the calculation resource demands of the task; constructing a task auction mechanism of the multi-agent non-cooperative game model, performing task allocation by adopting an anti-strategic auction rule and a fragmentation asynchronous protocol, and constraining resource declaration behaviors of the agents through a credit pledge mechanism; the real-time resource utilization rate of the system is monitored, the task priority is dynamically adjusted in combination with the task preemption historical state, a resource soft preemption strategy is triggered, and a compensation queue is established; a self-adaptive model splitting strategy is adopted to distribute reasoning tasks to end side equipment and edge computing nodes, cooperative execution is achieved through data compression and transmission, and task rescheduling is conducted according to feedback of an execution result; according to the method, the task execution efficiency, the resource utilization rate and the system stability can be remarkably improved in a high-concurrency scene.

The invention relates to the technical field of building model design, in particular to a building three-dimensional model lightweight design method and system based on artificial intelligence, and the method comprises the steps: extracting geometric-semantic features of a building model through a multi-scale curvature filtering and semantic segmentation network, constructing a fusion feature vector matrix, and obtaining a fusion feature vector matrix; by utilizing an integrated graph convolutional network and a self-adaptive neural simplification network of a double-branch attention mechanism, differential resampling is executed based on vertex importance weight, a simplified intermediate model is generated, a surface microstructure is recovered by means of a generative adversarial network, grid holes are corrected by combining Delou inner triangulation, and a surface microstructure is obtained. A non-uniform rational B-spline curved surface is adopted to reconstruct a key decoration component and output a lightweight model, so that the problems of insufficient geometric feature retention and semantic information splitting in the traditional technology are solved, intelligent, efficient and lightweight of a building three-dimensional model is realized, visual fidelity is ensured while data compression is performed, and the construction quality is improved. And the digital management requirement of the whole life cycle of the building is met.

A system and method for processing and generating multimodal data using a combination of Vector Quantized Variational Autoencoder (VQ-VAE) and Latent Transformer architectures. The system efficiently handles diverse data types including time-series, textual, sentiment, and structured tabular data through specialized encoding modules. A novel fusion module integrates these encodings, capturing cross-modal relationships. The fused representation is compressed into a discrete latent space, processed by a latent transformer, and then reconstructed and enhanced. This approach enables superior data compression, reconstruction, analysis, and generation of synthetic scenarios.

The invention relates to the field of wind power plants, in particular to a wind power plant edge calculation data cleaning and real-time transmission optimization method and system. The method comprises the following steps: forming a multi-source heterogeneous data stream by collecting SCADA operation data, CMS vibration monitoring data and meteorological environment data of a wind turbine generator in real time; performing standardization processing on the data at an edge computing node, constructing a multi-dimensional feature vector, and generating a fusion data set by adopting a self-adaptive fusion algorithm based on a dynamic weight; and constructing a fault prediction model based on the deep dual-channel convolutional neural network, and outputting a health state evaluation value and a fault risk level in real time. And when the risk level exceeds a threshold value, generating an early warning signal. The system also performs data compression transmission optimization according to the real-time bandwidth state, and dynamically adjusts the weight of the monitoring parameter to realize the closed-loop optimization of the preventive maintenance strategy.

The invention relates to the technical field of equipment monitoring, in particular to an equipment corrosion evaluation and life prediction method and application, and the method comprises the following steps: deploying a sensor network in an easily-corroded area of coal chemical equipment, and collecting multi-dimensional data; carrying out abnormal value elimination, data compression, time synchronization and space-time alignment preprocessing on the collected multi-source data; image features are extracted through a convolutional neural network, processed data are analyzed through an LSTM-attention model, and a fuzzy comprehensive evaluation matrix is established to evaluate the corrosion level; a physical model based on the Faraday electrolysis law and a data driving model based on the Transform network are constructed, and the residual life is predicted through Bayesian network fusion output and Monte Carlo simulation. Through fusion of multi-source data and an intelligent algorithm, accurate evaluation of the corrosion state of the equipment and accurate prediction of the residual life are realized, and safe and efficient operation of the coal chemical equipment is guaranteed.

