Real-time monitoring and self-adaptive adjusting method for wireless audio transmission link

Abstract

The invention relates to the technical field of wireless communication, and discloses a real-time monitoring and self-adaptive adjusting method for a wireless audio transmission link, which is used for monitoring the state of a wireless channel in real time, acquiring link quality parameters including signal strength, noise, interference strength, network load, signal attenuation and the like, and transmission quality indexes of audio streams. And further monitoring the link delay, packet loss rate and jitter, and predicting the link state, network load and signal strength by using a machine learning or deep learning algorithm according to the obtained real-time data, so as to judge the possible link quality change in advance. By means of intelligent prediction and dynamic optimization, transmission parameters can be adjusted in advance when a wireless environment changes, audio quality reduction caused by link fluctuation is effectively avoided, stable high-fidelity audio transmission is ensured, the adaptive resource allocation and interference source suppression technology is adopted, and the transmission efficiency is improved. The delay and packet loss rate of audio data can be reduced under variable network conditions, and the real-time performance is improved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and specifically to a method for real-time monitoring and adaptive adjustment of a wireless audio transmission link. Background Art

[0002] The method for real-time monitoring and adaptive adjustment of a wireless audio transmission link is a technology that automatically adjusts audio transmission parameters by detecting the transmission status and link quality of wireless signals in real time. This method continuously monitors factors such as network load, signal strength, interference level, and latency, and analyzes the performance of the audio transmission link in real time. When a change in the network environment or a decrease in link quality is detected, the system intelligently adjusts transmission parameters such as frequency, power, and bandwidth to optimize the stability and quality of audio transmission, ensuring that audio data can be transmitted with high quality and low latency in different wireless environments. This adaptive adjustment mechanism can effectively cope with the uncertainties of wireless networks and improve the robustness and reliability of audio transmission.

[0003] Existing methods for real-time monitoring and adaptive adjustment of wireless audio transmission links suffer from the following shortcomings: Latency: Existing systems typically rely on real-time monitoring and data feedback of link quality. However, since the monitoring and adjustment process can take time, especially in complex wireless environments, transmission delays can occur, impacting audio quality. This is particularly evident in latency-sensitive applications such as real-time voice calls or online live streaming. Incomplete interference suppression: While existing technologies can monitor and adjust interference sources, in complex wireless environments, interference sources can change rapidly and randomly, resulting in existing systems failing to respond to sudden interference in a timely and accurate manner in some cases, thus impacting audio transmission quality. Inadequate resource allocation: Existing adaptive adjustment methods may not be able to dynamically adjust wireless resources (such as bandwidth, spectrum, and power) based on actual needs. When network load fluctuates dramatically, the system may not adapt in a timely manner, resulting in unstable audio transmission quality. Lack of intelligent prediction: Most existing methods rely solely on the current link state for adjustment and lack the ability to intelligently predict future network conditions. In the presence of large fluctuations in network load, the system may not be able to make optimization decisions in advance, resulting in poor audio transmission quality. Inadequate energy efficiency: In some cases, existing adjustment methods fail to effectively balance audio quality and energy efficiency. While ensuring audio quality, unnecessary power consumption may be caused, especially in mobile devices or low-power applications, where there is a large room for energy efficiency optimization; Insufficient adaptability: Existing technologies have poor adaptability in multi-user environments, especially when multiple audio streams are transmitted simultaneously. Existing adjustment methods often cannot achieve priority, bandwidth allocation and power adjustment for different audio streams, resulting in resource conflicts and performance degradation; Strong hardware dependence: Some existing methods rely on specific hardware devices (such as high-performance sensors or dedicated signal processing chips), which limits their application in certain general-purpose devices or low-cost devices, reducing the universality and promotion of the technology.

