A method for improving earphone audio experience and device endurance

Abstract

This application provides a method for improving headphone audio experience and device battery life, comprising: acquiring high-frequency energy loss data from the headphone amplifier circuit, comparing it with a preset threshold, and determining whether material parameters need to be optimized; performing performance simulation on core circuit components based on the comparison result to determine the optimized material parameter configuration; acquiring circuit dynamic response data based on the material parameter configuration, analyzing fluctuation characteristics, and determining a response speed improvement scheme; adjusting high-frequency performance test signals based on the circuit stability enhancement model to determine a structural design correction path; acquiring audio purity sampling data through the structural design correction path, determining the distortion ratio, and obtaining sound quality improvement verification results; processing battery life simulation input based on the sound quality improvement verification results to determine a power consumption reduction configuration; and acquiring overall audio experience feedback through the power consumption reduction configuration, determining a balance index, and determining the final circuit deployment scheme.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to a method for improving headphone audio experience and device battery life. Background Technology

[0002] In the modern audio equipment field, sound quality and battery life have become key indicators for measuring product competitiveness. As users' demands for high-fidelity sound and portability continue to increase, the design of audio power amplifier circuits directly affects the overall performance of headphones, especially in applications such as professional monitoring and intelligent noise cancellation, where its importance is self-evident. How to achieve a balance between high efficiency and high sound quality within limited space has become a key area that the industry urgently needs to address.

[0003] However, current mainstream headphone amplifier circuits exhibit significant shortcomings when dealing with complex audio demands. Existing technologies often struggle to find a balance between power consumption control and signal processing accuracy, especially when handling wide-range dynamic audio signals, where circuit response speed and energy efficiency are often mutually exclusive. This limitation not only affects headphone usage time but also restricts the finesse of sound effects, significantly diminishing the user experience.

[0004] At a deeper level, the root of the problem lies in the reliance on component performance in circuit design. Traditional materials exhibit significant energy loss at high frequencies, leading to inefficiencies in circuits when processing rapidly changing audio signals. This inefficiency further exacerbates heat generation, forcing designers to add heat dissipation structures or reduce performance to maintain stability. This vicious cycle makes it difficult for headphones to maintain signal integrity and purity when driving high-impedance devices or implementing complex functions.

[0005] Therefore, optimizing the performance of core circuit components in a miniature device like headphones while simultaneously resolving the conflict between energy loss and signal distortion has become a key issue in improving the audio experience and battery life. For example, in everyday use, when a user wears headphones to enjoy high-resolution music, if the amplifier circuit cannot quickly respond to sudden volume changes or high / low frequency transitions, the sound will become blurry or intermittent, directly impacting the immersive experience. Summary of the Invention

[0006] This invention provides a method for improving headphone audio experience and device battery life, mainly including:

[0007] High-frequency energy loss data is obtained from the headphone amplifier circuit and compared with a preset threshold to determine whether material parameters need optimization. Based on the comparison results, performance simulations are performed on the core components of the circuit to determine the optimized material parameter configuration. Based on the material parameter configuration, dynamic response data of the circuit is obtained, fluctuation characteristics are analyzed, and a response speed improvement scheme is determined. Through the response speed improvement scheme, energy utilization indicators are integrated, heat generation control variables are optimized, and a circuit stability enhancement model is constructed. Based on the circuit stability enhancement model, high-frequency performance test signals are adjusted, signal accuracy is analyzed, and a structural design correction path is determined. Through the structural design correction path, audio purity sampling data is obtained, the distortion ratio is determined, and sound quality improvement verification results are obtained. Based on the sound quality improvement verification results, simulated input of battery life is processed to determine the power consumption reduction configuration. Through the power consumption reduction configuration, overall audio experience feedback is obtained, balance indicators are determined, and the final circuit deployment scheme is determined. Furthermore, the step of acquiring high-frequency energy loss data from the headphone amplifier circuit and comparing it with a preset threshold includes: acquiring a high-frequency electrical signal from the test node of the headphone amplifier circuit, and using a spectrum analyzer to separate signal components in a specific frequency band; calculating the power difference between the signal components at the start and end points of the transmission path to obtain an energy attenuation value, which is used as the high-frequency energy loss data; comparing the high-frequency energy loss data with a preset energy loss threshold to determine whether it exceeds the threshold; if it exceeds the threshold, triggering a performance simulation process for the core components of the circuit; if it does not exceed the threshold, recording the current data and ending the current process. Furthermore, the performance simulation of the core circuit components to determine the optimized material parameter configuration includes: establishing a finite element mesh model based on the geometry of the core circuit components, and loading a high-frequency electrical signal as an excitation source; assigning dielectric constant and magnetic permeability parameters to the traditional materials in the model, and performing electromagnetic-thermal coupling field simulation calculations; extracting temperature rise distribution and stress distribution data from the simulation results, and evaluating the improvement degree of candidate material parameters in conjunction with a material property database; determining the improvement quantification index by comparing the peak value reduction ratio of the distribution data under each candidate parameter; and iteratively adjusting the parameter combination of the candidate materials using a gradient descent algorithm until the convergence condition is met to obtain the optimized material parameter configuration. Furthermore, the step of acquiring circuit dynamic response data and analyzing fluctuation characteristics based on the material parameter configuration includes: acquiring the input excitation signal from the material parameter configuration and separating high-frequency components using a spectrum analyzer; obtaining dynamic response curve data by calculating the phase shift of the high-frequency components in the circuit path; extracting peak fluctuation characteristics from the dynamic response curve data using a convolutional neural network algorithm; determining the amplitude change trend of the peak fluctuation characteristics and comparing it with a preset range; if the amplitude change trend exceeds the preset range, triggering an impedance matching adjustment process, acquiring circuit impedance distribution data, and optimizing the curve smoothing version.Furthermore, the step of integrating energy utilization indicators and optimizing heat generation control variables through the response speed improvement scheme includes: obtaining heat generation control variables from the simulation environment, combining them with the energy utilization indicators, and using a genetic algorithm to optimize the variable combination; obtaining the optimized set of variable parameters and calculating the corresponding circuit impedance distribution data; extracting high-frequency fluctuation components as interference features from the impedance distribution data and calculating the impedance adjustment amount; if the impedance adjustment amount exceeds a preset threshold, selecting parameters strongly correlated with the interference features to form a core parameter subset; and using the core parameter subset, combined with dynamic response curve data, deriving the circuit damping coefficient and quality factor to construct the circuit stability enhancement model. Furthermore, the step of adjusting the high-frequency performance test signal and analyzing the signal accuracy based on the circuit stability enhancement model includes: extracting a high-frequency excitation signal at a preset frequency point from the circuit stability enhancement model as an initial test signal; acquiring the measured response waveform of the initial test signal at the target circuit port and calculating the initial signal accuracy index; adjusting the amplitude and phase parameters of the initial test signal using an iterative optimization algorithm to generate an adjusted test signal sequence; recalculating the signal accuracy index for the adjusted test signal sequence; and if the signal accuracy index is lower than a preset target accuracy value, analyzing the deviation source category using a support vector machine classification model to determine the structural design correction path. Furthermore, the step of obtaining audio purity sampling data and determining the distortion ratio through the structural design correction path includes: configuring the converter and amplifier parameters in the audio test environment according to the set of physical structural parameters in the structural design correction path; generating a test audio source file containing a specific frequency sweep signal; recording the output electrical signal using a high-precision audio acquisition device to obtain raw audio sampling data; performing frequency domain conversion on the raw audio sampling data to obtain a signal spectrum; extracting harmonic components and noise energy from the signal spectrum to calculate the overall distortion ratio; comparing the overall distortion ratio with a preset purity threshold to determine whether the distortion ratio has decreased, and obtaining the sound quality improvement verification result. Furthermore, the step of processing the simulated battery life input based on the sound quality improvement verification results and determining the power consumption reduction configuration includes: obtaining the simulated battery life input from the sound quality improvement verification results; calculating the battery life index using a power consumption model to obtain initial duration data; adjusting the power supply voltage and load distribution using energy allocation rules combined with a voltage regulation strategy based on the initial duration data to obtain the allocated duration; comparing the allocated duration with a preset duration benchmark; if the allocated duration exceeds the duration benchmark, determining that the power consumption optimization is effective and generating a configuration adjustment mechanism; and integrating circuit impedance and efficiency coefficients based on the configuration adjustment mechanism to determine the power consumption reduction configuration.

