356results 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 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 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.

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 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.

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 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 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.

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.

The invention relates to a rapid reasoning semantic communication method and system based on non-autoregressive decoding. The method comprises the following steps: step 1, constructing a semantic communication model of non-autoregressive decoding based on a neural network; 2, sequentially inputting the training batches into a semantic encoder and a source-channel joint encoder to obtain a semantic encoding matrix; step 3, obtaining a disturbed semantic coding matrix; 4, sequentially inputting the obtained disturbed semantic coding matrix into a source-channel joint decoder and a non-autoregressive semantic decoder to obtain a plurality of output probability matrixes; 5, training the semantic communication model of the non-autoregression decoding by using a cross entropy loss function, and finishing the training of the semantic communication model of the non-autoregression decoding when a preset number of times of training is reached; and step 6, reasoning through the trained non-autoregressive decoded semantic communication model, carrying out semantic communication, and obtaining a semantic reconstruction sentence. According to the method, the reasoning speed of the deployed semantic communication system is greatly improved, and the communication time delay is reduced.

The present invention relates to the technical field of haptic signal generation. Disclosed is a signal-to-noise ratio adaptive haptic signal reconstruction method with joint source-channel optimization, comprising: preprocessing an image corresponding to each object and a haptic vibration signal generated by sliding; constructing a cross-modal joint encoder for generating hierarchical fusion features, constructing a cross-modal joint decoder, and inputting preprocessed training data into the constructed cross-modal joint encoder and cross-modal joint decoder for training; and inputting in pairs haptic signals and image signals under test into the cross-modal joint encoder and the optimal cross-modal joint decoder to reconstruct a target haptic signal. The signal-to-noise ratio adaptive haptic signal reconstruction method with joint source-channel optimization provided by the present invention simplifies encoding training processes, better performs modal feature fusion, improves the robustness of models to channel variations, and allows the models to exhibit higher haptic signal reconstruction stability under a wider range of signal-to-noise ratio variations.

An electronic device and a method of operating the electronic device are provided. The electronic device includes at least one processor configured to receive an input data and perform an encoding operation based on the input data to generate a first latent data, through a first neural network, perform an encoding operation based on the input data to generate a second latent data different from the first latent data, through a second neural network different from the first neural network, and generate an index data corresponding to one of the first latent data and the second latent data, and a communication device configured to transmit the index data and one of the first latent data and the second latent data corresponding to the index data to an external destination.

The embodiment of the invention provides a semantic communication method and device and a storage medium, relates to the technical field of communication, and can realize matching of semantic features and channel features. The method comprises the following steps: a coding end obtains an original message; performing semantic coding on the original information based on the channel feature parameters to obtain first semantic feature information; the first semantic feature information is used for representing semantic features of the original information; the coding end sends the first semantic feature information; the decoding end obtains second semantic feature information after the first semantic feature information is transmitted through the channel; and obtaining decoding information based on the second semantic feature information.

The invention discloses a multi-mode joint information source channel coding system for satellite-ground semantic communication. The multi-mode joint information source channel coding system comprises a joint information source channel coder and a decoder, the encoder is used for sequentially performing feature extraction, feature fusion, data cutting and phase modulation operation on input multi-modal data to generate a transmitting signal, and transmitting the transmitting signal to a ground base station through a channel; and the decoder is used for sequentially performing real and virtual part separation, data expansion, feature extraction and modal data recovery operation on the received signal so as to reconstruct multi-modal data. According to the system, public semantic information among multi-modal data can be mined, the time-varying low-signal-to-noise-ratio channel characteristics of a satellite downlink are combined, the information bottleneck theory highly related to data reconstruction quality is used as guidance to optimize a coding space, complex field transmission signals with the high compression ratio are generated in a self-adaptive mode, and the multi-modal multi-domain transmission signals are obtained. The reconstruction quality of the multi-modal data can be ensured, and the bandwidth and the energy consumption overhead of satellite communication can be remarkably reduced.

The invention relates to a frequency-space hybrid joint source channel coding method and an image transmission method. The method comprises the following steps: inputting an image to be transmitted into a joint source channel coder, and mapping the image into a channel input vector; an image block embedding module and a frequency space mixing module are sequentially arranged in each encoder layer of the joint source channel encoder, the frequency space mixing module independently divides a high-frequency feature and a low-frequency feature into two branches for parallel processing, and outputs of the high-frequency and low-frequency branches are spliced along a channel dimension and then are subjected to spatial feature modeling. The output of the frequency space mixing module is input into a next encoder layer; a channel adaptive attention mechanism is introduced into an encoder layer, real-time channel SNR is embedded into attention calculation of each encoder layer, and attention focuses are dynamically adjusted according to the real-time channel SNR. Compared with the prior art, the self-adaption to different channel signal-to-noise ratios is realized, and the reconstruction quality of two types of information of high-frequency texture and global semantics is effectively considered under the same code length.

