A method for dynamically transmitting and processing audio data and an audio playback device

Abstract

The present invention relates to the field of data processing, and in particular to a method for dynamic transmission and processing of audio data and an audio playback device. The method comprises the following steps: identifying key transmission nodes in an audio transmission network, marking multiple transmission network nodes; performing multi-dimensional state perception on multiple transmission network nodes, and reconstructing multi-dimensional environmental indicators to build a dynamic twin network environment; obtaining audio data to be transmitted; performing low-frequency filtering optimization processing on the audio data to be transmitted, and performing feature segmentation processing to obtain multiple audio feature segments; performing data stream encapsulation processing on multiple audio feature segments, and performing lossless compression encoding to generate multiple audio lossless compression codes; performing intelligent segmented transmission processing on multiple audio lossless compression codes based on a dynamic twin network environment, and extracting audio segment transmission response data. The present invention achieves efficient audio data transmission and low audio errors.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to an audio data dynamic transmission processing method and an audio playback device. Background Art

[0002] With the rapid development of Internet technology, audio data is increasingly used in various application scenarios. From entertainment to office, from monitoring to remote conferencing, audio data has become one of the important carriers of information transmission. However, in the actual transmission process, audio data is often affected by various factors in the network environment, such as bandwidth fluctuations, transmission delays, data loss, etc. These problems will seriously affect the end-user experience quality.

[0003] Traditional audio data transmission methods usually use static encoding and transmission strategies, which cannot dynamically adapt to the ever-changing network environment. This method provides good transmission effects under certain stable network conditions, but once complex network conditions are encountered, it is difficult to ensure audio quality and real-time performance. At the same time, traditional methods that rely on manual observation and manual adjustment have low audio transmission efficiency and accuracy.

[0004] Therefore, there is an urgent need for an intelligent audio data transmission processing method that can actively perceive the network environment status and adopt corresponding optimization strategies according to real-time changes, so as to ensure that the audio data can maintain good transmission quality under various network conditions. Summary of the invention

[0005] In order to solve the above technical problems, the present invention proposes an audio data dynamic transmission processing method and an audio playback device to solve at least one of the above technical problems.

[0006] To achieve the above object, the present invention provides a method for dynamic transmission and processing of audio data, comprising the following steps:

[0007] Step S1: Identify key transmission nodes in the audio transmission network and mark multiple transmission network nodes; perform multi-dimensional state perception on multiple transmission network nodes, reconstruct multi-dimensional environmental indicators, and build a dynamic twin network environment;

[0008] Step S2: obtaining audio data to be transmitted; performing low-frequency filtering optimization processing on the audio data to be transmitted, and performing feature segmentation processing, thereby obtaining multiple audio feature segments;

[0009] Step S3: performing data stream encapsulation processing on the multiple audio feature segments, and performing lossless compression encoding, thereby generating multiple audio lossless compression codes;

[0010] Step S4: performing intelligent segmented transmission processing on multiple audio lossless compression codes based on the dynamic twin network environment, and extracting audio segmented transmission response data;

[0011] Step S5: performing multi-frame transmission error identification on the audio segment transmission response data, and performing frame-by-frame error correction, thereby generating error-corrected audio data;

[0012] Step S6: Obtain transmission delay points based on the audio segment transmission response data; dynamically optimize the transmission delay of the error-corrected audio data according to the transmission delay points, and build an intelligent audio delay optimization engine to perform dynamic transmission operations for audio data.

[0013] The present invention marks key transmission nodes and constructs a dynamic twin network environment to establish an overall understanding of the audio transmission network, optimize the transmission path and improve the transmission efficiency. Multi-dimensional state perception and environmental indicator reconstruction improve the system's ability to perceive environmental changes, provide more accurate environmental data support for subsequent audio transmission, and optimize low-frequency filtering to reduce noise and improve the clarity of audio data, improve transmission quality and reduce transmission errors. Feature segmentation processing divides audio data into smaller segments, improves transmission efficiency and achieves more precise control of audio data. Data stream encapsulation processing and lossless compression coding effectively reduce the size of audio data and improve transmission efficiency while ensuring that the quality of audio data is not affected. Lossless compression coding helps reduce data loss during transmission and ensures the integrity of audio data. It improves the quality of audio and video transmission and the fidelity of audio. It performs intelligent segmented transmission based on a dynamic twin network environment, adjusts the transmission strategy according to the real-time network status, improves the transmission success rate and speed, extracts audio segmented transmission response data, monitors the feedback information during the transmission process, and adjusts the transmission parameters in time to ensure the transmission quality. It effectively reduces data loss and distortion during transmission through transmission error identification and frame-by-frame error correction, improves the integrity and accuracy of audio data, and repairs audio data damaged during transmission through frame-by-frame error correction to ensure that the receiving end obtains high-quality audio content. It effectively reduces transmission delay through transmission delay points and dynamic transmission delay optimization, improves the real-time and stability of audio transmission, and builds an intelligent audio delay optimization engine to dynamically adjust the transmission delay according to different situations, providing a more intelligent and personalized audio transmission experience.

[0014] Preferably, step S1 comprises the following steps:

[0015] Step S11: identifying key transmission nodes in the audio transmission network and marking multiple transmission network nodes;

[0016] Step S12: performing multi-dimensional state perception on multiple transmission network nodes, and extracting real-time state parameters of the network nodes, wherein the real-time state parameters of the network nodes include node bandwidth, node packet loss rate, and node transmission delay parameters;

[0017] Step S13: Calculate the bandwidth utilization of the node bandwidth to obtain the network node bandwidth utilization;

[0018] Step S14: analyzing the time series variation of the node packet loss rate to generate a node packet loss time series variation curve;

[0019] Step S15: performing delay fluctuation trend identification on the node transmission delay parameter to obtain the node delay fluctuation trend;

[0020] Step S16: Reconstruct multi-dimensional environmental indicators of network node bandwidth utilization, node packet loss timing change curve and node delay fluctuation trend to build a dynamic twin network environment.

[0021] The present invention ensures that the system focuses on key nodes in the network, optimizes transmission paths and improves transmission efficiency by identifying key transmission nodes and marking transmission network nodes, extracts node parameters such as bandwidth, packet loss rate and transmission delay in real time, and the system understands the network status in real time, provides accurate data support for dynamically adjusting transmission strategies, effectively evaluates the use of network resources by calculating bandwidth utilization, and helps to reasonably allocate bandwidth resources to improve transmission efficiency and reduce congestion risks. The timing change analysis reveals the trend and law of node packet loss rate, timely discovers network problems and takes corresponding measures to reduce data loss and improve transmission quality. By identifying the delay fluctuation trend, the system better understands the change of node transmission delay, adjusts the transmission strategy to reduce delay and improve transmission efficiency. By identifying the delay fluctuation trend, the system better understands the change of node transmission delay, adjusts the transmission strategy to reduce delay and improve transmission efficiency.

[0022] Preferably, step S2 comprises the following steps:

[0023] Step S21: Acquire audio data to be transmitted;

[0024] Step S22: performing low-frequency filtering optimization processing on the audio data to be transmitted, thereby generating filtering optimized audio data;

[0025] Step S23: performing audio semantic feature analysis on the filtered optimized audio data to obtain audio semantic feature data;

[0026] Step S24: performing key semantic feature recognition based on the audio semantic feature data to extract key semantic feature points;

[0027] Step S25: performing feature segmentation processing on the filtered optimized audio data based on key semantic feature points, thereby obtaining a plurality of audio feature segments.

