Multi-device cooperative wireless audio transmission protocol optimization method and system

Abstract

The invention discloses a multi-device cooperative wireless audio transmission protocol optimization method and system, relates to the technical field of network communication technologies, and is used for solving the problems of heavy burden of embedded devices, unbalanced resource allocation and poor environmental adaptability caused by complex calculation in the prior art. The method comprises the following steps of: quickly matching services based on a matching ratio MR; dynamic bandwidth allocation based on the total terminal demand bandwidth TBR and the link terminal ratio LTR; adaptive transmission strategy adjustment is carried out according to the interference level IL and the congestion level CL; and iterative optimization based on the long-term matching success rate MSR and the resource utilization rate RUR. The system correspondingly comprises a multicast matching module, a resource allocation module, an environment self-adaption module and a long-term optimization module, by simplifying a calculation and judgment mechanism, the core audio service quality is guaranteed, meanwhile, the calculation overhead of the system is remarkably reduced, and high efficiency and stability of wireless audio transmission in the multi-device cooperative environment are achieved.

Description

Technical Field

[0001] This invention relates to the field of network communication technology, and more specifically, to a method and system for optimizing multi-device collaborative wireless audio transmission protocols. Background Technology

[0002] With the rapid development of wireless communication technology and the Internet of Things, the demand for collaborative playback using multiple wireless audio devices in scenarios such as education, conferences, and public broadcasting is increasing. Such multi-device collaborative wireless audio transmission systems need to solve the problem of how to enable numerous terminals to efficiently and stably receive and play one or more audio sources, involving multiple technical aspects such as service discovery, dynamic allocation of bandwidth resources, and network environment adaptation.

[0003] Existing wireless audio transmission protocols and solutions often suffer from the following shortcomings when facing large-scale, dynamic collaborative scenarios: In service matching and selection, many solutions rely on complex Quality of Service (QoS) assessment models or multi-attribute decision algorithms, resulting in significant computational overhead and difficulty in rapid execution on resource-constrained embedded terminals, impacting terminal access efficiency and real-time performance. Regarding bandwidth resource management, traditional methods typically employ allocation mechanisms based on fixed priorities or complex weight calculations, which are not only cumbersome to configure but also difficult to make flexible and fair adjustments when the number of terminals changes or the network topology shifts, easily leading to an imbalance where some links are congested while others are idle. Faced with common signal interference and network traffic fluctuations in wireless environments, existing solutions often lack efficient and lightweight adaptive mechanisms, either exhibiting slow response or overly complex adjustment strategies, failing to quickly adapt to environmental changes while ensuring the quality of core services. Most systems have fixed parameters after initial configuration, lacking self-optimization capabilities based on long-term operational data. As business needs evolve or equipment scales up, the initial configuration may gradually become ineffective, leading to a decline in system performance over time.

[0004] To address the above problems, this invention proposes a solution. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a method and system for optimizing multi-device collaborative wireless audio transmission protocols to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A method for optimizing a multi-device collaborative wireless audio transmission protocol, including the following steps; Step S1: Match the terminal attribute information with the description information of multiple audio services. By calculating the matching ratio between the service and the terminal, the services are classified into fully matched services and partially matched services. The terminal only adds its network interface to the multicast group corresponding to the fully matched service and only receives the audio data stream of the multicast group. Step S2: Calculate the total bandwidth requirement of each terminal for its fully matched service, summarize the total bandwidth requirements of all terminals, and compare them with the available total bandwidth. If resources are sufficient, allocate bandwidth as needed. If resources are scarce, allocate available bandwidth according to the proportion of the total bandwidth requirements of each terminal, and adjust it in combination with the load of the link where the terminal is located. If the allocated bandwidth of the terminal after adjustment still does not meet its basic requirements, perform a downgrade adaptation operation on the terminal, reduce the quality parameters of its non-critical services or suspend the reception of low-priority services. Step S3: Monitor the wireless interference intensity and network link congestion level of the terminal's environment in real time and divide them into multiple discrete levels. Based on the combination of interference level and congestion level, trigger the corresponding transmission strategy adjustment. The adjustment strategy includes maintaining the current configuration, optimizing local parameters or switching frequency bands for non-critical services, and entering the core service guarantee mode. When the environmental level is detected to be restored, gradually restore to the normal service configuration. Step S4: Calculate the service matching success rate and network resource utilization rate, compare the matching success rate and resource utilization rate with preset thresholds respectively, and execute the corresponding iterative optimization strategy according to the combination of comparison results to gradually adjust the service matching rules, bandwidth allocation parameters or environment adaptation thresholds.

[0007] In a preferred embodiment, step S1 includes the following: Extract the service description parameters and the terminal attribute parameters, and calculate the matching ratio (MR) between the service and the terminal; Based on the matching ratio MR, the services are classified into fully matched services and partially matched services; The terminal is made to receive only the multicast data stream corresponding to the fully matched service. The matching ratio MR is calculated by the number of matching tags MTN, which is based on the consistency determination of region tags, role tags, and priority tags.

[0008] In a preferred embodiment, step S2 includes the following: Calculate the total bandwidth required (TBR) for each terminal to provide a fully matched service; When total bandwidth is tight or link load is too high, the total available bandwidth of the system is allocated according to the total bandwidth demand of each terminal (TBR) ratio, and the allocated bandwidth is adjusted in combination with the link terminal ratio (LTR). When the allocated bandwidth of a terminal is lower than its minimum bandwidth requirement, a degradation adaptation strategy is triggered, which reduces non-critical service parameters or suspends low-priority services to ensure core services.