The invention belongs to the technical field of digital twinning, and particularly discloses a digital twinning data transmission optimization method and system and a program product, which realize real-time intelligent adjustment of data transmission priority and improve transmission efficiency by constructing a dynamic priority algorithm based on multi-dimensional data characteristics. The accuracy of digital twin data transmission is ensured through a dual guarantee mechanism of data integrity verification and time sequence verification; dynamic balance of transmission efficiency and accuracy is realized by combining bandwidth dynamic allocation and an intelligent retransmission strategy, and the method has extremely high industrial universality; and through dynamic bandwidth allocation and an intelligent compression strategy, the utilization rate of the network bandwidth is remarkably improved, and the data transmission cost of an enterprise is greatly reduced. The industrial problems of low data transmission efficiency and poor accuracy between the physical entity and the digital twinborn body can be effectively solved, and key support is provided for deep application of the digital twinborn technology.

The embodiment of the invention discloses a data transmission method and device, communication equipment and a storage medium. The method comprises the following steps: a first node performs semantic analysis on first data based on a local first knowledge base to obtain a plurality of semantic feature vectors and determine a first parameter of each semantic feature vector; the first data is from terminal equipment; the first parameter represents the semantic value of the semantic feature vector; and the first node encodes each semantic feature vector based on the first parameter and the first knowledge base to obtain second data, and sends the second data to a base station.

The invention relates to the technical field of electric power systems, Internet of Things and computing, in particular to an edge computing and data optimization technology suitable for an electric Hongshu Internet of Things operating system. According to the invention, by integrating edge computing and data optimization technologies, the data processing capability of the electric Hongyu Internet-of-Things operating system in an edge computing scene is improved. Specifically, real-time, efficient and accurate processing and analysis of Internet of Things data are realized through the steps of edge node deployment and data acquisition, data preprocessing and compression, edge calculation and preliminary analysis, data optimization and aggregation, intelligent model training and updating, data visualization and decision support and the like; and the response speed and the data processing efficiency of the system are improved. The method has the beneficial effects that the processing speed and accuracy of the Internet of Things data are improved, and the data transmission delay is reduced. The data transmission bandwidth and storage requirements are reduced, and the system cost is reduced. Decision support based on data is provided, and system performance and management efficiency are optimized. The expandability and flexibility of the system are enhanced, and the system is suitable for various application scenes of the Internet of Things. In conclusion, the invention provides an edge calculation and data optimization technology suitable for the electric Hongyu Internet of Things operation system, and the edge calculation and data optimization technology has remarkable technical advantages and application prospects.

An embodiment of the present application discloses a coding rate adjustment method and related equipment. In this method, a terminal device can determine the signal strength change rate of a wireless network and send the signal strength change rate to a first access network device. The terminal device receives a first coding rate from a media processing device, where the first coding rate is determined based on the signal strength change rate. The terminal device then encodes audio and video data based on the first coding rate. As can be seen, the first coding rate is more able to reflect the current wireless network environment. The terminal device encodes audio and video data based on the first coding rate, which is conducive to reducing the packet loss rate and thus reducing the probability of communication interruption of the terminal device.

The invention relates to the technical field of photovoltaic power station remote monitoring, and discloses a photovoltaic power station remote monitoring system based on the Internet of Things, and the system comprises the steps: carrying out the importance evaluation of collected original data through a data value evaluation model, and generating a data importance weight; performing hierarchical compression processing on the data based on the data importance weight; sensitive data are processed by adopting an on-loop learning and error fully homomorphic encryption algorithm; original data features are protected through a differential privacy enhanced model updating mechanism; converting the operation of the machine learning model into polynomial operation supported by homomorphic encryption; a distributed data recovery mechanism is established at the cloud end, and multi-power-station collaborative analysis is supported in combination with a secure multi-party computing protocol. The technical problem that the existing method is difficult to balance the relationship among the data transmission efficiency, the data security and the analysis performance is solved, and efficient data transmission, full-life-cycle data security and multi-power-station security collaboration are realized.

The invention discloses a semantic constellation shaping model optimization method and device, equipment and a storage medium, and relates to the technical field of digital communication, and the method comprises the steps: obtaining to-be-transmitted source data and current channel state information; performing semantic extraction on the to-be-transmitted source data according to the feature extractor to obtain semantic features; generating a constellation diagram coordinate corresponding to the current channel state information according to the constellation diagram generator; mapping the semantic feature vector to a channel symbol corresponding to the constellation diagram coordinate according to the mapper; and optimizing parameters of the feature extractor, the constellation diagram generator, the mapper and the decoder according to the channel symbols and the decoder to obtain a semantic constellation shaping optimization model. According to the method, efficient compression and transmission of semantic information are completed through channel self-adaptive constellation diagram shaping optimization, and the performance and the spectrum utilization rate of the model are improved.