[0008] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0009] This invention addresses the interconnected challenges in headphone amplifier circuits, including high-frequency energy loss, response speed, circuit stability, sound quality purity, and power consumption balance, proposing an integrated optimization solution. The core challenge lies in achieving a comprehensive improvement in audio performance and energy efficiency through material parameter adjustment, dynamic response optimization, and structural design modifications. This invention detects energy loss using preset thresholds, optimizes material configuration through performance simulation, analyzes response curve fluctuations using convolutional neural networks to determine speed enhancement schemes, and employs genetic algorithms to optimize heat control variables to enhance circuit stability. Simultaneously, iteratively adjusts signal accuracy by classifying accuracy deviation sources using support vector machines, ultimately determining power consumption reduction configurations and circuit deployment schemes based on energy allocation rules and feedback loops. The most significant technical effect of this invention is that, through multi-algorithm collaboration and multi-dimensional optimization, it significantly improves the sound quality purity and battery life of headphone amplifier circuits while ensuring a balanced optimization of high-frequency performance and overall audio experience. Attached Figure Description

[0010] Figure 1 This is a flowchart of a method for improving headphone audio experience and device battery life according to the present invention;

[0011] Figure 2 This is a module framework diagram of step S102 in a method for improving headphone audio experience and device battery life according to the present invention;

[0012] Figure 3 This is a module framework diagram of step S103 in a method for improving headphone audio experience and device battery life according to the present invention;

[0013] Figure 4 This is a module framework diagram of step S104 in a method for improving headphone audio experience and device battery life according to the present invention. Detailed Implementation

[0014] To further understand the content of this invention, a detailed description of the invention is provided in conjunction with the accompanying drawings and embodiments. The specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0015] like Figures 1-4 This embodiment of a method for improving headphone audio experience and device battery life may specifically include:

[0016] Step S101: High-frequency energy loss data of traditional materials is obtained from the headphone amplifier circuit and compared with a preset threshold. If the loss value exceeds the threshold, performance simulation is performed on the core component to obtain the optimized material parameter configuration.

[0017] High-frequency electrical signals are acquired from the test nodes of the headphone amplifier circuit. A spectrum analyzer is used to separate signal components in specific frequency bands. Energy attenuation is calculated by comparing the power difference between the signal components at the start and end points of the transmission path, yielding high-frequency energy loss data for the conventional material. This high-frequency energy loss data is compared with a preset energy loss threshold to determine if it exceeds the threshold. If it does, a multi-physics coupling simulation process for the core component of the circuit is triggered. A finite element mesh model is established based on the geometry of the core component. The high-frequency electrical signal is loaded into the model as the excitation source, and the conventional material is assigned its current dielectric constant and permeability parameters. Electromagnetic-thermal coupling field simulation calculations are performed, and the field distribution results are obtained by solving the coupled form of Maxwell's equations and the heat conduction equations. Temperature rise and stress distribution data of the core component under high-frequency excitation are extracted from the coupled field simulation results. Combined with a material property database, the improvement degree of different candidate material parameters on the temperature rise and stress distribution is evaluated. The improvement quantification index is determined by comparing the peak value reduction ratio of the distribution data under each candidate parameter. The gradient descent algorithm is used to iteratively adjust the combination of dielectric constant and magnetic permeability parameters of the candidate materials with the goal of minimizing the maximum value of the temperature rise and stress distribution, until the convergence condition is met, and the optimized material parameter configuration is obtained.

[0018] In one implementation, high-frequency energy loss data of conventional materials is obtained from the headphone amplifier circuit using specialized testing equipment.

[0019] Specifically, the process first involves placing the headphone amplifier circuit in an environment simulating a high-frequency signal input, such as an audio signal with a frequency range of 10kHz to 20kHz. Then, a power meter or spectrum analyzer is used to measure the energy loss of conventional materials in the circuit, such as copper wires or standard capacitors. These loss data reflect the heat dissipation and signal attenuation of the materials during high-frequency transmission.

[0020] It should be noted that high-frequency energy loss typically stems from intrinsic properties of materials such as resistivity and dielectric constant. Real-time data acquisition provides a set of quantitative indicators, such as power loss recorded in watts, thus offering fundamental data support for subsequent comparisons. This acquisition method ensures data accuracy and repeatability and is commonly used in headphone circuit optimization to evaluate material performance bottlenecks. Based on the acquired data, a preset threshold is used for comparison.

[0021] Preferably, the preset threshold can be set according to the standard operating parameters of the headphone amplifier circuit. For example, the threshold can be defined as high-frequency energy loss not exceeding 5% of the total circuit power. A simple numerical comparison algorithm is used to determine whether the loss value exceeds the threshold. If the loss value exceeds the threshold, it indicates that the performance of traditional materials is insufficient under high-frequency conditions, requiring further intervention. This comparison step is simple and efficient, and can quickly screen out circuit components that need optimization. It is often used as a preliminary screening mechanism in actual headphone production testing. Furthermore, if the loss value exceeds the threshold, performance simulation is performed on the core components to obtain optimized material parameter configurations.

[0022] Specifically, the performance simulation process involves building a virtual model to simulate the behavior of the headphone amplifier circuit, such as using finite element analysis to simulate the response of core components like amplifier chips or filter capacitors in high-frequency environments.

[0023] In one possible implementation, simulation input parameters are first defined, including the initial resistivity and dielectric constant of the conventional material, and then these parameters are adjusted through an iterative algorithm, for example, by gradually reducing the resistivity value to minimize the energy loss output in the simulation.

[0024] It's important to note that the core of this simulation lies in establishing a loss function model, where the loss value serves as the objective function. A gradient descent strategy is used to find the minimum loss configuration of the parameters, for example, adjusting the initial resistivity from 0.017 Ω·mm² / m to an optimized value of 0.015 Ω·mm² / m, thereby optimizing the material parameters. The technical goal of this process is to improve the overall efficiency of the circuit, reducing signal distortion and extending equipment lifespan in the headphone amplification field. Through this simulation, the optimization effect can be predicted without actually replacing materials, providing reliable configuration suggestions. This simulation step is the innovative point of the entire solution because it combines data-driven optimization, generating customized parameters for specific high-frequency loss problems, which is more accurate and efficient than traditional trial-and-error methods.

