Balance control system for dynamic power consumption and transmission delay of Bluetooth headset

Abstract

The invention relates to the technical field of Bluetooth earphones, and discloses a Bluetooth earphone dynamic power consumption and transmission delay balance control system, which comprises a state acquisition module, a strategy generation module, a factor analysis module, a quantitative evaluation module, a parameter adjustment module and an execution control module. The state acquisition module obtains working current, data packet state and the like to construct an evaluation model; the strategy generation module generates an initial association strategy table; the factor analysis module analyzes historical data to identify interference factors; the quantitative evaluation module calculates a stability deviation index; the parameter adjusting module updates the strategy table according to the index; and the execution control module triggers rate switching. The system is further provided with a data packet comparison unit, a timestamp marking unit and the like to process data transmission conflicts, and the transmission quality is optimized through a three-dimensional transmission quality field model, a genetic algorithm and a three-level response mechanism. The system realizes dynamic balance of power consumption and transmission delay, and is suitable for the field of power consumption and transmission control of the Bluetooth headset.

Description

Technical Field

[0001] This invention relates to the field of Bluetooth headset technology, specifically to a dynamic power consumption and transmission delay balance control system for Bluetooth headsets. Background Technology

[0002] In the field of wireless audio transmission, Bluetooth headsets have become mainstream devices due to their portability and wireless advantages. However, their performance is significantly constrained by the trade-off between power consumption and transmission latency. Traditional Bluetooth headsets have many shortcomings in balancing power consumption control and transmission efficiency, making it difficult to meet users' dual needs for long battery life and low-latency transmission.

[0003] From a power consumption perspective, current Bluetooth headset power management technologies largely rely on fixed strategies, failing to fully consider the dynamic changes in real-world usage scenarios. For example, headset power requirements vary significantly depending on environmental interference intensity, audio data type, and chip workload. Fixed power control parameters cannot adapt to these changes in real time, leading to increased power consumption in high-interference environments to maintain transmission stability, resulting in wasted power. Conversely, in low-load scenarios, excessively high power settings further shorten battery life. Furthermore, traditional power management lacks deep integration with transmission rates and dynamic adjustment mechanisms, making it difficult to optimize power consumption while maintaining transmission quality.

[0004] Regarding transmission latency, existing transmission systems lack sufficient intelligence in data packet processing. When transmitting data packets with the same encoding type consecutively, the lack of effective detection and control over transmission intervals easily leads to data collisions causing retrying and increasing latency. For data packets with different encoding types, the lack of flexible channel allocation strategies may result in competition for shared channel resources, further exacerbating latency issues. Furthermore, assessments of transmission link stability are often based on single indicators, failing to comprehensively consider the combined impact of multiple interference factors. This makes it impossible to accurately predict transmission quality fluctuations and to adjust transmission strategies in a timely manner to reduce latency.

[0005] From a system-wide perspective, the power consumption control and transmission latency management modules in existing Bluetooth headsets operate independently, lacking effective data interaction and coordination mechanisms. The status acquisition module can only obtain basic operating current and data packet status, failing to build a comprehensive transmission quality assessment model; the policy generation module generates policies based on fixed rules, lacking in-depth analysis and dynamic optimization capabilities based on historical data. This prevents the system from achieving a dynamic balance between power consumption and transmission latency in complex and ever-changing usage scenarios, severely impacting user experience.

[0006] As users' demands for Bluetooth headset performance continue to increase, such as the stringent requirements for low latency in scenarios like gaming and video calls, and the high demand for battery life during extended music playback, there is an urgent need for a control system that can sense environmental changes in real time and dynamically adjust power consumption and transmission rate to solve problems such as power waste and unstable transmission latency in existing technologies, and achieve the optimal balance between the two. Summary of the Invention

[0007] The purpose of this invention is to provide a dynamic power consumption and transmission delay balance control system for Bluetooth headsets, so as to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a dynamic power consumption and transmission delay balance control system for Bluetooth headsets, the system comprising: The status acquisition module is used to acquire the real-time operating current data of the headphones and the status of audio transmission data packets, and to build a transmission link quality assessment model. The strategy generation module is used to generate an initial correlation strategy table between transmission rate and power consumption based on the evaluation model, wherein the strategy table includes a transmission rate threshold and power consumption control parameters. The factor analysis module is used to analyze historical transmission record data, identify variables related to rate fluctuations, and classify core interference factors and auxiliary interference factors. The quantitative evaluation module is used to calculate the stability deviation index of the transmission link based on the core interference factor and the auxiliary interference factor. The parameter adjustment module is used to determine the calibration coefficient of the correlation strategy table based on the stability deviation index, and update the correlation strategy table according to the calibration coefficient to form a dynamic balance strategy table. The execution control module is used to trigger the transmission rate switching operation of the headphone chip according to the dynamic balancing strategy table.

[0009] Preferably, the system further includes: The data packet comparison unit is used to compare the current transmitted data packet format with the previous transmitted format to determine whether the two belong to the same type of encoding. The timestamp unit is used to record the start time of the current transmission and the completion time of the previous transmission if the encoding is determined to be of the same type. An interval detection unit is used to calculate the time interval difference between two transmissions and compare it with a preset transmission interval threshold to determine whether the current transmission is allowed to proceed. A transmission suppression unit is used to delay the current transmission and generate a data conflict record if the time interval difference is less than the preset transmission interval threshold. The channel allocation unit is used to allocate an independent transmission channel to perform the current audio data transmission if the encoding type comparison results are inconsistent.

[0010] Preferably, the factor analysis module includes: All rate fluctuation-related variables are arranged in a time series to form a detection queue. Adjacent variable groups are randomly selected to obtain the corresponding transmission rate readings and subsequent transmission rate readings, and the absolute value of the difference in the rates is calculated. Identify the peak and valley rates in the full transmitted data and calculate the overall rate fluctuation range; The ratio of the absolute value of the difference to the fluctuation range is calculated and denoted as the rate ratio. Calculate the interference correlation offset between the variable groups; Determine the maximum and minimum correlation among all variables, and calculate the range of global correlation variation; The ratio of the interference correlation offset to the range of global correlation variation is calculated and denoted as the correlation ratio value. Multiply the rate ratio by the correlation ratio and normalize the result to obtain the real-time interference coefficient. The remaining variable groups are traversed to generate a set of auxiliary interference factors, and the median value of all real-time interference coefficients is calculated as the core interference factor.