[0028] The present invention obtains the audio data to be transmitted as the starting point of the processing flow, ensures the accuracy and integrity of the audio data, and provides basic data for subsequent processing. Low-frequency filtering optimization processing removes high-frequency noise and interference, improves the clarity and quality of audio data, thereby improving the transmission effect and the auditory experience of the receiving end. Audio semantic feature analysis can deeply explore the semantic information of audio data, help the system better understand the audio content, and provide a more accurate data basis for subsequent processing. Through key semantic feature recognition, the system identifies the most important semantic feature points in the audio data, extracts the key information of the audio data, and optimizes transmission efficiency and accuracy. Feature segmentation processing based on key semantic feature points divides the audio data into different segments according to semantic features, which is conducive to personalized processing and transmission of different parts, and improves transmission efficiency and quality.

[0029] Preferably, the specific steps of step S22 are:

[0030] Performing wavelet transform decomposition on the audio data to be transmitted to obtain frequency domain data of multiple frequency bands;

[0031] Calculating noise components on frequency domain data of a plurality of frequency bands to generate noise components of each frequency band;

[0032] Performing audio noise distribution analysis on the noise components of each frequency band to obtain noise distribution data;

[0033] Adaptively denoising the audio data to be transmitted based on the noise distribution data to generate denoised audio data;

[0034] Perform multi-point sampling processing on the denoised audio data to extract multiple audio sampling points;

[0035] Perform frequency fluctuation calculation on multiple audio sampling points point by point to generate sampling point frequency fluctuation values;

[0036] The denoised audio data is subjected to low-frequency filtering optimization processing based on the frequency fluctuation value of the sampling point, thereby generating filtering-optimized audio data.

[0037] The present invention can decompose the original audio data into sub-signals of different frequency bands through wavelet transform, analyze the characteristics of the audio signal in the frequency domain, and obtain the frequency domain data of multiple frequency bands, so that the subsequent noise component calculation and denoising processing are more precise and accurate. By calculating the noise component, it helps to understand the noise level in each frequency band and determine the noise components that need to be removed. The generated noise component data can provide a basis for the subsequent denoising processing, ensuring that the denoising effect is more accurate and effective. The noise distribution is analyzed to understand the distribution of noise in different frequency bands, providing guidance for the subsequent denoising processing. The noise distribution data helps to determine the parameters and adjustment strategies of the denoising algorithm to minimize noise interference and adaptively Denoising can selectively reduce noise according to the noise distribution, retain the main information of the audio signal, generate denoised audio data to improve the quality of audio transmission, and reduce the impact of noise on audio quality. Multi-point sampling can more comprehensively understand the fluctuations of audio signals at different time points, analyze the frequency change characteristics of the audio, and extract multiple audio sampling points to more accurately evaluate the frequency fluctuations of audio signals, providing a basis for subsequent optimization processing. The frequency fluctuation degree of audio signals at different time points is quantified through point-by-point frequency fluctuation calculation, helping to discover the law of frequency fluctuations. Low-frequency filtering optimization processing reduces the impact of low-frequency noise, improves the clarity and quality of audio signals, and makes the audio smoother and more natural.

[0038] Preferably, step S3 specifically comprises the following steps:

[0039] Step S31: calculating entropy values ​​of multiple audio feature segments segment by segment to obtain an entropy value of each audio feature segment;

[0040] Step S32: dynamically adaptively entropy coding the multiple audio feature segments based on the entropy value of each audio feature segment to obtain multiple entropy coded audio segments;

[0041] Step S33: performing data stream encapsulation processing on the multiple entropy coded audio segments to obtain multiple audio data packets;

[0042] Step S34: performing lossless compression encoding on the plurality of audio data packets, thereby generating a plurality of audio lossless compression encodings.

[0043] The present invention evaluates the information volume and complexity of audio signals in different sections by calculating the entropy value of each audio feature segment. The entropy value calculation identifies important information and noise in the audio signal, providing a basis for subsequent encoding and transmission. The dynamic adaptive entropy coding is flexibly adjusted according to the entropy value of each audio feature segment to improve the coding efficiency and compression ratio. The audio signal is more effectively represented and transmitted through entropy coding, redundant information is reduced, and the efficiency of data transmission is improved. The data stream encapsulation combines multiple entropy-coded audio segments into an audio data packet to manage and transmit data. The data stream encapsulation processing can ensure the integrity and sequentiality of the audio data, and facilitate the subsequent transmission and processing flow. The lossless compression coding can reduce the size of the data file while maintaining the quality of the audio data, saving the transmission bandwidth and storage space. The lossless compression coding ensures that the audio data does not lose any information during the transmission process, and maintains the original quality and accuracy of the audio.

[0044] Preferably, the specific steps of step S4 are:

[0045] Step S41: performing feature priority evaluation on multiple audio feature segments to obtain a priority evaluation value of each segment;

[0046] Step S42: Perform transmission path mining on the dynamic twin network environment to extract multiple network transmission links;

[0047] Step S43: performing path transmission performance prediction on multiple network transmission links, thereby obtaining link transmission performance prediction values;

[0048] Step S44: making a dynamic priority matching transmission decision for the priority evaluation value of each segment based on the link transmission performance prediction value, and constructing a dynamic matching transmission strategy;

[0049] Step S45: performing intelligent segmented transmission processing on multiple audio lossless compression codes based on a dynamic matching transmission strategy, and extracting audio segmented transmission response data.

[0050] The present invention determines the importance of each segment in the transmission process by evaluating the priority of audio feature segments, thereby improving the efficiency of data transmission. By obtaining the priority evaluation value, the transmission resources are better allocated in the subsequent transmission decision-making, ensuring that important audio data obtains better quality transmission services, mining the transmission paths in the dynamic twin network environment to find the best transmission route in the network, improving transmission efficiency and stability, extracting multiple network transmission links to provide more options for subsequent transmission performance prediction and transmission decision-making, ensuring the reliability and speed of audio data transmission, predicting the transmission performance of multiple network transmission links to select the best transmission path, improving the efficiency and quality of audio data transmission, and obtaining the link transmission performance prediction value for dynamic priority matching transmission decision making. The system provides data support for the policy to ensure the stability and real-time performance of audio data during transmission. It makes matching transmission decisions based on the priority evaluation values ​​of audio feature segments based on the link transmission performance prediction values. It can dynamically adjust the transmission strategy to maximize the efficiency and quality of audio data transmission, and build a dynamic matching transmission strategy to achieve optimal data transmission based on real-time network conditions and data characteristics, ensuring the timeliness and integrity of audio data. It performs lossless compression encoding on the audio and performs intelligent segmented transmission processing to reduce data loss during transmission and ensure the integrity of audio data. It extracts audio segment transmission response data to help monitor the transmission status of each segment during the transmission process, and adjusts the transmission strategy in time to ensure the accuracy and integrity of audio data.

