131 results about "Huffman coding" patented technology

The invention provides an intelligent short message scheduling method and device based on multi-dimensional dynamic optimization, and the method comprises the steps: obtaining the performance data of a plurality of short message channels, and calculating a channel health score based on a weight dynamic adjustment model; determining a scheduling strategy according to the priority identifier of the to-be-sent message, and performing channel screening and optimal matching; executing message sending and monitoring a sending state; terminal state detection is carried out on the failure message through operator base station signaling, and a decision tree model is applied to determine a retry strategy; and performing Huffman coding compression processing on the P2-level marketing messages which fail in retry, and performing batch sending in an idle window. According to the method, a comprehensive performance evaluation index and reward function model is also constructed, and parameter optimization is performed by applying a reinforcement learning algorithm. According to the invention, multi-dimensional dynamic channel scoring, intelligent retry decision making based on terminal state perception, batch processing with balanced cost-time efficiency and a closed-loop self-optimization system are realized, the short message delivery rate is obviously improved, and the invalid retry rate and the sending cost are reduced.

The invention provides a BIM intelligent management platform oriented to a whole life cycle of project construction. A building information model, Internet of Things sensing data and a block chain evidence storage mechanism are integrated through a multi-source data fusion technology, and a whole-process data chain of association planning, design, construction, operation and maintenance is associated. The platform adopts space optimization Huffman coding to realize model lightweight, combines a constraint genetic algorithm to optimize a construction path, and applies a bidirectional long-short-term memory network to analyze an equipment state. A three-chain block chain system is reconstructed on the architecture, intelligent association of engineering quantity and payment nodes is realized through cooperation of a main chain, a calculation quantity side chain and an auditing side chain, and mobile terminal offline interaction is supported based on a digital-analog separation technology. The platform covers an intelligent design management unit, a block chain investment management unit, a dynamic correction management unit, a quality safety responsibility tracing unit, an NLP risk management unit and a digital twin operation and maintenance unit. The units achieve cross-system cooperation through a unified data bus, and a closed-loop management architecture covering the whole life cycle of project construction is formed.

The invention relates to the technical field of hydrogeological monitoring, in particular to a hydrogeological dynamic monitoring and analysis system and method based on big data. The method specifically comprises the steps that hydrogeological data are collected in real time, edge calculation is combined for data denoising, space-time calibration and error correction, variational mode decomposition, geologic model constraint and wavelet transform are adopted for eliminating noise and drift errors, storage and transmission efficiency are optimized through incremental coding and Huffman coding, and quadratic form calibration codes are generated to ensure data reliability; a hydrological evolution trend is predicted by fusing a space-time deep learning model and a self-attention mechanism, and adaptive anomaly detection is realized by combining DBSCAN clustering and prediction error analysis; and optimizing a water resource allocation strategy through reinforcement learning, hierarchically evaluating a risk level and generating a management scheme, and finally visually displaying monitoring data, an abnormal score and a decision result. According to the method, multi-source data fusion, space-time modeling and intelligent decision making are integrated, and hydrological monitoring precision and response efficiency are improved.

The invention discloses an intelligent water level monitoring system and a data transmission method thereof, and belongs to the technical field of Internet of Things sensing. The system comprises a distributed water level sensing node, an edge computing gateway and a cloud management platform, wherein the sensing node is integrated with a piezoresistive water level transmitter and an adaptive Kalman filtering module; the data transmission method comprises a dynamic threshold triggering mechanism and a compression algorithm based on improved Huffman coding, and low-power-consumption and high-reliability transmission is realized by optimizing transmission frequency and a data packet structure. The edge computing gateway is internally provided with an LSTM water level prediction model, historical data are analyzed through an LSTM neural network, and an early warning signal is generated in real time. And the cloud management platform stores the key data by adopting a block chain technology. Through multi-sensor data fusion and layered encryption transmission, the water level monitoring error rate is reduced to + / -0.5 cm, the data transmission energy consumption is reduced by 40%, and the monitoring efficiency and the data safety are remarkably improved in flood early warning and reservoir management scenes.