[0025] In one embodiment, for a wireless headphone amplifier circuit, the simulation process can be extended to consider different environmental factors, such as the impact of temperature changes on material loss. By incorporating temperature variables into the model and performing multiple iterations, a more adaptive parameter configuration can be obtained. In another embodiment, performance simulation can integrate machine learning elements, such as using neural networks to predict and optimize parameters, but it remains limited to the headphone circuit domain, ensuring that the simulation output directly serves the adjustment of material configuration.

[0026] Understandably, the optimized material parameter configuration is ultimately applied to actual circuit design. For example, the low-loss resistivity parameters obtained from simulation are used to select new alloy materials, thereby improving high-frequency energy transfer in headphone amplifier circuits. Furthermore, the versatility of this solution is reflected in various headphone types; for instance, the simulation optimization of wired headphone circuits is also applicable, adapting to different product requirements by adjusting thresholds and simulation parameters.

[0027] For example, in actual testing, if the initial loss data is 0.8W, exceeding the threshold of 0.5W, the simulation process will output an optimized configuration, such as reducing the dielectric constant from 4.0 to 3.5, to ensure improved circuit performance.

[0028] It should be noted that the logical chain from data acquisition to simulation optimization ensures the consistency of the solution and provides a systematic approach to headphone amplifier circuit optimization.

[0029] Step S102: Based on the obtained material parameter configuration, acquire the dynamic response curve data of the circuit, use a convolutional neural network algorithm to process the peak fluctuations in the curve, determine whether the fluctuation amplitude is within the preset range, and determine the response speed improvement scheme.

[0030] The input excitation signal of the circuit is obtained from the material parameter configuration. A spectrum analyzer is used to separate high-frequency components, and the dynamic response curve data is obtained by calculating the phase shift of the high-frequency components in the circuit path. For the dynamic response curve data, a convolutional neural network algorithm is used to extract peak fluctuation features from the curve, and the amplitude variation trend of these peak fluctuation features is determined. The amplitude variation trend is compared with a preset range. If the amplitude variation trend exceeds the preset range, an impedance matching adjustment process is triggered, and the impedance distribution data of the circuit is obtained by adjusting the resistance values ​​of the circuit components. Based on the impedance distribution data, a noise suppression strategy is implemented, and interference components in the dynamic response curve data are reduced by applying filters to obtain an optimized, smoothed version of the curve. The response time index is extracted from the optimized, smoothed version of the curve, and the potential for improvement of the response time index is determined to identify a response speed improvement scheme.

[0031] In one implementation, based on the obtained material parameters, the dynamic response curve data of the headphone amplifier circuit is first acquired.

[0032] Specifically, the process involves applying optimized material parameters, such as resistivity and dielectric constant, to a circuit model, and then inputting a frequency-converted signal, such as an audio pulse with a frequency ranging from 1 kHz to 15 kHz, using simulation tools to record the curve data of the circuit's output voltage changing over time. These curve data represent the circuit's instantaneous response to the input signal, including rise time and steady state.

[0033] It should be noted that dynamic response curves are typically presented with time on the horizontal axis and voltage amplitude on the vertical axis. By collecting multiple sets of data points to form a continuous curve, it is used in headphone amplifier circuit optimization to evaluate the real-time performance of signal transmission. This acquisition method ensures that the data reflects the impact of material configuration on circuit behavior, providing a foundation for subsequent analysis. Furthermore, a convolutional neural network algorithm is used to process peak fluctuations in the curve.

[0034] In one possible implementation, a convolutional neural network is a multi-layer neural network structure used to extract local features from curve data, such as by applying filters through convolutional layers to scan the curve and capture peak positions and amplitude changes.

[0035] Specifically, the algorithm first converts the dynamic response curve into an image format, such as expanding a one-dimensional sequence into a two-dimensional matrix, and then sets the convolution kernel size to 3x3 for feature mapping to generate an activation map to highlight the peak fluctuation region.

[0036] It's important to note that the core of a convolutional neural network lies in convolution operations and pooling layers. Convolution operations extract edge features by multiplying the weight matrix with the input data and accumulating the sum, while pooling layers reduce dimensionality through downsampling. When processing headphone circuit curves, this network can identify abnormal peaks in the high-frequency response, such as sudden voltage spikes.

[0037] For example, in a wireless headphone amplifier circuit, this algorithm can process multiple curve data points. The training process involves adjusting network parameters using a labeled dataset to ensure accurate detection of fluctuation patterns. This processing step enables automated analysis in the field of headphone optimization, providing quantitative metrics for peak fluctuations.

[0038] Preferably, the fluctuation amplitude is determined based on the output of the convolutional neural network.

[0039] Specifically, the preset range can be defined as the peak fluctuation amplitude not exceeding 10% of the average value of the curve. The extracted amplitude value is compared with the threshold by a comparison algorithm. If the amplitude is within the range, it indicates that the response is stable; otherwise, further processing is required.

[0040] In one embodiment, for wired headphone circuits, this determination is integrated into a software module, outputting the result in real time. Further, if the fluctuation amplitude exceeds a preset range, a response speed improvement scheme is determined. In one embodiment, the response speed improvement scheme involves adjusting circuit parameters, such as increasing the filter capacitor capacity or optimizing the amplifier gain, to reduce peak fluctuations.

[0041] Specifically, the scheme involves iteratively simulating different configurations, such as adjusting the initial gain from 20dB to 15dB, and observing the improvement in the response curve.

[0042] It should be noted that the process of determining the solution generates a recommendation list based on the judgment result, which is executed for the high-frequency part in the earphone amplifier circuit to ensure that the solution is applicable to actual production.

[0043] For example, in another embodiment, obtaining dynamic response curve data can be extended to the actual measurement environment. Connect the earphone amplifier circuit to an actual audio source, input complex signals such as music segments, and collect curve data to verify the effect of material configuration. This method emphasizes the diversity of data collection and covers different usage scenarios in earphone optimization.

[0044] It can be understood that when the convolutional neural network algorithm processes peak fluctuations, a fully connected layer can be added for classification. For example, the fluctuations are divided into normal and abnormal categories, and the probability distribution is output through the softmax function to improve the judgment accuracy in earphone circuit analysis. The flexibility of this algorithm is reflected in parameter adjustment, such as setting the learning rate to 0.001 for training.

[0045] In one embodiment, judging whether the fluctuation amplitude is within the preset range can be combined with historical data. For example, compare the current curve with the standard template. If the deviation is less than 5%, it is regarded as qualified. This judgment step is simple and serves as a screening mechanism in earphone amplifier circuit testing. Further, determining the response speed improvement solution may include software simulation. For example, use circuit simulation software to generate multiple solution options, such as replacing low-resistance materials or adding a buffer circuit, to achieve a configuration adjustment that reduces the response time from 50 ms to 30 ms in wireless earphones.

[0046] Exemplarily, the generation of this solution considers circuit load factors to ensure comprehensiveness.

[0047] Preferably, the entire process forms a closed loop from obtaining curve data to determining the solution and is applied to mass production in earphone amplifier circuit optimization. For example, execute the full process for a Bluetooth earphone model to provide a customized improvement path. This universality is reflected in various earphone types, such as similar applications for in-ear circuit.

[0048] Step S103, if the response speed improvement solution is determined, then by integrating the energy utilization rate index, obtain the heat generation control variables from the simulation environment, and use the genetic algorithm to optimize the association between variables to obtain a circuit stability enhancement model.

