280 results about "Hierarchical coding" patented technology

The invention discloses a full-life-cycle carbon footprint intelligent detection system and method, belongs to the technical field of carbon emission monitoring, and aims to solve the problems of insufficient standardization of full-life-cycle carbon emission data, inaccurate abnormal link identification and delayed carbon footprint risk early warning. Carbon emission data of a production link in a full life cycle of a target product is collected and transmitted to a block chain, and standardized coding is realized through a hierarchical coding rule; detecting the collected data based on a multi-dimensional triggering rule to judge whether carbon footprint updating is triggered or not, if so, configuring an anchoring reference of a quantization range according to a production mode, and screening the data to construct a quantization data set; after the quantitative data set is corrected, the total carbon emission amount of each production link is calculated, and a carbon emission report is generated; the abnormal links are identified based on the carbon emission and proportion of the production links in the report, the abnormal source is positioned through standardized coding, historical carbon emission data are tracked to predict the emission, and carbon footprint grade change risk early warning is realized.

The invention discloses a low-delay self-adaptive audio transmission method, system and device and a storage medium, and relates to the technical field of audio, and the method comprises the steps: dynamically recognizing an optimal transmission path in wireless dual bands based on an intelligent band detection and self-adaptive channel selection mechanism; decomposing the target audio data through a hierarchical coding algorithm, and executing self-adaptive adjustment of a differential transmission strategy and a quantization parameter; the dual-band performance is monitored in real time based on a load balancing algorithm, the decomposed transmission tasks are dynamically allocated, and path switching is triggered when it is monitored that the transmission quality of the current band is abnormal; the future buffer requirement is predicted based on a predictive buffer algorithm, the size of a buffer area is dynamically adjusted according to the actual playing condition and the network state change so as to ensure the playing continuity and minimize the delay, the technical bottleneck of traditional single-frequency wireless audio transmission is broken through, an effective frequency band coordination mechanism is realized, resource waste or transmission conflicts are avoided, and the transmission efficiency is improved. And the double-frequency advantage is fully exerted.

The invention provides an aeronautical part multi-process parameter cooperative surface treatment standardization optimization method, and relates to the technical field of aeronautical manufacturing surface engineering, and the method comprises the following steps: S1, constructing a process parameter hierarchical coding system and a correlation mapping model; s2, establishing a parameter solving method based on multi-objective collaborative optimization; s3, constructing a modular process chain; s4, establishing real-time monitoring and closed-loop control of multi-source data fusion; s5, establishing a digital simulation body and verifying the digital simulation body; and S6, constructing a whole-process quality tracing database. According to the method, dynamic collaborative optimization of multiple process parameters is realized through parameter hierarchical coding, association mapping and a cross-process-chain intelligent decision model, the parameter conflict rate is reduced, and the process decision efficiency is improved. Meanwhile, a self-adaptive optimization engine based on reinforcement learning can correct process parameters in real time, the batch consistency of key performance indexes is improved, and the process stability and the product quality are remarkably improved.

The invention relates to a building structure health state assessment method and device, and belongs to the technical field of building renovation, and the method comprises the steps: obtaining original stress time sequence data generated by dynamic strain response of a stress part of a target building under the action of a natural load or artificial excitation, building health state category labeling is carried out on the original stress time sequence data, and a multi-dimensional labeling vector with a timestamp is obtained; obtaining a health state evaluation model based on a hierarchical coding architecture of spatial-temporal feature decoupling, and training the health state evaluation model based on the clean stress time sequence data as labeled sample data in combination with unlabeled sample data to obtain a target health state evaluation model; and based on the target health state assessment model, data analysis is performed on the collected target building stress data, and the building structure health state of the target building is determined, so that the accuracy, continuity and early warning performance of building structure health state assessment can be improved.

The invention discloses a traffic flow prediction method and system based on feature fusion and frequency enhancement. The method comprises the following steps: acquiring traffic flow data of a traffic monitor and external meteorological data and carrying out preprocessing; a dynamic space-time diagram generation module constructs a dynamic space-time diagram fusing a geographic neighbor diagram and a function similar diagram based on the preprocessed traffic flow data; inputting the dynamic space-time diagram into a space-time attention convolution layer to obtain a space-time matrix; fusing the space-time matrix with the external meteorological data subjected to one or more times of long-short-term memory network processing, and executing near-time slicing processing on the fused data; and constructing a hierarchical encoder-decoder model based on Transform, and generating traffic flow prediction data after the data subjected to slicing processing passes through the model. According to the method, the traffic flow prediction precision can be improved, and a more reliable prediction result is provided.