[0011] Preferably, the quantitative evaluation module includes: Set upper and lower warning values ​​for the core interference factor; The set of high interference factors is selected based on the upper limit warning value, and the set of low interference factors is selected based on the lower limit warning value. Calculate the positive offset of the high interference factor from the upper limit warning value, and arrange them in reverse order according to the offset to form a high interference queue; Calculate the negative offset between the low interference factor and the lower limit warning value, and arrange them in ascending order of offset magnitude to form a low interference queue; The elements of the high-order queue and the low-order queue are combined one by one to form an interference comparison group, and the stability deviation index is calculated based on all interference comparison groups.

[0012] Preferably, the quantitative evaluation module further includes: The total number of interference comparison groups is counted, and the squared difference between the highest and lowest elements in each group is calculated. Identify the minimum and maximum values ​​among all squared differences, and calculate the distribution dispersion parameter of all squared differences; The stability deviation index is calculated based on the minimum, maximum, and distribution dispersion parameters.

[0013] Preferably, the parameter adjustment module includes: Set the basic interference threshold and the enhanced interference threshold; Configure initial calibration coefficients, intermediate calibration coefficients, and enhanced calibration coefficients; When the stability deviation index is lower than the basic interference threshold, the initial calibration coefficient is used; when the stability deviation index is between the basic and enhanced interference thresholds, the medium calibration coefficient is used; and when the stability deviation index exceeds the enhanced interference threshold, the enhanced calibration coefficient is used.

[0014] Preferably, the parameter adjustment module further includes: The target adjustment value is obtained by multiplying the calibration coefficient by the power consumption control parameter; The expected transmission rate corresponding to the target adjustment value is determined by matching a preset parameter mapping rule. The dynamic balancing strategy table is constructed by integrating the target adjustment value with the expected transmission rate.

[0015] Preferably, the status acquisition module further includes: Real-time aggregation of signal strength data, chip power consumption data, and environmental interference data is used to construct a three-dimensional transmission quality field model. The effective transmission coverage area is calculated based on the three-dimensional transmission quality field model. When the predicted transmission quality is lower than the set standard, the anti-interference coding mode is activated. The latency / power consumption relationship matrix under different combinations of working modes is generated by simulating using a genetic algorithm, and an optimized balanced solution set is generated.

[0016] Preferably, the system has a three-level response mechanism based on the degree of transmission quality degradation: Level 1 enables basic coding compensation, Level 2 switches to enhanced anti-interference protocol, and Level 3 initiates dual-channel redundant transmission.

[0017] Preferably, the data packet comparison unit includes: extracting key transmission features from non-standard data packets using feature encoding recognition technology, storing a vectorized database of historical transmission conflict events and their handling strategies, and when a data conflict is detected, retrieving an adaptation processing scheme from the strategy database through feature matching and generating a transmission priority adjustment instruction.

[0018] Compared with the prior art, the beneficial effects of the present invention are: The status acquisition module can acquire the headphone's operating current and audio transmission data packet status in real time. Simultaneously, it aggregates signal strength, chip power consumption, and environmental interference data to construct a three-dimensional transmission quality field model, accurately assessing the transmission link quality. This model not only calculates the effective transmission coverage area but also generates a latency / power consumption relationship matrix through a genetic algorithm, providing the system with comprehensive status awareness and optimization basis, enabling the system to make more scientific decisions based on multi-dimensional data.

[0019] The linkage mechanism between the strategy generation module and the parameter adjustment module enables dynamic optimization of the strategy. After generating an initial correlation strategy table based on the evaluation model, the factor analysis module analyzes historical transmission data, identifies rate fluctuation correlation variables, and classifies core and auxiliary interference factors. The quantitative evaluation module further calculates the stability deviation index, and the parameter adjustment module dynamically adjusts the calibration coefficients based on this index, updating the strategy table to form a dynamic balance strategy. This process allows the system to flexibly adjust transmission rate and power consumption parameters according to real-time interference conditions, avoiding the limitations of fixed strategies and effectively reducing power consumption while ensuring transmission quality, thus extending the headphone's battery life. For example, when the stability deviation index is low, the initial calibration coefficients are used to avoid over-adjustment and wasted power; when the index is high, enhanced calibration coefficients are used to ensure transmission stability in strong interference environments.

[0020] The packet processing unit effectively solves the data transmission conflict problem. The packet comparison unit determines the encoding type through feature comparison. The timestamp marking unit, in conjunction with the interval detection unit, controls the transmission interval when transmitting packets of the same encoding type, avoiding data conflicts caused by excessively short intervals, reducing the number of transmission retries, and thus lowering latency. The channel allocation unit allocates independent channels when encoding types are inconsistent, avoiding competition for channel resources between different types of data, further improving transmission efficiency. Furthermore, a vectorized database stores historical conflict events and handling strategies, enabling rapid response and intelligent conflict handling, enhancing the system's robustness.

[0021] The algorithm design of the factor analysis and quantitative evaluation modules improves the accuracy of interference factor identification and the scientific rigor of stability assessment. Through time series analysis, absolute difference calculation, and correlation offset analysis, core interference factors can be accurately identified, while interference from secondary factors is avoided, enabling the system to take effective measures against major interference sources. The quantitative evaluation module comprehensively assesses the stability of the transmission link by setting warning values, constructing interference comparison groups, and calculating the stability deviation index. This provides precise quantitative basis for strategy adjustments, ensuring that the system maintains stable transmission performance under different interference environments.

[0022] The three-tiered response mechanism enables the system to flexibly respond to the degree of transmission quality degradation. Tier 1 enables basic coding compensation to maintain transmission at a low cost when transmission quality is only slightly degraded; Tier 2 switches to enhanced anti-interference protocols to improve anti-interference capabilities when interference levels are moderate; Tier 3 activates dual-channel redundant transmission to ensure reliable data transmission in extreme conditions. This tiered response mechanism ensures transmission quality while rationally allocating system resources, avoiding resource waste, and further optimizing the balance between power consumption and latency. Attached Figure Description

[0023] Figure 1This is a schematic diagram illustrating the working principle of the Bluetooth headset dynamic power consumption and transmission delay balance control system described in this invention. Figure 2 Design diagram for a data packet transmission collision handling system; Figure 3 Design diagram of the interference factor analysis system; Figure 4 Design diagram of the stability deviation index calculation system; Figure 5 Design diagram for a parameter calibration and strategy table generation system. Detailed Implementation

[0024] 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.

