128 results about "Differential coding" patented technology

The invention discloses a cross-platform virtual-real fusion scene construction method and system based on AI space calculation, and relates to the technical field of artificial intelligence and space calculation, and the method comprises the steps: carrying out the multi-scale feature fusion based on a received cross-modal conversion instruction set, and generating an initial image sequence; carrying out implicit field coding on a target object by combining a three-dimensional reconstruction algorithm to obtain an initial parameterized model; performing space-time alignment on the multi-view video stream, loading a digital scene asset package in combination with physical sensing data and a preset spatial index structure, and establishing a bidirectional data channel between a virtual scene and a physical sensor; performing rendering and illumination parameter adjustment on the initial parameterized model to obtain an optimized parameter model; performing differential coding processing on the optimization parameter model to obtain a target virtual-real scene fusion model; and distributing the target virtual-real scene fusion model to a preset terminal. The invention provides a virtual-real fusion construction method for end-to-end collaborative optimization, which is suitable for cross-platform live broadcast or dynamic interaction scenes.

The invention provides an unstructured data compression method and device, equipment and a storage medium, and the method comprises the steps: carrying out the feature extraction and clustering processing of a to-be-compressed unstructured data set, and obtaining a data segment clustering group; performing semantic region segmentation processing on each data fragment in the data fragment clustering group to obtain a semantic important region and a semantic non-important region; performing differential coding processing on the semantic non-important regions in the same clustering group to obtain differential coding data; and performing high-quality compression processing on the semantic important regions in the same clustering group, and performing merging processing on the compressed important semantic data and the difference coding data to obtain an unstructured data compression result. According to the method, semantic region segmentation and differential compression processing are performed on the unstructured data, so that the data compression efficiency can be effectively improved on the premise of ensuring the quality of important information, and an efficient solution is provided for storage and transmission of large-scale unstructured data.

The invention relates to an intelligent water affair remote monitoring and control system based on the Internet of Things, in particular to the field of intelligent water affair remote monitoring and control, and the system comprises the steps: firstly, pre-training a long and short-term memory network model through a cloud, and generating a lightweight edge prediction model to reduce calculation and transmission loads; the data transmission module effectively reduces the occupation of transmission bandwidth based on a dynamic quantization threshold value of a residual error and a differential coding technology, meanwhile, the high precision of the data is kept, the verification feedback module monitors the accuracy of the transmission data in real time through confidence coefficient detection, and triggers model re-calibration when the confidence coefficient is reduced, so that the long-term stability of the system is ensured, and the reliability of the system is improved. The sampling optimization module dynamically adjusts the sampling frequency according to the confidence coefficient and the residual fluctuation characteristics, balance is achieved between bandwidth occupation and reconstruction precision by using the Lyapunov optimization theory, the real-time performance and accuracy of water affair monitoring are improved, and meanwhile bandwidth consumption and energy consumption are effectively reduced.

The invention discloses a method for aggregating ocean buoy data, which comprises the following steps of: establishing a space-time correlation model according to the spatial distribution density of buoy clusters and the dynamic characteristics of an ocean environment, and adaptively dividing buoy groups with a data compression cooperative relationship; performing a data-related compression operation within the buoy group; constructing a transmission priority dynamic sorting engine, and calculating a dynamic weight coefficient; performing hierarchical packaging processing on the differential coding data, distributing enhanced forward error correction coding resources for high-priority data, and reserving redundant transmission time slots; the method comprises the following steps: receiving a hierarchical data packet through a low-orbit satellite and executing on-satellite preprocessing, reconstructing an original data stream by using a distributed decoder, and meanwhile, fusing multi-dimensional space-time related data to generate an aggregated data product; and finally, the aggregated data product is transmitted to a ground data center through a satellite downlink. According to the invention, uplink data volume is reduced, disaster monitoring timeliness is improved, ground station processing load is reduced, and system environment adaptability is enhanced.