The invention discloses an image description generation method and system based on enhanced fine-grained information. The method comprises the following steps: 1) extracting regional features and global features of an image, and carrying out weighted fusion to generate multi-view feature information; 2) combining text feature extraction and cosine similarity screening, dynamically fusing text-image features, and optimizing cross-modal features through a dynamic multi-view enhancement mechanism; (3) a hierarchical memory enhancement encoder is adopted, multi-head attention and Transform structure hierarchical coding features are combined, and detail information is reserved through a memory unit; and 4) aggregating image and text weights in two stages through a double cross-modal attention decoder to generate high-quality description. The system comprises a feature extraction module, a text processing module, an encoding module and a decoding module. Through fine-grained feature enhancement and cross-modal interaction, the problems of detail loss and semantic deviation in a traditional method are effectively solved, and the method is suitable for image semantic analysis of complex scenes.

The invention relates to the technical field of speech synthesis, can be applied to business system platforms such as financial science and technology, medical health and the like, and discloses a layered coding and decoding speech synthesis method, device, equipment and medium, and the method comprises the following steps: carrying out semantic feature extraction on a pre-acquired original text to obtain text semantic features; performing prosodic feature extraction on the original text to obtain a text prosodic feature; performing feature fusion compression on the text semantic features and the text rhythm features to obtain fusion compression features; performing low-frequency decoding on the fusion compression features to obtain low-frequency decoding features; performing high-frequency decoding on the fusion compression feature to obtain a high-frequency decoding feature; and combining the low-frequency decoding features and the high-frequency decoding features, and converting the combined features into target synthetic speech. Through high and low frequency decoding, richer and more accurate voice details are restored, so that the synthesized voice is listened more naturally and smoothly, and the credibility and fluency of the voice are improved.

The invention relates to the technical field of data compression and storage, and discloses a monitoring data compression and storage method driven by a time sequence database, comprising the following steps: S1, preprocessing multi-source monitoring data, and constructing a dynamic tensor comprising a timestamp, a numerical index and a multi-dimensional label; s2, performing dynamic dimension reduction processing on the dynamic tensor to generate a core tensor and a multi-dimensional factor matrix; s3, based on the core tensor and the multi-dimensional factor matrix, determining a compression parameter, a decompression parallelism degree and an index granularity through a joint optimization model; and S4, performing hierarchical coding on residual data generated by the dynamic dimension reduction processing to generate a lightweight residual coding result. Through a dynamic tensor decomposition and incremental updating technology, low storage overhead and real-time dimension expansion capability of streaming monitoring data are realized, the problems of calculation redundancy and storage expansion caused by the fact that the streaming monitoring data cannot adapt to dynamic newly-added tags are solved, and meanwhile, frequent reconstruction cost caused by data dynamic expansion is avoided.

The invention provides a data management method and system based on hierarchical coding and dynamic authority, and relates to the technical field of big data technology and data management, and the method comprises the following steps: obtaining original enterprise access data through an internal database, an external service interface and a public webpage; preprocessing the original enterprise access data, including abnormal data cleaning, unstructured text processing, sensitive level marking and tree hierarchy coding, to generate target enterprise access data; constructing a mode recognition model fused with hierarchical coding features to analyze the access circulation behavior mode, and generating a mode analysis result combined with hierarchical permission rules; a dynamic data management strategy including an authority distribution mechanism and a data encryption mechanism is generated by combining the mode generation frequency, so that the real-time insight demand of mass data can be met, the dynamic characteristics and behavior rules of the data are deeply studied, the refinement degree of data access authority control and the access history tracking efficiency are improved, and the real-time insight demand of the mass data is met. And the data security and compliance risk is reduced.

The invention belongs to the technical field of image communication, and particularly relates to a salt tide real-time monitoring method and system based on satellite remote sensing, and the method comprises the steps: collecting a remote sensing image of a monitoring region in real time; allocating code words for pixel values in each channel of the remote sensing image by using a variable-length coding algorithm; dividing the code word of each pixel value into a plurality of layers according to the loss threshold value of each layer to obtain a sub-code word of each layer of each pixel value; and performing layered coding on each channel of the remote sensing image by using the sub-code words of each layer of each pixel value, and transmitting coding results of different layers to a monitoring terminal in sequence for salt moisture real-time monitoring. Layered compression transmission is performed on the remote sensing image, and the real-time performance of salt tide monitoring is improved.