[0049] Heating control variables are obtained from a simulated environment, and energy utilization efficiency indicators are integrated. A genetic algorithm is used to optimize the combination of heating control variables with the goal of maximizing energy utilization efficiency, resulting in an optimized set of variable parameters. For the optimized set of variable parameters, the corresponding circuit impedance distribution data is calculated, and the high-frequency fluctuation components in the circuit impedance distribution data are obtained as interference features. Based on the interference features, impedance adjustment is calculated. If the impedance adjustment exceeds a preset threshold, parameters strongly correlated with the interference features are selected from the optimized set of variable parameters to form a core parameter subset. Using the core parameter subset and peak time data extracted from the dynamic response curve, a low-pass filter is used to process the time-series change sequence in the core parameter subset to obtain a parameter smoothing sequence. The circuit damping coefficient and quality factor are derived from the parameter smoothing sequence, and the numerical relationship between the damping coefficient and the quality factor is determined to obtain a circuit stability enhancement model.

[0050] In one implementation, if a response speed improvement scheme has been determined, the subsequent optimization process is initiated by integrating energy utilization metrics.

[0051] Specifically, energy efficiency refers to the ratio of input energy to effective output energy of a circuit during operation. For example, in a headphone amplifier circuit, this metric is calculated by measuring the ratio of power input to audio signal output power. This integration process involves collecting multiple sets of analog data, standardizing the metric values, and merging them into a comprehensive parameter to evaluate the overall efficiency of the circuit.

[0052] It should be noted that this integration of indicators ensures the reliability of the optimization basis, reflecting the impact of material configuration on energy consumption in wireless earphone circuits. In this way, the process transitions from response speed considerations to energy management, providing data support for heat control. Furthermore, obtaining heat control variables from a simulation environment becomes a crucial step.

[0053] In one possible implementation, the simulation environment could be circuit simulation software, such as a specialized tool for headphone amplifier circuits, where optimized parameter configurations, such as resistance values ​​and capacitance, are input, and then a dynamic simulation is run to record temperature changes. Thermal control variables include the temperature thresholds of circuit components, heat dissipation rates, and power densities; these variables are extracted using the thermal analysis module of the simulation tool, for example, by monitoring the temperature rise curve of the amplifier chip when simulating input high-frequency audio signals.

[0054] For example, in the simulation of a wired headphone circuit, this acquisition process emphasizes the real-time nature of variables to ensure data coverage under different load conditions. This acquisition method is integrated with energy efficiency metrics to form an optimized input.

[0055] Preferably, optimizing the relationships between variables using a genetic algorithm is the core processing step. A genetic algorithm is an optimization method based on the principle of natural evolution. Its principle is to search for the optimal solution by simulating the population evolution process, including population initialization, selection, crossover, and mutation operations.

[0056] Specifically, in the optimization of headphone amplifier circuits, the heat control variable and energy utilization index are first used as individuals in the initial population. Each individual represents a set of variable combinations, such as the correlation between temperature threshold and power density. Then, the performance of each individual is evaluated through a fitness function, which calculates the contribution of the correlation between variables to circuit stability, such as reducing fluctuations caused by heat generation.

[0057] It should be noted that the selection operation retains individuals with high fitness, the crossover operation swaps variable values ​​to generate new individuals, and the mutation operation introduces random changes to avoid local optima.

[0058] In one embodiment, for a Bluetooth headset circuit, the algorithm iterates through multiple generations, for example, setting the population size to 50 and the number of iterations to 100. Through these steps, the variable association is gradually optimized to ensure the stability of the circuit under high load.

[0059] Understandably, the flexibility of this algorithm lies in parameter adjustment; for example, setting the crossover probability to 0.8 can adapt to the optimization needs of different headphone models. The process details the application of genetic algorithms in business: during initialization, variables such as heat dissipation rate are encoded as binary strings; fitness evaluation considers the negative correlation between energy utilization and heat generation, i.e., high utilization corresponds to low heat generation; selection uses a roulette wheel mechanism to select individuals proportionally; crossover points are randomly selected to achieve variable exchange; mutation introduces diversity by flipping bit values ​​with a low probability. Through multiple generations of evolution, the algorithm converges to the optimal configuration of variable associations, achieving a balance between minimizing heat generation and maximizing stability in headphone circuits. This optimization not only handles single variables but also explores their interactions, such as how temperature thresholds affect power density, thus providing a foundation for model construction. In actual business processes, this algorithm is applied to batch simulation testing, significantly improving the robustness of circuit design.

[0060] For example, in another implementation, the genetic algorithm optimization can be extended to in-ear headphone circuits, adjusting variable correlations to control heat generation for specific scenarios such as prolonged audio playback. Based on the above optimization, a circuit stability enhancement model is obtained as the final output.

[0061] Specifically, the model is a mathematical representation constructed by integrating optimized variable relationships, such as a parameterized function describing the circuit's stability under different conditions. In the headphone amplifier circuit, the model inputs include energy utilization and heat generation variables, and the output stability index is such as the fluctuation amplitude threshold. Furthermore, the model can be refined by validating simulation data to ensure its accuracy in practical applications.

[0062] In one embodiment, to enhance the stability of wireless earphone circuitry, the model emphasizes the dynamic relationships between variables, such as converting the results of genetic algorithm optimization into a lookup table for easy and fast querying.

[0063] Preferably, the entire process forms a closed loop, from integrating metrics to model generation, covering multiple scenarios in headphone optimization, such as testing under different frequency signal inputs. This versatility is reflected in the model's adaptability, providing guidance for production.

[0064] It should be noted that the process of building the circuit stability enhancement model involves result verification, such as comparing simulation data before and after optimization to confirm the stability improvement.

[0065] For example, in a wired headphone implementation, this model can be integrated into the design software to enable automated adjustments.

[0066] Understandably, through optimization using genetic algorithms, this model provides a reliable stability framework in the field of headphone circuits, supporting continuous improvement in response speed.

[0067] Step S104: Based on the circuit stability enhancement model, obtain the high-frequency performance test signal, perform iterative adjustment on the signal accuracy, and if the accuracy after adjustment is lower than the target value, use the support vector machine algorithm to classify the sources of accuracy deviation and determine the structural design correction path.

[0068] From the circuit stability enhancement model, a high-frequency excitation signal at a preset frequency point is extracted as the initial test signal, and the measured response waveform of this initial test signal at the target circuit port is obtained. Based on the amplitude and phase differences between the measured response waveform and the theoretical distortion-free response waveform, the initial signal accuracy index is calculated. An iterative optimization algorithm is used to adjust the amplitude and phase parameters of the initial test signal, generating an adjusted test signal sequence. For each signal in the adjusted test signal sequence, its response waveform at the target circuit port is re-acquired, and the corresponding signal accuracy index is calculated. If the maximum value of the signal accuracy index is lower than the preset target accuracy value, a deviation analysis process is triggered. After triggering the deviation analysis process, all response waveform data corresponding to the adjusted test signal sequence are collected to form a deviation dataset. From the deviation dataset, waveform distortion, resonant point offset, and impedance discontinuity features are extracted as classification feature vectors. The classification feature vectors are input into a support vector machine classification model, and the model outputs deviation source category labels. The deviation source category labels include trace crosstalk dominance, power integrity degradation dominance, and package parasitic effect dominance. Based on the deviation source category label, a set of associated physical structure parameters is retrieved from a preset process rule library. This set includes linewidth, line spacing, via anti-pad size, and power layer splitting spacing. Based on this set of physical structure parameters, a corresponding 3D model and material properties are set in an electromagnetic field simulation environment, and full-wave simulation is performed to obtain the response of the target circuit port under the high-frequency excitation signal. The magnitude of the response change when each physical structure parameter changes individually is analyzed to obtain the influence weight of each parameter on the signal accuracy index. Based on the ranking of the influence weights, the correction order and numerical adjustment direction of the set of physical structure parameters are determined, resulting in a structural design correction path.