The invention discloses a heterogeneous network converged communication control method and system for a vehicle-mounted terminal, and relates to the related technical field of communication control, and the method comprises the steps: determining a plurality of original signal fragments transmitted to a receiving end by a plurality of heterogeneous networks based on the vehicle-mounted terminal; a vehicle-mounted terminal is provided with an intermediate fusion node, and the intermediate fusion node is provided with an interference detection module and a differential processing module; carrying out adaptive signal differential coding processing according to a synchronous receiving constraint condition of a receiving end and the plurality of link interference detection signals, and outputting a plurality of differential signal fragments; and respectively sending to a receiving end for signal restoration. The technical problem that in the prior art, when a vehicle-mounted heterogeneous network sends the same or fragmented signals at the same time, due to dynamic variation of network link interference and transmission delay difference of all paths, signals at a receiving end are out of step and quality is deteriorated is solved. The technical effects that the receiving end efficiently and synchronously restores the signal, and the anti-interference performance of signal transmission and the reliability of heterogeneous network converged communication are improved are achieved.

The invention discloses a fire alarm and access control linkage method and system. The method comprises the steps of signal receiving and verification, target access control determination, intelligent control release execution, anti-interference processing and cross-brand adaptation. The system comprises a multi-channel signal transmission module, a double-loop verification module, a central control unit, an intelligent control release execution module, a secondary release trigger module, an anti-interference processing module and a protocol adaptive conversion module, the problem of signal interruption caused by dependence of a traditional single channel is solved; the intelligent unlocking execution module ensures thoroughness in unlocking through graded current monitoring and a secondary release mechanism; the anti-interference processing module adopts differential coding and frequency hopping communication to resist electromagnetic interference; the protocol adaptive conversion module realizes seamless integration of multi-brand equipment, reduces the cost and maintenance difficulty, and improves the reliability and practicability of the system.

The invention discloses a centralized phased array beam control method and system based on an FPGA, and relates to the technical field of phased array beam control, and the method comprises the steps: carrying out the real-time iterative operation through an incremental adaptive correction kernel in a centralized control FPGA, and obtaining an amplitude-phase correction increment and a corresponding target effective frame number; performing increment differential coding on the amplitude-phase correction increment and the corresponding target effective frame number in a centralized control FPGA (Field Programmable Gate Array) to generate an increment differential beam control message; sending the increment difference beam control message to an array element FPGA (Field Programmable Gate Array) at the front end of an array, executing frame-level atom switching according to a target effective frame number, and fusing the amplitude-phase correction increment and a locally stored old beam control weight in the switching process to obtain a final array element beam control weight; generating a real-time beam control directional diagram based on the final array element beam control weight; the stability and convergence precision of beam control are improved, and the bandwidth requirement of a control link is reduced.

The invention discloses a music database retrieval method and system based on feature extraction, and relates to the technical field of data analysis, and the method comprises the steps: carrying out the preprocessing of an audio signal of a query track input by a user, dividing the audio into equal-length frame sequences, and generating three-channel spectrogram tensors, including a Mel spectrogram, a logarithmic amplitude spectrogram and a CQT spectrogram; extracting multi-modal features of the equal-length frame sequence, dividing melody motivation segments according to pitch change and stability of the audio signal, performing differential coding, generating a melody differential sequence, and modeling the melody differential sequence into a melody topological graph; and calculating the structural similarity between the melody topological graph and a database melody graph, screening a similar melody candidate set P, mapping a melody curve into a topological manifold through a topological data analysis method, extracting persistent homology features, and converting the persistent homology features into a persistent bar graph. According to the method, the retrieval precision and the matching credibility of the complex melody in the music database are remarkably improved.

The invention provides a special high-throughput satellite communication terminal for electric power, and relates to the technical field of electric power communication, and the special high-throughput satellite communication terminal comprises a baseband processing module which is used for carrying out differential encryption processing on different types of electric power data; the high-throughput satellite radio frequency module is used for suppressing sidelobe interference while realizing automatic satellite beam tracking through a beam forming algorithm during satellite communication, and calculating the optimal transmitting power through a self-adaptive power regulation algorithm; the communication transmission module is used for presetting a data priority based on a priority mapping table during data transmission and preempting satellite beam resources according to the data priority; the electric power Internet of Things protocol adaptation layer is used for realizing conversion between an electric power dedicated protocol and a satellite IP protocol, differential coding compression of electric power data and interrupted transmission in a link; and the beam optimization module is used for dynamically optimizing the beam pointing angle based on the satellite orbit parameters and the terminal geographic position. According to the scheme, the coverage degree of electric power communication in remote areas can be improved, and the safety and reliability of data transmission are improved.

