452results about "Source coding adaptation" patented technology

The invention relates to a satellite-to-ground network adaptive image semantic communication method based on an NOMA technology, and the method comprises the following steps: S1, building a satellite-to-ground semantic communication frame integrated with the NOMA technology: carrying out the power superposition and serial interference elimination of different user transmission contents through the NOMA technology, a coding and decoding process is optimized through deep joint source channel coding based on hierarchical vision using a shift window; s2, establishing an adaptive semantic communication model: dynamically adjusting coding parameters based on a target bandwidth ratio and a signal-to-noise ratio of a satellite-ground channel; s3, proposing a dynamic weight adjustment algorithm: optimizing model convergence speed and transmission performance tradeoff under different channel conditions; and S4, designing a feature selection module: performing priority ranking on the image features, and selectively transmitting important features according to bandwidth resources. According to the method, the adaptive semantic communication model (ASeC-NOMA) which can be dynamically adjusted according to the target bandwidth ratio and the signal-to-noise ratio is constructed, and a large number of model storage parameters are saved while the transmission performance is ensured.

The invention provides a channel environment adaptive sampling-semantic-channel coding joint optimization method and system, and relates to the technical field of semantic communication, a sending end performs semantic sampling on an image needing to be transmitted to obtain a semantic sampling result; the sending end inputs the semantic sampling result into an encoder to obtain an image feature vector needing to be transmitted; a receiving end receives the image feature vector influenced by the physical channel noise and preprocesses the image feature vector; the receiving end inputs the preprocessing result into a decoder to obtain an image feature vector needing semantic reconstruction; and the receiving end performs semantic reconstruction according to the image feature vector needing semantic reconstruction to obtain a reconstructed image. According to the method provided by the invention, the characteristics of an information source and a channel are combined, image transmission in a variable signal-to-noise ratio environment can be supported while semantic sampling and reconstruction are considered, and the reconstruction quality of the image is ensured.

The invention discloses a water conservancy water regimen monitoring management system based on Beidou short message communication, which relates to the technical field of water regimen monitoring and comprises a scheduling module, a data compression module, a data preprocessing module and an early warning module. The scheduling module determines a transmission queue by marking a water level state, calculating various scores and correction parameters, and implements a preemption mechanism and setting optimal transmission in a silent period; the data compression module classifies and processes data, adjusts the compression rate according to channel signals, and adopts forward error correction coding and the like to guarantee the data accuracy; the data preprocessing module filters the original data, detects abnormity by using space-time correlation analysis, and fills missing values; the system effectively realizes water conservancy water regimen monitoring management, has significant advantages in the aspects of data transmission, processing, early warning and the like, improves the accuracy and timeliness of water regimen monitoring, and provides powerful support for water conservancy management.

The invention belongs to the crossing field of industrial control and edge computing, and particularly relates to an industrial personal computer data security dynamic protection method and system based on AI edge computing. Firstly, industrial control data encryption and standardization preprocessing are achieved through a hardware-level trusted execution environment; secondly, performing real-time anomaly detection on the encrypted data stream based on an AI model, generating a threat score by combining industrial control protocol feature analysis, and forming an edge layer intelligent detection capability; the authentication mode is dynamically switched based on the threat score, and self-adaptive strategy adjustment is realized in a fog layer; then optimizing edge resource allocation according to the threat index and the equipment load, and constructing a cloud elastic scheduling mechanism; and finally, a security system covering data encryption, real-time detection, dynamic protection and resource optimization is formed through grading component cooperation, and the method is suitable for real-time threat detection and self-adaptive response in an industrial internet scene.