[0069] In one implementation, the process of obtaining high-frequency performance test signals based on a circuit stability enhancement model first requires reviewing the model's foundation. This model originates from variable associations previously optimized using a genetic algorithm.

[0070] Specifically, it integrates energy efficiency metrics and heat control variables into a parameterized framework for predicting circuit behavior. In the headphone amplifier circuit business, this signal acquisition step is achieved by simulating high-frequency inputs through models, such as audio waveforms with input frequencies above 20kHz, and then extracting the output signal as a test benchmark.

[0071] It should be noted that high-frequency performance test signals refer to waveform data that reflect the circuit's response speed and stability. In wireless earphone circuits, this signal can be directly derived from the model's dynamic simulation module, ensuring coverage of performance under different load conditions. This method provides a reliable data foundation for subsequent accuracy adjustments. Furthermore, iterative adjustments to signal accuracy become a key step in the optimization process.

[0072] Specifically, signal accuracy refers to the degree of matching between the test signal and the ideal output, which can be quantified, for example, by calculating the mean square error.

[0073] In one possible implementation, the iterative adjustment uses a loop mechanism. First, an initial accuracy target value is set, such as an error of less than 0.05. Then, the actual signal is compared with the model prediction. If there is a deviation, the model parameters, such as the resistance value or the amplification gain, are adjusted.

[0074] For example, in the implementation of a Bluetooth headset circuit, the process is iterated multiple times, for example, by gradually reducing the deviation through a gradient descent method, and the test signal is regenerated after each iteration to verify the effect.

[0075] Understandably, this iteration ensures the gradual refinement of the signal, enabling it to adapt to high-frequency changes in audio signals in wired headphone circuits, thereby improving overall performance.

[0076] Preferably, if the adjusted accuracy is lower than the target value, using a Support Vector Machine (SVM) algorithm to classify the source of accuracy deviation is the core method for handling anomalies. SVM is a supervised learning algorithm that works by constructing a hyperplane to separate data points of different categories, maximizing the margin in a high-dimensional space to achieve classification. In the context of headphone circuitry, this algorithm is applied to analyze the sources of accuracy deviation, such as mapping deviation data to feature vectors including signal amplitude deviation, phase shift, and noise level, and then training the model to classify whether these deviations originate from component thermal effects or material configuration issues.

[0077] Specifically, the training process first collects historical simulation data as a training set, such as extracting deviation samples from multiple iterations of a wireless headphone circuit, and then uses kernel functions such as radial basis functions to project the data into a high-dimensional space.

[0078] It should be noted that after classification, the algorithm outputs category labels for the sources of deviation, such as "heat-related" or "structural inhomogeneity," which provides targeted guidance for correction.

[0079] In one embodiment, for in-ear headphone circuitry, the algorithm emphasizes data preprocessing steps, such as normalizing feature values, to improve classification accuracy. This classification shifts the process from simple adjustments to root cause analysis, ensuring optimization efficiency. In another embodiment, the detailed application of the support vector machine algorithm also includes a model optimization phase.

[0080] Specifically, the algorithm uses cross-validation to tune parameters, for example, by setting the regularization parameter C to 1.0 to balance the classification margin and error rate. In the scenario of headphone amplifier circuits, the process of identifying sources of classification accuracy bias involves multiple rounds of training. For example, 100 sets of biased samples are input, and the algorithm iteratively solves for support vectors to identify the dominant sources of bias, such as signal distortion caused by uneven power density.

[0081] For example, in a Bluetooth headset circuit, this algorithm can distinguish between deviations caused by environmental noise and deviations related to internal circuit variables, thereby forming a probability distribution of the deviation sources. This explanation highlights the algorithm's practicality in business applications; for instance, in high-frequency testing, the classification results directly influence decisions in subsequent steps, avoiding blind adjustments. Furthermore, the determination of the structural design correction path is based on the results of support vector machine classification.

[0082] Specifically, once the source of deviation is classified, such as being identified as a heat dissipation rate problem, path planning generates a correction scheme through mapping rules, such as adjusting the chip layout to improve heat distribution. In the implementation of the wireless earphone circuit, this path can be described as a series of steps, first prioritizing the correction of high-impact sources, and then verifying the model stability.

[0083] It should be noted that this determination process ensures the targeted nature of the correction. In wired headphone circuits, it can generate multiple path options, such as material replacement or parameter fine-tuning, to adapt to different precision requirements. Preferably...

[0084] In one embodiment, the extended application of the structural design correction path covers batch testing scenarios for headphone circuits.

[0085] Specifically, the path includes a feedback loop, such as re-inputting the revised design into the model, acquiring new test signals, and checking for accuracy improvements. In this way, the entire process forms a closed loop, enabling the handling of deviations during prolonged high-frequency audio playback in the Bluetooth headset business and ensuring the sustainability of structural optimization.

[0086] Understandably, the flexibility of this approach lies in the optional branches; if there are multiple sources of deviation, then low-cost correction is preferred. In another possible implementation, the integration of the entire scheme emphasizes the combination of real-time acquisition and adjustment of high-frequency performance test signals.

[0087] Specifically, after extracting signals from the circuit stability enhancement model, iterative adjustments are immediately initiated. If the accuracy is insufficient, support vector machine classification is used to generate a correction path. In in-ear headphone circuits, this integration reflects actual business needs. For example, in the audio amplification module, the final output of the correction path is a design blueprint adjustment suggestion, such as increasing capacitor capacitance to reduce deviation.

[0088] Furthermore, the principle behind the Support Vector Machine (SVM) algorithm in classifying sources of error requires in-depth explanation. Its core is solving a convex quadratic programming problem to find the optimal hyperplane. In headphone circuit optimization, this means the algorithm can effectively handle nonlinear errors, such as mapping signal features through a polynomial kernel function to achieve accurate classification of complex error sources.

[0089] For example, in a wireless headset implementation, the algorithm, after training, can achieve a classification accuracy exceeding 90%, thus providing a reliable basis for structural correction. This application not only improves diagnostic efficiency but also enables a rapid transition from problem identification to solution in business operations. In one implementation, the detailed steps for determining the structural design correction path include path evaluation, such as calculating the expected accuracy improvement for each correction option, and then selecting the optimal path. In the context of Bluetooth headset circuitry, this evaluation considers cost factors, such as the resource consumption of component replacement, to ensure the path is practical.

[0090] It should be noted that this determination process seamlessly integrates with the aforementioned classification, forming a complete optimization chain. Finally, in another embodiment of the wired headphone circuit, the versatility of the entire technical solution is reflected in its adaptability to multiple scenarios. For example, for testing different audio frequencies, the adjustment path can be dynamically changed, thereby supporting continuous iteration of circuit design. This approach provides a framework for structural optimization in business applications, ensuring stable improvement in high-frequency performance.