The invention provides an on-site video return coding method and system, and the method comprises the steps: collecting a video stream of an on-site scene, judging whether a current video frame in the video stream responds to a scene switching video frame or not, and marking the current video frame as a key frame when the current video frame responds to the scene switching video frame; after the current video frame is identified as a key frame, visual saliency analysis is performed on the key frame, and an entropy weight matrix representing regional information importance distribution in the key frame is constructed according to a visual saliency analysis result and texture information entropies of different blocks in the key frame; adjusting code rate allocation weights of different blocks in the key frame according to the entropy weight matrix and a code rate regulation and control strategy of the key frame to obtain adaptive coding configuration adaptive to content characteristics of the key frame; and performing optimization coding on the key frame through adaptive coding configuration to obtain a target code stream in response to a field video return demand. By adopting the scheme of the invention, the dynamic differential coding of the key video frame in a complex scene can be realized.

The invention discloses a pathogenic microorganism detection system based on nanopore sequencing and intelligent interpretation. The system comprises six modules including a biomolecule electric signal acquisition module and a pathogen characteristic sequence extraction module. Ion current change signals are collected through a nanopore chip array, a pathogen characteristic sequence is extracted through differential coding, a hypergraph topological structure self-adaption building module is used for building a pathogen relation model, a multi-dimensional heterogeneous pathogen parameter fusion analysis module integrates morphological parameters, metabonomics parameters and other multi-dimensional parameters, and the multi-dimensional heterogeneous pathogen parameter fusion analysis module is used for analyzing the multi-dimensional parameters. The optimized hypergraph neural network reasoning module realizes pathogen category prediction, and finally, the nanopore sequencing result intelligent interpretation output module generates a detection report containing pathogen categories, subtypes and risk levels. The system is combined with nanopore sequencing and an intelligent algorithm, pathogenic microorganism detection can be efficiently and accurately completed, the accuracy, reliability and efficiency of detection are greatly improved, and the requirement for rapid and accurate detection of pathogens in practical application is met.

The invention provides a power plant high-frequency data transmission method based on a lossless compression algorithm. The power plant high-frequency data transmission method comprises the following steps of performing quality preprocessing on original high-frequency data; implementing hierarchical acquisition and periodic packaging according to data real-time requirements; performing differential coding for the data type: performing run-length coding on the switching value, and performing differential coding on the analog quantity; carrying out unified lossless compression on the coded data by using a Zstand-ard algorithm; the compressed data is encrypted and transmitted to an industrial internet platform, challenges confronted by a speed regulation system are handled through the steps, automatic optimization and dynamic adjustment of a data compression algorithm are achieved so as to adapt to the requirements of different application scenes, and the stability and efficiency of power production are improved.

The invention provides a difference and dictionary mixed compression method and device for time series data of the Internet of Vehicles, and relates to the field of data processing of the Internet of Vehicles. The method comprises the following steps: acquiring original data corresponding to a target vehicle in the Internet of Vehicles; performing first-order differential coding on the original data to obtain a first-order differential sequence; obtaining a corresponding differential disturbance factor in the first-order differential sequence through a preset model, and calculating the redundancy corresponding to the first-order differential sequence based on the differential disturbance factor; when it is confirmed that the redundancy is greater than a preset redundancy threshold value, performing second-order differential coding on the first-order differential sequence based on a differential disturbance factor to obtain a second-order differential sequence; replacing a target character string in the second-order difference sequence by using a dictionary index through a dynamic dictionary; and carrying out compression packaging on the second-order difference sequence and the dictionary index, and uploading the second-order difference sequence and the dictionary index to an Internet of Vehicles cloud. The problem that the subsequent compression efficiency is greatly reduced due to the fact that an existing method cannot adapt to the time sequence structure difference of the Internet of Vehicles data in different abnormal scenes is solved.