[0009] In a preferred embodiment, step S3 includes the following: Based on real-time collected metrics, interference level IL and congestion level CL are classified. Based on the combination of interference level (IL) and congestion level (CL), adjustment strategies of varying intensities are triggered, including maintaining configuration, local optimization and adaptation, and ensuring core services. Local optimization and adaptation strategies include reducing the sampling rate or sampling depth of non-critical services, attempting to switch communication frequency bands, or adjusting the bandwidth allocation of non-critical services. The core service assurance strategy includes suspending non-core services, reducing the sampling rate of core services, and adopting encoding methods with higher compression ratios.

[0010] In a preferred embodiment, step S4 includes the following: The long-term matching success rate (MSR) and resource utilization rate (RUR) of the statistical system; Based on the comparison results of the matching success rate (MSR) and resource utilization rate (RUR) relative to their respective thresholds, the corresponding iterative optimization strategy is executed to adjust the service matching rules, bandwidth allocation parameters, or environment adaptation thresholds. When the matching success rate (MSR) is below its threshold and the resource utilization rate (RUR) is below its upper limit, the service matching conditions are relaxed. When the matching success rate (MSR) is higher than its threshold and the resource utilization rate (RUR) is higher than its upper limit, tighten the service matching conditions and adjust the bandwidth allocation parameters. The execution of the iterative optimization strategy is based on statistical data within a long time window and adopts a gradual adjustment and feedback closed-loop mechanism.

[0011] A multi-device collaborative wireless audio transmission protocol optimization system includes: a multicast matching module, a resource allocation module, an environment adaptation module, and a long-term optimization module, with signal connections between the modules; Multicast matching module: Matches terminal attribute information with description information of multiple audio services. By calculating the matching ratio between services and terminals, services are classified into fully matched services and partially matched services. Terminals only add their network interfaces to the multicast group corresponding to the fully matched service and only receive the audio data stream of that multicast group. Resource allocation module: Calculates the total bandwidth requirement of each terminal for its fully matched service, summarizes the total bandwidth requirement of all terminals, and compares it with the available total bandwidth. If resources are sufficient, they are allocated as needed. If resources are scarce, available bandwidth is allocated according to the proportion of the total bandwidth requirement of each terminal, and adjusted in combination with the load of the link where the terminal is located. If the allocated bandwidth of the terminal still does not meet its basic requirements after adjustment, a degradation adaptation operation is performed on the terminal, reducing the quality parameters of its non-critical services or suspending the reception of low-priority services. Environment Adaptive Module: Real-time monitoring of the wireless interference intensity and network link congestion level of the terminal's environment and classifying them into multiple discrete levels. Based on the combination of interference level and congestion level, it triggers corresponding transmission strategy adjustments. Adjustment strategies include maintaining the current configuration, optimizing local parameters or switching frequency bands for non-critical services, and entering core service guarantee mode. When the environmental level is detected to have recovered, it gradually restores to the normal service configuration. Long-term optimization module: Statistically calculates service matching success rate and network resource utilization rate, compares the matching success rate and resource utilization rate with preset thresholds respectively, and executes corresponding iterative optimization strategies based on the combination of comparison results, making gradual adjustments to service matching rules, bandwidth allocation parameters or environment adaptation thresholds.

[0012] The technical effects and advantages of the multi-device collaborative wireless audio transmission protocol optimization method of this invention are as follows: This invention significantly reduces the implementation complexity and resource overhead of multi-device collaborative wireless audio transmission systems by simplifying the calculation and decision-making process. It is particularly suitable for terminals with limited computing power, such as embedded devices. The specific benefits are as follows: In the service matching stage, a fast determination rule based on the matching ratio (MR) is adopted. The target service set can be constructed by simply comparing tags and calculating the ratio, avoiding complex weighted evaluation algorithms and greatly improving the efficiency of initial terminal access and service discovery. In the bandwidth allocation stage, dynamic adjustments are made based on the total bandwidth required by the terminal (TBR) and the link terminal ratio (LTR). A lightweight allocation strategy combining proportional reduction and link load suppression is proposed. While ensuring fairness and alleviating congestion, it does not rely on multi-dimensional weight coefficients and has a low computational burden. Facing dynamic network environments, this system innovatively adopts a discretized division of interference level (IL) and congestion level (CL), and triggers adaptive adjustment strategies of varying intensities based on their combinations. This enables the system to respond quickly to environmental changes with low computational cost, prioritizing the quality and continuity of core audio services under interference or congestion conditions. Furthermore, an iterative optimization mechanism based on long-term statistical indicators is introduced, allowing the system to continuously fine-tune matching rules and allocation parameters according to actual operational data, forming a closed-loop optimization. This allows the system to adapt to changes in the number of terminals, network topology, and service demands during long-term operation, maintaining high efficiency and stability in transmission performance. This solves the problems of rigid configuration and difficulty in long-term adaptation inherent in traditional solutions. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of the multi-device collaborative wireless audio transmission protocol optimization method of the present invention.

[0014] Figure 2 This is a schematic diagram of the multi-device collaborative wireless audio transmission protocol optimization system module of the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Example Please see Figure 1 As shown, this invention discloses a method for optimizing a multi-device collaborative wireless audio transmission protocol, including the following steps: Step S1: Match the terminal attribute information with the description information of multiple audio services. By calculating the matching ratio between the service and the terminal, the services are classified into fully matched services and partially matched services. The terminal only adds its network interface to the multicast group corresponding to the fully matched service and only receives the audio data stream of the multicast group. Step S2: Calculate the total bandwidth requirement of each terminal for its fully matched service, summarize the total bandwidth requirements of all terminals, and compare them with the available total bandwidth. If resources are sufficient, allocate bandwidth as needed. If resources are scarce, allocate available bandwidth according to the proportion of the total bandwidth requirements of each terminal, and adjust it in combination with the load of the link where the terminal is located. If the allocated bandwidth of the terminal after adjustment still does not meet its basic requirements, perform a downgrade adaptation operation on the terminal, reduce the quality parameters of its non-critical services or suspend the reception of low-priority services. Step S3: Monitor the wireless interference intensity and network link congestion level of the terminal's environment in real time and divide them into multiple discrete levels. Based on the combination of interference level and congestion level, trigger the corresponding transmission strategy adjustment. The adjustment strategy includes maintaining the current configuration, optimizing local parameters or switching frequency bands for non-critical services, and entering the core service guarantee mode. When the environmental level is detected to be restored, gradually restore to the normal service configuration. Step S4: Calculate the service matching success rate and network resource utilization rate, compare the matching success rate and resource utilization rate with preset thresholds respectively, and execute the corresponding iterative optimization strategy according to the combination of comparison results to gradually adjust the service matching rules, bandwidth allocation parameters or environment adaptation thresholds.

