140 results about "Adaptive encoding" patented technology

The invention provides a fragment data cross-modal label generation system and method based on deep transfer learning. The method comprises the following steps: extracting first high-dimensional feature vectors in different modes; mapping the first high-dimensional feature vectors of different modals into the same semantic space through a cross-modal comparison loss function to realize multi-modal alignment and fusion to obtain second high-dimensional feature vectors; labeling semantic tags corresponding to the second high-dimensional feature vectors based on the fragmented data components by adopting a small sample transfer learning algorithm; a multi-channel Hash encoder is adopted, a self-adaptive encoding strategy is called according to different modal data combinations, and the second high-dimensional feature vector is encoded into a multi-channel binary Hash code; in combination with an incremental graph neural network, the binary hash codes and the corresponding semantic tags are dynamically expanded into the historical knowledge graph; matched fine-grained tags are established for semantic differentiation features of different entity combinations in the target knowledge graph, and a cross-modal tag tree is obtained by combining three-matrix hierarchical construction.

The invention discloses an HPLCamp (High Performance Liquid Chromatography) based on deep learning. The invention discloses an HRF dual-mode communication adaptive coding modulation and anti-noise method. The method comprises the following steps: acquiring an optical radio frequency signal amplitude-phase change rate and synchronously sampling and normalizing; calculating a node amplitude-phase residual error to generate a nonlinear mapping coefficient; monitoring coherent change to solve a drift trend, adjusting a modulation coding optimization scheme, compensating distortion and outputting an anti-noise result. According to the method, the instantaneous amplitude and phase of the optical radio frequency dual-mode signal are extracted, a multi-dimensional amplitude-phase characteristic matrix is formed in combination with time domain synchronization and a normalization template, differential residual modeling and nonlinear mapping coefficient calculation are carried out between impedance nodes, and dynamic compensation of amplitude-phase mismatch and envelope offset is achieved. A drift trend quantity is generated based on coherent offset parameter differentiation, feedback is provided for modulation format and coding strategy optimization, amplitude equalization and phase correction are completed, the signal synchronization degree and amplitude-phase consistency are improved, and the steady-state response and anti-disturbance performance of a transmission link are enhanced.

The invention discloses an enterprise intelligent compliance auditing method based on data driving. The method comprises the following steps: S1, automatically collecting auditing data of various heterogeneous data sources in an enterprise in real time through a cross-domain data access interface; s2, performing feature automatic identification and standardization processing on the audit data by adopting a semantic adaptive coding method; s3, a cross-domain collaborative characterization model is constructed by extracting and fusing shared features through a sub-domain multi-expert structure; s4, generating a visual feature heat map by using a hierarchical attention mechanism of the model, and outputting an anomaly detection result; s5, extracting long and short period correlation mode features, inputting the features into a contrast learning framework, and outputting an abnormal risk score; s6, constructing a dynamic enterprise compliance risk knowledge graph; s7, strategy training is carried out, and risk rating is output; and S8, generating an audit report according to the risk rating. According to the invention, efficient and accurate enterprise compliance risk identification and audit decision support are realized.

The invention provides a multi-source data convolution fusion TBM electric drive system fault diagnosis method, and relates to the technical field of TBM electric drive control. The method comprises the following steps of: filtering and denoising operation data of an electric drive system by using variational mode decomposition (VMD), extracting time domain features, and constructing a multi-mode fusion data set; secondly, extracting short-term, medium-term and long-term time sequence features in parallel through a residual causal convolutional network, and enhancing key features by using a convolutional block attention module (CBAM) to highlight early weak fault signals; furthermore, working condition parameters are embedded into a feature space by adopting working condition adaptive coding, and cross-working-condition feature consistency constraint is realized based on a maximum mean difference (MMD) criterion. According to the method, the weak fault identification capability and the cross-working-condition diagnosis generalization performance are effectively improved, and the TBM operation stability and the tunnel construction safety are guaranteed.