[0025] Please see Figures 1-5 The present invention relates to a dynamic power consumption and transmission delay balance control system for Bluetooth headsets. The system includes a state acquisition module, a strategy generation module, a factor analysis module, a quantitative evaluation module, a parameter adjustment module, and an execution control module. These modules work together to achieve a balance between dynamic power consumption and transmission delay control. Specifically, the system includes the following steps: The status acquisition module obtains real-time operating current data and audio transmission data packet status of the headphones to construct a transmission link quality assessment model. This module aggregates signal strength data, chip power consumption data, and environmental interference data in real time to construct a three-dimensional transmission quality field model. The effective transmission coverage area is calculated using this three-dimensional transmission quality field model, and an anti-interference coding mode is activated when the predicted transmission quality is lower than a set standard. Simultaneously, a genetic algorithm is used to simulate and generate latency / power consumption relationship matrices under different combinations of operating modes, and an optimized balanced solution set is generated.

[0026] The strategy generation module generates an initial correlation strategy table between transmission rate and power consumption based on the aforementioned evaluation model. This table includes transmission rate thresholds and power consumption control parameters. The generation of this initial correlation strategy table is based on the analysis and modeling of a large amount of historical data. The evaluation model predicts and optimizes power consumption at different transmission rates, thereby determining reasonable transmission rate thresholds and corresponding power consumption control parameters to achieve an initial balance between power consumption and transmission latency.

[0027] The factor analysis module parses historical transmission record data, identifies rate fluctuation-related variables, and classifies core interference factors and auxiliary interference factors. This module arranges all rate fluctuation-related variables into a detection queue according to time series, randomly selects adjacent variable groups, obtains corresponding and subsequent transmission rate readings, and calculates the absolute value of rate differences. It identifies rate peaks and valleys in the full transmission data and calculates the overall rate fluctuation range; it calculates the ratio of the absolute value of the difference to the fluctuation range; it calculates the interference correlation offset between variable groups; it determines the maximum and minimum correlation among all variables and calculates the global correlation variation range; it calculates the ratio of the interference correlation offset to the global correlation variation range; it multiplies the rate ratio by the correlation ratio and performs normalization to obtain the real-time interference coefficient; it iterates through the remaining variable groups to generate a set of auxiliary interference factors, and calculates the median value of all real-time interference coefficients as the core interference factor.

[0028] The quantitative evaluation module calculates the stability deviation index of the transmission link based on the core interference factor and auxiliary interference factors. This module sets upper and lower warning values ​​for the core interference factor; it filters a set of high interference factors based on the upper warning value and a set of low interference factors based on the lower warning value; it calculates the positive offset of high interference factors from the upper warning value and arranges them in reverse order of offset magnitude to form a high-order interference queue; it calculates the negative offset of low interference factors from the lower warning value and arranges them in ascending order of offset magnitude to form a low-order interference queue; it combines elements from the high-order and low-order queues item by item to form interference comparison groups, and calculates the stability deviation index based on all interference comparison groups. Simultaneously, it counts the total number of interference comparison groups, calculates the squared difference between the high-order and low-order elements in each group; it identifies the minimum and maximum values ​​among all squared differences and calculates the distribution dispersion parameter of all squared differences; and it calculates the stability deviation index based on the minimum, maximum, and distribution dispersion parameter.

[0029] The parameter adjustment module determines the calibration coefficients of the associated strategy table based on the stability deviation index and updates the associated strategy table according to the calibration coefficients to form a dynamic balancing strategy table. This module sets a basic interference threshold and an enhanced interference threshold, and configures initial, medium, and enhanced calibration coefficients. When the stability deviation index is lower than the basic interference threshold, the initial calibration coefficient is used; when the stability deviation index is between the basic and enhanced interference thresholds, the medium calibration coefficient is used; and when the stability deviation index exceeds the enhanced interference threshold, the enhanced calibration coefficient is used. Then, the calibration coefficients are multiplied by the power consumption control parameters to obtain the target adjustment value. A preset parameter mapping rule is matched to determine the expected transmission rate corresponding to the target adjustment value, and the target adjustment value and the expected transmission rate are integrated to construct the dynamic balancing strategy table.

[0030] The execution control module triggers the transmission rate switching operation of the headphone chip according to the dynamic balance strategy table, thereby realizing dynamic balance control of Bluetooth headphone power consumption and transmission latency.

[0031] The technical solution of the present invention will be further described in detail below with reference to specific embodiments.

[0032] Example 1: This embodiment supplements the system by including a data packet comparison unit, a timestamp marking unit, an interval detection unit, a transmission suppression unit, and a channel allocation unit. This forms a complete data packet collision detection and handling mechanism. Through precise identification of data packet characteristics, accurate control of time intervals, and intelligent allocation of transmission channels, this mechanism effectively prevents and handles data collisions, further optimizing system performance.

[0033] After the system starts, the data packet comparison unit is activated first. This unit uses feature-encoding recognition technology to extract features from each data packet entering the system. This feature extraction is not just simple format matching, but delves into the encoding structure of the data packet to extract key transmission features. For example, for audio data packets, features such as audio encoding format, sampling rate, and bit rate are extracted. Simultaneously, the system maintains a vectorized database of historical transmission conflict events and their handling strategies. This database stores a large amount of historical data and processing experience, providing a basis for subsequent comparisons and decisions.

[0034] When a new data packet needs to be transmitted, the data packet comparison unit compares the format of the currently transmitted data packet with the format of the previous transmission. This comparison is based on the similarity calculation of feature vectors to determine whether the two belong to the same type of encoding. During the comparison process, the system considers various factors, such as subtle differences in encoding algorithms and changes in data packet header information, to ensure the accuracy of the comparison results.

[0035] If the encoding is determined to be of the same type, the timestamp unit immediately records the start time of the current transmission and the completion time of the previous transmission. These two timestamps are crucial, providing an accurate data foundation for subsequent interval detection. The timestamp unit employs a high-precision clock system to ensure that the recorded time is accurate to the microsecond level, meeting the demands of high-speed data transmission.

[0036] After receiving the timestamp information, the interval detection unit calculates the time difference between two transmissions. This difference reflects the time distance between two transmissions of the same type of data packets. Then, the interval detection unit compares the result with a preset transmission interval threshold to determine whether the current transmission is allowed. The preset transmission interval threshold is determined based on a combination of factors, including system hardware performance, transmission protocol, and actual application scenario. It represents the minimum time interval at which the system can stably handle continuous transmissions of the same type of data packets.

[0037] If the time interval difference is less than a preset transmission interval threshold, the transmission suppression unit will intervene immediately. This unit will delay the current transmission and generate a data conflict record. The transmission delay time is dynamically adjusted according to the system load and data priority to ensure orderly data transmission. The generated data conflict record contains information such as the time of the conflict, the characteristics of the involved data packets, and the time interval difference. These records will be stored in the system log to provide data support for subsequent analysis and optimization.