[0091] Step S105: By correcting the determined structural design path, obtain audio purity sampling data, determine whether the distortion ratio in the sampling data has been reduced, and obtain the sound quality improvement verification result.

[0092] Based on the set of physical structure parameters determined in the structural design correction path, corresponding digital-to-analog converter and power amplifier parameters are configured in the audio test environment to generate a test audio source file containing a specific frequency sweep signal and a multi-tone synthesized signal. Using high-precision audio acquisition equipment, the test audio source file is played in a shielded environment, and the electrical signal at the output of the target audio circuit is recorded synchronously to obtain raw audio sampling data, which is represented as a time-domain voltage waveform sequence. A Fast Fourier Transform is applied to the time-domain voltage waveform sequence to convert the time-domain data into frequency-domain data, obtaining a complete signal spectrum. The signal spectrum includes the fundamental component, all harmonic components, and the background noise floor. From the signal spectrum, the amplitudes of all harmonic components within a preset frequency band are extracted, and the total harmonic energy is calculated. Simultaneously, the average amplitude of the noise floor far from the signal frequency band is extracted, and the background noise energy is calculated. Based on the ratio of the sum of the total harmonic energy and the background noise energy to the fundamental energy, the overall distortion ratio of the current sampled data is determined. The overall distortion ratio is compared with a preset purity threshold. If the overall distortion ratio is lower than the purity threshold, the distortion ratio is determined to have decreased, and a sound quality improvement verification pass mark is generated; otherwise, a verification fail mark is generated.

[0093] In one implementation, to obtain audio purity sampling data through a determined structural design correction path, the correction path first needs to be converted into specific circuit adjustment operations.

[0094] Specifically, a structural design modification path may involve a series of instructions, such as adjusting the bias current of a specific amplifier stage, changing the dielectric material of the output coupling capacitor, or modifying the spacing of critical signal lines on a printed circuit board. In the context of headphone amplifier circuits, executing this path means physically modifying or resetting the parameters of the circuit prototype or simulation model according to the instructions.

[0095] For example, if the path indication requires improved power supply rejection ratio to reduce distortion introduced by power supply noise, the implementation might include adding a 10 microfarad tantalum capacitor at the power supply pin. Based on the modified circuitry, the process of acquiring audio purity sampling data then begins. Audio purity here specifically refers to the scarcity of harmonics and noise components in the output signal, in addition to the desired fundamental frequency component.

[0096] In one possible implementation, the sampling data is acquired through a standard audio testing procedure.

[0097] Specifically, a pure sine wave test signal with a single frequency and constant amplitude, such as 1kHz and 100mV, is input to the modified headphone amplifier circuit. The circuit output is connected to a high-precision audio analyzer, which digitizes the output waveform at a sampling rate tens of times higher than the signal frequency, obtaining a series of discrete voltage values. These data constitute the audio purity sampling dataset.

[0098] It should be noted that the frequency and amplitude of the test signal can be selected according to business needs. For example, when verifying high-frequency performance, a 20kHz sine wave can be used for testing. Furthermore, determining whether the distortion ratio in the sampled data has decreased hinges on calculating and comparing the distortion metric before and after correction. The distortion ratio is typically quantified as total harmonic distortion plus noise.

[0099] Specifically, for the acquired sampled data, the audio analyzer or subsequent processing algorithm performs a Fast Fourier Transform (FFT) to convert the time-domain signal into a frequency-domain spectrum. In this spectrum, in addition to the input fundamental frequency component, there are harmonic components at integer multiples of its frequency, as well as a widely distributed noise floor. The formula for THD+N is the ratio of the total effective value of all harmonic components and noise to the effective value of the fundamental component, usually expressed as a percentage.

[0100] In one embodiment, for the verification of in-ear headphone circuitry, the THD+N values ​​of the circuitry before and after the correction path is executed are calculated.

[0101] For example, the THD+N measurement value was 0.08% before correction at 1kHz and 100mV input, while the measurement value under the same conditions after correction decreased to 0.03%.

[0102] Understandably, by comparing the THD+N values ​​before and after correction, one can directly determine whether the distortion ratio has decreased. If the corrected THD+N value is lower than the original value, it indicates that the distortion ratio has decreased.

[0103] Preferably, this judgment process can set a specific threshold, such as requiring a THD+N reduction of more than 20% to be considered an effective reduction. Ultimately, based on the judgment of the reduction in distortion ratio, the system or engineer will obtain a clear sound quality improvement verification result. This result can be a Boolean value, such as "verification passed," or it can be accompanied by a detailed performance improvement data report. In batch verification scenarios for Bluetooth headphone amplifier circuits, this process can be automated, quickly evaluating the effectiveness of different correction paths, thereby selecting the optimal structural design scheme.

[0104] Step S106: Based on the sound quality improvement verification results, obtain the simulated input of battery life, process the input data using a preset energy allocation rule, and if the allocated duration exceeds the baseline, determine the power consumption reduction configuration.

[0105] The battery life simulation input is obtained from the sound quality verification results. A power consumption model is used to calculate battery life indicators based on the input audio circuit power and battery capacity, yielding initial duration data. Based on this initial duration data, an energy allocation rule combined with a voltage regulation strategy is employed to adjust the supply voltage and load distribution, resulting in a dynamically load-balanced allocated duration. This allocated duration is compared to a baseline duration; if it exceeds the baseline, the power consumption optimization is deemed effective, leading to a configuration adjustment mechanism. Based on this mechanism, audio circuit parameters and energy efficiency assessments are integrated, and circuit impedance and efficiency coefficients are combined to determine a power consumption reduction configuration.

[0106] In one implementation, to obtain the simulated input of battery life based on the sound quality improvement verification results, it is first necessary to establish a mapping relationship between circuit state and power consumption parameters.

[0107] Specifically, the sound quality improvement verification results indicate the final state of the circuit after specific modifications.

[0108] For example, successful verification might correspond to the use of low-leakage-current MOSFETs in the output stage, or the adjustment of the negative feedback network to a specific resistance value. These specific circuit component parameters and operating point changes directly alter the static operating current and dynamic switching losses of the corresponding functional modules. The process of acquiring analog input involves converting these determined circuit parameters into a set of power consumption characteristic data that can be used by the battery life simulation model.

[0109] For example, for a Bluetooth headset, its power consumption characteristic data can be a vector containing the operating current of the Bluetooth RF chip under a specific encoding format, the power consumption of the audio decoding chip when processing files at different sampling rates, and the efficiency value of the headphone amplifier circuit under corresponding load impedance and output power. This data can be obtained based on component datasheets, preliminary measurements, or circuit simulations, and used as input parameters for a battery life simulation model. Furthermore, the battery life simulation model operates based on the total capacity of the device's battery and the aforementioned input power consumption characteristic data. This model is not a simple division operation but needs to consider the dynamic power consumption sequence under typical usage scenarios of the device.

[0110] In one possible implementation, the model simulates a complete user cycle, including different states such as device wake-up, Bluetooth connection, audio stream decoding, signal amplification, and standby. Each state corresponds to a specific set of current values ​​in the power consumption characteristic data. The model estimates the rate of battery energy consumption by accumulating the product of the duration of each state and the corresponding current, and then calculates the theoretical battery life under a given battery capacity. This calculated duration is the "battery life simulation input" mentioned in the claims. Processing the input data using preset energy allocation rules is the core of this method. The energy allocation rules define how, during device operation, the limited battery energy budget is dynamically allocated among different functional modules according to task requirements, performance goals, or user settings.