The invention provides an array image demosaicing method based on dynamic convolution and adaptive coding, which relates to the technical field of image processing, and comprises the following steps: acquiring single-channel original image data and a corresponding color filtering array arrangement type identifier; converting the arrangement type identifier into a multi-dimensional physical feature vector, and inputting the multi-dimensional physical feature vector into a feature processor of a neural network model to generate a weighting coefficient vector; carrying out weighted combination on the plurality of special arrangement transformation matrixes through a weighting coefficient vector to obtain a transformation component, adding the transformation component and a basic convolution kernel parameter to obtain a dynamic convolution kernel parameter, and carrying out directional modulation on a specific spatial position of the dynamic convolution kernel parameter based on a direction weight component in a multi-dimensional physical feature vector; and performing convolution operation on the original image data by using the dynamic convolution kernel parameters, extracting multi-scale features, reconstructing image features, and outputting multi-channel color image data, so that the method can be adaptive to different color filter array types, and the demosaicing precision and generalization capability are improved.

The invention discloses a data full-link dynamic anti-interference optimization transmission method. The method comprises the following steps: S1, constructing a data transmission system architecture comprising a sending end, a transmission link and a receiving end; the sending end detects environment disturbance characteristics in real time, and the receiving end collects receiving signals containing background noise and link distortion; s2, designing a self-adaptive waveform coding strategy at a sending end; s3, performing active channel compensation on a transmission link; channel characteristics are extracted, and channel interference is counteracted in real time through pre-distortion waveform adjustment; s4, deploying an intelligent noise reduction decoding algorithm at a receiving end; a depth feature separation network is constructed, and link residual interference is filtered out by analyzing the time-frequency domain difference between a target signal and a noise component. Through a three-level anti-interference mechanism of adaptive coding of the sending end, active compensation of a transmission link and intelligent noise reduction of the receiving end, the problem that the transmission reliability of signals is reduced due to environment disturbance, channel distortion and noise pollution in a complex environment is solved.

The invention discloses a self-adaptive coding modulation method and system for relieving sun-borne interference in an ultra-dense low-orbit satellite network, and aims to improve the reliability and the spectrum utilization rate of satellite network communication. The method specifically comprises the following steps: modeling satellite-ground and inter-satellite communication links of the ultra-dense low-orbit satellite network, and modeling and calculating time generated by the sun and interference to the low-orbit satellite network; the ground station estimates and predicts the satellite-ground channel state, and returns the predicted SNR to the satellite; and according to the SNR returned by the ground station and the SNR generated by the sun, a deep reinforcement learning method is adopted to select a coding modulation scheme to perform downlink data transmission, and whether a lower satellite is selected to perform data unloading and then transmit the data to the ground station is determined. Simulation results show that the bit error rate of the ultra-dense low-orbit satellite network can be effectively reduced, the spectrum utilization rate can be remarkably improved, real-time response and efficient resistance to sun interference can be achieved, the satellite calculation load can be reduced, the comprehensive performance of the system can be improved, and the method has high practical application value.

The invention discloses a scene electromagnetic texture mapping method and system based on an autonomous mobile robot. The method comprises robot operation and development, radar sensor operation and DART implementation. The robot carries out data acquisition through a sensor and carries out data fusion and processing on the acquired information; the radar sensor operates to detect the surrounding environment through a signal transceiving mechanism, and the obtained signals are fused and processed; in the DART implementation step, visual results of different angles are provided through a visual angle synthesis technology and are finally output in a multi-visual-angle mode. Through a strong coupling mechanism of robot body motion control and radar scanning parameters, a personal error source is eliminated, high-precision motion control is realized based on a robot-radar cooperative control architecture, and based on a DART-NeRF hybrid modeling engine and a lightweight network architecture, model parameters are compressed through adaptive coding, and training and reasoning processes are accelerated.

The invention provides an LDPC (Low Density Parity Check) optimization method based on dynamic parameter adaptive coding and a Tanner graph neural network. The method comprises the following steps: acquiring a data set through a channel sensing module; the data set comprises channel state information, bit error rate statistics and QoS (Quality of Service) demand information; the collected data set is preprocessed; dividing the preprocessed data set according to a preset proportion; constructing an LSTM (Long Short Term Memory) prediction model; training and optimizing the LSTM prediction model by using the training set based on the loss function, and storing an optimal model; inputting the test set into the optimal model to predict the coding parameters of the channel transmission to obtain a prediction result of the adaptive coding parameters of the channel transmission; the method comprises the following steps: establishing an LDPC encoder and a Tanner-GNN decoder; building a joint optimization controller; the joint optimization controller is used for dynamically adjusting coding parameters through the LSTM prediction model and receiving the bit error rate BER counted by the Tanner-GNN decoder, the technical limitation of traditional coding and decoding separation optimization is broken through, and the performance of the LDPC code in a complex channel environment is remarkably improved.

