Intelligent audio power amplifier

Abstract

The invention relates to the technical field of audio power amplification, and discloses an intelligent sound power amplifier, which comprises a processor, and the processor is configured to execute the following steps: calculating a thermal state coefficient based on the temperature of a power amplifier chip and the temperature of a loudspeaker voice coil; calculating a signal load factor based on the signal peak value, the short-term RMS value and the low-frequency energy integral ratio; calculating a dynamic load-system tolerance matching degree based on the thermal state coefficient, the environmental noise sound pressure level and the signal load factor; calculating a dynamic response coefficient based on the signal parameter change acceleration and the power supply voltage drop rate; and integrating the signal load factor, the dynamic load-system tolerance matching degree and the dynamic response coefficient, and calculating and adjusting to a target compression ratio. According to the invention, the self-adaptive dynamic adjustment of the compression ratio is realized by sensing the signal characteristics, the thermal state of the system and the external environment in multiple dimensions, the sound quality and the hearing feeling can be guaranteed, the overload, overheating and fatigue damage of equipment can be effectively prevented, and the reliability, the environmental adaptability and the user experience of the system are improved.

Description

Technical Field

[0001] This invention belongs to the field of audio power amplification technology, and particularly relates to an intelligent speaker power amplifier. Background Technology

[0002] Power amplifiers in audio systems play a crucial role in efficiently converting low-power audio signals into high-power outputs to drive speakers. Their design must amplify energy while maintaining signal fidelity. However, traditional power amplifier dynamic range control and thermal management mechanisms generally rely on preset static rules, lacking adaptability to the complexities of real-world usage scenarios, leading to multiple challenges to system performance and reliability. In dynamic range processing, existing technologies often employ compression algorithms with fixed thresholds or ratios, failing to intelligently adjust based on the real-time characteristics of the audio content. For example, when playing high-dynamic-range classical music, excessive compression compresses the signal's dynamic layers, resulting in loss of musical detail and reduced artistic expression; conversely, when processing low-dynamic-range speech signals, especially in noisy environments, insufficient compression makes it difficult to improve speech clarity and intelligibility. Traditional methods fail to effectively integrate the transient characteristics of the signal (such as the ratio of instantaneous peak value to short-term RMS value) and low-frequency energy distribution (the proportion of low-frequency energy to total frequency energy), parameters crucial for accurately assessing the overall load of the signal on the amplifier. In terms of thermal protection mechanisms, conventional solutions only focus on monitoring the instantaneous temperature of the power amplifier chip. Once a preset threshold is exceeded, a forced shutdown or significant output attenuation is triggered. This "fuse-based" strategy has significant lag and completely ignores the temperature state of the speaker voice coil—a more vulnerable component susceptible to heat damage. Overheating of the voice coil is often the main cause of permanent speaker damage. Furthermore, existing technologies do not consider the cumulative effect of temperature and cannot identify or mitigate material fatigue caused by frequent short-term overheating events, thereby accelerating device aging and shortening its lifespan. At the same time, external factors such as fluctuations in ambient noise sound pressure levels and drops in power supply voltage are not incorporated into the system control logic, resulting in insufficient dynamic response and stability of the amplifier, making it difficult to achieve an optimal balance between sound quality and hardware protection in complex and ever-changing real-world scenarios.

[0003] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0004] The purpose of this invention is to provide a smart speaker power amplifier that addresses the aforementioned problems.

[0005] This invention is implemented as follows: a smart speaker power amplifier includes a power amplifier body, and a power interface and an audio signal input interface installed on the power amplifier body. The invention is characterized by further including a processor installed inside the power amplifier body, the processor being configured to perform the following steps: calculating the thermal state coefficient based on the power amplifier chip temperature and the speaker voice coil temperature; calculating the signal load factor based on the signal peak value, short-term RMS value, and low-frequency energy integral ratio; calculating the dynamic load-system tolerance matching degree based on the thermal state coefficient, ambient noise sound pressure level, and signal load factor; calculating the dynamic response coefficient based on the signal parameter change acceleration and power supply voltage sag rate; and calculating the target compression ratio based on the signal load factor, dynamic load-system tolerance matching degree, and dynamic response coefficient, and adjusting the current compression ratio to the target compression ratio.

[0006] A further technical solution involves the following steps for calculating the target compression ratio: obtaining the signal load factor, dynamic load-system tolerance matching degree, and dynamic response coefficient; based on the signal load factor, performing linear interpolation between the preset minimum compression ratio and the maximum compression ratio to obtain the base compression ratio; multiplying the base compression ratio by a correction factor to obtain the target compression ratio, wherein the correction factor increases as the dynamic load-system tolerance matching degree decreases and the dynamic response coefficient increases.