[0004] To this end, we propose a real-time monitoring and adaptive adjustment method for wireless audio transmission links. Summary of the Invention

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for real-time monitoring and adaptive adjustment of a wireless audio transmission link, comprising the following steps:

[0006] S1: Real-time link monitoring: Monitors the status of wireless channels in real time to obtain link quality parameters, including signal strength, noise, interference intensity, network load, signal attenuation, and transmission quality indicators of audio streams, and further monitors link latency, packet loss rate, and jitter.

[0007] S2: Intelligent Prediction and Analysis: Based on the acquired real-time data, use machine learning or deep learning algorithms to predict the link state, network load, and signal strength, so as to judge in advance the possible changes in link quality within a certain period of time in the future.

[0008] S3: Adaptive Resource Allocation: According to the prediction results of S2 and the real-time monitored link state, dynamically adjust the transmission parameters of the audio stream, including frequency, bandwidth, power, modulation method, etc., to ensure the audio transmission quality, stability, and low latency.

[0009] S4: Interference Source Identification and Suppression: Identify and locate interference sources in real time, analyze the interference type and take measures, select a less interfered frequency band or use interference suppression algorithms to avoid adverse effects on audio transmission.

[0010] S5: Dynamic Feedback and Optimization: Establish a feedback mechanism to monitor the real-time performance of the link, including the quality of audio transmission and the QoS requirements of users. Dynamically adjust the adjustment strategy according to the feedback data to optimize the performance of the audio transmission link.

[0011] Preferably, the intelligent prediction and analysis module adopts a deep learning model, uses a convolutional neural network (CNN) or a recurrent neural network (RNN), combines historical data with real-time monitoring data, makes an intelligent prediction of the future link state, and calculates parameters such as possible future network load and signal strength changes.

[0012] Preferably, the adaptive resource allocation module dynamically adjusts transmission parameters such as frequency, bandwidth, power, and modulation method according to the QoS requirements of the audio stream (such as delay, bandwidth, packet loss rate, delay jitter, etc.) to ensure that the priority of the audio stream is guaranteed.

[0013] Preferably, the interference source identification and suppression module identifies and suppresses real-time interference sources through spectrum analysis and signal processing technologies to avoid the influence of interference signals on the audio transmission link.

[0014] Preferably, the dynamic feedback and optimization module combines a reinforcement learning algorithm to continuously optimize the adjustment strategy, and further adjusts the resource allocation of the audio stream based on the link quality and user feedback to ensure the stability of the audio stream quality.

[0015] Preferably, the feedback mechanism dynamically adjusts the resource allocation by real-time evaluating the quality of the audio stream through an audio quality evaluation module, combining the user experience evaluation results (such as audio distortion, delay, etc.), and gives priority to ensuring the transmission of important audio streams when the network load increases.

[0016] Preferably, during the adaptive adjustment process, the number of users, the priority of audio streams, and the current network conditions are considered to flexibly adjust the bandwidth allocation and transmission power of audio streams to avoid network congestion and resource waste.

[0017] Compared with the prior art, the present invention provides a method for real-time monitoring and adaptive adjustment of a wireless audio transmission link, which has the following beneficial effects:

[0018] 1. The method for real-time monitoring and adaptive adjustment of the wireless audio transmission link can, through intelligent prediction and dynamic optimization, adjust transmission parameters in advance when the wireless environment changes, effectively avoid the decline in audio quality caused by link fluctuations, ensure stable high-fidelity audio transmission, and reduce the delay and packet loss rate of audio data and improve real-time performance by adopting adaptive resource allocation and interference source suppression technologies under changing network conditions.

[0019] 2. The method for real-time monitoring and adaptive adjustment of the wireless audio transmission link can predict the network state through deep learning algorithms, make intelligent decisions on future link conditions, take corresponding adjustment measures in advance, avoid network load overload or signal interference from affecting audio quality, and reduce unnecessary energy consumption while ensuring audio quality by finely adjusting resources such as frequency, power, and bandwidth, which is suitable for low-power devices.