[0051] Preferably, the specific steps of step S5 are:

[0052] Step S51: performing multi-frame transmission error identification on the audio segment transmission response data to obtain audio frame error data;

[0053] Step S52: performing error frame positioning on the audio frame error data to obtain audio frame error point position data;

[0054] Step S53: performing error point distribution analysis on the audio frame error point position data to generate audio frame error point distribution data;

[0055] Step S54: performing frame-by-frame error correction on the audio segment transmission response data according to the audio frame error point distribution data, thereby generating error-corrected audio data.

[0056] The present invention identifies multiple frame transmission errors in audio segment transmission response data, accurately captures errors and lost audio frames that occur during data transmission, obtains audio frame error data to identify problem areas during transmission, and provides a basis for subsequent correction and optimization. By positioning the error frame, the error point in the audio frame is accurately positioned to help determine the specific location of the problem in the audio frame. The audio frame error point position data is obtained to accurately identify and process errors in the audio data transmission process, and provide an accurate reference for subsequent correction. By performing distribution analysis on the audio frame error point position data, the distribution law of the error point in the audio frame is understood to help discover potential transmission problems. The audio frame error point distribution data is generated to identify common error patterns, and guidance is provided for subsequent correction strategies. The audio segment transmission response data is subjected to frame-by-frame error correction according to the audio frame error point distribution data to effectively correct errors during transmission, improve the quality of the audio data, and generate error-corrected audio data to reduce the impact of transmission errors on the audio data, and ensure that the quality of the transmitted audio data reaches a higher level.

[0057] Preferably, the specific steps of step S6 are:

[0058] Step S61: performing transmission timing delay analysis on the audio segment transmission response data to obtain audio transmission delay data;

[0059] Step S62: Perform network delay stagnation mining on the audio transmission delay data to obtain a transmission delay point;

[0060] Step S63: Calculate the delay state of the transmission hysteresis point to obtain the delay state parameter of the hysteresis point;

[0061] Step S64: dynamically optimize the transmission delay of the error-corrected audio data according to the delay state parameters of the hysteresis point, and construct an intelligent audio delay optimization engine to perform dynamic transmission of audio data.

[0062] The present invention analyzes the transmission timing delay of audio data, understands the delay of audio in the transmission process, evaluates the real-time performance and stability, obtains audio transmission delay data to provide basic data for subsequent network delay mining and delay optimization, helps optimize transmission efficiency, mines network delay stagnation points to identify delay peaks that occur during audio transmission, discovers potential transmission bottlenecks and problems, obtains transmission lag points as optimization focuses, helps the system to solve the impact of network delay stagnation in a targeted manner, calculates delay state parameters, and more carefully understands the delay status of the lag points, including information such as the degree of delay and delay fluctuation. The acquisition of delay state parameters quantifies the degree of influence of network delay, and provides quantitative indicators and reference basis for subsequent delay optimization. Based on the delay state parameters, dynamic transmission delay optimization is performed on error-corrected audio data to realize intelligent transmission adjustment, improve transmission efficiency and quality, and construct an intelligent audio delay optimization engine to dynamically adjust the transmission delay according to the real-time network status to ensure the real-time performance and stability of audio data transmission.

[0063] Preferably, the specific steps of step S64 are:

[0064] Perform time series evolution analysis on the delay state parameters of the time delay point to obtain the delay state evolution data;

[0065] Perform multi-time-point delay learning on the delay state evolution data to obtain the delay evolution law;

[0066] Identify multiple available transmission links based on a dynamic twin network environment;

[0067] According to the delay evolution law, delay distribution prediction is performed on multiple available transmission links to obtain delay distribution prediction data of the transmission links;

[0068] Perform transmission congestion risk time window inference on the delay distribution prediction data of the transmission link to obtain the congestion risk time window of each transmission link;

[0069] Calculate the delay peak value of the congestion risk time window of each transmission link to obtain the delay peak value of the congestion risk time window;

[0070] Perform dynamic delay avoidance path planning based on the delay peak value in the congestion risk time window and generate a dynamic avoidance path strategy;

[0071] The error-corrected audio data is dynamically optimized for transmission delay based on the dynamic path avoidance strategy, and an intelligent audio delay optimization engine is built to perform dynamic transmission of audio data.

[0072] The present invention understands the changing trend of the delay in the network by analyzing the time-series evolution of the delay state parameters, provides basic data for subsequent transmission optimization, obtains the delay state evolution data to reveal the changing law of the delay, helps identify the transmission bottlenecks and problem points in the network, and better understands the dynamic characteristics of the delay by learning the law of delay evolution, provides a basis for predicting and avoiding transmission delays, and understands the law of delay evolution to help network operation and maintenance personnel make corresponding adjustments and optimizations, improve transmission efficiency and stability, identify multiple callable transmission links to help select the best transmission path, improve transmission efficiency and stability, and dynamically identify the twin network environment to quickly adapt to changes in the network environment and ensure audio data The real-time and quality of transmission, predict the delay distribution of the transmission link to make transmission strategy adjustments in advance, reduce transmission delay and risk, infer the congestion risk time window to respond to network congestion in a timely manner, avoid data loss and transmission failure, calculate the delay peak of the congestion risk time window to clarify the congestion situation, and provide data support for dynamic avoidance. Dynamic avoidance path planning adjusts the transmission path according to the real-time network situation, reduces delay and improves the transmission success rate. Through dynamic transmission delay optimization, the delay in the audio data transmission process is reduced to ensure the real-time and quality of audio, and build an intelligent audio delay optimization engine to dynamically adjust the transmission strategy according to the real-time network situation to improve transmission efficiency and stability.

[0073] In this specification, an audio playback device is provided, which is used to execute the above-mentioned audio data dynamic transmission processing method, including:

[0074] The dynamic twin module identifies key transmission nodes in the audio transmission network and marks multiple transmission network nodes; it performs multi-dimensional state perception on multiple transmission network nodes, reconstructs multi-dimensional environmental indicators, and builds a dynamic twin network environment;

[0075] The filtering optimization module obtains the audio data to be transmitted; performs low-frequency filtering optimization processing on the audio data to be transmitted, and performs feature segmentation processing to obtain multiple audio feature segments;

[0076] The lossless compression coding module performs data stream encapsulation processing on multiple audio feature segments and performs lossless compression coding, thereby generating multiple audio lossless compression codes;

[0077] The segment transmission module performs intelligent segment transmission processing on multiple audio lossless compression codes based on a dynamic twin network environment and extracts audio segment transmission response data;

[0078] An error correction module is used to identify multi-frame transmission errors of the audio segment transmission response data and perform frame-by-frame error correction to generate error-corrected audio data;

[0079] The delay optimization module obtains the transmission delay point based on the audio segment transmission response data; dynamically optimizes the transmission delay of the error-corrected audio data according to the transmission delay point, and builds an intelligent audio delay optimization engine to perform dynamic transmission operations of audio data.