[0111] Specifically, the rule can be embodied as a set of configurable strategy tables or a lightweight decision logic.

[0112] In one embodiment, the rules are indexed by "performance mode". For example, a preset "high-fidelity mode" rule requires the audio decoding chip to always operate at the highest precision, the headphone amplifier circuit to be biased in Class A mode to pursue the lowest distortion, but at the same time limits the Bluetooth chip's transmit power to save some energy. A "long battery life mode" rule, on the other hand, may instruct the decoding chip to enable a simplified algorithm, the headphone amplifier circuit to switch to the more efficient Class B or Class D operating mode, and allow the Bluetooth chip to reduce transmit power when the signal is good. When processing input data, the system combines the analog input of battery life with the preset rules.

[0113] For example, based on the power consumption characteristic data obtained above, the system first adjusts the original power consumption data according to the "high-fidelity mode" rule to generate a new set of power consumption parameters that meets the requirements of the mode.

[0114] Specifically, the rules might instruct setting the quiescent current of the headphone amplifier circuit to a higher value while reducing the transmit current of the Bluetooth chip by a percentage. The battery life simulation model then uses these adjusted parameters to recalculate the simulated battery life under the "high fidelity mode" rules.

[0115] It is understandable that the same set of basic circuit power consumption data will yield different simulated battery life results when different energy allocation rules are applied.

[0116] It should be noted that the energy allocation rules can be processed more finely. In another possible implementation, the rules are not simply mode switching, but include a feedback-based adaptive adjustment logic.

[0117] For example, the rules can monitor the battery voltage drop curve during simulation. When the simulated voltage falls below a certain threshold, it automatically triggers power-limiting commands for non-core modules (such as auxiliary sensors or lights on the headphones) and reallocates the saved energy budget to the main audio link, prioritizing the preservation of core sound quality within overall battery life constraints. This dynamic adjustment process also falls within the scope of "processing input data." If the allocated time exceeds the baseline, a power-reduction configuration is determined. Here, the "baseline" is a preset battery life target value, such as the minimum battery life required by the product specifications.

[0118] In one embodiment, the system compares the simulated battery life calculated after applying a certain power allocation rule with a baseline duration. For example, if the baseline duration is 8 hours, and the duration simulated using the "high-fidelity mode" rule is 9 hours, it is determined to be "exceeding the baseline." At this point, the system identifies a series of specific parameters associated with that rule as valid "power reduction configurations." Determining power reduction configurations means translating the parameter adjustments defined in these rules into configuration instructions executable by the device firmware or hardware.

[0119] For example, corresponding to the aforementioned "high-fidelity mode" rule, the determined configuration might include: setting the audio decoding chip's operating clock to 48MHz, writing 0x1F to the headphone amplifier circuit's bias voltage register, and setting the Bluetooth module's transmit power control word to the "medium power" level. This collection of specific register values ​​or control commands constitutes a complete, deployable power reduction configuration. This configuration directly stems from a previously verified and effective power distribution strategy.

[0120] Preferably, to ensure the reliability of the configuration, multi-rule iterative simulation can be introduced before determining the configuration.

[0121] For example, the system sequentially applies multiple preset energy allocation rules (such as Mode A, Mode B, and Mode C) to process the same set of input data and obtains simulated battery life for each. Then, the system filters out all rules whose simulated battery life exceeds a benchmark and selects the rule that minimizes the impact on sound quality while meeting battery life requirements (this can be aided by the distortion ratio data in the sound quality improvement verification results). Finally, the system determines the corresponding parameter set as the power reduction configuration. This method achieves a better balance between battery life and sound quality. In the later stages of Bluetooth headset product development, this method can be automated. Engineers input a series of candidate circuit modification schemes and their sound quality verification results into the system. The system automatically completes the entire process from power consumption feature extraction and multi-rule battery life simulation to final configuration determination, and outputs a report listing which modification schemes meet sound quality targets while also achieving battery life through specific energy allocation, and providing specific configuration parameters. This greatly improves the efficiency of comprehensive performance evaluation of circuit design.

[0122] Step S107: Based on the determined power consumption reduction configuration, obtain the overall audio experience feedback loop, determine whether the balance index in the feedback is optimized, and obtain the final circuit deployment scheme.

[0123] By establishing a power consumption reduction configuration, overall loop data is obtained from user interactions using audio experience feedback. The balance index in this loop data is assessed by comparing audio quality and power consumption ratios to obtain preliminary optimization results. Based on these preliminary optimization results, energy distribution rules are obtained and combined with voltage regulation strategies to adjust power supply levels and handle load distribution. A time-based benchmark comparison after dynamic load balancing is determined, resulting in a configuration adjustment mechanism. According to this mechanism, circuit parameters and efficiency coefficients are integrated to assess audio signal stability by combining impedance values ​​and transmission efficiency. If the assessment exceeds a preset threshold, the balance index optimization is deemed effective, leading to an expanded circuit deployment scheme. Through this expanded scheme, a time-based benchmark comparison is used to verify the overall audio experience feedback loop from initial duration data, determining the final circuit deployment scheme.

[0124] In one implementation, the initial parameters of the audio circuitry must first be evaluated to determine a power reduction configuration. Specifically, the power reduction configuration refers to selecting appropriate voltage and current adjustment strategies based on the power consumption model of the audio device.

[0125] For example, in an audio amplifier circuit, power consumption can be reduced by lowering the supply voltage of non-critical components. The process of determining this configuration involves analyzing the load distribution of the circuit to ensure that audio signal processing is not affected.

[0126] It should be noted that the power consumption model is based on the static and dynamic power calculations of the circuit, where static power originates from leakage current and dynamic power is related to the switching frequency. This configuration provides the foundation for subsequent feedback loops. Based on the above configuration, an overall audio experience feedback loop is further obtained. This loop includes real-time acquisition of subjective evaluation data from the user on the audio output, such as feedback on sound clarity and volume balance recorded by built-in sensors.

[0127] Specifically, the feedback loop process starts with audio playback, where the device automatically triggers the evaluation mechanism. Users can input data such as a sound effect satisfaction score, and the system then forms a closed loop with these data, iterating in a loop to accumulate experience metrics.

[0128] In one possible implementation, feedback data is stored as a vector, encompassing dimensions such as pitch, loudness, and distortion, ensuring that multiple aspects of the audio experience are covered in a loop. This looping mechanism helps to dynamically adjust circuit parameters, avoiding the limitations of static configuration. Determining whether the balance metrics in the feedback are optimized is a crucial step. Balance metrics refer to the degree of equilibrium among various elements of the audio experience, such as the trade-off between sound quality and power consumption.

[0129] Specifically, the judgment process is carried out by comparing feedback data with preset thresholds. For example, if the sound quality score is higher than 80 points and the power consumption is reduced by more than 15%, it is considered as optimization.

[0130] For example, in an audio headphone circuit, the system analyzes feedback loop data and calculates the deviation value of the balance index. If the deviation is less than a set range, optimization is confirmed. The principle behind this process is to use statistical methods to evaluate data distribution, ensuring that the index reflects the real experience. Furthermore, this judgment can identify potential problems, such as a decline in experience due to uneven volume, thereby guiding configuration iterations.