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

A system and method for adaptive data processing combining compression and encryption. The system analyzes input data characteristics, compares probability distributions, and creates a transformation matrix to convert data into a dyadic distribution. It generates a main data stream of transformed data and a secondary stream of transformation information. The system dynamically selects and applies processing techniques, including transformation, encoding, compression, and encryption algorithms, based on analyzed characteristics and real-time performance metrics. It compresses the main data stream using Huffman coding and implements security measures to protect the output. A feedback loop monitors technique effectiveness, updates a knowledge base, and influences future selections. The system can operate in lossless, lossy, or modified lossless modes, adapting to different application requirements. This approach offers an efficient solution for scenarios where both data reduction and security are critical concerns.

The invention relates to the technical field of multi-sensor information fusion and intelligent manufacturing, and discloses a guide rail precision evaluation method and system based on multi-sensor fusion, and the method comprises the steps: constructing a multi-modal sensor value quantification model based on information entropy, calculating the contribution degree of sensor information and constructing a multi-sensor mutual information coupling model; constructing a self-optimization sensor network configuration system, abstracting a multi-modal sensor system into a graph structure, and generating optimal sensor combination configuration by applying a graph reinforcement learning algorithm; constructing a multi-time scale optimization framework and realizing adaptive data compression based on Huffman coding; constructing a dynamic Bayesian network model to fuse heterogeneous sensor data; constructing a self-evolution evaluation strategy system, and continuously improving an evaluation strategy through historical performance data; through information entropy-driven resource allocation, resources can be dynamically allocated according to the information value of the sensor.

A system and method for optimizing data compression and encryption using reinforcement learning. The system analyzes incoming data streams to extract statistical features and data characteristics, which are processed by a reinforcement learning engine to automatically configure a multi-stage compression pipeline. Each compression stage transforms data into optimized distributions, applies Huffman coding, and maintains full encryption using homomorphic operations. A performance monitor tracks compression efficiency, processing speed, and output quality in real-time, providing feedback to continuously improve the reinforcement learning model's decisions. The system can dynamically adjust between one to five compression stages and select appropriate compression methods, including traditional algorithms or neural network-based approaches, based on data characteristics and performance requirements. All processing occurs on encrypted data without requiring decryption, ensuring complete data security throughout the pipeline. The adaptive nature of the system enables optimal compression performance across diverse data types while maintaining encryption integrity.

The invention relates to the technical field of data processing, and discloses a logistics data transmission verification method and system based on hierarchical priority. The method comprises the steps of collecting logistics data, establishing a grading system according to business importance, and generating a weight matrix and a sensitivity matrix; calculating a data change difference value, and performing weighted operation with the matrix to output a deviation vector; extracting position, state and time information in the vector, and calculating correlation degree and consistency to generate a multi-dimensional check value; allocating coding bit length according to the check value, and performing dynamic Huffman coding to generate a data packet; and verifying the data packet, performing incremental decoding to recover information, and reconstructing a database state. The logistics data transmission verification method and device solve the problems that an existing logistics data transmission verification method is low in verification efficiency, cannot adapt to business differentiation requirements and is too large in verification overhead in a bandwidth limited environment, and the efficiency and pertinence of logistics data transmission verification are improved.

The invention relates to the technical field of space flight and aviation, in particular to a low-delay communication control system and method for an analog machine in a fixed flight training course. The control system comprises a multi-source sensor fusion module, a data simplification processing module, a dynamic time delay optimization communication framework, a distributed intelligent decision-making terminal, a virtual-real mapping synchronous controller, a milli delay regulation and control module and an intelligent sensing alarm interaction module. According to the invention, 4G, 5G and satellite double links are integrated, an optimal link is dynamically selected by adopting a Q-Learning algorithm, millisecond-level time delay control is realized, transmission reliability is improved through a packet loss rate compensation algorithm, multi-sensor data is fused based on an FPGA hardware accelerator and an EKF algorithm, and a data compression ratio is improved in combination with Huffman coding and Kalman filtering. The bandwidth requirement is greatly reduced, through software and hardware collaborative optimization, the end-to-end time delay is reduced to be within hundreds of milliseconds, the virtual-real synchronization precision is improved to the centimeter level, and the decision response speed is improved by two times.