[0080] The present invention monitors the network status in real time by identifying key transmission nodes and constructing a dynamic twin network environment, thereby improving transmission efficiency and stability. Multi-dimensional state perception and environmental indicator reconstruction can make the transmission network more intelligent and adapt to different working environments and network conditions. Low-frequency filtering optimization processing removes low-frequency noise in the audio signal and improves audio quality and clarity. Feature segmentation processing can divide audio data according to features, and subsequent processing and analysis, lossless compression encoding reduces the volume of audio data, improves transmission efficiency and saves bandwidth, data stream encapsulation processing maintains the integrity and accuracy of audio data and avoids information loss. Intelligent segmented transmission processing dynamically adjusts the transmission strategy according to the network status and environment to improve the transmission success rate and speed, extracts audio segmented transmission response data to monitor the transmission situation in real time, and adjusts the transmission strategy in time. Multi-frame transmission error identification and frame-by-frame error correction improve the accuracy and reliability of data transmission, and generates error-corrected audio data to effectively reduce data loss and damage during transmission. Dynamic transmission delay optimization can adjust the transmission delay in real time according to the transmission delay point, improve the real-time and stability of audio transmission, and the intelligent audio delay optimization engine intelligently manages the transmission delay according to the actual situation to provide better user experience and data transmission efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 A schematic diagram of the steps of a method for dynamic transmission and processing of audio data according to the present invention;

[0082] Figure 2 Detailed implementation flow chart of step S1;

[0083] Figure 3 Detailed implementation flow chart of step S2;

[0084] Figure 4 Detailed implementation flow chart of step S3. DETAILED DESCRIPTION

[0085] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0086] The present application example provides an audio data dynamic transmission processing method and an audio playback device. The execution subjects of the audio data dynamic transmission processing method and the audio playback device include but are not limited to the following: mechanical equipment, data processing platform, cloud server node, network upload device, etc. equipped with the system can be regarded as the general computing node of the present application, and the data processing platform includes but is not limited to: at least one of an audio image management system, an information management system, and a cloud data management system.

[0087] See also Figures 1 to 4 The present invention provides a method for dynamic transmission and processing of audio data, and the method for dynamic transmission and processing of audio data comprises the following steps:

[0088] Step S1: Identify key transmission nodes in the audio transmission network and mark multiple transmission network nodes; perform multi-dimensional state perception on multiple transmission network nodes, reconstruct multi-dimensional environmental indicators, and build a dynamic twin network environment;

[0089] Step S2: obtaining audio data to be transmitted; performing low-frequency filtering optimization processing on the audio data to be transmitted, and performing feature segmentation processing, thereby obtaining multiple audio feature segments;

[0090] Step S3: performing data stream encapsulation processing on the multiple audio feature segments, and performing lossless compression encoding, thereby generating multiple audio lossless compression codes;

[0091] Step S4: performing intelligent segmented transmission processing on multiple audio lossless compression codes based on the dynamic twin network environment, and extracting audio segmented transmission response data;

[0092] Step S5: performing multi-frame transmission error identification on the audio segment transmission response data, and performing frame-by-frame error correction, thereby generating error-corrected audio data;

[0093] Step S6: Obtain transmission delay points based on the audio segment transmission response data; dynamically optimize the transmission delay of the error-corrected audio data according to the transmission delay points, and build an intelligent audio delay optimization engine to perform dynamic transmission operations for audio data.

[0094] The present invention marks key transmission nodes and constructs a dynamic twin network environment to establish an overall understanding of the audio transmission network, optimize the transmission path and improve the transmission efficiency. Multi-dimensional state perception and environmental indicator reconstruction improve the system's ability to perceive environmental changes, provide more accurate environmental data support for subsequent audio transmission, and optimize low-frequency filtering to reduce noise and improve the clarity of audio data, improve transmission quality and reduce transmission errors. Feature segmentation processing divides audio data into smaller segments, improves transmission efficiency and achieves more precise control of audio data. Data stream encapsulation processing and lossless compression coding effectively reduce the size of audio data and improve transmission efficiency while ensuring that the quality of audio data is not affected. Lossless compression coding helps reduce data loss during transmission and ensures the integrity of audio data. It improves the quality of audio and video transmission and the fidelity of audio. It performs intelligent segmented transmission based on a dynamic twin network environment, adjusts the transmission strategy according to the real-time network status, improves the transmission success rate and speed, extracts audio segmented transmission response data, monitors the feedback information during the transmission process, and adjusts the transmission parameters in time to ensure the transmission quality. It effectively reduces data loss and distortion during transmission through transmission error identification and frame-by-frame error correction, improves the integrity and accuracy of audio data, and repairs audio data damaged during transmission through frame-by-frame error correction to ensure that the receiving end obtains high-quality audio content. It effectively reduces transmission delay through transmission delay points and dynamic transmission delay optimization, improves the real-time and stability of audio transmission, and builds an intelligent audio delay optimization engine to dynamically adjust the transmission delay according to different situations, providing a more intelligent and personalized audio transmission experience.

[0095] In the embodiment of the present invention, refer to Figure 1 , is a schematic flow chart of the steps of a method for dynamic transmission and processing of audio data of the present invention. In this example, the steps of the method include:

[0096] Step S1: Identify key transmission nodes in the audio transmission network and mark multiple transmission network nodes; perform multi-dimensional state perception on multiple transmission network nodes, reconstruct multi-dimensional environmental indicators, and build a dynamic twin network environment;

[0097] In this embodiment, through network topology analysis and traffic monitoring, key transmission nodes on the audio transmission path are identified. These nodes are routers, switches, servers and other devices, which have an important impact on the quality of audio transmission. These key nodes are marked to provide targets for subsequent status perception. Various sensing devices are deployed for the marked key transmission nodes to collect multi-dimensional status data of the nodes in real time, including bandwidth utilization, CPU / memory utilization, queuing delay, packet loss rate and other performance indicators. The original monitoring data are subjected to feature extraction and indicator reconstruction to form a multi-dimensional model that highly describes the state of the network environment. Based on the multi-dimensional indicator data of the network environment, a digital twin model that is highly synchronized with the actual physical network environment is constructed. This model can accurately reflect the real-time changes in the network state, provide a reliable basis for subsequent transmission optimization, and ensure that the twin environment and the physical network remain highly synchronized through continuous online monitoring and model updates.

[0098] Step S2: obtaining audio data to be transmitted; performing low-frequency filtering optimization processing on the audio data to be transmitted, and performing feature segmentation processing, thereby obtaining multiple audio feature segments;

[0099] In this embodiment, audio data to be transmitted and to be processed is obtained, and digital signal processing technology is used to perform low-frequency filtering optimization on the obtained audio data to remove some useless low-frequency components, thereby improving the quality and transmissibility of the audio signal. The audio data that has undergone low-frequency filtering optimization is divided into a plurality of meaningful semantic segments, and different audio semantic blocks are divided according to features such as the coherence of the audio content and the pause rhythm, thereby generating a set of structured data describing the features of each audio segment.

[0100] Step S3: performing data stream encapsulation processing on the multiple audio feature segments, and performing lossless compression encoding, thereby generating multiple audio lossless compression codes;

[0101] In this embodiment, the multiple audio feature segmented data obtained in the combined steps are packaged and encapsulated, and the audio data of each segment is converted into a standard data stream format to facilitate subsequent encoding and transmission, ensure the structured description and integrity of the audio segmented data, and use a professional audio lossless compression algorithm to encode and compress the generated data stream. Without losing audio quality, the data volume is minimized and the transmission efficiency is improved. Commonly used lossless compression formats include FLAC, Apple Loss less, etc. For each audio feature segment obtained, the above-mentioned packaging and encoding operations are performed, and finally a group of multiple lossless compression encoding data blocks describing the entire audio are formed, which provide basic data for subsequent dynamic transmission optimization.