The invention provides a video coding and compression transmission method for intelligent equipment, which belongs to the technical field of video coding and compression transmission, realizes advanced channel quality estimation by establishing a channel prediction model enhanced by a spatial transformation network, configures layered coding parameters according to a prediction result and optimizes motion search in combination with inertial measurement unit data. An optimal coding mode is selected by an application rate distortion optimization algorithm, enhancement layer sending is adjusted according to real-time feedback by adopting a dynamic transmission scheduling strategy, and differential forward error correction protection and a time-space domain error concealment mechanism are matched; the technical problem that video transmission is unstable due to violent fluctuation of channel quality when intelligent equipment moves and works in complex electromagnetic environments such as a transformer substation is solved.

The invention belongs to the technical field of crossing of satellite navigation and edge computing, and relates to a differential data broadcasting method based on edge computing, which comprises the following steps: accessing multi-source data through a dynamic data acquisition processing module, reducing noise, scheduling computing power through an edge node cooperative computing module, and fusing data to generate a differential factor; the spatio-temporal characteristic difference generation module generates adaptive difference data in combination with spatio-temporal and environmental parameters, then the intelligent broadcast resource management and control module selects channels and performs layered coding broadcast, the system health degree management module guarantees stability, the cross-domain collaborative application interface module adapts to multiple scenes, and after the system is started, all the modules operate in sequence, data are collected and processed firstly, and then the data are transmitted to the intelligent broadcast resource management and control module. And then generating factors through cooperative calculation, generating adaptive differential data, intelligently broadcasting the adaptive differential data, monitoring and maintaining the adaptive differential data by a health degree module, and finally docking each application scene through an interface module to realize high-precision positioning service.

The invention particularly relates to a multi-level self-cognition system based on a large model, and relates to the technical field of large models. A neural symbol world model module; a large language model cognition core module; and a hierarchical decision planning system module. According to the method, deep integration of perception, cognition and decision making is achieved through the hierarchical fusion architecture, and compared with the prior art, the method has remarkable advantages; the multi-modal perception encoder adopts layered encoding and a cross-modal attention mechanism, so that the semantic alignment problem of multi-source perception data is effectively solved, and the understanding ability of the system to a complex scene is greatly improved; according to the neural symbol world model, the neural network and symbol reasoning are combined, the limitation of a pure neural network method in physical modeling is overcome, meanwhile, the calculation complexity of a pure symbol system is avoided, and efficient and accurate environment characterization and prediction are achieved.

The invention discloses a water conservancy project management method and system based on multi-source inspection data fusion and correlation analysis, and relates to the technical field of data processing. According to the method, hierarchical coding is performed on the inspection data in a unified acquisition period, and a three-layer envelope structure is constructed, so that time-space integrated organization of multi-source inspection information is realized, cross-source data is enabled to obtain structural consistency expression, and the accuracy and traceability of data fusion are improved; according to the method, a joint positioning point is established in a time envelope layer and a collaborative labeling identifier is distributed in a space envelope layer, so that collaborative labeling entries with synchronous semantics are formed by data of different sources, and the alignment capability and the contrastive analysis efficiency of inspection information are remarkably improved; by means of construction of a step-by-step linkage association link, continuous evolution of a water conservancy structure state can be presented on a time sequence, meanwhile, spatial-temporal characteristics can be synchronously captured in combination with generation of dynamic association paragraphs, and the recognition sensitivity of abnormal changes is enhanced.

A system for 3D medical image segmentation includes a medical imaging device for obtaining a plurality of 2D images forming a volumetric image, processing circuitry, and a display. The processing circuitry is configured with a first stage to divide the volumetric image into 3D image patches, a hierarchical encoder-decoder structure in which resolution of features of the 3D image patches is decreased by a factor of two in each of a plurality of stages of the encoder, an encoder output connected to the decoder via skip connections, and a convolutional block to produce a voxel-wise final segmentation mask. The encoder includes a plurality of efficient paired attention blocks each with a spatial attention branch and a channel attention branch that learn respective spatial and channel attention feature maps. The display displays the final segmentation mask.