The invention relates to the technical field of industrial data compression, in particular to an industrial operation and maintenance data hybrid compression storage method, system and device and a medium. High compression rate and lossless storage are realized by distinguishing time sequence data from non-time sequence feature vectors and respectively adopting Huffman coding and LLM-driven arithmetic coding; and through efficient compression processing, especially effective reduction of feature vector redundancy, the space required by data storage is greatly reduced, so that the storage cost of the operation and maintenance data of the generic semiconductor industrial equipment is reduced.

The invention discloses a four-dimensional multi-component phase modulation transmission and receiving method based on information source entropy driving symbol extraction in the technical field of high-dimensional phase modulation, and the method comprises the steps: obtaining the information source entropy of a target information source, calculating the number of information source symbols, and generating a simplified four-dimensional symbol unit; determining a bit index of each symbol to construct a mapping table; selecting the number of constellations, and distributing a corresponding Huffman code for each constellation point; performing interleaving processing on the bit sequence after Huffman coding by combining a bit interleaver, converting the bit sequence into a corresponding decimal numerical value, and performing bit-to-symbol mapping by matching a corresponding bit index through a mapping table to obtain a mapped symbol sequence; and modulating the mapped symbol sequence to a target wave band for transmission. According to the method, the spectrum efficiency and the transmission rate of a wireless communication system are remarkably improved, the symbol mapping efficiency is improved, the anti-interference capability of the system is effectively enhanced, the stability of data transmission is improved, and the error rate is also remarkably reduced.

The invention relates to a method for improving real-time performance of a tactile sensor. The method comprises the following steps: acquiring a vibration signal of the tactile sensor, predicting vibration signal distribution by using Gaussian process regression (GPR), predicting a potential range of an effective sensing point in a sampling stage, obtaining a two-dimensional Gaussian probability distribution function, and determining a central point of the two-dimensional Gaussian probability distribution function and variances in x and y directions; setting the number M of randomly sampled points according to the resolution of the tactile sensor, randomly generating a plurality of groups of M two-dimensional points by using a two-dimensional Gaussian probability density function, and calculating the distance from each two-dimensional point in each group to a central point as the radius of a circular ring; each two-dimensional point corresponds to a circular ring radius, and the rings are arranged in sequence to form a differential pressure value matrix; and quantizing the matrix through a DPCM algorithm to obtain a differential pressure value matrix, and then performing Huffman coding compression to realize real-time transmission of the tactile sensor data. The real-time performance can be improved on the premise of ensuring the sampling accuracy.

The codebook-based homomorphic compression system is a novel approach that combines data compression and homomorphic encryption to enable efficient and secure computation on compressed data. It involves quantizing the input data, generating an optimized codebook using techniques like Huffman coding or deep learning, and compressing the data by replacing each value with its corresponding codeword. The compressed data is then encrypted using a homomorphic encryption scheme, such as the Paillier cryptosystem, allowing computations to be performed directly on the encrypted compressed data without decryption. Homomorphic properties of the encryption scheme enable operations like addition and multiplication on the ciphertexts, while preserving the confidentiality of the underlying data. The system also incorporates error correction techniques to mitigate the impact of quantization and encryption on the accuracy of the computations. This approach combines the benefits of data compression and homomorphic encryption, enabling efficient storage, transmission, and secure computation on compressed data.