The invention discloses a highway bridge bearing capacity detection device and a use method thereof, and belongs to the technical field of highway bridge detection. The device comprises a multi-source data acquisition module for realizing omnibearing real-time data acquisition of bridge structural stress, environmental parameters and apparent characteristics; the edge calculation processing module is used for executing basic data processing, primary feature extraction, intelligent data compression and abnormal preliminary screening at a data source, and ensuring the balance between the data transmission efficiency and the processing real-time performance; the knowledge graph management module is used for constructing and maintaining a multi-level knowledge graph system covering bridge structure features, damage modes and historical cases; the intelligent analysis decision module is used for realizing intelligent decisions of bridge bearing capacity evaluation, risk early warning and detection strategy optimization; and the visual interaction module is used for realizing visual display, intelligent interaction, report management and authority management.

The invention discloses a time sequence data management method of an edge computing gateway, which relates to the technical field of edge computing and industrial Internet of Things, and comprises the following steps of: respectively recording a communication bandwidth occupancy rate, a buffer area residual rate and a scheduling thread occupancy rate of the edge computing gateway in a preset fixed time period; and constructing a resource use original data matrix covering all time points in the fixed time period. According to the method, by periodically monitoring the resource use state and fusing the high-priority task scheduling performance, the scheduling resource abnormal occupancy index is dynamically generated, and intelligent sensing and scheduling optimization of the edge computing gateway on the resource pressure are achieved. When the abnormal index is increased, the system automatically triggers buffer area redistribution and low-optimal task data compression, data writing and scheduling real-time performance of key tasks are guaranteed preferentially, the problems of task starvation and data loss are effectively avoided, and the stability and the response capability of the system in a high-pressure environment are improved.

The invention discloses a rural industry agricultural product full-process traceability method based on a block chain. The method comprises the following steps: collecting agricultural product full-life-cycle process data and environmental parameter data through Internet of Things sensing equipment; preprocessing the agricultural product traceability data by using a space-time agricultural data compression technology; storing the compressed key traceability data in a block chain network, and storing high-capacity original data through a content addressing interstellar file system; maintaining the consistency of the block chain network based on an adaptive agricultural season consensus algorithm; automatically executing data verification and business logic processing through the smart contract; the authenticity of agricultural product traceability information is verified based on a zero-knowledge proving technology, and meanwhile peasant household sensitive data are protected.

The invention provides a multi-dimensional visual analysis method for meteorological observation and detection data, and relates to the field of visualization. According to the method, an unmanned aerial vehicle platform carries a meteorological sensor to collect data, and primary processing, abnormal value detection and intelligent data compression are carried out; transmitting the processed data to a ground measurement and control terminal; the ground terminal fuses the multi-source data to generate a three-dimensional meteorological data set; constructing an interactive three-dimensional visual scene by utilizing self-adaptive interaction logic and a dynamic data display technology; a user can check and perform multi-view linkage operation through the interactive operation interface; a severe weather and weather raster data set can be constructed based on the three-dimensional weather data set and historical severe weather records, and simulation and display are carried out; and finally exporting the data, verifying the precision and integrity, and generating a visual analysis report.

The invention discloses an intelligent bid evaluation expert extraction and management system, and relates to the technical field of expert extraction systems. Comprising a data cleaning and compression module, a database logic and table relation design module, an expert information collection standard and automation module, an information real-time updating and principal management module, an extraction algorithm design module, a safety and authority management module and a real-time monitoring and auditing function module. Through a self-adaptive sparse auto-encoder and a dynamic clustering technology in the data cleaning and compression module, abnormal values in a bid evaluation expert database can be detected and cleaned, data compression is performed by adopting a self-adaptive dictionary coding algorithm, and the storage efficiency and query performance of the database are improved; based on a multi-modal information fusion and reinforcement learning method, accurate collection, dynamic updating and intelligent management of expert information are realized. The randomness and fairness of the bid evaluation expert extraction process are ensured by using a mode of combining deep reinforcement learning and a quantum random number generator in an extraction algorithm design module.