The invention relates to the technical field of unmanned aerial vehicle image processing, and discloses a high-definition image processing and transmission method based on an unmanned aerial vehicle. The method comprises the following steps: acquiring a multi-frame high-definition image sequence obtained in the flight process of an unmanned aerial vehicle; analyzing the image sequence frame by frame, extracting key feature points and generating feature descriptors; establishing a feature matching relationship between adjacent frames, and calculating an inter-frame motion vector; constructing a global motion model according to the motion vector and performing motion compensation; performing region segmentation on the compensated image sequence to divide the image sequence into a dynamic region and a static region; performing multi-scale noise reduction on the dynamic region, and performing texture enhancement on the static region; carrying out fusion reconstruction on the processed dynamic and static regions to generate an optimized image sequence; performing layered coding on the optimized sequence according to a transmission bandwidth condition, and generating base layer and enhancement layer data; dynamically adjusting the priority of two layers of data transmission according to the network state; and sending the data to a ground receiving end through a multi-path transmission protocol.

The invention discloses a tunnel smooth blasting parameter selection method, which aims at the tunnel back-break and back-break problem and comprises the following steps of: constructing a blasting parameter optimization model by determining the back-break and back-break amount minimization as a target function and taking the tunnel section size, the lithology grade and the geological condition as constraint conditions; an improved genetic algorithm which introduces dynamic constraint, hierarchical coding and multi-stage fitness evaluation is utilized, and optimized blasting parameters under the minimum back break amount are screened out through continuous selection, intersection and mutation operations. Compared with a traditional genetic algorithm, global convergence and parameter practicability are improved, and meanwhile the problems of premature convergence and invalid solutions of the traditional genetic algorithm are solved.

The invention provides a virtual data transmission flow control system for an MR head-mounted display. The virtual data transmission flow control system comprises a dynamic key area identification module, a self-adaptive flow scheduling module, a low-delay transmission optimization module and a cross-layer collaborative optimization module, wherein the dynamic key region recognition module is used for recognizing a complex key region in an MR environment in real time based on a user fixation point, a head movement track and scene semantic analysis, and the complex key region comprises a high-dynamic interaction object, a visual focus region and a real-time changing virtual scene element; the dynamic key region identification module obtains user behavior data through multi-modal sensor fusion, predicts the change trend of a user concerned region in combination with a deep learning model, and outputs the priority weight of a key region; the adaptive traffic scheduling module comprises a hierarchical coding unit, a dynamic bandwidth allocation unit and a redundancy control unit; and the self-adaptive flow scheduling module is connected with the dynamic key area identification module.

The invention discloses a psychological state dynamic evaluation and early warning system based on multi-modal behavior data, which belongs to the field of medical care informatics and comprises a multi-modal behavior data hierarchical coding module, a time sequence causal atlas construction and reasoning module, a double-stage self-adaptive early warning decision module and a context awareness intervention strategy generation module. A cross-modal association mode is extracted through a double-layer coding mechanism, a time sequence graph containing a causal relationship is constructed, causal reasoning is performed, a double-stage mechanism of short-term mutation detection and long-term trend prediction is adopted to generate graded early warning, and an optimal intervention strategy is selected based on a deep Q network according to a user situation. According to the method, the accuracy, timeliness and intervention effectiveness of psychological health assessment are improved, and dynamic monitoring and early warning of the psychological state are realized.

The invention discloses a hierarchical coding system-based intelligent recognition and language conversion method for traditional building-riding facade components, which realizes automatic coding and standardized description generation by constructing a six-level hierarchical coding system and training a special component recognition model, and lays a data foundation for evolution analysis of large-scale building-riding buildings. The method comprises the following steps: S1, constructing a six-level standard coding system; s2, collecting, preprocessing and labeling images, and constructing a labeling data set; s3, constructing a building-riding part identification model, and training and identifying the building-riding part identification model by using the existing data set and the annotation data set; s4, allocating coding information of a standard coding system for the recognition result of S3, extracting multi-dimensional features, performing component correlation analysis, and constructing a component feature database; s5, standard component description texts of three levels are automatically generated based on the steps S3 and 4; s6, constructing a structured knowledge graph of the apartment facade components based on the steps S3 to S5; and S7, integrating the processing capabilities in the steps S2 to S6 into a complete system.