[0017] In step S1, the terminal attribute information is matched with the description information of multiple audio services. By calculating the matching ratio between the service and the terminal, the services are classified into fully matched services and partially matched services. The terminal only adds its network interface to the multicast group corresponding to the fully matched service and only receives the audio data stream of that multicast group. Specific details include: During the initial access phase, multi-terminal systems need to determine which audio streams each terminal should receive data from. For a single terminal, subscribing to all available audio services would consume excessive bandwidth and cause redundant data transmission; selecting too few services might cause it to miss the services it needs. Therefore, it is necessary to select the most suitable service from the multiple audio services provided based on the terminal's location, business role, and minimum service priority. Simple parameters are extracted from the service description table and the terminal attribute table, and the suitability of a service for a particular terminal is determined through an easily calculated method. The service description table is maintained by the system management terminal and includes the service area label, service role label, service priority level, multicast address, port, and corresponding audio sampling rate and sampling depth for each service. The terminal attribute table is set by the terminal at the factory or during configuration and includes information such as the terminal's area identifier, role identifier, and minimum service priority threshold. The following parameters are used for matching decisions: The number of matching tags (MTN) represents the number of tags in the service description table that match the terminal attribute table. There are three types of tags: region tags, role tags, and priority judgment. During the judgment, each type of tag matching is scored as 1 point, and non-matching is scored as 0 points. That is, if the service region tag is completely consistent with the terminal region identifier, the region tag is scored as 1 point; if the service role tag is consistent with the terminal role identifier, it is scored as 1 point; if the service priority level is higher than or equal to the terminal's lowest service priority threshold, it is scored as 1 point. If a tag only partially matches, such as the region tag corresponding to the same building but different floors, it is scored as 0.5 points. Therefore, 0 ≤ MTN ≤ 3. Match Ratio (MR) measures the degree of matching between a service and a terminal by the proportion of the number of matched tags to the total number of tags. The formula is as follows: The value of this ratio ranges from [0,1], with a higher value indicating a better match between the service and the terminal. During initial access, the terminal calculates the MR (Mean Match) for all services in a loop and decides whether to add the service to the terminal candidate list based on the following rules: Exact match rule: when 1. When MTN=3, the service is considered to be a complete match with the terminal. At this time, no further judgment is needed, and the terminal directly adds the service to the complete match set and the complete match list. Semi-matching rule: when When MTN=3 or MTN=2.5, the service is considered a partial match, and the service is added to the partial match set and the half-match list. Mismatch rule: When That is, when MTN < 2, the service is considered to have limited help to the terminal and is not included in the candidate set; The complete matching set is used as the target service, and the partial matching set is used as the alternative service. The constructed data structure should at least include the service identifier, multicast address, port, sampling rate and sampling depth, and matching ratio. Based on the items in the complete matching set, the local network interface is added to the corresponding multicast address and port through the multicast addition interface provided by the operating system. Only data packets from the complete matching set are received and handed over to the audio decoding module for playback; services for partial matching sets are not included for the time being. By using simple matching ratios (MR), terminals can quickly determine service compatibility and construct a target service set without complex weighting.