[0102] Step S4: performing intelligent segmented transmission processing on multiple audio lossless compression codes based on the dynamic twin network environment, and extracting audio segmented transmission response data;

[0103] In this embodiment, for multiple audio lossless compression encoded data blocks, combined with the network environment status, the transmission cost of each segment under the current network conditions is predicted, and multiple factors such as data volume, delay, and packet loss risk are considered to calculate the transmission priority of each segment. The audio segment transmission cost data is used to make dynamic transmission decisions using an intelligent scheduling algorithm, giving priority to transmitting low-cost critical segments, and appropriately delaying or discarding high-cost non-critical segments to meet the audio transmission requirements under the current network conditions to the greatest extent possible. After the actual execution of the intelligent segment transmission decision, the transmission response of each audio segment is recorded, including key indicators such as transmission delay and packet loss rate, reflecting the actual performance of segment transmission in the network environment, and providing basic data for subsequent transmission quality analysis and optimization.

[0104] Step S5: performing multi-frame transmission error identification on the audio segment transmission response data, and performing frame-by-frame error correction, thereby generating error-corrected audio data;

[0105] In this embodiment, the error characteristics of each segment during the transmission process are analyzed. This includes various transmission quality issues such as delay jitter and data loss. By comparing the original audio segment data and the actual transmission response data, the transmission error of each segment is identified. According to the various transmission errors found, a special correction algorithm is used to perform frame-by-frame error compensation. This includes using the previous and next frame data to correct the delay jitter, and using interpolation and other methods to fill in the lost data. The corrected audio segment data is reassembled into a complete audio data stream. A high-quality audio data that has been corrected for transmission errors is formed.

[0106] Step S6: Obtain transmission delay points based on the audio segment transmission response data; dynamically optimize the transmission delay of the error-corrected audio data according to the transmission delay points, and build an intelligent audio delay optimization engine to perform dynamic transmission operations for audio data.

[0107] In this embodiment, the delay characteristics of each segment in the actual transmission are analyzed, and the key time nodes that cause the delay problem, namely the transmission delay points, are identified, including the time point when the delay peak occurs, the duration and other key characteristics. The obtained transmission delay point data is combined with the error-corrected audio data to perform dynamic delay optimization processing, including reasonably arranging the transmission timing of the audio segments, avoiding the key time window when the delay peak occurs, etc., to minimize the delay fluctuation in the audio transmission process and ensure the overall delay performance. Based on the various optimization data and strategies obtained in the above steps, an engine system that can continuously perform intelligent delay optimization is constructed, which can perceive the network environment status in real time, predict the transmission delay points, and dynamically schedule the transmission of audio data according to the optimization strategy, to ensure that the end user experiences high-quality, low-latency intelligent audio transmission services.

[0108] In this embodiment, refer to Figure 2 , is a flowchart of detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include:

[0109] Step S11: identifying key transmission nodes in the audio transmission network and marking multiple transmission network nodes;

[0110] Step S12: performing multi-dimensional state perception on multiple transmission network nodes, and extracting real-time state parameters of the network nodes, wherein the real-time state parameters of the network nodes include node bandwidth, node packet loss rate, and node transmission delay parameters;

[0111] Step S13: Calculate the bandwidth utilization of the node bandwidth to obtain the network node bandwidth utilization;

[0112] Step S14: analyzing the time series variation of the node packet loss rate to generate a node packet loss time series variation curve;

[0113] Step S15: performing delay fluctuation trend identification on the node transmission delay parameter to obtain the node delay fluctuation trend;

[0114] Step S16: Reconstruct multi-dimensional environmental indicators of network node bandwidth utilization, node packet loss timing change curve and node delay fluctuation trend to build a dynamic twin network environment.

[0115] In this embodiment, multi-dimensional sensors deployed on network nodes collect and monitor key performance parameters of nodes in real time, including operation status information of multiple dimensions such as node bandwidth, node packet loss rate, and node transmission delay. These node status parameters collected in real time are effectively organized and stored to provide basic data for subsequent dynamic analysis. The current bandwidth utilization is calculated in combination with the actual capacity of the node bandwidth. Through time series analysis, a change curve of the bandwidth utilization of each node is drawn. These bandwidth utilization data reflect the overall resource utilization of the network. The packet loss rate data collected by the node in real time is analyzed to depict the change trend of the packet loss rate in the time series, and a set of time series curves that can reflect the fluctuation characteristics of the node data transmission quality are generated. Based on the real-time monitoring data of the node transmission delay, its fluctuation characteristics in the time series are analyzed. Through the machine learning algorithm, the fluctuation trend pattern of the node transmission delay is extracted. According to the network performance indicator data of each dimension, it is integrated through data fusion technology to construct a multi-dimensional virtual model that can reflect the overall operation status of the network in real time. This dynamic network environment twin provides valuable data support for subsequent intelligent perception and optimization decision-making.

[0116] In this embodiment, refer to Figure 3 , is a flowchart of detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include:

[0117] Step S21: Acquire audio data to be transmitted;

[0118] Step S22: performing low-frequency filtering optimization processing on the audio data to be transmitted, thereby generating filtering optimized audio data;

[0119] Step S23: performing audio semantic feature analysis on the filtered optimized audio data to obtain audio semantic feature data;

[0120] Step S24: performing key semantic feature recognition based on the audio semantic feature data to extract key semantic feature points;

[0121] Step S25: performing feature segmentation processing on the filtered optimized audio data based on key semantic feature points, thereby obtaining a plurality of audio feature segments.

[0122] In this embodiment, the original audio data file to be transmitted is obtained from the audio source device or database, ensuring that the basic parameters such as the format and sampling rate of the obtained audio data meet the transmission requirements, selecting a suitable low-frequency filter (such as Butterworth filter, Chebyshev filter, etc.), determining the cutoff frequency, inputting the audio data to be transmitted into the filter, performing low-frequency filtering optimization processing, removing unnecessary low-frequency noise, and using a speech analysis algorithm to perform in-depth semantic feature analysis on the optimized audio data, including extraction of semantic feature information at multiple levels such as phonemes, pitches, and rhythms. Based on the audio semantic feature data, key semantic information in the audio is identified, such as keywords and key sentences in the speech, as the focus of audio transmission. The audio segmentation strategy is determined based on the extracted key semantic feature points, such as segmentation based on feature points, and the audio data after filtering optimization is segmented according to the key feature points to generate multiple audio feature segments.

[0123] In this embodiment, the specific steps of step S22 are:

[0124] Performing wavelet transform decomposition on the audio data to be transmitted to obtain frequency domain data of multiple frequency bands;

[0125] Calculating noise components on frequency domain data of a plurality of frequency bands to generate noise components of each frequency band;

[0126] Performing audio noise distribution analysis on the noise components of each frequency band to obtain noise distribution data;

[0127] Adaptively denoising the audio data to be transmitted based on the noise distribution data to generate denoised audio data;

[0128] Perform multi-point sampling processing on the denoised audio data to extract multiple audio sampling points;

[0129] Perform frequency fluctuation calculation on multiple audio sampling points point by point to generate sampling point frequency fluctuation values;

[0130] The denoised audio data is subjected to low-frequency filtering optimization processing based on the frequency fluctuation value of the sampling point, thereby generating filtering-optimized audio data.