The invention discloses an AI-based motion subject tracking and streaming media optimization live broadcast system, and particularly relates to the technical field of motion subject tracking and streaming media optimization, AI-driven motion subject detection and tracking and streaming media coding adaptive control are closely fused, and code rate fluctuation of a coding end and bandwidth change of a network end are sensed in real time, so that the accuracy of the motion subject tracking and streaming media optimization live broadcast system is improved. A continuous and explainable live broadcast jitter risk analysis index is constructed, potential image quality jitter and delay risks are identified in advance through fusion of causal rule scoring and time sequence prediction, and the live broadcast jitter risk analysis index is directly mapped into an adaptive coding and transmission adjustment instruction. Priority guarantee, code rate distribution smoothing and delay control optimization of a key area are realized, the definition and playing smoothness of a live broadcast picture are remarkably improved, and sudden jitter and jamming phenomena are reduced.

The invention relates to the field of hybrid coding of video engines, and provides a hybrid coding processing method, system and equipment based on a video engine, which comprises the step of intelligently switching an H.265 inter-frame predictive coding mode and an MJPEG (Multijoint Joint Photographic Experts Group) intra-frame compression coding mode by dynamically monitoring the proportion of a motion area in a video picture. When a scene is static, discrete cosine transform is adopted to compress space redundancy, motion vector compensation is started to eliminate time redundancy when motion is violent, and meanwhile, key frames are doubly screened by using pixel difference analysis and a perceptual hash algorithm, and repeated frames of visual redundancy are eliminated. And finally, space-time association is established for the optimized double-code-stream data through a timestamp index system, and efficient storage and accurate reconstruction of the mixed code stream are realized. The problems that the coding efficiency is low, no self-adaptive coding mode exists, redundant frames are not optimized, and code stream management is insufficient are solved.

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 relates to a variable input measuring point-oriented physical field prediction neural operator model and a construction method thereof, the model constructed by the method adopts a two-layer architecture of a backbone network and a branch network, the backbone network encodes query coordinates of an output function by using a full-connection neural network, and the branch network encodes the query coordinates of the output function by using the full-connection neural network; and the branch network realizes adaptive coding of the measuring points at different positions by fusing the permutation invariance and the geometric feature extraction capability of the point cloud network. According to the model and the construction method disclosed by the invention, high-precision solution is kept, meanwhile, the change of the position of an input function measuring point can be flexibly adapted, and the strict limitation of a traditional neural operator model on the spatial consistency of input data is effectively overcome.

The invention relates to the technical field of electronic information data processing, in particular to an electronic information data AI anomaly detection and repair method, which comprises the steps of preprocessing multi-modal data, automatically identifying multi-format data and adaptively encoding and normalizing; fusing a convolutional neural network, a bidirectional long-short-term memory network and a Transform architecture to extract multi-modal features, and judging abnormality through an improved deep isolation forest model; the exceptions are subdivided through a lightweight convolutional neural network in combination with a rule base, and service scene labels are automatically matched; based on the abnormal type, grade, scene and semantic four-dimensional degree, a reinforcement learning model intelligently matches a repair strategy such as filling, correction or manual intervention; a data, model and repair three-level parallel architecture and a sliding window mechanism are adopted to realize real-time stream processing; incremental learning closed-loop optimization is driven through validity and rationality dual verification, and the method is suitable for high-reliability data governance scenes such as finance and communication.

The invention relates to the technical field of intelligent costume design, and discloses an intelligent costume pattern generation system based on enhanced multi-modal generation, which comprises a multi-module input module, a layered attention fusion module, a self-adaptive position coding module and a hierarchical decoding module, the multi-module input module is used for receiving a clothing reference image, a version design demand text and structured data, and performing feature extraction respectively to obtain image features, semantic features and structural features; the layered attention fusion module adopts a three-layer attention mechanism to perform fusion processing on the image features, the text features and the structural features to obtain final fusion features; the self-adaptive coding module carries out self-adaptive coding on the final fusion feature and the model parameter of the clothing template, and outputs a coding result; and the hierarchical decoding module is used for decoding the coding result to generate a complete garment pattern.