The invention discloses a progressive multi-modal medical image fusion method based on multiple scales. Relates to the technical field of multi-modal medical image fusion, in particular to a progressive multi-modal medical image fusion method based on multiple scales. Through the integrated design of multi-scale extraction, hierarchical coding, layer-by-layer fusion and adaptive modeling, efficient and accurate fusion of multi-modal medical images is realized, and reliable technical support is provided for clinical application. The method comprises the following steps: acquiring images of a mode A and a mode B, and preprocessing the images; performing multi-modal parallel coding on the preprocessed image, wherein the multi-modal parallel coding comprises four coding stages; the input of each coding stage passes through a self-adaptive visual RWKV module, an efficient cross-modal module and a progressive fusion module in sequence to obtain the output of multi-modal parallel coding; and carrying out image conversion to obtain an RGB fusion image.

A video compression system and method integrates geometric compression with cognitive understanding through a persistent cognitive machine interface. The system employs a hierarchical encoder generating multi-scale compressed representations organized within a Lorentzian manifold structure. A geometric processor maintains temporal causality through time-like geodesics and light cone constraints while organizing video content according to semantic relationships. A cognitive interface creates thought bundles as navigable submanifolds, enabling semantic access to compressed content beyond traditional temporal indexing. The system supports real-time processing through progressive refinement, streaming coarse representations immediately while adding detail in parallel. Symbolic anchors mark semantically significant points, enabling concept-based navigation through compressed video. Federated learning capabilities allow distributed systems to share geometric patterns while preserving content privacy. The architecture enables improved compression ratios while maintaining both temporal causality and semantic navigability, transforming video from sequential media into an intelligently accessible information space.

The invention discloses a monitoring video processing method and device in a public security scene and a medium, and the method comprises the steps: obtaining the current network state information of a video transmission network in real time in the process of carrying out normal compression coding on a monitoring video stream collected by a monitoring device in real time and transmitting the monitoring video stream to a receiving control end in real time; when the current network state information satisfies a degradation coding condition, determining each monitoring video stream to be processed as a degradation video stream, and respectively identifying a target monitoring area and a non-monitoring area in each degradation video stream; and performing at least one round of degradation coding processing on each degradation video stream according to the target monitoring area and the non-monitoring area in each degradation video stream until a degradation processing ending condition is met, according to the technical scheme of the embodiment of the invention, a reliable video transmission guarantee scheme is provided for public security scenes such as urban security and protection through intelligent area identification and dynamic hierarchical coding technologies.

The invention discloses an artificial leather quality rapid detection method based on spectral analysis, and particularly relates to the field of quality detection.The method comprises the steps that component composition characteristics, surface and microcosmic characteristics and dynamic response characteristics of artificial leather are synchronously obtained through multi-dimensional original data acquisition, and a time-space correlation data set is formed through timestamp alignment and space calibration; constructing component analysis, surface association and dynamic response models, and eliminating space-time migration through a synchronous optimization model; after multi-dimensional features are extracted, optimized features are generated through cross-direction tensor fusion, attention enhancement and gating cycle unit time sequence fusion; and finally, quantizing the quality index based on a multi-task model, and outputting a quality grade and a traceable report in combination with a dynamic threshold value through hierarchical coding, graph neural network association enhancement and evidence fusion. The single sample collection duration is less than 3 minutes, the response time is less than 1 second, and full-dimension, rapid and accurate detection and quality traceability are realized.

The invention belongs to the technical field of image processing, and particularly relates to a Chinese painting style migration method based on multi-scale color embedding, which comprises the following steps: acquiring a content image data set and a style image data set, and constructing a Chinese painting style migration network with multi-scale color embedding, including a content-aware position encoder, a hierarchical encoder and a multi-mode decoder; inputting the content image into a content perception position encoder, and extracting through linear mapping and a Transform encoder to obtain a global semantic feature sequence of the content image; inputting the style image into a hierarchical encoder, and generating image embedding fusing content and style through a progressive decoder in combination with the global semantic feature sequence; embedding and inputting the image with the fused content and style into a multi-mode decoder, and dynamically optimizing color embedding representation; according to the method, efficient fusion of the content image and the polychrome wood print style is realized through a deep learning technology, artistic expressive force and semantic consistency are achieved, and the quality and stability of polychrome wood print style migration are improved.