[0131] In one embodiment, for portable audio devices, the balance index optimization also takes into account battery life factors, and a stable judgment is achieved through multiple cycle verifications.

[0132] Preferably, the final circuit deployment scheme is obtained based on the aforementioned judgment results.

[0133] Specifically, if the balance metrics are optimized, the current power consumption reduction configuration is solidified into the deployment plan, such as embedding the adjusted voltage parameters in the audio processor chip.

[0134] In one embodiment, this solution is applied to smart speaker circuitry, achieving a 20% reduction in power consumption while maintaining the same audio experience. The deployment process includes solution verification and hardware integration to ensure versatility. In another implementation, for in-vehicle audio systems, the solution similarly optimizes deployment through feedback, demonstrating the technology's flexible application within the same domain.

[0135] Understandably, through the above steps, the technical solution can provide effective power management and experience optimization in the audio field.

[0136] For example, in conference audio equipment, feedback loops and indicator judgments help to make real-time adjustments, resulting in improved circuit efficiency.

[0137] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations.

Claims

1. A method for improving headphone audio experience and device battery life, characterized in that, include: High-frequency energy loss data is obtained from the headphone amplifier circuit and compared with a preset threshold to determine whether material parameters need to be optimized. Based on the comparison results, performance simulations are performed on the core components of the circuit to determine the optimized material parameter configuration. Based on the material parameter configuration, dynamic response data of the circuit is acquired, fluctuation characteristics are analyzed, and a response speed improvement scheme is determined. Using the response speed improvement scheme, energy utilization indicators are integrated, heat generation control variables are optimized, and a circuit stability enhancement model is constructed. Based on the circuit stability enhancement model, high-frequency performance test signals are adjusted, signal accuracy is analyzed, and a structural design correction path is determined. Using the structural design correction path, audio purity sampling data is acquired, the distortion ratio is determined, and the sound quality improvement verification results are obtained. Based on the sound quality improvement verification results, process the simulated battery life input to determine the power consumption reduction configuration; By using the power reduction configuration, overall audio experience feedback is obtained, balance indicators are determined, and the final circuit deployment scheme is identified.

2. The method for improving headphone audio experience and device battery life as described in claim 1, characterized in that, The step of acquiring high-frequency energy loss data from the headphone amplifier circuit and comparing it with a preset threshold includes: acquiring a high-frequency electrical signal from the test node of the headphone amplifier circuit and separating specific frequency band signal components using a spectrum analyzer; calculating the power difference between the signal components at the start and end points of the transmission path to obtain an energy attenuation value, which is used as the high-frequency energy loss data; comparing the high-frequency energy loss data with a preset energy loss threshold to determine whether it exceeds the threshold; if it exceeds the threshold, triggering a performance simulation process for the core components of the circuit; if it does not exceed the threshold, recording the current data and ending the current process.

3. The method for improving headphone audio experience and device battery life as described in claim 1, characterized in that, The process of performing performance simulations on the core components of the circuit to determine the optimized material parameter configuration includes: establishing a finite element mesh model based on the geometry of the core components and loading a high-frequency electrical signal as an excitation source; assigning dielectric constant and magnetic permeability parameters to the traditional materials in the model and performing electromagnetic-thermal coupled field simulation calculations; extracting temperature rise and stress distribution data from the simulation results and evaluating the improvement degree of candidate material parameters in conjunction with a material property database; determining the quantitative improvement index by comparing the peak value reduction ratio of the distribution data under each candidate parameter; and iteratively adjusting the parameter combination of the candidate materials using a gradient descent algorithm until the convergence condition is met to obtain the optimized material parameter configuration.

4. The method for improving headphone audio experience and device battery life as described in claim 1, characterized in that, The step of acquiring circuit dynamic response data and analyzing fluctuation characteristics based on the material parameter configuration includes: acquiring an input excitation signal from the material parameter configuration and separating high-frequency components using a spectrum analyzer; obtaining dynamic response curve data by calculating the phase shift of the high-frequency components in the circuit path; extracting peak fluctuation characteristics from the dynamic response curve data using a convolutional neural network algorithm; determining the amplitude change trend of the peak fluctuation characteristics and comparing it with a preset range; if the amplitude change trend exceeds the preset range, triggering an impedance matching adjustment process, acquiring circuit impedance distribution data, and optimizing the curve smoothing version.

5. The method for improving headphone audio experience and device battery life as described in claim 1, characterized in that, The method of improving response speed by integrating energy utilization indicators and optimizing heat generation control variables includes: obtaining heat generation control variables from the simulated environment, combining them with the energy utilization indicators, and optimizing the variable combination using a genetic algorithm; obtaining the optimized set of variable parameters and calculating the corresponding circuit impedance distribution data; extracting high-frequency fluctuation components as interference features from the impedance distribution data and calculating the impedance adjustment amount; if the impedance adjustment amount exceeds a preset threshold, selecting parameters strongly correlated with the interference features to form a core parameter subset; and using the core parameter subset, combined with dynamic response curve data, deriving the circuit damping coefficient and quality factor to construct the circuit stability enhancement model.

6. The method for improving headphone audio experience and device battery life as described in claim 1, characterized in that, The step of adjusting the high-frequency performance test signal and analyzing the signal accuracy based on the circuit stability enhancement model includes: extracting a high-frequency excitation signal at a preset frequency point from the circuit stability enhancement model as the initial test signal; acquiring the measured response waveform of the initial test signal at the target circuit port and calculating the initial signal accuracy index; adjusting the amplitude and phase parameters of the initial test signal using an iterative optimization algorithm to generate an adjusted test signal sequence; recalculating the signal accuracy index for the adjusted test signal sequence; and if the signal accuracy index is lower than a preset target accuracy value, analyzing the deviation source category using a support vector machine classification model to determine the structural design correction path.

7. The method for improving headphone audio experience and device battery life as described in claim 1, characterized in that, The step of obtaining audio purity sampling data and determining the distortion ratio through the structural design correction path includes: configuring the converter and amplifier parameters in the audio test environment according to the set of physical structural parameters in the structural design correction path; generating a test audio source file containing a specific frequency sweep signal, recording the output electrical signal using a high-precision audio acquisition device, and obtaining raw audio sampling data; performing frequency domain conversion on the raw audio sampling data to obtain a signal spectrum; extracting harmonic components and noise energy from the signal spectrum and calculating the overall distortion ratio; comparing the overall distortion ratio with a preset purity threshold to determine whether the distortion ratio has decreased, and obtaining the sound quality improvement verification result.

8. The method for improving headphone audio experience and device battery life as described in claim 1, characterized in that, The step of processing the simulated battery life input based on the sound quality improvement verification results and determining the power consumption reduction configuration includes: obtaining the simulated battery life input from the sound quality improvement verification results, calculating the battery life index using a power consumption model to obtain initial duration data; adjusting the supply voltage and load distribution using energy allocation rules combined with a voltage regulation strategy based on the initial duration data to obtain the allocated duration; comparing the allocated duration with a preset duration benchmark; if the allocated duration exceeds the duration benchmark, determining that the power consumption optimization is effective and generating a configuration adjustment mechanism; and determining the power consumption reduction configuration by integrating circuit impedance and efficiency coefficient based on the configuration adjustment mechanism.

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