The deep joint source channel coding method comprises the following steps: a sending end obtains a first semantic feature of a first image, and performs constellation mapping modulation on the first semantic feature to obtain a first initial constellation point; the sending end extracts semantic information of the first initial constellation point, generates a rotation angle corresponding to the semantic information, and rotates the first initial constellation point according to the rotation angle to obtain a first rotation constellation point; the sending end generates a real part interleaving matrix and an imaginary part interleaving matrix based on the first rotating constellation point and the real part and the imaginary part of the channel state information, resorts the real part and the imaginary part of the symbol sequence respectively and then recombines the real part and the imaginary part into a complex signal to obtain a first interleaving signal; the sending end transmits the first interleaved signal to a receiving end through a fading channel, and the receiving end receives a second interleaved signal; the receiving end performs semantic reconstruction decoding on the second interleaved signal to obtain a second image; or, the receiving end performs semantic classification decoding on the second interlaced signal to obtain a second image category.

The invention discloses a resource allocation digital semantic communication system and method based on semantic importance guidance, and relates to the technical field of wireless communication, and the system comprises a semantic encoder module, a semantic importance module, a resource allocation module and a semantic decoder module. The semantic encoder module is used for performing semantic feature extraction on the source data; the semantic importance module is used for calculating feature importance weights of different features according to the extracted semantic features, and obtaining a semantic feature data stream with importance tags according to the semantic features and the feature importance weights; the resource allocation module is used for determining power allocation of each semantic feature data stream with an importance label and an allocation strategy of subcarriers based on feature importance weights of different features; and the semantic decoder module is used for carrying out semantic reconstruction on the source data. According to the invention, the most critical information can be transmitted preferentially in a complex communication environment, so that the reliability and efficiency of the whole system are improved.

The invention discloses an efficient data compression and transmission method based on a computing power host management operation platform, and relates to a large-scale data concurrent management technology. Comprising the steps of 1, integrating a lightweight data feature analyzer at a Kafka producer end, extracting feature indexes of data streams in real time through the data feature analyzer, and generating compression strategy parameters according to the feature indexes, and 2, performing feature recognition on structured data and unstructured data, extracting data features, and generating compression strategy parameters according to the compression strategy parameters. And step 3, based on data characteristics, CPU load of producers / consumers and network bandwidth prediction, constructing a multi-objective optimization model, dynamically selecting a compression algorithm and a compression level, selecting compression strategy parameters according to the compression algorithm, introducing a compression income ratio index, calculating a ratio of compression rate increase to compression time consumption, and performing compression strategy optimization. And preferentially selecting a compression algorithm with a high gain ratio to perform data compression and transmission.

The invention discloses an air quality monitoring data processing transmission method and device, and relates to the technical field of data transmission, and the method comprises the steps: collecting multi-modal environment parameters through a distributed sensor array, calculating a pollutant concentration covariance matrix of adjacent monitoring nodes, and judging an abnormal monitoring event according to a condition number; hierarchical compression is carried out by adopting a compressed sensing algorithm driven by space-time correlation; calculating a channel comprehensive quality index, predicting a channel state in combination with a deep learning model, dynamically selecting a transmission protocol combination, and obtaining a multi-protocol data stream; performing space-time interpolation reconstruction, calculating a reconstructed hash, writing the reconstructed hash into the block chain, and performing integrity verification; the weight of the deep learning model is updated, the compressed sensing algorithm is optimized, energy distribution is optimized according to the transmission load, and iterative optimization of the deep learning model is carried out. According to the invention, data compression is carried out by using the spatial-temporal correlation driven compressed sensing algorithm, so that the data transmission burden is reduced.

In aspects of a Selective Retransmission Request (SeRQ) protocol proposed herein for wireless communication, a UE may group Retransmission Request (RTQ) feedback information bits for a plurality of transport blocks (TBs). The RTQ feedback information may be understood to indicate, to a non-terrestrial transmit receive point (NT-TRP), which TBs, among the plurality of TBs in the window, were not decoded properly. UEs may generate RTQ feedback information bits for a group of TBs received during a single window or TBs that have been received during multiple, distinct windows. The generation, by the UEs, of the RTQ feedback information bits for a group of TBs may be encoded based on, e.g., source coding, codebooks, binomial coefficients, etc. Upon receipt of a retransmitted version of a given TB, the UE may combine previously received bits of the given TB with the retransmitted TB version of the given TB.