[0020] 3. The method for real-time monitoring and adaptive adjustment of the wireless audio transmission link adapts to a multi-user environment, intelligently allocates spectrum resources, flexibly adjusts bandwidth and power according to the priority of different user audio streams, avoids resource conflicts, improves the adaptability and stability of the system, and can identify interference sources in real time and avoid or suppress them to ensure that audio streams are not affected by sudden interference, thereby enhancing the stability of transmission. Specific embodiments

[0021] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0022] Embodiment

[0023] An embodiment of a method for real-time monitoring and adaptive adjustment of a wireless audio transmission link

[0024] A method for real-time monitoring and adaptive adjustment of a wireless audio transmission link includes the following steps:

[0025] S1: Real-time Link Monitoring: Continuously monitor the status of the wireless channel to obtain link quality parameters, including signal strength, noise, interference intensity, network load, signal attenuation, etc., as well as the transmission quality metrics of the audio stream. Further monitor link latency, packet loss rate, and jitter.

[0026] S2: Intelligent Prediction and Analysis: Based on the acquired real-time data, use machine learning or deep learning algorithms to predict the link status, network load, and signal strength, so as to pre-judge possible link quality changes in the future for a certain period of time.

[0027] S3: Adaptive Resource Allocation: According to the prediction results of S2 and the real-time monitored link status, dynamically adjust the transmission parameters of the audio stream, including frequency, bandwidth, power, modulation method, etc., to ensure audio transmission quality, stability, and low latency.

[0028] S4: Interference Source Identification and Suppression: Real-time identify and locate interference sources, analyze the interference type and take measures, select a less interfered frequency band or adopt interference suppression algorithms to avoid adverse effects on audio transmission.

[0029] S5: Dynamic Feedback and Optimization: Establish a feedback mechanism to monitor the real-time performance of the link, including the quality of audio transmission and the QoS requirements of users. Dynamically adjust the adjustment strategy according to the feedback data to optimize the performance of the audio transmission link.

[0030] Specifically, the intelligent prediction and analysis module uses a deep learning model, utilizes convolutional neural network (CNN) or recurrent neural network (RNN), combines historical data with real-time monitoring data, makes intelligent predictions about the future link status, and calculates parameters such as possible future network load and signal strength changes.

[0031] Specifically, the adaptive resource allocation module dynamically adjusts transmission parameters such as frequency, bandwidth, power, modulation method, etc. according to the QoS requirements of the audio stream (such as latency, bandwidth, packet loss rate, delay jitter, etc.) to ensure that the priority of the audio stream is guaranteed.

[0032] Specifically, the interference source identification and suppression module identifies and suppresses real-time interference sources through spectrum analysis and signal processing technologies to avoid the impact of interference signals on the audio transmission link.

[0033] Specifically, the dynamic feedback and optimization module combines reinforcement learning algorithms to continuously optimize the adjustment strategy, and further adjusts the resource allocation of the audio stream based on link quality and user feedback to ensure the stability of the audio stream quality.

[0034] Specifically, the feedback mechanism evaluates the quality of the audio stream in real time through the audio quality assessment module, dynamically adjusts resource allocation based on the user experience evaluation results (such as audio distortion, delay, etc.), and prioritizes the transmission of important audio streams when the network load increases.

[0035] Specifically, the adaptive adjustment process takes into account the number of users, audio stream priority, and current network conditions, and flexibly adjusts the bandwidth allocation and transmission power of the audio stream to avoid network congestion and resource waste.

[0036] Through the above technical solution, in the present invention, through intelligent prediction and dynamic optimization, it is possible to adjust the transmission parameters in advance when the wireless environment changes, effectively avoid the degradation of audio quality caused by link fluctuations, and ensure stable high-fidelity audio transmission. The use of adaptive resource allocation and interference source suppression technology can reduce the delay and packet loss rate of audio data under changing network conditions, improve real-time performance, and predict the network status through deep learning algorithms. It can make intelligent decisions on future link conditions and make corresponding adjustment measures in advance to avoid network load overload or signal interference affecting audio quality. By fine-tuning resources such as frequency, power and bandwidth, it can reduce unnecessary energy consumption while ensuring audio quality. It is suitable for low-power devices, adapts to multi-user environments, intelligently allocates spectrum resources, and flexibly adjusts bandwidth and power according to the priority of different user audio streams to avoid resource conflicts, improve the adaptability and stability of the system, identify interference sources in real time and avoid or suppress them, ensure that the audio stream is not affected by sudden interference, thereby enhancing the stability of transmission.

