87 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 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.

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.

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 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.

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.

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 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 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.

The present invention relates to a method and system for encrypted domain image steganography based on Huffman coding. The method comprises: obtaining the pixel value of each pixel block in the original image and encoding it based on Huffman coding to obtain an initial code table; encrypting the secret information based on an encryption algorithm to obtain encrypted data; embedding the encrypted data based on a least significant bit algorithm to obtain an embedded pixel value for each pixel block; and updating the initial code table based on the embedded pixel value to obtain an embedded code table. The present invention reduces the total number of bits of transmitted data and increases capacity, thereby increasing the amount of information embedded without affecting image distortion and providing high security.

This invention provides a method and apparatus for safety fusion source coding of the main longitudinal beam of a hydraulic flatbed trailer. Based on the characteristics of important safety parameter signals from the hydraulic sensors of the hydraulic flatbed trailer, a base-64 encoding method is used, and the code length is calculated. Then, Huffman coding is used, and the code length is calculated again. The base-64 encoding code length is compared with the Huffman encoding code length. If the base-64 encoding code length is less than or equal to the Huffman encoding code length, the base-64 encoding is output; otherwise, the Huffman encoding is output. This invention overcomes the deficiency of Huffman coding in terms of low coding rate for shorter data and employs a fusion source coding strategy. The two methods complement each other, achieving more stable wireless communication of important safety parameters of the main longitudinal beam of the hydraulic flatbed trailer.

The application discloses a lossless compression method and device for highway structure information monitoring data, and solves the problem of difficult transmission and storage of high-frequency massive highway structure information monitoring data, and belongs to the field of road engineering.The application comprises the following steps: intercepting original highway structure information monitoring data, storing the intercepted monitoring data and start-stop time stamp into a file f; performing first-order difference on the monitoring data in the file f to obtain f1; expanding the monitoring data after the first-order difference to remove zero bits and decimal point bits in the monitoring data to obtain a file f2; performing Huffman coding processing on the monitoring data in the f2, wherein each monitoring data is taken as a single value, each single value is coded according to the frequency of the single value, a binary file f3 is obtained, and the compression is completed.When it is necessary to restore the monitoring data, the binary file f3 is processed in a reverse process according to the characteristic of a symmetric algorithm, a decoding file f4 is obtained, and the data restoration is completed.

Methods and computing devices configured to perform enhanced coding operations (e.g., compression, decompression, LZ-based compression techniques, etc.) are provided. A computing device processing system may be configured to: receive an input data stream; querying a search table with a sequence of bytes (or characters, symbols, etc.) in the received input data stream to receive one or more location vectors for each byte; querying a matching table using the received location vectors to identify a start and an end of a matching sequence; and performing a historical buffer check in response to determining that the match fails in the match table. A computing device processing system may determine copy and text commands for an input data stream based on heuristics and results of queries to a search table, match table, or historical buffer, and perform enhanced Huffman coding operations to encode the copy and text commands into a compressed bitstream.

The invention discloses a GPU memory optimization method and system, and the method comprises the steps: obtaining the real-time change characteristics of a task load through obtaining the dynamic data flow information of the task load and an intermediate data generation rule; according to the real-time change characteristics of the task load, an optimization algorithm based on gradient descent is adopted to determine the distribution proportion and the reserved space of a memory pool, and adjusted memory pool configuration is obtained; extracting available memory block information from the adjusted memory pool configuration, and compressing the intermediate data with low access frequency by adopting a Huffman coding algorithm to obtain a compressed intermediate data storage unit; and if the access frequency of the compressed intermediate data storage unit is lower than a preset threshold value, temporarily storing the compressed intermediate data storage unit to a low-speed storage area, and reallocating memory resources according to the task priority to obtain an optimized memory allocation scheme. According to the method, the memory utilization rate is remarkably improved, the access delay is reduced, and efficient memory resource management is realized.