The invention provides a frequency difference compressor based on precision perception, a gradient compression method, equipment and a medium, which are used for carrying out data compression and transmission between a server and a client so as to reduce communication overhead in personalized federated learning, and relates to the technical field of data compression. The frequency difference compressor comprises an information bottleneck rarefaction unit, a frequency domain compression unit, a dynamic quantization unit and a differential coding unit. Non-key gradient redundant components of the original gradient data are removed through an information bottleneck rarefaction unit to generate sparse gradient data; generating a metadata packet containing the first N high-energy frequency domain coefficients and the positions and the number of the first N high-energy frequency domain coefficients through a frequency domain compression unit; performing adaptive bit width mapping on the high-precision floating point gradient data into low-order integer representation through a dynamic quantization unit to generate dynamic quantization gradient data; and the differential gradient is transmitted to the client through the differential coding unit, so that data compression and transmission are completed. The method solves the problems that the communication overhead is large and gradient information cannot be reserved as far as possible in gradient transmission.

The invention provides a real-time deep sea subsurface buoy monitoring system, which belongs to the technical field of deep sea measurement, and comprises a control chip, a multi-parameter sensor array, a data storage device and a power supply, high-precision monitoring is realized through the following steps: recording ocean data acquired by a multi-parameter sensor; correcting the sound wave propagation model and calculating the relative position of the subsurface buoy; constructing a three-dimensional data matrix and realizing data compression; identifying abnormal data by using a matrix disorder index; processing the time sequence data by adopting a sliding window weighted average method and eliminating jump; carrying out distributed preprocessing on the multi-source heterogeneous data and carrying out parallel processing by applying a matrix partitioning technology; accumulated errors are corrected by combining historical data and applying a Kalman filtering algorithm, and the problems of environment interference and sensor drift are solved through a deep sea environment disturbance compensation network model and a self-adaptive weighted error optimization function.

The invention provides a data interaction method and a data interaction system for a concentrator and a collector, which are applied to the technical field of data interaction. And the protocol adaptation layer dynamically selects a protocol conversion module to realize data protocol conversion based on the protocol type of the collector, the equipment ID and the network environment of the system and transmits the converted data to the concentrator. And the concentrator unpacks, analyzes, stores or decides the received data. After the concentrator processes the data, generated feedback data are returned to the corresponding collector through the protocol adaptation layer, and closed-loop control is completed. Based on a real-time data analysis result of the concentrator, the protocol adaptation layer automatically adjusts a protocol adaptation mode, a data compression strategy or a load balancing mechanism. Through multi-layer protocol discovery and dynamic adaptation, intelligent data flow optimization and real-time conversion, and distributed intelligent decision and autonomous adjustment processes, the data transmission efficiency is improved, the flexibility of the system is enhanced, and the data security is ensured.

The invention relates to the technical field of information processing, in particular to a hard mountain side slope digital twinborn monitoring method and system based on a cloud platform. The method comprises the following steps: obtaining real-time monitoring data of a slope in a hard mountain area, and carrying out data preprocessing to obtain standardized slope dynamic monitoring data; performing data compression and feature extraction on the standardized slope dynamic monitoring data, and transmitting a feature extraction result to a cloud platform to obtain cloud slope key information data; the method comprises the following steps: constructing a digital twinborn model by utilizing cloud side slope key information data based on a cloud platform to obtain a preliminary side slope digital twinborn model; through construction and optimization of the digital twinborn model, stability evaluation and risk early warning, the precision and real-time performance of slope monitoring in the hard mountainous area are remarkably improved, and a scientific basis and an executable long-term scheme are provided for slope treatment.

The invention provides a construction site safety information supervision system based on the Internet of Things, and relates to the technical field of data processing, the risk assessment accuracy is remarkably improved and the data noise interference is inhibited through a deep reinforcement learning dynamic optimization grading early warning strategy and in combination with a multi-source data compression and federated learning framework; a risk coupling model is constructed based on a complex network theory, a conduction path and a linkage effect between regions are analyzed, a global early warning scheme is optimized, and the adaptability of the system to a complex construction environment is enhanced; the model is continuously optimized through a dynamic weight updating mechanism, high adaptability is kept under the environment change of a construction site, meanwhile, a resource dynamic allocation strategy is adopted, the problem of low high-concurrency processing efficiency is effectively solved, and the overall accident early warning accuracy is improved.