[0131] In this embodiment, a suitable wavelet basis (such as Haar, Daubechies, etc.) is selected for transformation, the number of decomposition layers is determined, and a wavelet transform algorithm (such as discrete wavelet transform) is used to decompose the audio data to be transmitted to obtain frequency domain data of multiple frequency bands, the frequency domain data of each frequency band is organized into a structured format, the frequency domain data of each frequency band is analyzed, the noise component is extracted using a noise analysis algorithm, the size of the noise component in each frequency band is calculated, and a noise distribution data is formed. These noise component data reflect the noise characteristics of the audio in different frequency bands, and the noise component data of each frequency band is further analyzed to depict the overall noise distribution characteristics, identify the main noise components of the audio data in different frequency bands and their distribution laws, select a suitable adaptive denoising algorithm, such as a wavelet threshold denoising method or a spectral subtraction method, and perform the denoising on the audio data to be transmitted according to the noise distribution data. The audio data is denoised to eliminate noise components, retain audio signals, determine the interval and number of sampling points, ensure the representativeness of sampling, extract multiple audio sampling points from the denoised audio data, record the value of each sampling point and its timestamp, determine the method of frequency fluctuation calculation, such as fast Fourier transform (FFT) or time domain analysis, perform point-by-point frequency fluctuation calculation on each audio sampling point, obtain the corresponding frequency fluctuation value, select a suitable low-frequency filter, determine its cutoff frequency and type (such as Butterworth, Chebyshev, etc.), use the designed filter to process the denoised audio data, eliminate low-frequency noise, retain important frequency components, and save the audio data after filtering optimization as a new file to ensure its quality and integrity.

[0132] In this embodiment, refer to Figure 4 , is a flowchart of detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include:

[0133] Step S31: calculating entropy values ​​of multiple audio feature segments segment by segment to obtain an entropy value of each audio feature segment;

[0134] Step S32: dynamically adaptively entropy coding the multiple audio feature segments based on the entropy value of each audio feature segment to obtain multiple entropy coded audio segments;

[0135] Step S33: performing data stream encapsulation processing on the multiple entropy coded audio segments to obtain multiple audio data packets;

[0136] Step S34: performing lossless compression encoding on the plurality of audio data packets, thereby generating a plurality of audio lossless compression encodings.

[0137] In this embodiment, according to multiple audio feature segments, the information entropy value of each segment is calculated respectively. The information entropy reflects the uncertainty and complexity of the audio data in the segment, and a group of data describing the entropy value of each audio feature segment is obtained. The calculated entropy value of each segment is used to dynamically compress and encode the audio data using an adaptive entropy coding algorithm. For segments with higher entropy values, a more efficient coding method is used; for segments with lower entropy values, a simpler coding method is used. The various entropy-coded audio segments are assembled into a data packet format that meets the transmission protocol standard, and necessary header information and checksums are added to ensure the integrity and reliability of the data packet. The obtained audio data packet is further compressed using a lossless compression algorithm to ensure that the audio data does not lose any original information during the transmission process, thereby generating a group of audio lossless compression coding data with smaller volume and higher transmission efficiency.

[0138] In this embodiment, step S4 includes the following steps:

[0139] Step S41: performing feature priority evaluation on multiple audio feature segments to obtain a priority evaluation value of each segment;

[0140] Step S42: Perform transmission path mining on the dynamic twin network environment to extract multiple network transmission links;

[0141] Step S43: performing path transmission performance prediction on multiple network transmission links, thereby obtaining link transmission performance prediction values;

[0142] Step S44: making a dynamic priority matching transmission decision for the priority evaluation value of each segment based on the link transmission performance prediction value, and constructing a dynamic matching transmission strategy;

[0143] Step S45: performing intelligent segmented transmission processing on multiple audio lossless compression codes based on a dynamic matching transmission strategy, and extracting audio segmented transmission response data.

[0144] In this embodiment, according to the audio feature segmentation data, the importance of the semantic features of each segment is analyzed, and the priority evaluation value of each segment is calculated according to the importance indicators such as keywords and key sentences, the status of each node and link in the dynamic twin network environment is monitored, real-time data is obtained, and a suitable path mining algorithm (such as Di jkstra algorithm, A* search, etc.) is selected for link analysis. According to the network status and transmission requirements, multiple available network transmission links are extracted, and the real-time transmission performance of each link is predicted by using a machine learning model, including transmission performance indicators in multiple dimensions such as bandwidth, delay, and packet loss rate. A dynamic priority matching algorithm is designed, considering the relationship between link performance and segment priority, matching the link transmission performance prediction value with the audio segment priority evaluation value, determining the best transmission link for each segment, generating a dynamic matching transmission strategy, clarifying the transmission path and priority of each audio segment, and according to the dynamic matching transmission strategy, segmenting and transmitting multiple audio lossless compression codes one by one, monitoring the transmission response data of each segment during the transmission process, including the success rate, delay, and packet loss, and organizing the transmission response data into a report to record the transmission effect and performance indicators of each segment.

[0145] In this embodiment, the specific steps of step S5 are:

[0146] Step S51: performing multi-frame transmission error identification on the audio segment transmission response data to obtain audio frame error data;

[0147] Step S52: performing error frame positioning on the audio frame error data to obtain audio frame error point position data;

[0148] Step S53: performing error point distribution analysis on the audio frame error point position data to generate audio frame error point distribution data;

[0149] Step S54: performing frame-by-frame error correction on the audio segment transmission response data according to the audio frame error point distribution data, thereby generating error-corrected audio data.

[0150] In this embodiment, error detection standards are defined, such as packet loss rate, delay threshold, and signal distortion, and the audio segment transmission response data is decomposed into multiple audio frames for frame-by-frame analysis. Each audio frame is analyzed, and frames with errors are identified and recorded as audio frame error data. Based on a positioning technology based on a timestamp or signal strength, the specific position of each error frame is identified according to the error frame data and recorded as audio frame error point position data. Based on the audio frame error point position data, the overall error point distribution characteristics are depicted, the main error concentration areas of the audio data in the time domain and their distribution patterns are identified, and a suitable error correction algorithm is selected, such as an interpolation method, a resampling method, or a model-based correction method. Based on the error point distribution data, each audio frame with errors is corrected to generate error-corrected audio data.

[0151] In this embodiment, the specific steps of step S6 are:

[0152] Step S61: performing transmission timing delay analysis on the audio segment transmission response data to obtain audio transmission delay data;

[0153] Step S62: Perform network delay stagnation mining on the audio transmission delay data to obtain a transmission delay point;

[0154] Step S63: Calculate the delay state of the transmission hysteresis point to obtain the delay state parameter of the hysteresis point;

[0155] Step S64: dynamically optimize the transmission delay of the error-corrected audio data according to the delay state parameters of the hysteresis point, and construct an intelligent audio delay optimization engine to perform dynamic transmission of audio data.