The invention relates to a dynamic environment adaptive semantic communication system and method, which are used for realizing the whole task-oriented semantic communication, the system comprises a transmitting terminal network, a codebook space, a wireless channel and a receiving terminal network, the transmitting terminal network comprises a feature extractor and a joint source channel encoder, namely a JSC encoder; the feature extractor is used for identifying and extracting features related to a specific task from the original input data; the JSC encoder is used for: mapping the continuous feature vector output by the feature extractor to a discrete symbol representation suitable for wireless transmission; the codebook space is used for mapping a continuous feature vector output by the feature extractor into a discrete codeword index through a vector quantization mechanism; the wireless channel is used for converting the discrete code word index into an electromagnetic signal capable of being physically transmitted and completing a transmission process of the electromagnetic signal in a noise and fading environment; according to the method, the comprehensive performance optimization of task-oriented semantic communication is realized.

The invention discloses a coding method and system based on deep learning and compressed sensing assistance, and belongs to the technical field of optical transmission. The method comprises the following steps: carrying out coding mapping on bit data, carrying out codebook design by using an AlexNet model, and training each codebook through five convolutional layers to obtain an error rate in a current environment; performing data mapping on the codebook with the lowest bit error rate; performing compressed sensing on the data signal mapped to the codebook; the method comprises the following steps of: firstly carrying out OFDM (Orthogonal Frequency Division Multiplexing) modulation on a signal subjected to compressed sensing, then carrying out signal transmission and signal demodulation, reconstructing the signal, demapping the reconstructed signal into a codebook signal, and finally decoding the codebook signal into an original signal through a decoder. The SCMA modulation is combined with the convolutional neural network technology and the compressed sensing technology, an encoder and a decoder of the SCMA are established, the bit error rate is minimized, signals are processed by using the compressed sensing theory, the amount of data needing to be collected, transmitted and processed is greatly reduced, and data are transmitted by occupying less subcarriers.

The invention provides a communication method and device, electronic equipment, a chip and a readable storage medium, and the method comprises the steps: obtaining communication parameters of communication transmission between terminal equipment and network equipment; under the condition that the communication parameter indicates that the communication quality does not meet the set requirement, a first coding and decoding mode adopted by communication transmission is switched to a second coding and decoding mode; wherein the minimum bandwidth required by the second coding and decoding mode is smaller than the minimum bandwidth required by the first coding and decoding mode. The coding data volume corresponding to the second coding and decoding mode is lower than the coding data volume corresponding to the first coding and decoding mode, and the first coding and decoding mode is switched into the second coding and decoding mode, so that the transmission data volume can be reduced, the network bandwidth occupation can be reduced, the problem of data loss caused by the fact that the bandwidth required by data transmission exceeds the capability is solved, and the probability of data loss is further reduced.

The invention relates to a satellite network semantic communication method based on reliable joint source channel coding. The satellite network semantic communication method comprises a signal transmitting method and a signal receiving method. The method comprises the following steps of: 1, extracting features of an image by utilizing a Vision Transform model to obtain semantic representation of the image; 2, constructing a channel estimation and joint loss function; 3, encoding the semantic representation in the step 1 by using a joint source channel encoder and combining the channel estimation in the step 2 and the joint loss function; 4, NOMA power distribution is carried out on the coding result in the step 3, and the coding result is transmitted through a wireless link; the signal receiving method comprises the following steps: step 5, a receiving end receives a signal transmitted in step 4 from a link; and 6, recovering the original user data from the noise by using a joint source channel decoder. According to the method, the semantic information is extracted from the image by using the pre-trained Vision Transform model, so that the amount of data to be transmitted is remarkably reduced, and meanwhile, the communication efficiency is kept.