[0038] If the encoding type comparison results are inconsistent, the channel allocation unit will assign an independent transmission channel to perform the current audio data transmission. The allocation of an independent transmission channel is intelligently selected based on factors such as channel load and transmission quality. The system monitors the status of each transmission channel in real time and selects the optimal channel to ensure reliable data transmission. Simultaneously, the channel allocation unit also considers interference between channels to avoid mutual interference between different types of data packets transmitted on adjacent channels.

[0039] When a data conflict is detected, the packet comparison unit retrieves an appropriate handling solution from the policy database through feature matching. The policy database stores handling strategies for different types of data conflicts, which have been derived through extensive experimentation and optimization. Through feature matching, the system can quickly find the most suitable handling solution for the current conflict situation and generate a transmission priority adjustment instruction. This instruction adjusts the transmission priority of different data packets based on factors such as packet importance and real-time requirements, ensuring that important data is transmitted first, thereby improving overall system performance and user experience.

[0040] During actual operation, the data packet comparison unit continuously extracts and compares features of incoming data packets. The timestamp unit records transmission time in real time, and the interval detection unit continuously calculates the time interval difference and performs threshold comparisons. The transmission suppression unit and channel allocation unit take corresponding measures based on the detection results to ensure the accuracy and stability of data transmission. The entire process forms a closed-loop control system that continuously monitors and adjusts the transmission of data packets.

[0041] Simultaneously, the system periodically analyzes historical data conflict records to summarize the patterns and characteristics of conflict occurrences. Based on the analysis results, the system optimizes and adjusts preset transmission interval thresholds and processing strategies in the policy database to adapt to the ever-changing transmission environment and user needs. This self-learning and optimization capability enables the system to continuously improve performance during long-term operation, better achieving a balance between dynamic power consumption and transmission latency control in Bluetooth headsets.

[0042] In addition, the system has a fault-tolerance mechanism. When a unit fails, the system can automatically switch to a backup unit to ensure the normal operation of the entire conflict detection and handling mechanism. At the same time, the system records fault information for subsequent maintenance and repair.

[0043] The packet comparison unit, timestamp marking unit, interval detection unit, transmission suppression unit, and channel allocation unit communicate and exchange data via a high-speed data bus. This communication method ensures fast and reliable data transmission between the various units, enabling the entire collision detection and processing process to operate efficiently.

[0044] This mechanism also works closely with other modules of the system. For example, it shares data packet characteristic information with the status acquisition module, jointly formulates transmission strategies with the strategy generation module, and collaborates with the parameter adjustment module to optimize system parameters. This inter-module collaboration makes the entire Bluetooth headset dynamic power consumption and transmission latency balance control system an organic whole, achieving a comprehensive improvement in system performance.

[0045] Example 2: The factor analysis module accurately identifies and quantifies various factors affecting transmission rate fluctuations, providing a reliable basis for subsequent system strategy adjustments. This process involves multi-dimensional data processing and logical judgment to ensure that the system can accurately capture key variables affecting transmission performance.

[0046] After the system starts, the factor analysis module first arranges all rate fluctuation-related variables into a detection queue according to time series. These variables cover data from multiple dimensions such as signal strength, environmental interference, and chip operating status, and their order on the time axis implies important causal relationships. For example, a sudden drop in signal strength may lead to subsequent fluctuations in transmission rate, and such fluctuations may be related to changes in environmental interference factors.

[0047] After forming the detection queue, the system randomly selects adjacent variable groups. This random selection helps avoid human selection bias and ensures the objectivity of the analysis results. For each group of adjacent variables, the system obtains its corresponding transmission rate reading and subsequent transmission rate readings. Obtaining these two readings is crucial, as they form the basis for calculating the absolute value of the rate difference. By comparing the changes in transmission rates corresponding to adjacent variable groups, the degree of correlation between the variables can be preliminarily determined.

[0048] After calculating the absolute value of the rate difference, the system identifies the rate peaks and valleys in the entire transmitted data. This step requires traversing and analyzing a large amount of historical transmission data. Rate peaks and valleys represent extreme cases of transmission rate, and their occurrence is often related to specific interference factors or changes in system state. By determining these two extreme values, the system can calculate the overall rate fluctuation range, thereby understanding the magnitude of transmission rate variation across the entire dataset.

[0049] Next, the ratio of the absolute value of the difference to the fluctuation range is calculated. This ratio reflects the relative importance of the rate changes of adjacent variable groups in the overall fluctuation. If a certain ratio is large, it indicates that the change of that variable group has a significant impact on the overall transmission rate; conversely, the impact is smaller. This step provides an important reference for subsequently determining the core interference factor.

[0050] When calculating the interference correlation offset between variable groups, the system considers the interactions between the variables. These interactions can be direct or indirect. For example, changes in signal strength may directly affect the transmission rate, while changes in ambient temperature may indirectly affect the transmission rate by influencing chip performance. By analyzing this correlation offset, the system can gain a deeper understanding of the complex relationships between variables.

[0051] After determining the maximum and minimum correlation among all variables, the system calculates the global correlation range. This range reflects the degree of difference in the correlation between all variables. A larger range indicates greater variation in the correlation between variables, requiring a more detailed analysis of the impact of each variable; a smaller range indicates a more uniform correlation between variables.

[0052] The ratio of the interference correlation offset to the global correlation variation range is calculated. This ratio reflects the relative position of the current variable group's interference correlation offset within the global variation. Multiplying the rate ratio by the correlation ratio and normalizing the result yields the real-time interference coefficient. This coefficient comprehensively considers both rate and correlation variations, providing a more accurate reflection of the interference level of the variable group.

[0053] During the process of generating the auxiliary interference factor set by traversing the remaining variable groups, the system performs the same analysis and calculations on each variable group. In this way, the system can comprehensively understand the impact of all variable groups on the transmission rate, thus forming a complete auxiliary interference factor set.

[0054] Calculating the median of all real-time interference coefficients as the core interference factor is a crucial step. Selecting the median avoids the influence of extreme values, making the determination of the core interference factor more reasonable and stable. Extreme values ​​may be caused by random factors or data noise; if the average value is used directly as the core interference factor, these extreme values ​​may introduce significant bias into the results. The median, however, better represents the central tendency of the data, improving the reliability of the core interference factor.

[0055] During actual operation, the factor analysis module continuously monitors and analyzes the transmitted data. As new data continuously enters the system, the detection queue is constantly updated, and the system calculates new real-time interference coefficients and core interference factors in real time. This real-time capability enables the system to respond quickly to changes in the transmission environment and adjust control strategies promptly.