[0018] In step S2, the total bandwidth requirement of each terminal for its fully matched service is calculated, the total bandwidth requirements of all terminals are aggregated, and compared with the available total bandwidth. If resources are sufficient, bandwidth is allocated as needed; if resources are scarce, available bandwidth is allocated according to the proportion of the total bandwidth requirements of each terminal, and adjustments are made based on the load of the link where the terminal is located. If the allocated bandwidth of the terminal still does not meet its basic requirements after adjustment, a degradation adaptation operation is performed on the terminal, reducing the quality parameters of its non-critical services or suspending the reception of low-priority services. Specific details include: After service matching is completed, each terminal will receive a certain number of audio streams. When there are many devices or some streams have high audio quality, the system needs to allocate network bandwidth resources reasonably while ensuring the quality of the main services to avoid link congestion. Traditional bandwidth allocation methods often use complex weighted algorithms to adjust the allocation ratio based on numerous weights. Considering the limited computing resources of embedded devices, this step proposes a bandwidth allocation method based on simple statistics, which does not rely on multiple weights and can still dynamically adjust the bandwidth usage of each terminal. The Total Bandwidth Required by the Terminal (TBR) represents the total bandwidth required by the terminal. It is the sum of the products of the sampling rate and sampling depth of all audio streams in the terminal's perfectly matched list, which is also the sum of the theoretical transmission rates of these audio streams. Let the terminal's perfectly matched list contain... The first service, the first The sampling rate for each service Sampling depth is Then according to as well as The theoretical bandwidth requirement of the service is expressed by the TBR calculation formula as follows: ; Link-to-Terminal Ratio (LTR) indicates the occupancy status of a link, representing the ratio of the number of currently connected terminals to the maximum number of terminals supported by the link. Let the number of terminals currently connected to the audio service on the link be... The maximum number of terminals that the link design can support is ,but The calculation formula is expressed as: ; The sum of the TBR values ​​of all terminals is calculated and then compared with the total available bandwidth of the entire network. Compare the results and adopt different strategies accordingly: Normal state: When the total demand of all terminals Furthermore, the LTR of each link is less than a certain link security threshold. For example, when the value is 0.8, it means that the network still has spare capacity. The bandwidth requirements of the terminals are not adjusted. Each terminal obtains the required bandwidth according to its TBR. At this time, the system adopts a full allocation strategy, does not adjust the bandwidth requirements of any terminal, and each terminal obtains the required bandwidth according to its calculated TBR value, ensuring that the terminal can receive high-quality audio streams without compression or degradation. Bandwidth strain: when Or the LTR of some links exceeds the threshold. When this occurs, it indicates that the network load is too high and there is a risk of congestion. At this time, a simple proportional reduction strategy is adopted, combined with LTR to suppress congested links: calculate the demand ratio of each terminal. This refers to the proportion of a single terminal's TBR value to the total TBR value of all terminals. The calculation formula is as follows: ;in, Let the demand ratio of terminal j be , Let j be the total bandwidth required by terminal j. This represents the total number of terminals connected to the system. Total bandwidth requirements for all terminals; Calculate the theoretical allocated bandwidth of terminal j That is, the total available bandwidth of the system is determined according to the demand ratio. The allocation to each terminal is calculated using the following formula: ;in, Allocate bandwidth theoretically for terminal j. Let the demand ratio of terminal j be , This represents the total available bandwidth of the system. Link load suppression adjustment. If the LTR of the link where terminal j is located exceeds the threshold... If this is the case, the allocated bandwidth for the terminal needs to be further reduced to alleviate the load pressure on the link and avoid link congestion. Adjusted terminal allocated bandwidth. The calculation formula is as follows: ;in, The final bandwidth allocated to terminal j after link load suppression adjustment. The theoretical bandwidth is allocated to terminal j, and LTR is the link terminal ratio of the link where terminal j is located; The aforementioned proportional reduction strategy ensures fair bandwidth allocation. Combined with link load suppression adjustments, it can specifically alleviate the pressure on heavily loaded links, preventing localized link congestion from impacting the overall network transmission performance. This strategy eliminates the need for multi-dimensional weighting coefficients, relying solely on simple calculations based on terminal bandwidth requirements and link load conditions. Its low computational complexity makes it suitable for efficient execution in embedded devices and core network equipment. If, after the above bandwidth allocation, the allocated bandwidth for a certain terminal... Below the minimum bandwidth required for the terminal to maintain basic audio service quality , Determined by factors such as the terminal's audio decoding capabilities and basic service requirements, this can be preset during terminal configuration. If the terminal's bandwidth resources are severely insufficient, it cannot maintain normal transmission for all services in the complete matching list. In this case, the system initiates a degradation adaptation strategy, notifying the terminal to take the following measures: Reduce the sampling rate or sampling depth of some non-critical services to reduce the bandwidth requirements of individual services; Temporarily cancel the reception of some low-priority services, that is, the services with lower priority in the full match list, and move them to the partial match list, while only retaining the reception of high-priority core services to ensure that core business is not affected; Since the canceled service has a high MR value and is considered a fully matched service, but has a low priority, the terminal can move it back from the partially matched list to the fully matched list and resume receiving the service when there is sufficient network bandwidth or the link load is reduced.

[0019] In step S3, the wireless interference intensity and network link congestion level of the terminal's environment are monitored in real time and divided into multiple discrete levels. Based on the combination of interference level and congestion level, corresponding transmission strategy adjustments are triggered. The adjustment strategies include maintaining the current configuration, optimizing local parameters or switching frequency bands for non-critical services, and entering core service guarantee mode. When the environmental level is detected to have recovered, the system gradually restores to the normal service configuration. Specific details include: In the actual operation of multi-device collaborative audio transmission systems, the network and physical environments are not static but constantly changing. For example, in educational examination scenarios, peak examination periods may lead to a sharp increase in network traffic, causing network congestion. In places such as teaching buildings and conference centers, the addition of new wireless hotspots and the operation of other wireless devices may introduce new radio interference. These environmental changes directly affect the audio transmission quality, leading to problems such as audio stuttering, distortion, and increased packet loss rates. In severe cases, they may even cause the interruption of critical audio services. To meet the computing resource limitations of embedded devices, adjustment strategies need to be based on simple parameter judgments, avoiding complex multi-index fusion and optimization calculations. Two simple real-time statistical metrics are used to describe the environmental state, and environmental levels are classified based on threshold determination to ensure that the embedded terminal can efficiently collect and process these parameters, specifically including interference level and congestion level: Interference level (IL) is used to quantitatively describe the signal interference intensity in the current terminal's environment, reflecting the degree of impact of external interference on audio transmission. Through the terminal's network interface and sensors, within a fixed time window (set to 1 second in this embodiment, but adjustable according to actual needs), the following three individual indicators are collected in real time: packet loss rate, signal strength fluctuation range, and number of external interference sources. Packet loss rate: The ratio of the number of data packets lost during the reception of audio data packets by the terminal to the total number of data packets received; Signal strength fluctuation range: The difference between the maximum and minimum values ​​of the signal strength received by the terminal within a time window, reflecting the stability of the signal; Number of external interference sources: The number of wireless devices operating in the vicinity that the terminal detects through the spectrum detection module that may interfere with the current transmission frequency band; The interference level (IL) is divided into three discrete levels based on the above three indicators and preset thresholds. The specific division rules are as follows: Low interference IL = 0: Meets all of the following conditions: packet loss rate less than 1%, signal strength fluctuation range less than 10%, number of external interference sources ≤ 2; Medium interference IL = 1: meets any of the following conditions but does not meet the high interference condition: packet loss rate is between 1% and 5%, signal strength fluctuation range is between 10% and 30%, and the number of external interference sources is between 3 and 5. High interference: IL = 2: Meets any of the following conditions: packet loss rate exceeds 5%, signal strength fluctuation range exceeds 30%, number of external interference sources exceeds 5; This threshold-based discretization method can quickly determine the interference level of the current environment, providing a basis for the subsequent adjustment strategy. Congestion level (CL) describes the degree of congestion on a network link, reflecting the busy state of the network transmission channel. It is monitored in real-time by core network devices such as routers, switches, or terminals, using the following three metrics: bandwidth utilization, queue length, and link latency. Bandwidth utilization: The ratio of the actual bandwidth used by the current link to the total bandwidth of the link; Queue length: The average length of the queue of audio data packets waiting to be forwarded in a network device; Link latency: The average delay time for audio data packets to travel from the sending end to the receiving end.