[0156] In this embodiment, audio segment transmission response data is collected, including the timestamp and transmission status information of each frame, the actual transmission delay information of each audio segment is extracted, the timing changes of the audio data during the transmission process are analyzed, a detailed transmission delay data is obtained, and the detection standard of delay stagnation is determined, such as the frame whose delay exceeds the preset threshold. The network delay stagnation point is mined by using the timing analysis algorithm, that is, the audio data encounters obvious transmission delay blockage at certain time points. According to each transmission delay point, the specific characteristics of its delay state are analyzed, including state parameters of multiple dimensions such as delay duration and accumulated delay value. According to the state parameters of the delay point, a dynamic transmission delay optimization strategy is designed, such as adjusting the transmission rate, selecting a backup link, etc. An intelligent audio delay optimization engine is developed, which integrates the optimization algorithm and the real-time monitoring function. The optimization engine is used to dynamically transmit the error-corrected audio data, monitor the transmission effect in real time, and automatically adjust the strategy.

[0157] In this embodiment, the specific steps of step S64 are:

[0158] Perform time series evolution analysis on the delay state parameters of the time delay point to obtain the delay state evolution data;

[0159] Perform multi-time-point delay learning on the delay state evolution data to obtain the delay evolution law;

[0160] Identify multiple available transmission links based on a dynamic twin network environment;

[0161] According to the delay evolution law, delay distribution prediction is performed on multiple available transmission links to obtain delay distribution prediction data of the transmission links;

[0162] Perform transmission congestion risk time window inference on the delay distribution prediction data of the transmission link to obtain the congestion risk time window of each transmission link;

[0163] Calculate the delay peak value of the congestion risk time window of each transmission link to obtain the delay peak value of the congestion risk time window;

[0164] Perform dynamic delay avoidance path planning based on the delay peak value in the congestion risk time window and generate a dynamic avoidance path strategy;

[0165] The error-corrected audio data is dynamically optimized for transmission delay based on the dynamic path avoidance strategy, and an intelligent audio delay optimization engine is built to perform dynamic transmission of audio data.

[0166] In this embodiment, the delay state parameters of the obtained time delay point are combined to further analyze their variation characteristics in the time series, describe the evolution law and trend of the delay state over time, and form a set of time series data that records the evolution process of the delay state in detail. The machine learning technology is used to learn the potential law of delay variation based on the delay state evolution data, including key features such as the time point of the delay peak, the duration, and the variation trend. The path planning algorithm is used to mine the available transmission link candidate set, and multiple performance indicators such as the link bandwidth, delay, and packet loss are considered to obtain a set of optional transmission paths. According to the delay evolution law, the delay distribution of each transmission link in the future period is predicted, including the delay distribution characteristics of multiple dimensions such as the delay peak, the average delay, and the fluctuation range. The time window where each transmission link will be congested in the future is analyzed to determine The potential congestion risk time interval of each link is obtained, and the maximum delay peak in this time period is predicted according to the congestion risk time window of each link, which provides a key reference for the subsequent dynamic avoidance path planning. The obtained delay peak data is combined with the optional transmission links to perform dynamic path planning and scheduling, and try to avoid links with high congestion risks, select reliable transmission paths with low delay peaks, and generate an intelligent transmission path strategy that suits the actual network conditions. Through intelligent scheduling and path switching, network congestion is avoided to the greatest extent, and the delay performance of audio transmission is guaranteed. An audio transmission engine system that can dynamically perform intelligent delay optimization is constructed, and the optimization engine is used to dynamically transmit error-corrected audio data, adjust the transmission path in real time to optimize the delay, monitor the performance indicators during the transmission process, and further optimize the transmission strategy based on feedback.

[0167] In this embodiment, an audio playback device is provided, which is used to execute the above-mentioned audio data dynamic transmission processing method, including:

[0168] The dynamic twin module identifies key transmission nodes in the audio transmission network and marks multiple transmission network nodes; it performs multi-dimensional state perception on multiple transmission network nodes, reconstructs multi-dimensional environmental indicators, and builds a dynamic twin network environment;

[0169] The filtering optimization module obtains the audio data to be transmitted; performs low-frequency filtering optimization processing on the audio data to be transmitted, and performs feature segmentation processing to obtain multiple audio feature segments;

[0170] The lossless compression coding module performs data stream encapsulation processing on multiple audio feature segments and performs lossless compression coding, thereby generating multiple audio lossless compression codes;

[0171] The segment transmission module performs intelligent segment transmission processing on multiple audio lossless compression codes based on a dynamic twin network environment and extracts audio segment transmission response data;

[0172] An error correction module is used to identify multi-frame transmission errors of the audio segment transmission response data and perform frame-by-frame error correction to generate error-corrected audio data;

[0173] The delay optimization module obtains the transmission delay point based on the audio segment transmission response data; dynamically optimizes the transmission delay of the error-corrected audio data according to the transmission delay point, and builds an intelligent audio delay optimization engine to perform dynamic transmission operations of audio data.

[0174] The present invention monitors the network status in real time by identifying key transmission nodes and constructing a dynamic twin network environment, thereby improving transmission efficiency and stability. Multi-dimensional state perception and environmental indicator reconstruction can make the transmission network more intelligent and adapt to different working environments and network conditions. Low-frequency filtering optimization processing removes low-frequency noise in the audio signal and improves audio quality and clarity. Feature segmentation processing can divide audio data according to features, and subsequent processing and analysis, lossless compression encoding reduces the volume of audio data, improves transmission efficiency and saves bandwidth, data stream encapsulation processing maintains the integrity and accuracy of audio data and avoids information loss. Intelligent segmented transmission processing dynamically adjusts the transmission strategy according to the network status and environment to improve the transmission success rate and speed, extracts audio segmented transmission response data to monitor the transmission situation in real time, and adjusts the transmission strategy in time. Multi-frame transmission error identification and frame-by-frame error correction improve the accuracy and reliability of data transmission, and generates error-corrected audio data to effectively reduce data loss and damage during transmission. Dynamic transmission delay optimization can adjust the transmission delay in real time according to the transmission delay point, improve the real-time and stability of audio transmission, and the intelligent audio delay optimization engine intelligently manages the transmission delay according to the actual situation to provide better user experience and data transmission efficiency.