[0056] This module also possesses self-learning capabilities. Through the analysis and summarization of a large amount of historical data, the system can gradually optimize calculation methods and parameter settings, improving the accuracy of interference factor identification and analysis efficiency. For example, the system can automatically adjust the weight parameters for calculating the real-time interference coefficient according to different usage scenarios and environmental conditions to adapt to different transmission requirements.

[0057] The factor analysis module collaborates closely with other modules in the system. It transmits the identified core and auxiliary interference factors to the quantitative evaluation module, providing data support for subsequent stability assessments. Simultaneously, it receives feedback from other modules and adjusts the analysis methods and parameters based on the system's actual operation.

[0058] To ensure the accuracy of the analysis results, the system employs multiple data verification mechanisms. During the data acquisition phase, the data obtained from the sensors is filtered and calibrated to remove noise and outliers. During the data analysis phase, multiple analysis methods are used to verify the same set of data, ensuring the consistency and reliability of the results.

[0059] The factor analysis module also features data visualization capabilities. It can display the analysis results in intuitive charts and graphs, making it easy for system administrators and developers to view and analyze. Through visualization, they can more clearly understand the relationships between various variables and their impact on transmission rates, thus enabling targeted system optimization.

[0060] The algorithm design of this module considers a balance between computational efficiency and resource consumption. While ensuring analytical accuracy, it employs simple and efficient algorithms to reduce the system's computational burden and energy consumption. This is especially important for power-sensitive devices like Bluetooth headsets.

[0061] When faced with complex and ever-changing transmission environments, the factor analysis module can adaptively adjust its analysis strategy. For example, when numerous and complex interference factors are detected in the transmission environment, the system increases the sampling frequency and analysis depth to more accurately identify key interference factors; when the transmission environment is relatively stable, the system appropriately reduces the analysis frequency to save system resources.

[0062] The factor analysis module, through precise data acquisition, scientific algorithm analysis, and reasonable parameter settings, effectively identifies and quantifies the variables related to Bluetooth headset transmission rate fluctuations. This module provides crucial decision-making support for the entire control system, enabling it to dynamically adjust its operating strategy based on different interference conditions. This ensures transmission quality while optimizing power consumption, ultimately achieving a balance between dynamic power consumption and transmission latency in the Bluetooth headset.

[0063] Example 3: The quantitative evaluation module accurately assesses the stability of the transmission link. This process involves classifying and screening core interference factors, constructing interference comparison groups, and calculating the stability deviation index, ensuring that the system can accurately capture changes in the transmission environment and respond promptly.

[0064] After the system starts, the quantitative evaluation module first sets the upper and lower warning values ​​for the core interference factors. These two warning values ​​are based on extensive experimental data and practical application experience, representing the boundaries of the interference factor range within which the system can operate stably. The upper warning value is a high threshold; when the core interference factor exceeds this value, the system's stability will be severely threatened. The lower warning value is a low threshold; interference factors below this value have a smaller impact on system stability.

[0065] Based on the set upper warning value, the system filters out a set of high interference factors. These high interference factors refer to core interference factors whose values ​​exceed the upper warning value, posing a significant potential threat to the stability of the transmission link. Similarly, based on the lower warning value, the system filters out a set of low interference factors whose values ​​are below the lower warning value, having a relatively smaller impact on system stability.

[0066] Next, the positive offset between the high interference factors and the upper warning value is calculated. This offset reflects the degree to which each high interference factor exceeds the upper warning value. These positive offsets are arranged in descending order to form a high-order interference queue. The earlier an element appears in the high-order interference queue, the greater the threat that high interference factor poses to system stability.

[0067] Simultaneously, the negative offset between the low-interference factor and the lower warning limit is calculated. The negative offset indicates the degree to which the low-interference factor falls below the lower warning limit. These negative offsets are arranged in ascending order to form a low-order interference queue. The earlier an element appears in the low-order interference queue, the greater its contribution to system stability.

[0068] The elements of the high-order and low-order queues are combined item by item to form interference comparison groups. Each interference comparison group contains a high interference factor and a low interference factor, and their combination can reflect the stability changes of the system when facing different levels of interference. Based on all interference comparison groups, the system begins to calculate the stability deviation index.

[0069] The total number of interference comparison groups is counted, and the squared difference between the highest and lowest elements in each group is calculated. The squared difference reflects the degree of difference between high and low interference factors in each group. The greater the difference, the more the system's stability may be affected when facing this combination of interference.

[0070] Identify the minimum and maximum values ​​among all squared differences, and calculate the dispersion parameter of the distribution of all squared differences. The dispersion parameter reflects the distribution of squared differences across the entire dataset, and it is of great significance for understanding the range of fluctuations in system stability.

[0071] Finally, the stability deviation index is calculated based on the minimum, maximum, and distribution dispersion parameters. The formula for calculating the stability deviation index is:

[0072] in, Indicates the stability deviation index. Indicates the first The squared difference of each interference control group This represents the minimum value among the squares of all differences. This represents the maximum value among all squared differences. This represents the total number of interference control groups. This index, through normalization, maps stability deviations to a range of 0% to 100%, facilitating intuitive judgment and decision-making within the system.

[0073] During actual operation, the quantitative evaluation module continuously monitors and analyzes the core interference factors. Whenever new interference factor data is received, the system immediately updates the high and low interference factor sets and recalculates the stability deviation index. This real-time capability enables the system to promptly detect potential instability factors in the transmission link and take corresponding measures to adjust them.

[0074] The system also analyzes the trend of the stability deviation index. If the index continues to rise, it indicates that the stability of the transmission link is gradually decreasing, and the system needs to take more proactive measures to deal with interference; if the index remains at a low level and fluctuates little, it indicates that the transmission link is in a relatively stable state, and the system can maintain its current operating mode.

[0075] The quantitative evaluation module collaborates closely with other modules of the system. It transmits the calculated stability deviation index to the parameter adjustment module in real time. The parameter adjustment module uses this index to determine the calibration coefficients of the correlation strategy table, thereby dynamically adjusting the system's operating parameters. Simultaneously, the quantitative evaluation module also receives real-time data from the status acquisition module, ensuring the accuracy and reliability of the analysis results.

[0076] To improve the accuracy of the assessment, the system employs multiple data verification mechanisms. During the data acquisition phase, the interference factor data acquired by the sensors is filtered and calibrated to remove noise and outliers. During the data analysis phase, various statistical methods are used to verify the same set of data, ensuring the consistency and reliability of the results.

[0077] This module also features adaptive adjustment capabilities. As the system operates and the environment changes, the quantitative assessment module automatically adjusts the upper and lower warning values ​​based on historical data and actual operating conditions. This adaptive adjustment allows the system to better adapt to different usage scenarios and environmental conditions, improving the accuracy and effectiveness of the assessment.