[0020] Similarly, the system simply integrates the above three indicators without involving weighted average calculations, and directly divides the congestion level (CL) into three discrete levels based on preset thresholds. The specific division rules are as follows: Low congestion CL = 0: Meets all of the following conditions: bandwidth utilization is less than 50%, queue length is stable and does not exceed 10 packets, and link latency is less than 50ms; Medium congestion CL = 1: Meets any of the following conditions but does not meet the high congestion condition: bandwidth utilization is between 50% and 80%, queue length fluctuates significantly (the difference between the maximum and minimum values ​​exceeds 5 data packets), and link latency is between 50ms and 100ms. High congestion CL = 2: meets any of the following conditions: bandwidth utilization exceeds 80%, queue length continuously exceeds 20 packets, link latency exceeds 100ms; This level classification method is also based on a simple threshold determination. Network devices or terminals can quickly obtain the current link congestion level, ensuring that adjustment strategies can respond to network congestion changes in a timely manner. Based on the combination of interference level (IL) and congestion level (CL), adaptive adjustment strategies of varying strengths are triggered to ensure the stability of audio transmission quality. The specific strategies are as follows: Normal mode, maintaining the current configuration: When IL = 0 and CL = 0, it indicates a stable network environment, low interference level, no link congestion, and good audio transmission conditions. In this mode, the system makes no adjustments, maintaining the current bandwidth allocation scheme, audio sampling rate, sampling depth, and encoding method to ensure the terminal can receive a high-quality audio stream. Mild adjustment mode, local optimization and adaptation: When IL = 1 or CL = 1, that is, one of the interference level or congestion level reaches medium level and the other is low level, it indicates that the network environment or link status has slightly deteriorated. If no adjustment is made, it may lead to a decrease in audio transmission quality. At this time, the system starts a mild adjustment strategy, and the specific measures are as follows: For non-critical services in the terminal's complete match list or services in the partial match list, temporarily reduce their sampling rate or sampling depth to reduce the bandwidth consumption of individual services and alleviate network load pressure. If the current IL = 1, the terminal scans the surrounding available frequency bands through the spectrum detection module and attempts to switch to a frequency band with less interference to reduce the impact of external interference on audio transmission. If the current CL = 1, the demand ratio of each terminal is recalculated based on the bandwidth allocation results. and theoretically allocated bandwidth For terminals corresponding to non-critical services, bandwidth allocation should be appropriately reduced, and the saved bandwidth resources should be prioritized for terminals corresponding to core services in order to alleviate network congestion. Heavy Adjustment Mode, Core Service Guarantee: When IL = 2 or CL = 2, meaning either the interference level or congestion level reaches high, regardless of the other level, it indicates a severe deterioration in the network environment or link status. Without strong adjustment measures, this will lead to severe stuttering, distortion, or even interruption of audio transmission. In this situation, the system triggers a heavy adjustment strategy, prioritizing the normal transmission of core services. Specific measures are as follows: The terminal retains only the top 1-2 core services with the highest priority in the fully matched list, while all other services, including low-priority services in the fully matched list and all services in the partially matched list, are temporarily suspended. Bandwidth resources are concentrated on core services. The audio sampling rate of the core services is reduced to the minimum value allowed by the system. In this embodiment, the minimum sampling rate is set to 16kHz, which can be adjusted according to the terminal's hardware capabilities, for example, from 48kHz to 16kHz or 24kHz. A higher compression ratio is adopted, such as switching from the 320kbps compression mode of MP3 to the 128kbps compression mode or a smaller data packet size encoding method, to minimize the bandwidth requirements of a single core service. For a short period of time, each terminal only maintains basic functional audio stream transmission, ensuring only the transmission of critical information. All other non-critical audio streams, such as background music and supplementary audio, are suspended to avoid non-critical data occupying limited network resources.

[0021] When the system detects that both IL and CL have returned to the low level of IL = 0 and CL = 0, it gradually restores the suspended services: first restores the low priority services in the complete match list, then restores the services in the partial match list, and at the same time gradually increases the audio sampling rate and sampling depth until it is restored to the configuration of normal mode. After the adaptive adjustment strategy is implemented, the relevant configuration information will be updated, and the adjustment process and results will be recorded in the log. The specific update content is as follows: Record the adjusted parameters such as sampling rate, sampling depth, and encoding compression method for each service to ensure that the terminal subsequently decodes and plays audio according to the new parameters. Generate a list of disabled services, clearly indicating the service identifier, reason for discontinuation, and conditions for restoration, so as to facilitate quick query when services are restored. If the terminal switches frequency bands or changes multicast address, record the new frequency band number, new multicast address and port configuration to ensure that the communication parameters between the terminal and the network device are consistent. Recalculate the TBR value for each terminal and the LTR value for each link to provide the latest basic data for the next round of bandwidth allocation; Through the above adaptive strategy based on simple discrete parameters and threshold determination, this step achieves dynamic adjustment of the transmission strategy in complex and ever-changing environments with low computational cost. It effectively solves the key problem of how to maintain the quality of core audio services when interference or congestion occurs, and ensures that the system can maintain the normal operation of basic functions even in harsh environments.