[0037] 1. System hardware composition

[0038] The present invention can be implemented in a wireless audio transmission system, which includes the following hardware modules:

[0039] Sensor module: used to monitor channel quality in real time and obtain link quality parameters such as wireless signal reception strength, signal-to-noise ratio, noise, interference level, network delay, etc.

[0040] Prediction module: Based on deep learning algorithms (such as convolutional neural networks (CNN) or recurrent neural networks (RNN), it analyzes historical data and real-time link status to predict future link quality, network load, etc., and calculates the most likely resource requirements.

[0041] Adjustment module: Dynamically adjusts the transmission parameters of the audio stream (such as frequency, bandwidth, power, etc.) based on intelligent prediction results and real-time monitoring data to ensure audio quality and stability.

[0042] Interference source identification module: Identifies interference sources through spectrum analysis technology and uses technologies such as filtering, avoiding interference frequency bands, or selecting other modulation methods to suppress interference to reduce the impact on audio transmission.

[0043] Feedback and Optimization Module: Based on the real-time feedback data of the audio transmission link (such as packet loss rate, latency, jitter, etc.), continuously optimize the adjustment strategy to ensure the stability of audio quality, and adjust resource allocation according to user requirements and priorities.

[0044] 2. Process Flow

[0045] Step 1: Link Monitoring and Data Collection

[0046] The system collects real-time link status data through sensors, including received signal strength, noise, interference source information, network latency, packet loss rate, QoS metrics of audio stream transmission (such as latency and jitter of audio stream, etc.).

[0047] The data is transmitted to the central processing unit through the wireless interface for further analysis and processing.

[0048] Step 2: Intelligent Prediction and Analysis

[0049] Based on the collected real-time data, use deep neural network (DNN) or convolutional neural network (CNN) to predict information such as network load, channel quality, and interference sources.

[0050] According to the model trained from historical data, predict the change trend of the future link status, and calculate possible network load, signal attenuation, etc.

[0051] Step 3: Resource Allocation and Adjustment

[0052] According to the prediction results and real-time data, the adjustment module automatically adjusts the transmission parameters of the audio stream. For important audio streams, the system will give priority to allocating bandwidth and power to ensure low-latency and high-quality audio transmission.

[0053] If the predicted network quality deteriorates, the system will actively reduce the bandwidth or adopt a more efficient coding method to optimize the transmission and prevent the audio quality from decreasing.

[0054] Step 4: Interference Source Identification and Suppression

[0055] The system detects interference sources in the wireless environment in real time through spectrum analysis and interference source location algorithms.

[0056] Once an interference source is detected, the system immediately switches to a clearer frequency band or uses a modulation method with stronger anti-interference ability to reduce the impact on audio transmission.

[0057] Step 5: Dynamic Feedback and Optimization

[0058] The system evaluates the quality of the audio stream in real time through a feedback mechanism and adjusts the resource allocation according to the QoS requirements of the audio stream (such as latency, bandwidth, packet loss rate).

[0059] In the case of an increase in network load, the system dynamically adjusts the audio stream priority to ensure the transmission quality of real-time voice streams and high-priority streams.

[0060] 3. Implementation of intelligent algorithms

[0061] Intelligent prediction: Deep learning models (such as CNN, RNN) are trained based on historical link quality data to predict information such as future link quality, interference sources, and network load in real time.

[0062] Reinforcement learning: Use reinforcement learning algorithms to optimize the resource adjustment strategy, and adjust the system's resource allocation according to real-time feedback to ensure stable audio quality and improve network efficiency.