The invention relates to a ship mail adaptive coding and dynamic bandwidth allocation method, belongs to mobile voice service, mobile data communication service and other telecommunication services, and particularly relates to the field of digital information transmission. The method comprises the following steps: intelligently predicting the total number of transmitted mails and the total number of transmitted mail data of a target ship in a current time segment by using an AI network data prediction model according to various basic data which are fully and comprehensively selected; and determining an adaptive coding strategy and a dynamic bandwidth allocation strategy for the current time segment of the target ship based on the intelligent prediction result. According to the invention, in order to solve the technical problems that effective and smooth transmission of all mail data of each future time segment of each ship is difficult to consider and the utilization rate of network transmission resources is difficult to improve, A I network data prediction models of different structures customized and designed for different ships are used; and intelligent prediction of the mail transmission state of each ship in the future time segment is completed, so that the technical problem is solved.

The invention discloses a real-time video stream transmission method and device based on an unmanned aerial vehicle and electronic equipment, and relates to the technical field of unmanned aerial vehicle communication.The method comprises the steps that network state data, position information and historical transmission records of the unmanned aerial vehicle are obtained, and the historical transmission records of the unmanned aerial vehicle are obtained according to the network state data and the position information; adjusting a coding parameter of the to-be-transmitted video stream by adopting a self-adaptive coding strategy to obtain a target coding parameter, and coding the to-be-transmitted video stream based on the target coding parameter to obtain a target video stream; and screening the transmission path set based on the network state data, the position information and the historical transmission record to obtain a plurality of optimal transmission paths, and transmitting the target video stream by adopting the plurality of optimal transmission paths. According to the invention, the technical problem that the transmission delay is increased and the image quality is reduced due to the instability of the network state and the unicity of the transmission path in the prior art is solved.

The invention relates to the technical field of subway security and protection monitoring and video transmission, in particular to a subway security and protection monitoring video quick calling and multi-channel transmission optimization system, which comprises an analysis module used for extracting a feature set of multi-source heterogeneous metadata in a subway station and carrying out matching calculation on the feature set and an abnormal event template to obtain a matching confidence coefficient; if the matching confidence exceeds a preset threshold, judging that an abnormal event occurs and outputting an event feature identifier; determining an influence range and an association path of the event by combining space and scene information in the metadata according to the event feature identifier; and based on the influence range and the association path, obtaining a target equipment calling list containing the equipment identifier, the space-time weight and the initial priority. Intelligent sensing is realized through multi-source data fusion, dynamic resource allocation is realized through scheduling and channelization, and stable delivery of streaming media is guaranteed through adaptive coding and reliable transmission, so that the emergency response capability of a subway security monitoring system is comprehensively improved.

The invention discloses an industrial Internet of Things equipment collaborative management and control system based on data and knowledge driving. The system comprises six units including a heterogeneous multi-source data fusion interaction unit, an improved knowledge graph construction reasoning unit and an optimized hypergraph neural network feature extraction unit. Heterogeneous data acquisition and fusion are realized through a customized industrial-grade data interface protocol and a self-adaptive coding conversion mechanism; constructing a multi-level knowledge graph by using a semantic relationship mining algorithm and performing logical reasoning; deeply extracting data features by means of an optimized hypergraph neural network; combining equipment collaborative parameter generation, executing and dynamically adjusting a strategy; and monitoring the running state of the system in real time and performing feedback optimization. The method corresponds to six steps of the system, and equipment collaborative full-process management and control are achieved. The problems that a traditional system is difficult in data fusion, low in strategy making and execution efficiency and the like are effectively solved, the industrial Internet of Things equipment collaboration efficiency and production benefits are remarkably improved, and the system is suitable for various industrial production scenes.