[0022] In step S4, the service matching success rate and network resource utilization rate are statistically analyzed. These are compared with preset thresholds. Based on the combination of comparison results, corresponding iterative optimization strategies are executed to progressively adjust the service matching rules, bandwidth allocation parameters, or environment adaptation thresholds. Specific details include: Two simple and intuitive long-term statistical indicators are used to evaluate the long-term performance of the system, providing a basis for iterative optimization decisions. These indicators are calculated based on historical data within a long-term time window, as follows: The Match Success Rate (MSR) is used to evaluate the rationality of service matching rules and reflects the probability that a terminal can find a fully matched service. It is defined as: the ratio of the number of times a fully matched service is successfully found through the service matching process to the total number of matching attempts within a relatively long time window (set to 24 hours in this embodiment, but adjustable according to system operating frequency); Suppose that the terminal makes T matching attempts within the time window, including the initial matching during access and the rematching during subsequent service updates, and S of these attempts successfully match at least one service (i.e., the complete match list is not empty). Then the formula for calculating the matching success rate (MSR) is as follows: MSR is the matching success rate, with a value range of [0,1]. The higher the MSR value, the more reasonable the current service matching rules are, and the better they can meet the service needs of the terminal. If the MSR value is consistently low, such as being below 0.7 for a long time, it indicates that the current matching rules are too strict, or that the service configuration in the service description table does not match the actual needs of the terminal and needs to be adjusted. The management system aggregates and calculates the average MSR value of all terminals based on the matching results reported periodically by the terminals, which serves as a system-level indicator for evaluating the matching success rate, thus avoiding the influence of special cases of individual terminals on overall decision-making. Resource utilization rate (RUR) is used to evaluate the efficiency of system network resource utilization and reflects the overall occupancy of system bandwidth resources. It is defined as: the ratio of the total network bandwidth actually occupied by all terminals to the total available bandwidth of the system within the same long-term time window; Assume the total available bandwidth of the system is At any time t within the time window, the actual bandwidth usage of terminal j is Then, the average actual bandwidth usage of terminal j within the time window is In the discrete case, it can be calculated by sampling and summation: Where N is the number of samples; The formula for calculating system resource utilization (RUR) is as follows: Where RUR is the resource utilization rate, with a value range of [0,1], and n is the total number of terminals connected to the system. This represents the average actual bandwidth usage of terminal j within the time window. This represents the total available bandwidth of the system. The RUR value reflects the utilization level of system bandwidth resources: if the RUR is consistently close to 1, such as greater than 0.85, it indicates that the system is in a high-load operation state for a long time, network resources are tight, and congestion is likely to occur; if the RUR is consistently too low, it indicates that the system configuration is too conservative, a large amount of bandwidth resources are idle, the utilization rate is low, and there is room for optimization. To achieve long-term iterative optimization of the system, two core criterion thresholds are set as the basis for adjusting system parameters and rules: Match success rate threshold In this embodiment, the value is set to 0.7, meaning that when the system's average MSR is below 0.7, the matching rules are considered to need adjustment. Resource utilization cap In this embodiment, it is set to 0.85. That is, when the system RUR exceeds 0.85, it is considered that the system resource usage is too high and needs to be adjusted to reduce the load.

[0023] The system management console periodically calculates MSR and RUR, and executes corresponding iterative optimization strategies based on the following four combinations: Scenario 1: MSR < And RUR < The success rate of terminals matching fully matched services is low, and the system resource utilization is also low. The core reason is that the current service matching rules are too strict, which causes a large number of services suitable for terminals to be excluded from the fully matched set, while the system has enough bandwidth resources to support the transmission of more services. The iterative optimization strategy for this situation is as follows: Adjust the matching ratio threshold: Adjust the MR threshold for perfect matches from 1 to 0.8. This can be fine-tuned according to the actual situation. That is, when MR ≥ 0.8, the service can be included in the perfect match set, expanding the scope of perfect match services and improving the matching success rate. Optimize the service description table: Adjust the regional division in the service description table, merge regions with similar geographical locations or related business logic to increase the matching probability of regional tags; at the same time, appropriately adjust the role tags of some services to better match the actual business role needs of the terminal and improve the matching score of role tags. Adjust priority thresholds: Without affecting core services, appropriately reduce the priority level of some services or lower the minimum service priority threshold for some terminals to increase the success rate of priority matching; Scenario 2: MSR ≥ And RUR> The terminal matching success rate is relatively high, but the resource utilization rate remains high for a long time. The core reason is that the matching rules are too lenient, which causes the terminal to match too many unnecessary services, consuming a lot of bandwidth resources.