The invention discloses a graph convolutional network accelerator and equipment based on dynamic compression and sparse reordering, the graph convolutional network accelerator comprises a combination engine, an aggregation engine, a controller and a storage unit, the aggregation engine comprises an ECSR decoder, a scheduler and a plurality of multi-core SIMD processors which are connected in sequence, the ECSR decoder is used for extracting side information from an adjacency matrix represented by a sparse compression format ECSR, the adjacency matrix represented by the sparse compression format ECSR comprises a row pointer array and a column index array, the row pointer array comprises row numbers containing non-zero elements in the adjacency matrix, the column index array stores column position indexes of all rows containing the non-zero elements, and the column position indexes of all the rows containing the non-zero elements are stored in the column index array. The column position index is generated by adopting differential coding and variable byte coding. The method aims at improving the effective data throughput rate and the memory access efficiency of graph convolutional network acceleration, improving the calculation performance during feature updating and accelerating the reasoning process of the graph convolutional network.

The invention discloses a smart city-oriented multi-modal data transmission optimization method. The method comprises the following steps of collecting and preprocessing multi-modal original data generated in a city; performing joint feature extraction by using an improved CorrNet network and outputting an importance label; collecting operation data and carrying out regular analysis on the urban network to generate network state information; the multi-modal features, the importance labels and the network state information are associated and analyzed, and a coding control instruction is generated; performing differential compression and coding processing on the multi-modal data to generate a to-be-transmitted data queue; path calculation and resource allocation analysis are carried out, and a scheduling control instruction is generated and implemented through a tabu search method; and generating and uploading a transmission performance evaluation result based on the feedback index and the network state information. According to the method, multi-modal feature extraction, differential coding and intelligent scheduling technologies are fused, efficient and reliable transmission of urban data is realized, and the method has the advantages of high accuracy, controllable time delay and high resource utilization rate.

The invention relates to a dam safety state intelligent prediction method and system based on regression modeling, and the method comprises the steps: carrying out the self-adaptive wavelet packet decomposition of structural data collected by a sensor, so as to extract a time sequence feature, and carrying out the differential coding of an environment variable, so as to generate a covariable feature; the method comprises the following steps: mapping a text event into vector features through a knowledge graph, inputting the features into a mixed primary function library formed by a physical primary function and a data primary function, and constructing a sparse regression model through a dynamic gating mechanism; a physical constraint term based on a stiffness matrix and an external load matching error is introduced into the model, a causal graph is dynamically updated through an incremental PC algorithm, and a causal regularization term is added into a loss function to improve the adaptability of the model. The method is suitable for dam structure health monitoring, intelligent early warning, long-term trend analysis and other application scenes, and has good real-time performance, interpretability and small sample generalization ability.

The invention provides an edge side industrial data lightweight preprocessing caching method, and belongs to the technical field of industrial data. According to the method, the data acquisition quality is improved through Gray code anti-interference coding, dynamic sampling is realized through adaptive sampling rate adjustment and weak abnormal feature weight vectors, a fault position is accurately positioned through wavelet decomposition and a high-frequency fault feature sparse matrix, a semantic perception compression encoder is matched with a dispersion evaluation matrix to carry out differential coding, and the accuracy and the reliability of the algorithm are improved. According to the overall systematic technical scheme, the transmission path is optimized through a network transmission graph model, and all links from signal acquisition, self-adaptive processing, intelligent compression, dynamic cache management to network optimization are optimized. The technical problems that the cache occupancy rate is too high and the data transmission delay is large due to the fact that the multi-source signals of the industrial equipment are difficult to pre-process efficiently on the edge side are solved.

A method and apparatus for loop filtering are provided, which reduce the computational complexity of loop filtering, accelerate the computational speed, and improve the encoding and decoding performance by optimizing the encoding method in the encoding and decoding loop filtering process. A method for loop filtering includes: determining multiple sets of filter coefficients for loop filtering; and encoding the multiple sets of filter coefficients using a non-differential encoding method. Another method for loop filtering includes: determining correction index parameters for loop filtering; and encoding the correction index parameters using a non-exponential Golomb encoding method. A method for loop filtering is also provided, including: determining loop filtering correction parameters for the luminance component and loop filtering correction parameters for the chrominance component of an image frame; wherein the loop filtering correction parameters for the luminance component and the loop filtering correction parameters for the chrominance component are parameters selected from the same parameter list.