The invention provides a mine video semantic communication transmission method and system based on deep learning, which can realize the recovery of observable data and the execution of pragmatic tasks. According to the method, a semantic communication system model under a mining working face is constructed, the semantic communication system model comprises a semantic encoder and a semantic decoder which are realized based on a deep neural network, and the semantic encoder is located at a transmitting end and used for extracting data containing observable data and semantic information; the semantic decoder is located at the receiving end and used for reconstructing data related to empirical data and pragmatic tasks. According to the method, a semantic encoder and a semantic decoder are trained based on a DL-SC algorithm, image restoration and pragmatic task execution are considered in the aspect of design of a loss function, appropriate distortion measure functions are selected for the image restoration and pragmatic task execution respectively, and then the weighted sum of the distortion measure functions is solved to form a semantic distortion measure function to serve as the loss function. According to the invention, more efficient information interaction can be realized with lower data transmission quantity.

According to various embodiments, an electronic device includes a communication circuit for supporting a Bluetooth™ communication, and at least one processor functionally connected to the communication circuit. The at least one processor is configured to establish, via the communication circuit, a Bluetooth™ low energy (BLE) communication link with an external electronic device, to generate a first data packet from first audio data using a first coding scheme, and generate a second data packet from first audio data using a second coding scheme, and through the BLE communication link, to transmit the first data packet to the external electronic device in a first time interval of a predetermined time interval, and transmit the second data packet to the external electronic device in a second time interval of the predetermined time interval.

The invention provides a semantic communication method and device, network equipment and a medium, and relates to the technical field of communication. The method comprises the following steps: judging whether an information source channel joint encoder between a sending end and a receiving end has corresponding semantic coding processing capability or not under the condition of determining that information to be transmitted needs to be transmitted through a semantic coding channel; and when it is determined that the source-channel joint encoder does not have the semantic encoding processing capability, the to-be-transmitted information is sent to the cloud server, and the cloud server transmits the to-be-transmitted information to the receiving end, so that target communication in different scenes is realized, the communication requirements of different users are met, and the user experience is improved. Customized communication services are provided for diversification of communication scenes and diversification of user communication targets, communication resources are saved, and network working efficiency is improved.

Disclosed are a data processing method, an electronic device, and a computer-readable storage medium. The data processing method may include: determining, in response to acquiring at least one source data packet, a first network coding mode for the at least one source data packet according to a pre-configured first network coding parameter; encoding the at least one source data packet according to the first network coding mode to obtain a first encoded data packet; and sending the first encoded data packet to a first receiving end, such that the first receiving end processes the first encoded data packet.

The invention relates to a semantic-driven holographic communication combined information source channel coding method, which belongs to the technical field of wireless communication and comprises the following steps of: establishing an end-to-end point cloud semantic communication system model; the method comprises the following steps: designing a joint source channel coding model architecture, combining a 3D convolutional layer, a Voxception-Resnet module and a 3D attention feature 3D-AF module, and defining a basic unit to realize multi-scale feature extraction; establishing a voxel importance function based on a neuroscience inhibition effect, and deducing a closed-form solution of the voxel importance function; a three-dimensional attention feature module is designed, channel state information and a voxel importance function are fused, and dynamic semantic adjustment is achieved; and performing semantic communication by using the constructed end-to-end point cloud semantic communication system model. According to the invention, the stable performance of a single network in a continuous SNR range from-6dB to 8dB is realized, and compared with a fixed SNR training model, the performance is improved by 30% at most.

The embodiment of the invention provides a communication method and device, a storage medium and a program product. In the method, a data compression device transmits a first data matrix. Furthermore, the data compression device transmits a projection result and a projection residual error of the second data matrix. Wherein the projection result is determined according to a projection matrix determined by the second data matrix relative to the first data matrix, and the projection residual error is determined according to the second data matrix and the projection result. Thus, the correlation between the first data matrix and the second data matrix can be fully utilized, the compression efficiency is improved, and the transmission bandwidth or storage resources are saved.