[0078] The algorithm design of the quantitative evaluation module considers a balance between computational efficiency and resource consumption. While ensuring evaluation accuracy, it strives to use simple and efficient algorithms to reduce the system's computational burden and energy consumption. This is especially important for power-sensitive devices like Bluetooth headsets.

[0079] When faced with complex and ever-changing transmission environments, the quantification and evaluation module can adaptively adjust its analysis strategy. For example, when numerous and complex interference factors are detected in the transmission environment, the system increases the sampling frequency and analysis depth to more accurately assess the stability of the transmission link; when the transmission environment is relatively stable, the system appropriately reduces the analysis frequency to save system resources.

[0080] The quantitative evaluation module, through precise data acquisition, scientific algorithm analysis, and reasonable parameter settings, effectively assesses the stability of the Bluetooth headset transmission link. This module provides crucial decision-making support for the entire control system, enabling it to dynamically adjust its operating strategy based on different interference conditions. This ensures transmission quality while optimizing power consumption, ultimately achieving a balance between dynamic power consumption and transmission latency for the Bluetooth headset.

[0081] Example 4: The parameter adjustment module calibrates and updates the correlation strategy table based on the stability deviation index, forming a dynamic balance strategy table, thereby achieving precise control over the power consumption and transmission latency of the Bluetooth headset. This process involves multiple steps, including threshold setting, coefficient configuration, parameter adjustment, and strategy integration, ensuring that the system can make optimal decisions based on real-time interference conditions.

[0082] After system startup, the parameter adjustment module first sets the basic interference threshold and the enhanced interference threshold. These two thresholds are determined based on the system's design requirements and the actual application scenario. The basic interference threshold represents the upper limit of the interference level under which the system can operate normally. When the stability deviation index is below this threshold, the system considers the interference level to be relatively small and can adopt a more conservative adjustment strategy. The enhanced interference threshold is a higher threshold. When the stability deviation index exceeds this threshold, the system considers the interference level to be relatively large and requires more aggressive adjustment measures.

[0083] Three calibration coefficients—initial, medium, and enhanced—are configured, each corresponding to different interference levels. The initial calibration coefficient is used for low interference levels, the medium coefficient for moderate interference levels, and the enhanced coefficient for high interference levels. These calibration coefficients were obtained through extensive experimentation and optimization, accurately reflecting the adjustment requirements of system parameters under different interference levels.

[0084] After the quantitative evaluation module calculates the stability deviation index, the parameter adjustment module compares this index with the basic interference threshold and the enhanced interference threshold. If the stability deviation index is lower than the basic interference threshold, it indicates that the system is experiencing less interference. In this case, the initial calibration coefficient is used to calibrate the correlation strategy table. For example, when Bluetooth headphones are used in a quiet indoor environment, the surrounding electromagnetic interference is low, resulting in a low stability deviation index. The system will use the initial calibration coefficient for fine-tuning to maintain low power consumption and stable transmission latency.

[0085] If the stability deviation index is between the base and enhanced interference thresholds, it indicates that the system is experiencing moderate interference, and a medium calibration coefficient is used for calibration. Taking the use of Bluetooth headsets in an office environment as an example, there may be electromagnetic interference from devices such as computers and routers in the vicinity. The stability deviation index is at a medium level, and the system will use a medium calibration coefficient to adjust the association strategy table to balance power consumption and transmission latency.

[0086] When the stability deviation index exceeds the enhanced interference threshold, it indicates that the system is experiencing significant interference, requiring a substantial adjustment to the correlation strategy table using enhanced calibration coefficients. For example, in complex electromagnetic environments such as subway stations, Bluetooth headsets may be subject to substantial electromagnetic interference, resulting in a high stability deviation index. In such cases, the system will activate enhanced calibration coefficients to more aggressively adjust transmission rate and power consumption control parameters to ensure data transmission stability.

[0087] The target adjustment value is obtained by multiplying the calibration coefficient by the power consumption control parameter. The power consumption control parameter is a predefined parameter in the association strategy table used to control the power consumption level of the Bluetooth headset. By multiplying by the calibration coefficient, the system can dynamically adjust the power consumption level according to the current interference situation. For example, in an environment with less interference, the system can appropriately reduce power consumption to extend the headset's battery life; while in an environment with more interference, the system will increase power consumption to improve transmission stability.

[0088] The system matches preset parameter mapping rules to determine the expected transmission rate corresponding to the target adjustment value. These preset parameter mapping rules are pre-defined based on the Bluetooth headset's hardware performance and transmission protocol, establishing a correspondence between power consumption adjustment values ​​and transmission rates. Through these rules, the system can convert the target adjustment value into a specific transmission rate, ensuring that adjusting power consumption does not excessively impact transmission quality.

[0089] A dynamic balancing strategy table is constructed by integrating the target adjustment value and the expected transmission rate. This table is the core decision-making basis of the system, containing the optimal combination of operating parameters under different interference conditions. For example, when the stability deviation index is low, the strategy table may instruct the system to reduce the transmission rate to decrease power consumption; while when the stability deviation index is high, the strategy table will require the system to increase the transmission rate to ensure reliable data transmission, even if this increases power consumption.

[0090] During actual operation, the parameter adjustment module continuously monitors changes in the stability deviation index and adjusts the dynamic balancing strategy table in real time based on these changes. For example, when a user moves from a quiet indoor environment to a noisy outdoor environment, the stability deviation index will increase accordingly, and the system will automatically adjust the strategy table to improve transmission power and anti-interference capabilities to ensure audio transmission quality.

[0091] The system also records historical data for each parameter adjustment, including indicators such as stability deviation index, power consumption level, and transmission latency before and after the adjustment. By analyzing this historical data, the system can gradually learn and optimize parameter adjustment strategies, improving the effectiveness of dynamic balancing. For example, the system may discover that a certain combination of parameters can achieve better balancing results under specific interference environments, and thus prioritize using that combination when encountering similar situations in the future.

[0092] The parameter adjustment module works closely with other modules of the system to form a complete closed-loop control system. The status acquisition module is responsible for collecting real-time operating status and environmental data of the headphones. The factor analysis module and the quantitative evaluation module process and analyze this data to obtain a stability deviation index. The parameter adjustment module adjusts the strategy table according to this index, and finally, the execution control module adjusts the operating parameters of the headphones according to the updated strategy table. This collaboration ensures that the system can respond quickly to environmental changes and always maintain optimal operating conditions.