The invention relates to the technical field of data transmission, and discloses a multi-mode intelligent modulation data transmission method based on channel state perception, and the method comprises the steps: obtaining channel state data, and determining a channel state parameter and a fluctuation parameter of a channel according to the channel state data; determining a channel state comprehensive parameter according to the channel state parameter and the fluctuation parameter, and determining a modulation mode of transmission data according to the channel state comprehensive parameter; transmitting the transmission data according to the modulation mode, monitoring a signal transmission error rate and a signal transmission delay of the transmission data, and determining a coding parameter of the transmission data according to the signal transmission error rate and the signal transmission delay; and carrying out Huffman coding on the transmission data according to the coding parameters, and carrying out compression transmission on the coded transmission data. According to the invention, the optimal modulation mode can be selected for the channel state to modulate the transmission data, and the transmission data is compressed and transmitted through the signal quality, so that the data transmission efficiency is effectively improved.

The invention relates to the technical field of spacecraft satellite-ground communication, and provides a low-earth-orbit satellite communication time delay optimization method and system aiming at the defects that a traditional low-earth-orbit satellite communication satellite system and a satellite communication frame have high time delay and poor real-time communication function. The core of the invention comprises three parts: firstly, an efficient receiving and transmitting end coding and decoding technology: a transmitting end adopts a subblock coding technology of a Turbo code; and a receiving end uses an MAX-Log-MAP algorithm to carry out rapid iterative decoding on the word block code of the Turbo code. And secondly, compressing the user data: carrying out lossless compression on the user data and a satellite communication frame header by using Huffman coding, reducing the transmission data volume, improving the data processing speed, and thus reducing the processing time delay. And finally, simplifying the frame header design: optimizing the frame header of the communication frame so as to reduce the total length of each frame. The method is particularly suitable for communication satellites (the orbit height is less than or equal to 1500km) in a low earth orbit.

The invention discloses a hardware acceleration method and system for a ZSTD data compression algorithm based on an FPGA. Unified hardware implementation and collaborative optimization are carried out on three key links of LZ77 character matching, Huffman coding and finite state entropy coding in the ZSTD data compression algorithm. According to the system, a multi-channel parallel LZ77 character matching hardware architecture is adopted, and character matching is executed on continuous positions in a plurality of parallel windows at the same time. A modular design and a deep pipeline structure are adopted, and a buffer and pipeline mechanism is introduced among modules such as an LZ77 matching module, a Huffman coding module and a finite state entropy coding module. The 375MB / s compression throughput can be achieved under the 100MHz clock frequency, the compression speed is remarkably increased while the compression ratio is guaranteed, and the method is suitable for scenes with high requirements for compression performance of data centers, edge computing and embedded systems and has high practicability and popularization value.

The application discloses a cryptographic algorithm debugging data hierarchical grouping compression method for a reconfigurable cryptographic chip. In the debugging work of the reconfigurable cryptographic chip, the debugging data is divided into reconfigurable cryptographic algorithm configuration information and operation intermediate data. The debugging data is processed by hierarchical grouping, the debugging data of each level module is divided into one or more groups, then the debugging data is filled or spliced, and then the reconfigurable debugging data is compressed by using a Huffman coding mode. After the hierarchical grouping compression method of the debugging data is used, the transmission performance of the debugging data can be effectively improved while the storage resource of the debugging data is reduced.

A system and method for adaptive data processing combining compression and encryption. The system analyzes input data characteristics, compares probability distributions, and creates a transformation matrix to convert data into a dyadic distribution. It generates a main data stream of transformed data and a secondary stream of transformation information. The system dynamically selects and applies processing techniques, including transformation, encoding, compression, and encryption algorithms, based on analyzed characteristics and real-time performance metrics. It compresses the main data stream using Huffman coding and implements security measures to protect the output. A feedback loop monitors technique effectiveness, updates a knowledge base, and influences future selections. The system can operate in lossless, lossy, or modified lossless modes, adapting to different application requirements. This approach offers an efficient solution for scenarios where both data reduction and security are critical concerns.