The application relates to the technical field of data storage, and discloses a data storage method based on a lossless compression algorithm, which receives data to be stored through a data interface, automatically identifies the data format and carries out feature analysis by using a data analysis tool to obtain the size, type and complexity of the data; through deep analysis of the data features, the application can flexibly select a suitable Huffman coding processing mode, optimize the compression process according to different data types and complexity; in addition, the application introduces an upper and lower context modeling optimization mechanism, dynamically adjusts the coding mode, and further improves the compression ratio and storage efficiency; through a block storage strategy and in combination with metadata management, the efficient storage and reliability verification of the data are ensured; the application not only solves the problem of low efficiency of the existing compression algorithm, but also improves the integrity and reliability of data storage, and is suitable for the storage requirements of large-scale data and diversified data.

This invention relates to the field of data processing technology, specifically to a blockchain-based method and apparatus for storing civil engineering test data. The method includes: acquiring civil engineering test data stored on the blockchain and real-time collected civil engineering test data, and preprocessing it to obtain analysis data; determining the importance, access frequency, and final repetition rate of character data based on the analysis data, and obtaining the encoding priority of the character data, adjusting the Huffman coding table; compressing all civil engineering test data according to the adjusted Huffman coding table, and re-storing it in the blockchain; and comprehensively adjusting the Huffman coding table based on the obtained encoding priority of each character data, compressing the data, and re-storing it in the blockchain. This method reduces the amount of data while maintaining access speed, thereby improving the reading speed for high-access data during subsequent data access.

Decoding a data stream which includes variable-length character-strings representing symbols which have been encoded in accordance with a variable-length prefix encoding scheme (such as Huffman encodi

The invention relates to the technical field of video compression, in particular to an underground coal mine video monitoring method and system, and solves the problems that in the prior art, traditional Huffman coding only depends on frequency distribution weight and ignores low-frequency important safety information in underground coal mine video compression, so that images in a potential safety hazard area after compression are blurred, and the video compression efficiency is low. And abnormal identification is influenced. The method comprises the following steps: acquiring an underground video monitoring image of a coal mine; performing region division on the video monitoring image, and determining importance weights of a plurality of regions based on image features and motion features in the regions; correcting an initial weight parameter used in video coding according to the importance weight to obtain a corrected target weight parameter; the initial weight parameter is determined based on the occurrence frequency of a gray value in the image; and performing Huffman coding compression on the video monitoring image by using the target weight parameter to generate compressed video data for transmitting to a ground monitoring center.

The invention discloses an information physical system safety protection method based on measurement output watermark coding, and relates to the technical field of control system safety. According to the method, a chi-square detector is helped to identify replay attacks and error data injection attacks, the method is designed based on a watermark encryption technology, a watermark element set is constructed, and watermark elements are selected from the watermark element set with equal probability, so that the aim of quickly and circularly generating watermarks is fulfilled. Sensor measurement data is marked by a randomly generated watermark, and is transmitted in a network in a ciphertext form after being coded. And the data transmission quantity can be effectively reduced while the data privacy is ensured by the safe Huffman coding. Compared with a protection strategy of adding a watermark into a control input signal, the method has more excellent performance in the aspect of identifying the replay attack, and due to the fact that the introduced watermark can be completely removed, zero control performance loss can be achieved.

This invention discloses a lossless data compression and encrypted transmission method based on federated middleware, belonging to the field of computer application technology. The method includes: performing run-length encoding on the data to be processed based on federated middleware to obtain first compressed data; processing the first compressed data using a compression algorithm based on a raw-middleware dual dictionary to obtain second compressed data. This compression algorithm uses both the raw dictionary and the middleware dual dictionary, adding only values ​​that appear at a set frequency to the dictionary; sequentially performing Huffman coding and DES processing on the second compressed data to obtain encrypted data and a key; performing RSA processing on the key to obtain an encrypted key; and transmitting the encrypted data and the encrypted key to the target end using an encrypted compression transmission plugin of an experiment-and-train-enabled architecture. This method achieves lossless data compression and encryption based on federated middleware, with fast algorithm speed and high security.