[0063] Interference source suppression: Identify interference sources through spectrum analysis technology and use adaptive filtering algorithms for real-time interference suppression to ensure that the transmission of audio data is not affected by sudden interference.

[0064] 4. Performance evaluation and optimization

[0065] Dynamic adjustment: The system automatically optimizes according to real-time monitored network load changes, link quality, and user requirements to ensure low latency and stable audio quality even under high load conditions.

[0066] Adaptability to multi-user environments: This method can intelligently allocate resources in the case of concurrent transmission of multiple audio streams to ensure the quality of each audio stream. Especially in a high-density wireless environment, it can effectively avoid resource conflicts and network congestion.

[0067] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A real-time monitoring and adaptive adjustment method for a wireless audio transmission link, characterized in that: It includes the following steps: S1: Link real-time monitoring: Real-time monitor the status of the wireless channel, obtain link quality parameters, including signal strength, noise, interference strength, network load, signal attenuation, etc., as well as the transmission quality indicators of the audio stream, and further monitor link delay, packet loss rate, and jitter. S2: Intelligent prediction and analysis: Based on the obtained real-time data, use machine learning or deep learning algorithms to predict the link status, network load, and signal strength, so as to judge in advance the possible changes in link quality within a certain period in the future. S3: Adaptive resource allocation: According to the prediction results of S2 and the real-time monitored link status, dynamically adjust the transmission parameters of the audio stream, including frequency, bandwidth, power, modulation method, etc., to ensure audio transmission quality, stability, and low latency. S4: Interference source identification and suppression: Real-time identify and locate interference sources, analyze the interference type and take measures, select a less interfered frequency band or use interference suppression algorithms to avoid adverse effects on audio transmission. S5: Dynamic feedback and optimization: Establish a feedback mechanism to monitor the real-time performance of the link, including the quality of audio transmission and the QoS requirements of users. Dynamically adjust the adjustment strategy according to the feedback data to optimize the performance of the audio transmission link.

2. The real-time monitoring and adaptive adjustment method for a wireless audio transmission link according to claim 1, characterized in that: The intelligent prediction and analysis module adopts a deep learning model, uses a convolutional neural network (CNN) or a recurrent neural network (RNN), combines historical data with real-time monitoring data, intelligently predicts the future link status, and calculates parameters such as possible future network load and signal strength changes.

3. The real-time monitoring and adaptive adjustment method of a wireless audio transmission link according to claim 1, characterized in that: The adaptive resource allocation module dynamically adjusts transmission parameters such as frequency, bandwidth, power, and modulation method according to the QoS requirements of the audio stream (such as delay, bandwidth, packet loss rate, delay jitter, etc.) to ensure that the priority of the audio stream is guaranteed.

4. The real-time monitoring and adaptive adjustment method of a wireless audio transmission link according to claim 1, characterized in that: The interference source identification and suppression module identifies and suppresses real-time interference sources through spectrum analysis and signal processing technologies to avoid the impact of interference signals on the audio transmission link.

5. The real-time monitoring and adaptive adjustment method for a wireless audio transmission link according to claim 1, characterized in that: The dynamic feedback and optimization module combines reinforcement learning algorithms to continuously optimize the adjustment strategy, and further adjusts the resource allocation of the audio stream based on link quality and user feedback to ensure the stability of audio stream quality.

6. The real-time monitoring and adaptive adjustment method of a wireless audio transmission link according to claim 1, characterized in that: The feedback mechanism real-time evaluates the quality of the audio stream through an audio quality evaluation module, dynamically adjusts resource allocation in combination with the user experience evaluation results (such as audio distortion, delay, etc.), and gives priority to ensuring the transmission of important audio streams when the network load increases.

7. The real-time monitoring and adaptive adjustment method of a wireless audio transmission link according to claim 1, characterized in that: During the adaptive adjustment process, consider the number of users, the priority of the audio stream, and the current network condition, and flexibly adjust the bandwidth allocation and transmission power of the audio stream to avoid network congestion and resource waste.