The invention relates to the technical field of 5G wireless image transmission, in particular to a 5G wireless image transmission low-latency transmission method based on dynamic code rate hierarchical coding, which comprises the following steps of: coding each frame of image according to divided levels; dividing the transmission priority of each frame of image; and periodically determining whether the processing for the video is qualified based on the determined jitter influence parameter, adjusting a preset kurtosis used for dividing the hierarchy to a corresponding value when the processing for the video is determined to be abnormal, and adjusting the processing parameter for the video based on the transmission elasticity quantized value and the data retention characterization value. The transmission priority and the coding hierarchy are divided based on the actual recording condition and the transmission condition, and the coding transmission is carried out by adopting the targeted code rate, so that the video transmission efficiency is improved.

The invention relates to the technical field of wireless communication, in particular to a secure communication perception integrated system optimization method based on rate segmentation multiple access and a reconfigurable holographic surface, which comprises the following steps: constructing a secure communication perception integrated system model; constructing a multi-objective joint optimization problem; a multi-objective joint optimization problem is decomposed into three sub-problems, and solving algorithms are designed respectively; and sequentially updating the solutions of the three sub-problems in an alternate iteration mode until a convergence condition is met, outputting an optimal parameter, and carrying out multi-dimensional verification on the system performance. According to the invention, through a rate segmentation multiple access public stream-private stream layered coding mechanism, the system can intelligently convert part of signal energy into man-made interference to potential eavesdroppers, and at the same time, a high-dimensional beam forming degree of freedom provided by a reconfigurable holographic surface is utilized; accurate orientation of legal user signals and accurate control of null depth of an eavesdropping direction are realized.

The invention discloses a drainage basin water environment ecological safety early warning method and a drainage basin water environment ecological safety early warning system, particularly relates to the technical field of drainage basin water environment monitoring, and is used for solving the problem of pollution diffusion path prediction distortion caused by the fact that an existing model does not fully represent a river network hierarchical structure and flow direction constraint. The method comprises the steps of obtaining river network topology data, performing hierarchical coding on nodes, and determining a hierarchical attenuation factor and a flow direction constraint factor in combination with hydrological flow direction data; establishing a hierarchical weighted information transfer rule, so that the information transfer weight from a high-order node to a low-order node is decreased progressively along with the hierarchical difference; in the pollution diffusion deduction, executing one-way confluence constraint check and illegal path truncation based on the rule, and fusing a node hierarchy gradient field and a flow direction vector tensor to couple and analyze a topological stress state so as to output a deduction result; when the topological stress state continuously crosses the boundary, a flow direction filtering threshold value is deconstructed based on hierarchical difference characteristics; and finally, ecological safety early warning information is generated according to the standard exceeding nodes.

The invention discloses a multi-modal remote sensing classification method and device based on spatial covariance, and belongs to the technical field of remote sensing image processing, and the method comprises the steps: obtaining multi-modal remote sensing images of the same region, and carrying out the image registration of the obtained remote sensing images; extracting spatial covariance of each modal remote sensing image so as to quantitatively represent spatial correlation characteristics of ground features in the remote sensing image; constructing a channel attention mechanism for a remote sensing image with rich spectral information; for each modal remote sensing image, multi-modal features are extracted in a layered coding mode; extracting covariance features, respectively fusing the covariance features, and finally fusing the fused multi-modal features with the spatial covariance features; carrying out convolution operation on the fused features to reduce the number of channels, and realizing classified output; an optimizer with weight attenuation is adopted, a cross entropy loss function is used as a classification error measure, and a regular term is added to control the complexity of the model. According to the invention, the ground feature classification precision in a complex scene is improved.

An embodiment of the present invention discloses a method and related devices for streaming media data transmission, characterized in that the method includes: determining M key frames in a target group of pictures (GOP); the target GOP includes L frames; M is an integer greater than 0, and L is an integer greater than 2; respectively performing hierarchical encoding on the source data of each of the M key frames to generate M base layer data and N enhancement layer data; the data quality of the enhancement layer data corresponding to each key frame is higher than the data quality of the base layer data; N is an integer greater than or equal to M; performing differential protection on the base layer data and the enhancement layer data of each key frame to obtain first encoded data of the M key frames. Using the embodiment of the present invention can reduce the delay in the process of streaming media data transmission.