Implementations of the disclosure provide a video processing apparatus and method for intra prediction in video coding with an adaptive coding order. The method may include receiving, by one or more processors, one or more reference blocks from a video frame of a video for performing intra prediction on a video block of the video frame. The method may include dividing, by the one or more processors, the video block into a plurality of video sub-blocks. The method may include determining, by the one or more processors, the adaptive coding order for the plurality of video sub-blocks. The method may include generating, by the one or more processors, a plurality of reconstructed sub-blocks for the plurality of video sub-blocks based on the adaptive coding order and the one or more reference blocks. The plurality of reconstructed sub-blocks form a reconstructed block for the video block.

The invention provides an adaptive rate wireless image communication method based on channel attention and a mask auto-encoder, which comprises the following steps of: preprocessing an original image data set to obtain a preprocessed image; inputting the preprocessed image into a semantic encoder, sequentially performing down-sampling, feature extraction and channel noise self-adaptive encoding, and iterating for a plurality of rounds to obtain encoding features; performing channel-level adaptive mask processing on the coding features according to the compression ratio to obtain unmasked semantic features to be transmitted; carrying out power normalization on the unmasked semantic features, and sending the unmasked semantic features to a physical channel for transmission to obtain channel output features; zero filling is carried out on the channel output features, the channel output features are input into a semantic decoder after dimension recovery, and an output image is obtained through reconstruction; and calculating a loss function according to the output image and the original image, and performing model parameter optimization through the loss function. According to the invention, bandwidth occupation and transmission delay can be significantly reduced.

The invention discloses a gray value dynamic adjustment and risk assessment method in power system network security, and belongs to the technical field of power system network security assessment, and the method comprises the following steps: S1, feature dynamic collection, S2, gray value dynamic coding, S3, topology perception risk assessment, S4, risk alarm threshold dynamic adjustment, and S5, risk prevention and control feedback. Through cooperative operation of four stages of dynamic feature extraction, gray value adaptive coding, topology perception risk assessment and dynamic decision feedback, real-time and accurate assessment of the network security risk of the power system is realized, the gray value is dynamically adjusted along with a real-time risk index, historical risk interference is reduced in combination with a time decay factor, and the network security risk assessment accuracy is improved. The evaluation model effectively solves the hysteresis problem caused by static processing in the prior art by means of a reinforcement learning autonomous optimization strategy, and dynamic threshold adjustment can flexibly change the alarm sensitivity according to the system load change, thereby reducing the false alarm and missing alarm conditions.

A distributed adaptive coding and modulation network includes multiple network hubs that move relative to Earth, communicate among each other using intra-hub links, communicate with endpoint devices via wireless links, and elect one network hub to operate as a leader hub. A remainder of the network hubs operate as follower hubs. The leader hub is operational to negotiate a current modulation and coding scheme with the follower hubs based on a session management protocol, communicate with a first endpoint device, relay data between the endpoint devices using the current modulation and coding scheme, and dynamically renegotiate the current modulation and coding scheme in response to a change in link conditions based on the session management protocol. An edge follower hub is operational to communicate with a second endpoint device, and relay the data between the endpoint devices through the inter-hub links using the current modulation and coding scheme.

The present application relates to a kind of data reliable storage methods based on erasure code adaptive coding, the application is saved by being encoded as erasure code to data, and under different workloads, different coding strategies are converted, so that the storage system can adapt to different working environment, maintain the reliability of data.The present application is composed of two components of improved erasure code and adaptive coding, improved erasure code is improved based on existing erasure code, the performance of existing erasure code is enhanced by the XOR grouping coding XGC proposed in the present application, to provide the basis for adaptive coding.Component two judges work load and changes coding strategy based on the hot and cold characteristics of data and the use rate of storage system.

Aspects of the present disclosure relate to adaptive coding of digital communications, such as for a free-space optical channel. Some implementations can transmit, via a first transceiver over a communications channel, a first series of data packets to a second transceiver. The first transceiver can receive a second series of data packets from the second transceiver, and an adaptive coder can analyze the second series of data packets to detect transmission conditions of the communications channel. The adaptive coder can execute one or more proactive actions based on the detected transmission conditions, such as sending error correction information for the first series of data packets and / or modifying one or more future transmission characteristics, and can do so before information regarding the reception of the first series of data packets is received from the second transceiver.