[0024] The iterative optimization strategy for this situation is as follows: Increase matching requirements: Increase the MR threshold for a perfect match in step one from 1 to 1, while strictly judging partial match rules, increasing the lower limit of the MR for half-match rules from 0.67 to 0.75, and reducing the number of partially matched services; for services in the perfect match list, further refine the priority division, and lower the priority level of some non-critical services, so that their bandwidth can be reduced first when bandwidth is tight in the future. Optimize bandwidth allocation parameters: Adjust the link security threshold in step two. The bandwidth requirement was reduced from 0.8 to 0.7, enabling the link to trigger bandwidth reduction strategies when the load is low, thus preventing link congestion in advance; at the same time, the minimum bandwidth requirement of the terminal was appropriately increased. This avoids terminals frequently triggering downgrade adaptation due to insufficient allocated bandwidth, thus reducing the frequency of bandwidth adjustments. Service optimization: Analyze the usage frequency of low-priority services in the exact match list. For services with consistently low usage, consider merging or canceling the service to reduce unnecessary bandwidth consumption. Scenario 3: MSR ≥ And RUR ≤ The terminal matching success rate is high, the system resource utilization rate is within a reasonable range, the system is running well, and the current matching rules, bandwidth allocation parameters, and environmental adaptation strategies can meet the system requirements well. Therefore, there is no need to adjust the parameters and rules. Maintain the existing configuration, continue to observe the system's operating status, and reserve adjustment space for possible changes in the future. Scenario 4: MSR < And RUR> The low terminal matching success rate and high system resource utilization indicate a serious system configuration problem. The core reason may be a serious mismatch between the service description table and the terminal requirements, or a defect in the bandwidth allocation strategy. The iterative optimization strategy for this situation is as follows: Emergency service adjustments: First, the service description table was thoroughly reviewed. Based on the actual regional distribution of terminals and business roles, the regional tags, role tags, and priority levels of services were redefined to ensure that the service configuration was aligned with the terminal requirements. Bandwidth allocation reconfiguration: Reassessing the maximum number of terminals supported by each link. Adjust according to the actual bandwidth capacity of the link and the average bandwidth requirement of the terminal. The value of [value] is determined to avoid inaccurate link load assessment; at the same time, the calculation logic of the proportional reduction strategy is optimized, and the bandwidth allocation weight of core service terminals is appropriately increased to ensure the bandwidth requirements of core services. Environmental adaptation threshold optimization: Appropriately reduce the threshold for judging high interference and high congestion so that the system can trigger environmental adaptation adjustment strategies earlier, avoiding bandwidth waste and service quality degradation caused by environmental deterioration; The iterative optimization mechanism, based on long-term statistical data rather than instantaneous states, has the following characteristics, ensuring system stability and optimization effectiveness: Data reliability: Both MSR and RUR are average values ​​over a long time window, which can stably reflect the actual operating status of the system and avoid erroneous adjustments caused by short-term sudden events; Adjustment in a gradual manner: Each iteration of optimization only makes minor adjustments to system parameters and rules, such as adjusting the MR threshold by no more than 0.2. The adjustment range should not exceed 0.1 to avoid drastic fluctuations in system operation caused by large adjustments and to ensure a smooth system transition; Feedback loop: After each round of iteration and optimization, the system continuously monitors the changing trends of MSR and RUR. If the adjusted indicators enter the ideal range (MSR ≥ 0.5%), the system will initiate a feedback loop. And RUR ≤ If the indicators do not improve or even worsen, the configuration will be rolled back to the previous configuration, and the reasons will be re-analyzed and the optimization direction adjusted. Lightweight computation: The calculation of all statistical indicators and the execution of optimization strategies are carried out using simple mathematical operations and logical judgments. No complex algorithm models are required. Both the system management end and the terminal can complete the task efficiently without increasing the system's operating burden.

[0025] Through this iterative optimization mechanism based on long-term statistical data, the system can continuously adapt to dynamic changes in the number of terminals, network topology, and business needs, and continuously optimize service matching rules, bandwidth allocation parameters, and environmental adaptation strategies to ensure that the system maintains efficient and stable transmission performance during long-term operation and avoids system performance degradation caused by initial configuration failure.

[0026] Please see Figure 2 As shown, the present invention discloses a multi-device collaborative wireless audio transmission protocol optimization system, including: a multicast matching module, a resource allocation module, an environment adaptation module, and a long-term optimization module, with signal connections between the modules; Multicast matching module: Matches terminal attribute information with description information of multiple audio services. By calculating the matching ratio between services and terminals, services are classified into fully matched services and partially matched services. Terminals only add their network interfaces to the multicast group corresponding to the fully matched service and only receive the audio data stream of that multicast group. Resource allocation module: Calculates the total bandwidth requirement of each terminal for its fully matched service, summarizes the total bandwidth requirement of all terminals, and compares it with the available total bandwidth. If resources are sufficient, they are allocated as needed. If resources are scarce, available bandwidth is allocated according to the proportion of the total bandwidth requirement of each terminal, and adjusted in combination with the load of the link where the terminal is located. If the allocated bandwidth of the terminal still does not meet its basic requirements after adjustment, a degradation adaptation operation is performed on the terminal, reducing the quality parameters of its non-critical services or suspending the reception of low-priority services. Environment Adaptive Module: Real-time monitoring of the wireless interference intensity and network link congestion level of the terminal's environment and classifying them into multiple discrete levels. Based on the combination of interference level and congestion level, it triggers corresponding transmission strategy adjustments. Adjustment strategies include maintaining the current configuration, optimizing local parameters or switching frequency bands for non-critical services, and entering core service guarantee mode. When the environmental level is detected to have recovered, it gradually restores to the normal service configuration. Long-term optimization module: Statistically calculates service matching success rate and network resource utilization rate, compares the matching success rate and resource utilization rate with preset thresholds respectively, and executes corresponding iterative optimization strategies based on the combination of comparison results, making gradual adjustments to service matching rules, bandwidth allocation parameters or environment adaptation thresholds.