[0093] To cope with potential sudden interference, the parameter adjustment module also has an emergency handling mechanism. When a sudden and significant increase in the stability deviation index is detected, the system will immediately activate the emergency plan, temporarily increasing the transmission power and reducing the data rate to ensure communication continuity. Simultaneously, the system will record the characteristics and handling process of this sudden interference for subsequent analysis and optimization.

[0094] The parameter adjustment module achieves dynamic balance control of Bluetooth headset power consumption and transmission latency through precise threshold settings, tiered calibration coefficients, and intelligent parameter mapping rules. This module can automatically adjust operating parameters based on real-time interference conditions, minimizing power consumption while ensuring audio transmission quality, thus providing users with a better experience.

[0095] Example 5: The status acquisition module achieves comprehensive perception of the transmission environment and intelligent optimization of the operating mode by constructing a three-dimensional transmission quality field model and combining it with a genetic algorithm. This process involves multi-dimensional data aggregation, spatial modeling, intelligent algorithm application, and a hierarchical response mechanism to ensure that the system can operate efficiently in complex and ever-changing environments.

[0096] After system startup, the status acquisition module first aggregates signal strength data, chip power consumption data, and environmental interference data in real time. Signal strength data, sourced from the Bluetooth headset's antenna receiver module, reflects the wireless connection quality between the headset and the audio source device. Chip power consumption data is obtained by monitoring the operating current and voltage of each chip within the headset, providing a clear picture of the headset's power consumption status. Environmental interference data is collected through multi-band sensors, including ambient electromagnetic interference, Bluetooth device density, and interference from other wireless signals.

[0097] Based on this real-time collected data, the system constructs a three-dimensional transmission quality field model. This model uses spatial coordinates as the horizontal and vertical axes, and the transmission quality assessment value as the vertical axis, forming a three-dimensional quality distribution field. In this three-dimensional model, each point represents the transmission quality status at a specific spatial location and time point. For example, in an office environment, locations near the router may experience lower transmission quality due to Wi-Fi signal interference, while locations in corners away from electronic devices may have higher transmission quality.

[0098] Using this three-dimensional transmission quality field model, the system can calculate the effective transmission coverage area. The effective transmission coverage area refers to the spatial range within which the headphones can operate stably while meeting certain transmission quality requirements. During the calculation process, the system delineates the boundaries of the required areas in the three-dimensional model according to preset transmission quality standards. For example, if a transmission success rate of no less than 95% is set as the standard, the system will find all spatial points in the three-dimensional model that meet this condition, thereby determining the effective transmission coverage area.

[0099] When the predicted transmission quality falls below a set standard, the system activates an anti-interference coding mode. Anti-interference coding mode is a technique that improves data transmission reliability by adding redundant information. For example, in Bluetooth audio transmission, the system might employ forward error correction (FEC) coding, adding extra check bits to the original data, allowing the receiver to correct errors that occur during transmission to a certain extent. The activation of this mode can be controlled in stages according to the degree of transmission quality degradation to balance power consumption and anti-interference capability.

[0100] Simultaneously, the system uses a genetic algorithm to simulate and generate latency / power consumption matrices under different combinations of operating modes. A genetic algorithm is an optimization algorithm that simulates natural selection and genetic mechanisms. It searches for the optimal solution step-by-step by encoding, selecting, crossovering, and mutating candidate solutions. In this process, the system uses operating parameters such as transmission power, encoding method, and data rate as genes, iteratively generating various possible combinations of operating modes. For each combination, the system evaluates its corresponding transmission latency and power consumption, thereby constructing the latency / power consumption matrix.

[0101] Based on this matrix, the system generates an optimized equilibrium solution set. This set contains a series of optimal combinations of operating modes under different constraints. For example, under low-power constraints, the system identifies the lowest-power operating mode that meets basic transmission requirements; while under low-latency constraints, the system selects the operating mode that achieves the fastest data transmission. These solutions provide a rich selection space for subsequent dynamic adjustments.

[0102] The system employs a three-level response mechanism based on the degree of transmission quality degradation. Level 1 response corresponds to a slight degradation in transmission quality, in which case the system only activates basic coding compensation. Basic coding compensation is a lightweight anti-interference measure, such as adding simple parity bits or using lower-strength forward error correction coding. This measure has minimal impact on power consumption but can improve transmission reliability to some extent.

[0103] When transmission quality degrades to a certain level, the system enters a secondary response phase, switching to an enhanced anti-interference protocol. This enhanced protocol may include more complex coding methods, adaptive frequency hopping techniques, or additional retransmission mechanisms. These measures effectively improve transmission stability, but will correspondingly increase power consumption and transmission latency.

[0104] In the event of a significant degradation in transmission quality, the system initiates a three-level response: dual-channel redundant transmission. Dual-channel redundant transmission refers to using two independent transmission channels simultaneously for data transmission. The receiving end compares the data from the two channels to improve reception accuracy. This method greatly improves transmission reliability but significantly increases power consumption and system resource usage.

[0105] During actual operation, the status acquisition module continuously updates the three-dimensional transmission quality field model. For example, when a user moves from indoors to outdoors, the surrounding electromagnetic environment changes, and the signal strength and interference level also change accordingly. The system collects these changing data in real time and updates the three-dimensional model, thereby dynamically adjusting the effective transmission coverage area and anti-interference strategies.

[0106] The genetic algorithm also continuously optimizes itself during system operation. As new data accumulates, the algorithm adjusts and improves the combination of working modes based on the actual transmission effect. For example, if a certain working mode is found to perform better than expected in a specific environment, the algorithm will increase the weight of that mode in the solution set; conversely, if a certain mode performs poorly, its weight will be reduced.

[0107] The triggering conditions for the Level 3 response mechanism will also be adaptively adjusted based on actual usage. The system records changes in transmission quality and the effectiveness of the response mechanism under different environments, analyzes this data using machine learning algorithms, and gradually optimizes the triggering threshold. For example, in environments where brief, strong interference frequently occurs, the system may appropriately raise the triggering threshold for Level 2 response to avoid the additional overhead caused by frequent switching of operating modes.

[0108] The status acquisition module maintains close collaboration with other modules in the system. For example, it shares data from the 3D transport mass field model with the factor analysis module in real time, helping it to more accurately identify interference factors; at the same time, it also receives feedback from the parameter adjustment module and optimizes the frequency and method of data acquisition based on the adjusted strategy.

[0109] To ensure system reliability, the status acquisition module also features data verification and backup mechanisms. During data acquisition, the system verifies the data obtained by the sensors multiple times to ensure accuracy. Simultaneously, important data is backed up to the headset's storage module to prevent data loss or transmission interruption.