This invention discloses an end-to-end spectral reconstruction method, comprising the following steps: S1, reconstructing a hyperspectral image; S2, using the obtained hyperspectral image as input to an optimized neural network, training the neural network parameters according to the end-to-end spectral reconstruction model, and calculating the loss function; S3, backpropagating errors to the coding template to be designed, changing the design parameters of the coding template, and continuing to train the model until a high-quality hyperspectral image is reconstructed. By employing the above technical solution, the design of the optical system is combined with the reconstruction method. Based on the coded aperture snapshot system, an adaptive coding template is added according to the end-to-end spectral reconstruction method, replacing the traditional random coding template, thereby improving the quality of spectral reconstruction.

The invention provides an encoding device, a decoding device, and a non-transitory storage medium. The encoding device encodes a flag by context-adaptive encoding if the number of processing times of context-adaptive encoding is limited to allow context-adaptive encoding of a plurality of coefficient information flags together in both cases of applying a different syntax and skipping an orthogonal transform in residual encoding of a current block, and then performs encoding of the flag by the context-adaptive encoding if the number of processing times of the context-adaptive encoding is limited to allow context-adaptive encoding of a plurality of coefficient information flags. And encoding the residual value of the coefficient by Golomb encoding, if not allowed, skipping the encoding of the flag, encoding the value of the coefficient by Golomb encoding, and if the orthogonal transformation is skipped, encoding the coefficient information flag other than the plurality of absolute value flags after encoding and before encoding the residual value of the coefficient. A plurality of absolute value flags related to whether or not the absolute value of the coefficient is greater than a prescribed value, which is an integer greater than 1, are encoded by context adaptive coding, and if the orthogonal transformation is skipped, the flags further include a flag indicating whether the coefficient is a positive value or a negative value.

Aspects of the present disclosure relate to adaptive coding of digital communications, such as for a free-space optical channel. Some implementations can transmit, via a first transceiver over a communications channel, a first series of data packets to a second transceiver. The first transceiver can receive a second series of data packets from the second transceiver, and an adaptive coder can analyze the second series of data packets to detect transmission conditions of the communications channel. The adaptive coder can execute one or more proactive actions based on the detected transmission conditions, such as sending error correction information for the first series of data packets and / or modifying one or more future transmission characteristics, and can do so before information regarding the reception of the first series of data packets is received from the second transceiver.

This invention relates to the field of subway security monitoring and video transmission technology, specifically a subway security monitoring video rapid retrieval and multi-channel transmission optimization system. The system includes: an analysis module for extracting feature sets of multi-source heterogeneous metadata within the subway station and matching them with abnormal event templates to obtain a matching confidence level; if the matching confidence level exceeds a preset threshold, an abnormal event is determined and an event feature identifier is output; based on the event feature identifier and combined with spatial and scene information in the metadata, the scope of influence and associated paths of the event are determined; based on the scope of influence and associated paths, a target device retrieval list containing device identifiers, spatiotemporal weights, and initial priorities is obtained. This invention achieves intelligent perception through multi-source data fusion, dynamic resource allocation through scheduling and channelization, and ensures stable delivery of streaming media through adaptive encoding and reliable transmission, thereby comprehensively improving the emergency response capability of the subway security monitoring system.

The invention discloses a maritime mobile channel adaptive code modulation transmission method and system, and relates to the technical field of wireless communication, and the method comprises the steps: obtaining the dynamic sensing data, spatial position and channel state information historical sequence of a mobile platform; reconstructing an absolute motion velocity vector of the antenna under the reference navigation coordinate system based on the antenna installation position offset characteristic; extracting an effective radial speed scalar in combination with the sight distance direction vector; determining a dynamic channel coherence time boundary, and performing truncation processing on the channel sequence to obtain a target prediction input sequence; generating a reference signal-to-noise ratio predicted value by using the time sequence prediction model; determining a nonlinear back-off compensation amount based on an angular acceleration obtained by rotation angular velocity difference, further correcting a reference predicted value to obtain a target signal-to-noise ratio, and executing physical layer resource mapping; the method can effectively cope with the prediction failure caused by the violent sudden change of the carrier attitude, and improves the robustness and throughput of a maritime communication link.