The invention belongs to the technical field of digital image transmission, and particularly relates to a digital image transmission system based on ultra-low time delay, which comprises the following steps: firstly, acquiring digital image data in a scene in real time by using a high-frame-rate and low-noise image sensor, and transmitting the acquired original image data; then, carrying out preprocessing operation on the collected original image data; then, a deep neural network model is utilized to adaptively adjust coding parameters and a coding mode according to the content characteristics of the image and the real-time transmission bandwidth condition; on the basis, the adaptive lightweight coding module is arranged, a complex inter-frame prediction algorithm in a traditional coding standard is abandoned, a block-level differential coding mode is adopted, and coding parameters and modes are adaptively adjusted according to image content characteristics and real-time transmission bandwidth; the method has the advantages that the coding delay is effectively reduced, and the high-real-time scene coding requirement is met.

The invention discloses a cross-document type retrieval method and system based on differentiated nested coding. The method comprises the following six steps: document preprocessing, document type identification, differential coding, index construction and optimization, online retrieval and result fusion. The method comprises the steps of firstly, performing standardized preprocessing on a document, and extracting structured information; secondly, a BERT classifier is used for analyzing document features, and document types are recognized; then, selecting a corresponding encoder to generate an original encoding vector according to the document type; thirdly, constructing a differentiated nested coding index, and optimizing the differentiated nested coding index; in the online retrieval stage, user query is preprocessed, related document types are predicted, and approximate nearest neighbor retrieval is executed. And finally, sorting the retrieval results through a stepped weight fusion strategy, and returning a Top-100 retrieval result. According to the method, efficient retrieval of different types of documents is realized through differential coding and index optimization, and the method has remarkable practical value and application prospect.

In various examples there is a method for encoding a binary image, the method comprising receiving the binary image; performing run-length encoding on pixel data of the binary image to produce run-length encoded data; performing differential encoding on the run-length encoded data to produce differential encoded data; performing variable length encoding on the differential encoded data to produce variable length encoded data; and applying a lossless compressor to the variable length encoded data to produce compressed data, the compressed data being an encoded binary image.

The application belongs to the technical field of data processing, and particularly relates to a processing process parameter monitoring method and system for glass plate processing, which comprises the following steps: performing adaptive differential coding on data sequences of a glass plate processing production line; obtaining the correlation of any two data sequences according to the similarity of the coding sequences of the different data sequences and the similarity in the adaptive differential coding process; obtaining the data quality of each data sequence based on the volatility of the coding sequences of the data sequences and the correlation of the different data sequences; and establishing a real-time monitoring model of the glass plate processing production line according to the data quality of the data sequences, the coding sequences of the data sequences and the correlation of any two data sequences. The application improves the accuracy and efficiency of real-time data monitoring of the glass plate processing production line.

The invention belongs to the technical field of image processing, and particularly discloses a RAW data encoding device and method, and the RAW data encoding device comprises an image sensor, a main control unit, a data fusion unit, a data analysis unit and a storage device. The image sensor outputs original RAW image data; the data fusion unit is based on instruction control of the main control unit, and realizes dynamic shunting of RAW data and digital image processing data through single-interface time division multiplexing; the data analysis unit performs data type identification and differential coding to a memory based on instruction control of the main control unit; and reading the coded data from the memory, and storing the coded data by using a storage device. Aiming at the problems of low distribution efficiency and interface redundancy of RAW data and processed image data in the prior art, the invention provides a software-hardware collaborative solution, so that the problem of efficiency bottleneck of collaborative processing of multi-format image data is effectively solved, and data coding and storage of the RAW data are realized.

The invention discloses an intelligent staged compression method and system for radio astronomical data based on deep learning. The method comprises the following steps: 1) constructing a celestial body signal frequency domain template library according to observation data in the radio astronomy field; 2) mapping current observation data to be processed to a frequency domain to generate a frequency point matrix; 3) extracting a frequency domain feature of each frequency point from the frequency point matrix, performing similarity calculation on the frequency domain feature and a feature corresponding to each template, if the similarity is greater than a set threshold value, determining that the corresponding frequency point is an effective frequency point, otherwise, determining that the corresponding frequency point is an invalid frequency point; (3) the effective frequency points are analyzed and processed according to channel dimensions, and if the difference between the adjacent channel data is smaller than or equal to a difference threshold value, the difference value between the adjacent channel data is recorded in a difference coding mode; 4) performing secondary compression on the output data in the step 3); and 5) generating a standard Fits format file from the secondary compressed coding data and adding metadata parameters. According to the method, the bottleneck problem of mass data storage and transmission caused by multi-beam observation is effectively solved.