[0110] The implementation of the state acquisition module achieves comprehensive perception of the Bluetooth headset's transmission environment and intelligent optimization of its operating mode by constructing a three-dimensional transmission quality field model and applying a genetic algorithm. A three-level response mechanism ensures that the system can take appropriate measures based on different levels of interference, minimizing power consumption while maintaining transmission quality. This comprehensive optimization method enables the Bluetooth headset to maintain good performance in various complex environments.

[0111] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0112] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A dynamic power consumption and transmission delay balance control system for Bluetooth headsets, characterized in that, include: The status acquisition module is used to acquire the real-time operating current data of the headphones and the status of audio transmission data packets, and to build a transmission link quality assessment model. The strategy generation module is used to generate an initial correlation strategy table between transmission rate and power consumption based on the evaluation model, wherein the strategy table includes a transmission rate threshold and power consumption control parameters. The factor analysis module is used to analyze historical transmission record data, identify variables related to rate fluctuations, and classify core interference factors and auxiliary interference factors. The quantitative evaluation module is used to calculate the stability deviation index of the transmission link based on the core interference factor and the auxiliary interference factor. The parameter adjustment module is used to determine the calibration coefficient of the correlation strategy table based on the stability deviation index, and update the correlation strategy table according to the calibration coefficient to form a dynamic balance strategy table. The execution control module is used to trigger the transmission rate switching operation of the headphone chip according to the dynamic balancing strategy table.

2. The Bluetooth headset dynamic power consumption and transmission delay balance control system according to claim 1, characterized in that, Also includes: The data packet comparison unit is used to compare the current transmitted data packet format with the previous transmitted format to determine whether the two belong to the same type of encoding. The timestamp unit is used to record the start time of the current transmission and the completion time of the previous transmission if the encoding is determined to be of the same type. An interval detection unit is used to calculate the time interval difference between two transmissions and compare it with a preset transmission interval threshold to determine whether the current transmission is allowed to proceed. A transmission suppression unit is used to delay the current transmission and generate a data conflict record if the time interval difference is less than the preset transmission interval threshold. The channel allocation unit is used to allocate an independent transmission channel to perform the current audio data transmission if the encoding type comparison results are inconsistent.

3. The Bluetooth headset dynamic power consumption and transmission delay balance control system according to claim 1, characterized in that, The factor analysis module includes: All rate fluctuation-related variables are arranged in a time series to form a detection queue. Adjacent variable groups are randomly selected to obtain the corresponding transmission rate readings and subsequent transmission rate readings, and the absolute value of the difference in the rates is calculated. Identify the peak and valley rates in the full transmitted data and calculate the overall rate fluctuation range; The ratio of the absolute value of the difference to the fluctuation range is calculated and denoted as the rate ratio. Calculate the interference correlation offset between the variable groups; Determine the maximum and minimum correlation among all variables, and calculate the range of global correlation variation; The ratio of the interference correlation offset to the range of global correlation variation is calculated and denoted as the correlation ratio value. Multiply the rate ratio by the correlation ratio and normalize the result to obtain the real-time interference coefficient. The remaining variable groups are traversed to generate a set of auxiliary interference factors, and the median value of all real-time interference coefficients is calculated as the core interference factor.

4. The Bluetooth headset dynamic power consumption and transmission delay balance control system according to claim 1, characterized in that, The quantitative evaluation module includes: Set upper and lower warning values ​​for the core interference factor; The set of high interference factors is selected based on the upper limit warning value, and the set of low interference factors is selected based on the lower limit warning value. Calculate the positive offset of the high interference factor from the upper limit warning value, and arrange them in reverse order according to the offset to form a high interference queue; Calculate the negative offset between the low interference factor and the lower limit warning value, and arrange them in ascending order of offset magnitude to form a low interference queue; The elements of the high-order queue and the low-order queue are combined one by one to form an interference comparison group, and the stability deviation index is calculated based on all interference comparison groups.

5. The Bluetooth headset dynamic power consumption and transmission delay balance control system according to claim 4, characterized in that, The quantitative evaluation module also includes: The total number of interference comparison groups is counted, and the squared difference between the highest and lowest elements in each group is calculated. Identify the minimum and maximum values ​​among all squared differences, and calculate the distribution dispersion parameter of all squared differences; The stability deviation index is calculated based on the minimum, maximum, and distribution dispersion parameters.

6. The Bluetooth headset dynamic power consumption and transmission delay balance control system according to claim 1, characterized in that, The parameter adjustment module includes: Set the basic interference threshold and the enhanced interference threshold; Configure initial calibration coefficients, intermediate calibration coefficients, and enhanced calibration coefficients; When the stability deviation index is lower than the basic interference threshold, the initial calibration coefficient is used; when the stability deviation index is between the basic and enhanced interference thresholds, the medium calibration coefficient is used; and when the stability deviation index exceeds the enhanced interference threshold, the enhanced calibration coefficient is used.

7. The Bluetooth headset dynamic power consumption and transmission delay balance control system according to claim 1, characterized in that, The parameter adjustment module further includes: The target adjustment value is obtained by multiplying the calibration coefficient by the power consumption control parameter; The expected transmission rate corresponding to the target adjustment value is determined by matching a preset parameter mapping rule. The dynamic balancing strategy table is constructed by integrating the target adjustment value with the expected transmission rate.

8. The Bluetooth headset dynamic power consumption and transmission delay balance control system according to claim 1, characterized in that, The status acquisition module also includes: Real-time aggregation of signal strength data, chip power consumption data, and environmental interference data is used to construct a three-dimensional transmission quality field model. The effective transmission coverage area is calculated based on the three-dimensional transmission quality field model. When the predicted transmission quality is lower than the set standard, the anti-interference coding mode is activated. The latency / power consumption relationship matrix under different combinations of working modes is generated by simulating using a genetic algorithm, and an optimized balanced solution set is generated.

9. The Bluetooth headset dynamic power consumption and transmission delay balance control system according to claim 8, characterized in that, Based on the degree of transmission quality degradation, a three-level response mechanism is established: Level 1 activates basic coding compensation, Level 2 switches to enhanced anti-interference protocols, and Level 3 initiates dual-channel redundant transmission.

10. The Bluetooth headset dynamic power consumption and transmission delay balance control system according to claim 2, characterized in that, The data packet comparison unit includes: extracting key transmission features from non-standard data packets using feature encoding recognition technology, storing a vectorized database of historical transmission conflict events and their handling strategies, and when a data conflict is detected, retrieving an appropriate handling scheme from the strategy database through feature matching and generating a transmission priority adjustment instruction.

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