[0027] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0028] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0029] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and inventive constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0030] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0031] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0032] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing multi-device collaborative wireless audio transmission protocols, characterized in that, Includes steps; Step S1: Match the terminal attribute information with the description information of multiple audio services. By calculating the matching ratio between the service and the terminal, the services are classified into fully matched services and partially matched services. The terminal only adds its network interface to the multicast group corresponding to the fully matched service and only receives the audio data stream of the multicast group. Step S2: Calculate the total bandwidth requirement of each terminal for its fully matched service, summarize the total bandwidth requirements of all terminals, and compare them with the available total bandwidth. If resources are sufficient, allocate bandwidth as needed. If resources are scarce, allocate available bandwidth according to the proportion of the total bandwidth requirements of each terminal, and adjust it in combination with the load of the link where the terminal is located. If the allocated bandwidth of the terminal after adjustment still does not meet its basic requirements, perform a downgrade adaptation operation on the terminal, reduce the quality parameters of its non-critical services or suspend the reception of low-priority services. Step S3: Monitor the wireless interference intensity and network link congestion level of the terminal's environment in real time and divide them into multiple discrete levels. Based on the combination of interference level and congestion level, trigger the corresponding transmission strategy adjustment. The adjustment strategy includes maintaining the current configuration, optimizing local parameters or switching frequency bands for non-critical services, and entering the core service guarantee mode. When the environmental level is detected to be restored, gradually restore to the normal service configuration. Step S4: Calculate the service matching success rate and network resource utilization rate, compare the matching success rate and resource utilization rate with preset thresholds respectively, and execute the corresponding iterative optimization strategy according to the combination of comparison results to gradually adjust the service matching rules, bandwidth allocation parameters or environment adaptation thresholds.

2. The method for optimizing multi-device collaborative wireless audio transmission protocol according to claim 1, characterized in that, Extract the service description parameters and the terminal attribute parameters, and calculate the matching ratio (MR) between the service and the terminal; Based on the matching ratio MR, the services are classified into fully matched services and partially matched services; The terminal is made to receive only the multicast data stream corresponding to the fully matched service.

3. The method for optimizing multi-device collaborative wireless audio transmission protocol according to claim 2, characterized in that, The matching ratio MR is calculated by the number of matching tags MTN, which is based on the consistency determination of region tags, role tags, and priority tags.

4. The method for optimizing multi-device collaborative wireless audio transmission protocol according to claim 1, characterized in that, Calculate the total bandwidth required (TBR) for each terminal to provide a fully matched service; When total bandwidth is tight or link load is too high, the total available bandwidth of the system is allocated according to the total bandwidth demand of each terminal (TBR ratio), and the allocated bandwidth is adjusted in combination with the link terminal ratio (LTR).

5. The method for optimizing multi-device collaborative wireless audio transmission protocol according to claim 4, characterized in that: When the allocated bandwidth of a terminal is lower than its minimum bandwidth requirement, a degradation adaptation strategy is triggered, which reduces non-critical service parameters or suspends low-priority services to ensure core services.

6. The method for optimizing a multi-device collaborative wireless audio transmission protocol according to claim 1, characterized in that, Based on real-time collected metrics, interference level IL and congestion level CL are classified. Based on the combination of interference level (IL) and congestion level (CL), adjustment strategies of varying intensities are triggered, including maintaining configuration, local optimization and adaptation, and ensuring core services.

7. The method for optimizing multi-device collaborative wireless audio transmission protocol according to claim 6, characterized in that, Local optimization and adaptation strategies include reducing the sampling rate or sampling depth of non-critical services, attempting to switch communication frequency bands, or adjusting the bandwidth allocation of non-critical services. The core service assurance strategy includes suspending non-core services, reducing the sampling rate of core services, and adopting encoding methods with higher compression ratios.

8. The method for optimizing multi-device collaborative wireless audio transmission protocol according to claim 1, characterized in that, The long-term matching success rate (MSR) and resource utilization rate (RUR) of the statistical system; Based on the comparison results of the matching success rate (MSR) and resource utilization rate (RUR) relative to their respective thresholds, the corresponding iterative optimization strategy is executed to adjust the service matching rules, bandwidth allocation parameters, or environment adaptation thresholds. When the matching success rate (MSR) is below its threshold and the resource utilization rate (RUR) is below its upper limit, the service matching conditions are relaxed. When the matching success rate (MSR) is higher than its threshold and the resource utilization rate (RUR) is higher than its upper limit, tighten the service matching conditions and adjust the bandwidth allocation parameters.

9. The method for optimizing a multi-device collaborative wireless audio transmission protocol according to claim 8, characterized in that, The execution of the iterative optimization strategy is based on statistical data within a long time window and adopts a gradual adjustment and feedback closed-loop mechanism.

10. A multi-device collaborative wireless audio transmission protocol optimization system, used to implement the multi-device collaborative wireless audio transmission protocol optimization method according to any one of claims 1-9, characterized in that... ； Multicast matching module: Matches terminal attribute information with description information of multiple audio services. By calculating the matching ratio between services and terminals, services are classified into fully matched services and partially matched services. Terminals only add their network interfaces to the multicast group corresponding to the fully matched service and only receive the audio data stream of that multicast group. Resource allocation module: Calculates the total bandwidth requirement of each terminal for its fully matched service, summarizes the total bandwidth requirement of all terminals, and compares it with the available total bandwidth. If resources are sufficient, they are allocated as needed. If resources are scarce, available bandwidth is allocated according to the proportion of the total bandwidth requirement of each terminal, and adjusted in combination with the load of the link where the terminal is located. If the allocated bandwidth of the terminal still does not meet its basic requirements after adjustment, a degradation adaptation operation is performed on the terminal, reducing the quality parameters of its non-critical services or suspending the reception of low-priority services. Environment Adaptive Module: Real-time monitoring of the wireless interference intensity and network link congestion level of the terminal's environment and classifying them into multiple discrete levels. Based on the combination of interference level and congestion level, it triggers corresponding transmission strategy adjustments. Adjustment strategies include maintaining the current configuration, optimizing local parameters or switching frequency bands for non-critical services, and entering core service guarantee mode. When the environmental level is detected to have recovered, it gradually restores to the normal service configuration. Long-term optimization module: Statistically calculates service matching success rate and network resource utilization rate, compares the matching success rate and resource utilization rate with preset thresholds respectively, and executes corresponding iterative optimization strategies based on the combination of comparison results, making gradual adjustments to service matching rules, bandwidth allocation parameters or environment adaptation thresholds.

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