Disclosed in the present application are a firmware update method for an embedded device, an embedded device, a development end device, and a firmware update system for an embedded device. The embedded device acquires patch data from a source device and decompress the patch data to obtain original differential coding data; acquires data blocks in the original differential coding data in batches, and respectively perform corresponding operations on the data blocks according to a state identifier configured to indicate the currently performed operation, until differential decoding of the original differential coding data is accomplished.

The invention relates to a remote desktop transmission method and system based on focus prediction and hierarchical optimization, and the method comprises the steps: S100, a client captures a user interaction event, calculates a prediction focus region PoR and an active context region ACR through a focus prediction model, and transmits the coordinate information and context labels of the PoR and the ACR to a server through an independent metadata channel; s200, after receiving the information, the server sends a layered rendering instruction to a graphics processor; s300, the server executes differential coding; s400, the server executes adaptive transmission; and S500, the client outputs and displays the complete desktop frame. The main purpose of the invention is to convert the graphic processing of a remote desktop from a traditional passive mode of taking a frame as the center to an active prediction and optimization mode of taking a user focus and content as the center.

A highly efficient distributed synchronization (DS) uses waveforms that can be easily detected by the receiver while possessing desirable LPI / LPD characteristics. Differentially encoding the synchronization pattern reduces receiver computation because signal detection search space is collapsed to a single dimension. Dissimilar bursts are used for synchronization to remove correlated energy between different sets, while maintaining the same differentially encoded pattern. The DS functions only as a whole and not as individual carriers or channels. No single carrier conveys any useful information. All cross-channel coherence required for proper alignment and subsequent detection of the distributed synchronization set of bursts is possible due to large transmission hardware IF bandwidth. The synchronization pattern is differentially encoded by randomly generating two sets of complex numbers that are the same size as the synchronization pattern where the point-wise product of the second set with the conjugate of the first set comprises the synchronization pattern.

Methods, systems, and devices for wireless communication are described. A user equipment (UE) can receive an indication of one or more encoding operations for encoding a compressed data set, the one or more encoding operations including a differential encoding operation or an entropy encoding operation or both. In some examples, using a neural network, the UE can first encode the data set based on an additional encoding operation to generate a compressed data set, and then quantize the compressed data set encoded based on the additional encoding operation. Subsequently, after the data set has been initially encoded and then quantized, the UE can further encode and compress the data set using the indication of the one or more encoding operations. The UE can then transmit the data set to a second device based on the one or more encoding operations.

The invention discloses an interference technical scheme based on GMSK signal characteristics, and relates to the technical field of wireless communication interference. According to the scheme, firstly, differential coding is carried out on a binary random code to obtain a relative code, then a GMSK baseband signal is generated through GMSK modulation, and carrier modulation is carried out; the method is characterized in that a self-adaptive phase modulation forwarding interference signal generation mechanism is designed based on the characteristics of constant envelope and continuous phase change of GMSK signals, and interference signals are generated by constructing a time-varying amplitude disturbance model, a dynamic time delay model and an interference phase model; and finally, superposing the interference signal with the GMSK signal passing through the Gaussian white noise channel to realize effective interference on the GMSK communication signal. According to the method, the GMSK signal characteristics are fully utilized, the interference pertinence is high, the interference effect on a GMSK communication system can be remarkably improved, the interference parameters can be flexibly adjusted according to actual scenes, and the applicability is wide.

Encoding device for encoding vibrotactile multichannel signals, includes an encoder input module configured to receive a multichannel signal; a transform module adapted to execute a discrete wavelet transform of each channel of the multichannel signal and to generate a respective frequency range representation of each channel; a psychohaptic model unit designed to allocate to each channel, based on the respective frequency range representation, a mathematical representation of human perception of the channel; a clustering module configured to group, based on the allocated mathematical representation and a similarity measure of the channels, the wavelet-transformed channels of each multichannel signal into clusters, wherein each cluster is allocated a reference channel; a reference encoding module designed to quantize and compress wavelet coefficients of the reference channels which result from the performed discrete wavelet transform of the reference channels; a differential encoding module configured to encode the channels within a cluster, which are not a reference channel, in relation to the reference channel or at least one other channel of the cluster; and an encoder output module which outputs the clustered, compressed channels of each multichannel signal as a bit stream.