The present invention relates to the technical field of data processing. More specifically, the present invention relates to a method and system for off-site data migration and backup. The method includes: obtaining the temperature and current during machine operation and constructing a collaborative relationship curve; using the extreme points of the collaborative relationship curve to segment the collaborative relationship curve, calculating the difference between the normality of any data in any segment and the average normality of adjacent data, and obtaining the anomaly index of the data. Calculating the interference degree of the data, correcting the temperature or current at the corresponding moment based on the interference degree, the correction direction is towards the adjacent data, the correction amplitude is positively correlated with the interference degree, and using the Huffman coding algorithm to compress the corrected values of the temperature and current for migration and backup. The present invention can improve the compression efficiency of data and the accuracy of the compression result during the data migration and backup process.

The invention relates to the technical field of data compression, in particular to a real-time lossless compression method for linear scanning panoramic infrared image data. The method comprises the following steps: receiving current column original pixel data output by a linear column scanning infrared sensor; taking the original pixel data of the current column and the coded pixel data of the adjacent column in one frame as reference columns, and performing backward inter-column difference prediction to obtain predicted residual data of the current column; mapping the predicted residual data into a symbol value; carrying out entropy coding on the symbol value by using a static Huffman coding table which is pre-generated and solidified based on the statistical characteristics of the linear scanning infrared image, and outputting a compressed code stream; according to the scheme, the technical problem that high real-time performance and high compression ratio are difficult to consider when real-time lossless compression is carried out on linear scanning panoramic infrared image data in the prior art is solved.

A multi-stage process for homomorphic end-to-end data compression and encryption is claimed. The first stage analyzes input data, applies transformations to increase compressibility and security, produces conditioned data and error streams, transforms data into a dyadic distribution, creates a transformation matrix, performs Huffman coding, and combines Huffman-encoded data with a secondary transformation stream. A second stage further processes the compressed and encrypted output, either repeating the first stage's steps or using an autoencoder for additional compression. The system supports multiple compression stages, each building on the previous one. Decompression reverses these stages while maintaining homomorphic encryption throughout. This approach enables secure data transmission, storage, and analysis in bandwidth-constrained environments, adapting to various data types and compression levels while preserving data quality and security.

This invention discloses a hardware acceleration method and accelerator for Transformer based on mixed-precision quantization and Huffman coding. The acceleration method includes: using a genetic algorithm to obtain several configuration schemes for mixed-precision quantization of Transformer network layers; performing mixed-precision quantization on each Transformer network layer based on each quantization configuration scheme to obtain a corresponding KL divergence; training a multilayer perceptron to obtain a quantization configuration prediction network using the quantization configuration scheme and the corresponding KL divergence as the output label and input feature, respectively; receiving a user-set target KL divergence value, using the quantization configuration prediction network to obtain the corresponding quantization configuration scheme, and performing mixed-precision quantization on each network layer based on the quantization scheme; and using Huffman coding to encode and compress all quantization weights before on-chip storage. This invention can reduce storage and computational overhead while maintaining model accuracy.

The application provides a decoding method, device and equipment for a Huffman coded image based on a maximum code word, wherein whether the current group of data to be decoded in a data buffer is a polysemous word is judged, if not, the current group of data to be decoded is compared with each maximum extended code word value in turn, if the current group of data to be decoded is less than or equal to a first maximum extended code word value, a first real code length corresponding to the first maximum extended code word value is found in a mapping relationship, and bit data with the first real code length is read from the current group of data to be decoded as a first Huffman code word. In the embodiment of the application, the data to be decoded is first distinguished, the real code length of the current group of data to be decoded is determined after it is determined that the data to be decoded is not a polysemous word, the number of invalid comparison times in the decoding process is reduced, and the decoding efficiency is improved.

The present invention relates to the field of video compression technology, and specifically relates to a belt coal conveying video transmission system for intelligent coal mine monitoring. The system includes a video data acquisition module for extracting data; a video compression feature extraction module for determining the compression feature indexes corresponding to pixel points in the belt video frames; a video compression priority division module for determining the compression priorities of pixel points in the belt video frames; and a video compression transmission module for compressing and transmitting the belt video frames and the corresponding three-dimensional point cloud data using Huffman coding according to the compression priorities. The present invention improves the compression efficiency of relevant data with coal conveying features in the belt video frames.