The invention discloses a bridge monitoring data storage system based on cloud computing, particularly relates to the technical field of bridge monitoring, and comprises an optical fiber sensing network acquisition unit, an edge computing preprocessing unit, a multi-modal data fusion storage module, a dynamic hierarchical compression engine, a fault-data association analysis unit and a distributed cloud storage cluster. According to the invention, a full-link optimization system is constructed, a front end accurately captures key responses, a middle end filters abnormal data to reduce invalid transmission, and a rear end dynamically compresses based on health rating and combines a cloud storage mechanism, so that the storage efficiency is improved by more than 40%, and the storage cost and bandwidth pressure are solved; key data are compared and marked through a knowledge graph, and a triple protection system is constructed in combination with a fault-tolerant strategy; a fusion index is formed by space-time correlation, multi-dimensional analysis is supported, authority control gives consideration to safety and operation and maintenance, storage cost is optimized by cold and hot stratification, and a high-availability and extensible data management platform is provided for bridge monitoring.

The invention discloses a big language model reasoning scheduling method and device based on semantic communication. The method comprises the steps that edge equipment extracts semantic information of a reasoning request through a combined lightweight model, predicts the length of an output token and uploads the length to a big language model server; the large language model server sorts the waiting requests according to a semantic scheduling scoring function, the scoring function performs dynamic weighted calculation based on token length, channel quality and historical retransmission times, and a request group with the highest priority is selected and a corresponding edge device is indicated to upload semantic tensors of reasoning requests extracted by the joint lightweight model; and the large language model server receives the semantic tensor and then reconstructs information for batch reasoning. According to the method, the throughput capacity of an inference system in a high-concurrency environment can be effectively improved, the task response time delay is reduced, the stability and the service quality of an edge semantic communication system are enhanced, and the method has wide applicability and popularization value for deployment of a large language model in an edge calculation and multi-user semantic communication scene.

The invention provides a semantic communication method and system, electronic equipment and a computer readable storage medium, and relates to the field of communication, in particular to the field of semantic communication and artificial intelligence. According to the specific implementation scheme, the method comprises the steps of inputting information source data into a pre-trained semantic encoder for semantic encoding, obtaining information source semantic features of the information source data, mapping the information source semantic features to a target codebook, and generating an information source index vector; transmitting the information source index vector from the transmitting end to the receiving end through the communication channel, and obtaining a channel index vector; obtaining a channel semantic feature according to the target codebook and the channel index vector, inputting the channel semantic feature into a pre-trained semantic decoder for semantic decoding, and generating reconstruction data; wherein the target codebook is determined according to the signal quality of the communication channel and an initial codebook; the initial codebook is obtained through joint training with the semantic encoder and the semantic decoder.

Embodiments of the present application provide a method for communication, a device, a storage medium, and a program product. In the method, a data compression apparatus sends a first data matrix. Then, the data compression apparatus sends a projection result of a second data matrix and a projection residual. The projection result is determined on the basis of a determined projection matrix of the second data matrix relative to the first data matrix, and the projection residual is determined on the basis of the second data matrix and the projection result. In this way, the correlation between the first data matrix and the second data matrix can be fully utilized, thereby improving compression efficiency, and saving the transmission bandwidth or storage resources.

The invention provides a resource scheduling method and a communication device, which can be applied to uplink data transmission. In the embodiment of the invention, the terminal equipment sends a buffer status report to the network equipment to report the data volume of uplink data to be sent, and meanwhile, the terminal equipment also sends compression information of the uplink data to be sent to the network equipment, and the compression information indicates a compression parameter related to physical layer processing of the uplink data to be sent. The physical layer processing may include a coding mode in which compression and channel coding are separated on the TB, or the physical layer processing may include a coding mode in which compression and channel coding are combined on a plurality of sub-TBs obtained according to the TB, respectively. In the embodiment of the invention, the network equipment schedules the air interface resource for the uplink data to be sent of the terminal equipment according to the data volume and the compression information of the uplink data to be sent, so that the problem of uplink resource mismatch caused by introduction of a compression and channel coding separated coding mode or a compression and channel coding combined coding mode in a physical layer can be solved.