The invention provides an edge fusion diagnosis system and method for multi-source heterogeneous power transmission monitoring data, and belongs to the technical field of power transmission line monitoring. The multi-source heterogeneous power transmission monitoring data edge fusion diagnosis system comprises a multi-source heterogeneous data access module, a data preprocessing and standardization module, an edge computing node, an edge node collaborative diagnosis module, a data compression and feature extraction module and a cloud fusion analysis platform. The method comprises the following steps: accessing multi-source heterogeneous data, preprocessing and standardizing, locally diagnosing by an edge computing node, triggering multi-node collaborative diagnosis when the credibility is insufficient, and uploading key feature data to a cloud for global analysis and model optimization. According to the method, comprehensive integration and efficient processing of multi-source data are realized, the real-time performance, accuracy and comprehensiveness of power transmission monitoring diagnosis are improved, and the efficient monitoring requirement of a complex power transmission network can be met.

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.

The invention relates to a method and a system for storing and backing up electric power data of a data-in-data station based on use popularity, and belongs to the technical field of data-in-data stations, and the method comprises the following steps: classifying electric power business data into hot data, temperature data and cold data based on preset access frequency, time window and data volume index; according to a classification result and a business importance degree, storing hot data in an SSD cloud disk or a memory database, storing temperature data in a distributed column database, and compressing cold data and then archiving the cold data to an object storage or a tape library; dynamically configuring differentiated backup strategies for different types of data; automatic scheduling of storage and backup is realized through a cloud platform component, and hot switching of master and slave databases, OSS archived data recovery by a DataWorks tool and a remote multi-copy recovery mechanism are supported; and migrating data in advance in combination with a historical access prediction popularity trend, and triggering cold data compression and archiving when storage resources exceed 80%. The method is suitable for power industry scenes, the storage cost is optimized, and the data calling efficiency is improved.

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.

The invention discloses an intelligent operation and maintenance method for modular data acquisition and analysis sewage treatment equipment, which comprises the following steps: designing a modular data acquisition unit, configuring an acquisition module with a standardized interface, and realizing quick access of multi-source equipment; protocol conversion, data compression and timestamp completion are completed by using an edge computing gateway, and stable data reporting under a complex network is realized by combining an MQTT protocol; a time sequence database is adopted to store and fuse OA / ERP system data, and an information island is broken; remote diagnosis, intelligent early warning and visual decision making are realized by means of a remote fault analysis interface, a user-defined rule engine and a configurable billboard; and operation and maintenance closed-loop management is achieved in cooperation with a workflow engine. The method effectively solves the problems of low data acquisition integration level, unstable communication, weak analysis capability and the like in traditional operation and maintenance, has the characteristics of modularization, intelligence, high efficiency and the like, remarkably improves the operation and maintenance management level of the sewage treatment equipment, reduces the operation and maintenance cost, and has a wide application prospect.

A unified platform for multi-modal data compression and decompression that enables efficient processing of correlated data streams while preserving relationships between different modalities. The platform employs a virtual management layer to analyze and route input streams, implementing correlation analysis to identify temporal and spatial relationships between streams. Multiple compression methods, including neural network-based approaches, are utilized to compress data sets while maintaining cross-modal dependencies. A neural upsampling system leverages learned correlations between streams to enhance reconstruction quality. The platform includes a synchronization manager that maintains temporal alignment and relationship preservation throughout processing. By integrating correlation-aware compression with neural upsampling techniques, the platform provides comprehensive multi-modal compression capabilities while preserving critical relationships between different data types. The system is particularly suited for applications involving synchronized audio-visual data, sensor streams, and other multi-modal content.