The present application provides a method, apparatus, device and storage medium for embedding parameter caching. The method obtains the embedding parameters of at least one embedding table into the CPU memory and stores some hot spots of the embedding parameters into the GPU memory. Based on the coding mechanism of Huffman coding, the feature ID of the at least one embedding table is encoded to generate a global key with a unified format and global uniqueness. The global embedding cache table is used as the storage form of the GPU cache, and the global embedding cache table uses the global key as the key to index the embedding table. The unified indexing technology is used to offload the index queries in the preset number of CPU memories to the GPU memory. When reading and writing the cache, the copy operation of the embedded parameters is decoupled from the index operation of the cache. The adoption of this technical solution can solve the embedded access requirements and alleviate the performance problems during model estimation.

The present invention relates to the field of data processing, and particularly to an intelligent electric meter data optimization storage method. It performs normalization, smoothing, and outlier processing on multi-dimensional time series data such as electric energy, current, voltage, and power. Subsequently, it uses the autocorrelation function to perform periodic analysis on time series such as power, voltage, and current to determine the periodic intensity of the data. Finally, it compresses the data through pulse code modulation (PCM), differential pulse code modulation (DPCM), and Huffman coding, thereby effectively improving the data compression rate and reducing redundancy, ensuring the optimization of data transmission efficiency and storage space. The present invention compresses the electric meter data through periodic analysis and Huffman coding, thereby improving the storage efficiency and transmission efficiency.

The invention discloses a heart sound signal acquisition and automatic classification method based on deep learning, and aims to solve the problems that wireless transmission time delay of heart sound signals is high, weak pathological features are difficult to extract and classification accuracy is low. The method comprises the following steps: acquiring signals of four auscultation areas by using a high-fidelity wireless digital stethoscope; high-speed and low-delay transmission of medical-level signals is realized by utilizing a star flash transmission module and combining predictive coding and Huffman coding compression mechanisms; in the preprocessing stage, self-adaptive wavelet denoising based on signal energy entropy is adopted, a multi-resolution fusion feature extraction strategy is provided, the problem of time-frequency resolution tradeoff is solved through narrow window and wide window dual-channel parallel processing, and transient positioning features and pathological frequency domain textures are captured at the same time; the classification model adopts a multi-scale parallel convolution structure and is matched with a recurrent neural network and an attention mechanism, so that the problem that transient and continuous heart sound features are difficult to consider at the same time in a single scale is solved. According to the method, the accuracy and robustness of heart sound classification are remarkably improved.

The present invention proposes a normalized Huffman coding method and a neural network computing chip, comprising: performing run-length all-zero coding on the neural network data to obtain run-length compressed data, wherein the run-length all-zero coding includes run-length coding only for zero characters in the neural network data; performing Huffman coding on the run-length compressed data, and adding 1 to the leaf node codes in each level of nodes in the coding result from top to bottom and shifting them left to move the leaf nodes in each level of nodes in the coding result to the left side of the tree structure, thereby generating a normalized Huffman coding of the coding result as the compression result of the neural network data.

The invention discloses a compressed XML database type, system and method supporting efficient query, and relates to the technical field of XML databases, and the database type adopts a hierarchical compression storage architecture and a reverse search query architecture; in the storage stage, XML non-keyword information is coded through Huffman coding, recoverable ordered arrangement is carried out on the coded information through a tree structure, then transcoding is carried out, the information is stored in a disk in a binary form, and compared with a traditional XML database storage mode, at least 50% of storage space is saved. In the query stage, a reverse search mode is adopted, a corresponding Huffman code initial position is positioned through a keyword, then under the optimization effect of a reverse index, reverse search is performed to recover a complete XML structure, and the query complexity is reduced from traditional O (n) to O (1). According to the method, the storage efficiency and the query performance of the XML data are greatly improved, and the method is suitable for large-scale XML data management and application scenes.