The invention discloses a wireless communication method, device and equipment and a readable storage medium, and belongs to the field of communication, and the method comprises the steps that first equipment obtains target output related information, the target output related information is output related information meeting a first channel condition and / or a first quality requirement, and the target output related information is output related information meeting the first channel condition and / or the first quality requirement; the first channel condition is a channel condition for transmitting a first information source, and the first quality requirement is a quality requirement of second equipment on the first information source; the first device performs coding processing on the first information source according to the target output related information to obtain first coding information; and the first device sends the first coding information to the second device.

The invention particularly relates to a task-driven semantic communication method oriented to AUV collaborative awareness. The task-driven semantic communication method comprises the steps of semantic triple extraction, wherein a structured semantic triple is extracted from a multi-view underwater image; the semantic triad comprises an object, a relationship and an attribute; sorting the semantic triples: sorting the semantic triples based on a personalized semantic perception sorting method, and screening out the semantic triples with representativeness and difference; semantic triad compression: performing discretization coding on the screened semantic triads to generate a low-bit-rate compressed representation; semantic triple transmission: in a communication process, calculating confidence based on normalized entropy to judge whether current information is sufficient to complete a task, so as to realize dynamic suspension of communication; meanwhile, task states, historical transmission behaviors and channel conditions are comprehensively considered, an attention scheduling module is constructed, and communication nodes needing to be activated are dynamically selected based on attention scores. And efficient cooperation among multiple AUVs in a complex underwater environment is realized.

A modulation and coding scheme indication method and a corresponding apparatus in a wireless local area network system that supports one or more of a next-generation Wi-Fi protocol of IEEE 802.11ax (for example, 802.11be, Wi-Fi 7, or EHT), a next-generation Wi-Fi protocol of IEEE 802.11be (for example, 802.11bn, Wi-Fi 8, or UHR), Wi-Fi AI, a millimeter wave (mm Wave), an ultra wideband (UWB), and sensing (sensing). A method includes: generating a physical layer protocol data unit PPDU, where the PPDU includes a first field and a second field, the first field indicates a first modulation and coding scheme MCS group, and the second field indicates a number of spatial streams corresponding to one or more MCSs in the first MCS group; and sending the PPDU. This can reduce bit overheads.

A cloud-edge collaborative data processing method and system, a device, and a storage medium are provided, relating to the field of telecommunication technology. The method includes extracting, in an edge encoder, intermediate features of data to be processed, inputting the intermediate features into a lightweight edge decoder for decoding to obtain an edge decoding result; and in response to a feature uncertainty of the edge decoding result being less than or equal to a preset threshold, taking the edge decoding result as a target processing result, or in response to the feature uncertainty being greater than the preset threshold, compressing the intermediate features using an edge compression model and then sending the compressed intermediate features to a cloud server in the cloud-edge collaborative data processing system.

The invention provides a message transmission method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a to-be-transmitted message, determining a path quality score according to network occupation information corresponding to each node in each message transmission link, and screening a target transmission path corresponding to the highest path quality score; determining an incremental message between the to-be-transmitted message and the historical version message and a memory occupancy amount corresponding to the incremental message, and selecting a corresponding preset compression rule to compress the incremental message according to a numerical value interval in which the memory occupancy amount is located; and transmitting the compressed incremental message to a message receiver through a target transmission path, writing the compressed incremental message into a preset persistent queue after the compressed incremental message arrives, and when a preset batch writing condition corresponding to the persistent queue is triggered, storing the incremental message in a cache layer which is preset and matched with the access frequency of the incremental message in batches. According to the invention, the whole message transmission process can be optimized, the system throughput is improved, the network overhead is reduced, and the reliability is enhanced.