[0175] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0176] The above is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for dynamic transmission and processing of audio data, characterized in that: The following steps are involved: Step S1: Identify key transmission nodes in the audio transmission network and mark multiple transmission network nodes; perform multi-dimensional state perception on multiple transmission network nodes, reconstruct multi-dimensional environmental indicators, and build a dynamic twin network environment; Step S2: obtaining audio data to be transmitted; performing low-frequency filtering optimization processing on the audio data to be transmitted, and performing feature segmentation processing, thereby obtaining multiple audio feature segments; Step S3: performing data stream encapsulation processing on the multiple audio feature segments, and performing lossless compression encoding, thereby generating multiple audio lossless compression codes; Step S4: performing intelligent segmented transmission processing on multiple audio lossless compression codes based on the dynamic twin network environment, and extracting audio segmented transmission response data; Step S5: performing multi-frame transmission error identification on the audio segment transmission response data, and performing frame-by-frame error correction, thereby generating error-corrected audio data; Step S6: obtaining a transmission time delay point based on the audio segment transmission response data; Dynamically optimize the transmission delay of error-corrected audio data according to the transmission delay point, and build an intelligent audio delay optimization engine to perform dynamic audio data transmission operations; The specific steps of step S6 are: Step S61: performing transmission timing delay analysis on the audio segment transmission response data to obtain audio transmission delay data; Step S62: Perform network delay stagnation mining on the audio transmission delay data to obtain a transmission delay point; Step S63: Calculate the delay state of the transmission hysteresis point to obtain the delay state parameter of the hysteresis point; Step S64: dynamically optimizing the transmission delay of the error-corrected audio data according to the delay state parameters of the hysteresis point, and constructing an intelligent audio delay optimization engine to perform a dynamic transmission operation of the audio data; The specific steps of step S64 are: Perform time series evolution analysis on the delay state parameters of the time delay point to obtain the delay state evolution data; Perform multi-time-point delay learning on the delay state evolution data to obtain the delay evolution law; Identify multiple available transmission links based on a dynamic twin network environment; According to the delay evolution law, delay distribution prediction is performed on multiple available transmission links to obtain delay distribution prediction data of the transmission links; Perform transmission congestion risk time window inference on the delay distribution prediction data of the transmission link to obtain the congestion risk time window of each transmission link; Calculate the delay peak value of the congestion risk time window of each transmission link to obtain the delay peak value of the congestion risk time window; Perform dynamic delay avoidance path planning based on the delay peak value in the congestion risk time window and generate a dynamic avoidance path strategy; The error-corrected audio data is dynamically optimized for transmission delay based on the dynamic path avoidance strategy, and an intelligent audio delay optimization engine is built to perform dynamic transmission of audio data.

2. The method for dynamic transmission and processing of audio data according to claim 1, characterized in that: The specific steps of step S1 are: Step S11: identifying key transmission nodes in the audio transmission network and marking multiple transmission network nodes; Step S12: performing multi-dimensional state perception on multiple transmission network nodes, and extracting real-time state parameters of the network nodes, wherein the real-time state parameters of the network nodes include node bandwidth, node packet loss rate, and node transmission delay parameters; Step S13: Calculate the bandwidth utilization of the node bandwidth to obtain the network node bandwidth utilization; Step S14: analyzing the time series variation of the node packet loss rate to generate a node packet loss time series variation curve; Step S15: performing delay fluctuation trend identification on the node transmission delay parameter to obtain the node delay fluctuation trend; Step S16: Reconstruct multi-dimensional environmental indicators of network node bandwidth utilization, node packet loss timing change curve and node delay fluctuation trend to build a dynamic twin network environment.

3. The method for dynamic transmission and processing of audio data according to claim 1, characterized in that: The specific steps of step S2 are: Step S21: Acquire audio data to be transmitted; Step S22: performing low-frequency filtering optimization processing on the audio data to be transmitted, thereby generating filtering optimized audio data; Step S23: performing audio semantic feature analysis on the filtered optimized audio data to obtain audio semantic feature data; Step S24: performing key semantic feature recognition based on the audio semantic feature data to extract key semantic feature points; Step S25: performing feature segmentation processing on the filtered optimized audio data based on key semantic feature points, thereby obtaining a plurality of audio feature segments.

4. The method for dynamic transmission and processing of audio data according to claim 3, characterized in that: The specific steps of step S22 are: Performing wavelet transform decomposition on the audio data to be transmitted to obtain frequency domain data of multiple frequency bands; Calculating noise components on frequency domain data of a plurality of frequency bands to generate noise components of each frequency band; Performing audio noise distribution analysis on the noise components of each frequency band to obtain noise distribution data; Adaptively denoising the audio data to be transmitted based on the noise distribution data to generate denoised audio data; Perform multi-point sampling processing on the denoised audio data to extract multiple audio sampling points; Perform frequency fluctuation calculation on multiple audio sampling points point by point to generate sampling point frequency fluctuation values; The denoised audio data is subjected to low-frequency filtering optimization processing based on the frequency fluctuation value of the sampling point, thereby generating filtering-optimized audio data.

5. The method for dynamic transmission and processing of audio data according to claim 1, characterized in that: The specific steps of step S3 are: Step S31: calculating entropy values ​​of multiple audio feature segments segment by segment to obtain an entropy value of each audio feature segment; Step S32: dynamically adaptively entropy coding the multiple audio feature segments based on the entropy value of each audio feature segment to obtain multiple entropy coded audio segments; Step S33: performing data stream encapsulation processing on the multiple entropy coded audio segments to obtain multiple audio data packets; Step S34: performing lossless compression encoding on the plurality of audio data packets, thereby generating a plurality of audio lossless compression encodings.

6. The method for dynamic transmission and processing of audio data according to claim 1, characterized in that: The specific steps of step S4 are: Step S41: performing feature priority evaluation on multiple audio feature segments to obtain a priority evaluation value of each segment; Step S42: Perform transmission path mining on the dynamic twin network environment to extract multiple network transmission links; Step S43: performing path transmission performance prediction on multiple network transmission links, thereby obtaining link transmission performance prediction values; Step S44: making a dynamic priority matching transmission decision for the priority evaluation value of each segment based on the link transmission performance prediction value, and constructing a dynamic matching transmission strategy; Step S45: performing intelligent segmented transmission processing on multiple audio lossless compression codes based on a dynamic matching transmission strategy, and extracting audio segmented transmission response data.

7. The method for dynamic transmission and processing of audio data according to claim 1, characterized in that: The specific steps of step S5 are: Step S51: performing multi-frame transmission error identification on the audio segment transmission response data to obtain audio frame error data; Step S52: performing error frame positioning on the audio frame error data to obtain audio frame error point position data; Step S53: performing error point distribution analysis on the audio frame error point position data to generate audio frame error point distribution data; Step S54: performing frame-by-frame error correction on the audio segment transmission response data according to the audio frame error point distribution data, thereby generating error-corrected audio data.

8. An audio playback device, characterized in that: The method for dynamically transmitting and processing audio data according to claim 1 comprises: The dynamic twin module identifies key transmission nodes in the audio transmission network and marks multiple transmission network nodes; it performs multi-dimensional state perception on multiple transmission network nodes, reconstructs multi-dimensional environmental indicators, and builds a dynamic twin network environment; The filtering optimization module obtains the audio data to be transmitted; performs low-frequency filtering optimization processing on the audio data to be transmitted, and performs feature segmentation processing to obtain multiple audio feature segments; The lossless compression coding module performs data stream encapsulation processing on multiple audio feature segments and performs lossless compression coding, thereby generating multiple audio lossless compression codes; The segment transmission module performs intelligent segment transmission processing on multiple audio lossless compression codes based on a dynamic twin network environment and extracts audio segment transmission response data; An error correction module is used to identify multi-frame transmission errors of the audio segment transmission response data and perform frame-by-frame error correction to generate error-corrected audio data; The delay optimization module obtains the transmission delay point based on the audio segment transmission response data; dynamically optimizes the transmission delay of the error-corrected audio data according to the transmission delay point, and builds an intelligent audio delay optimization engine to perform dynamic transmission operations of audio data.

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