The invention provides a data acquisition method and device based on an industrial internet identification analysis system, which are applied to the technical field of industrial internet, and are used for realizing data grading processing by acquiring data and priority parameters and distributing transmission queues according to the priority parameters; the method comprises the following steps: compressing and coding data, evaluating the quality of a wireless channel, selecting a transmission path, avoiding an interference channel, determining a retransmission strategy by combining a data priority and a network congestion state when transmission fails, receiving a data packet at an edge computing node according to the retransmission strategy, and uploading the data packet after local processing. The pressure of a core system is dispersed by utilizing edge computing power, and the overall data processing time delay can be reduced. Therefore, according to the method, through the steps of data priority distinguishing, data compression, channel quality evaluation, intelligent retransmission strategy, edge computing application and the like, the beneficial effect of anti-interference and low-delay data transmission in a wireless communication factory environment is achieved.

The invention provides a data transmission method in an edge computing power secure channel, and the method comprises the steps: obtaining a real-time residual energy value, node position distribution information and node historical energy consumption data of each edge node in a network, constructing a node energy prediction model through a time sequence analysis method, obtaining a predicted energy consumption rate and predicted residual energy of the node in a future period of time; the signal-to-noise ratio, the stability and the interference level of links between weak nodes and adjacent nodes in the energy depression area are obtained, the weak nodes are nodes of which the predicted residual energy is lower than a network average value preset threshold value and which are located in the energy depression area, and adaptive power control and data compression are performed on the weak nodes; and in combination with edge computing power secure channel connectivity, edge computing power secure channel coverage rate and end-to-end data transmission delay factors, network life cycles under different data transmission strategies are evaluated through a Monte Carlo simulation method, and optimal transmission parameter configuration for prolonging the network life cycles is obtained.

The invention discloses a one-way optical shutter video stream transmission method based on weak supervision semantic segmentation, and belongs to the technical field of one-way optical shutter information transmission. Comprising the following steps: carrying out key region and background region classification and marking on all video data received by an optical shutter outer end through a weak supervision semantic segmentation model; performing priority division on a key region and a background region in the video frame; dividing the data packets according to time windows, and packaging the data packets; performing dynamic data compression and segmented coding on the packaged data packet; reed-Solomon redundant coding is carried out on all the data packets of the key area after compression coding is carried out on the data packets of the key area; dynamically adjusting the transmission sequence of the data packets to perform link scheduling; and a receiving end carries out de-encapsulation and data restoration, and reconstructs the restored video frame. According to the method, redundancy and compression of data transmission contents are accurately controlled, so that the transmission efficiency and reliability are remarkably improved, the bandwidth utilization is optimized, and the transmission reliability and stability are enhanced.

The invention relates to the technical field of video compression, in particular to a video monitoring data compression and transmission method and system, and the method comprises the following steps: analyzing the texture features of a video frame, calculating the gray gradient of each pixel, obtaining the texture change rate, detecting edge information, calculating the distribution density of edge pixels, and extracting a color histogram. And calculating the dispersion degree of color distribution. In the invention, a dynamic coding parameter adjustment mechanism based on content is introduced to analyze the video frame, a compression strategy is adjusted according to the content complexity, and the coding process can accurately meet the requirements of different video contents through comprehensive analysis on the texture change rate, the edge density and the color dispersion degree; according to the scheme, the coding parameters can be dynamically adjusted by monitoring the coded video quality in real time and comparing the coded video quality with the preset quality threshold value, and the storage and transmission of data are further optimized while the video quality is ensured.

A system and method for cross-domain knowledge transfer in federated compression networks. The system enables efficient lossless data compression across diverse data types by intelligently sharing compression strategies between domains. A cross-domain knowledge transfer system identifies relationships between different data domains, adapts compression parameters accordingly, and optimizes learning processes to maximize knowledge reuse. The architecture may include a knowledge repository for storing domain features and compression patterns, domain mapping components that identify similarities, and transfer learning optimization that enables efficient adaptation with minimal examples. This approach significantly accelerates model training for new domains while improving compression performance. Applications include satellite telemetry systems where efficient compression is critical for transmitting large information sets between distant locations. The system may employ probability prediction driven arithmetic coding paired with long short-term memory networks, enhanced by cross-domain knowledge sharing that adapts successful compression strategies from one domain to another while preserving domain-specific optimization.