[0007] A further technical solution involves calculating the dynamic load-system tolerance matching degree as follows: obtaining the thermal state coefficient, ambient noise sound pressure level, and signal load factor; performing maximum-minimum normalization on the ambient noise sound pressure level to obtain the environmental interference coefficient; and importing the thermal state coefficient, environmental interference coefficient, and signal load factor into the formula. Obtain dynamic load-system tolerance matching degree , The output range is 0-1, reflecting the degree of matching between the current signal load and the system's thermal tolerance and environmental requirements. 1 indicates a perfect match, meaning the system can easily handle the current signal; 0 indicates a severe mismatch, requiring strong intervention. The thermal state coefficient, This represents the environmental interference coefficient. Environmental noise weighting factor, For signal load factor, It is a very small positive number.

[0008] A further technical solution involves the following steps for calculating and obtaining the signal load factor: obtaining the signal peak value, short-term RMS value, and low-frequency energy integral ratio; performing ratio processing on the signal peak value and short-term RMS value to obtain the instantaneous peak factor; performing maximum-minimum normalization processing on the instantaneous peak factor to obtain the instantaneous peak factor exponent; and taking the square root of the weighted sum of squares of the instantaneous peak factor exponent and the low-frequency energy integral ratio to obtain the signal load factor, wherein the weighting coefficient is used to adjust the relative contribution of the two to the overall load.

[0009] A further technical solution involves the following steps for calculating and obtaining the dynamic response coefficient: obtaining the signal parameter change acceleration and the power supply voltage drop rate; performing maximum-minimum normalization on the signal parameter change acceleration to obtain the signal parameter change acceleration exponent; performing ratio processing on the current power supply voltage drop rate and the voltage drop rate threshold, and then using a min function to limit the upper limit to 1 to obtain the power supply voltage drop rate exponent; and taking the larger value between the signal parameter change acceleration exponent and the power supply voltage drop rate exponent as the dynamic response coefficient. , The output range is 0-1, reflecting the urgency of the system's need for a rapid response. 0 indicates no emergency, while 1 indicates that the compression ratio needs to be increased significantly immediately to prevent distortion or crashes.

[0010] A further technical solution involves the following steps for obtaining the thermal state coefficient: acquiring the power amplifier chip temperature and the speaker voice coil temperature; performing maximum-minimum normalization on the power amplifier chip temperature and the speaker voice coil temperature to obtain the power amplifier chip temperature index and the speaker voice coil temperature index; calculating the fatigue factor based on the power amplifier chip temperature and the speaker voice coil temperature; and taking the maximum value among the power amplifier chip temperature index, the speaker voice coil temperature index, and the fatigue factor as the thermal state coefficient. , Output range 0-1, A value of 0 indicates no thermal risk. A value of 1 indicates that the thermal protection limit has been reached, and maximum compression is required to reduce heat generation.

[0011] A further technical solution involves calculating the fatigue factor as follows: obtaining the power amplifier chip temperature and speaker voice coil temperature at the current sampling point, as well as the fatigue accumulation value at the previous sampling point; attenuating the fatigue accumulation value at the previous sampling point with a forgetting factor, and then adding the temperature exceedance level of the current sampling point to obtain the fatigue accumulation value at the current sampling point; the temperature exceedance level is calculated based on the maximum value between the power amplifier chip temperature and the speaker voice coil temperature relative to the reference temperature threshold and the maximum allowable temperature; the fatigue accumulation value at the current sampling point is compared with the maximum fatigue accumulation value, and then the upper limit is set to 1 using a min function to obtain the fatigue factor.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0013] 1. Breaking through the limitations of traditional amplifiers that rely on a single or few static parameters, it achieves accurate and real-time assessment of system load and tolerance by comprehensively sensing audio signal characteristics (transient, low frequency), thermal state of key system components (power amplifier chip, speaker voice coil) and external environment (noise, power supply), providing a comprehensive basis for dynamic processing decisions.

[0014] 2. Based on the calculated thermal state coefficient, signal load factor, dynamic load-system tolerance matching degree, and dynamic response coefficient, the target compression ratio is calculated in real time and adaptively. It can increase the compression ratio to protect hardware and improve speech clarity when the system has high thermal risk, heavy signal load, or noisy environment; and decrease the compression ratio to preserve musical details and artistic expression when the system is relaxed and the signal dynamics are rich, achieving an optimal balance between safety protection and sound quality experience.

[0015] 3. Innovatively, a fatigue factor based on temperature history is introduced and incorporated into the thermal state assessment. It not only focuses on instantaneous overheating but also identifies and responds to long-term fatigue risks caused by repeated, cumulative thermal stress, thereby triggering proactive protection. This effectively extends the service life of core components such as power amplifier chips and speakers, improving the long-term reliability of the system.

[0016] 4. By introducing environmental noise interference coefficient and power supply voltage drop monitoring, the system can automatically adjust the output strategy according to the external environmental noise level to optimize the listening experience, and can respond quickly to sudden situations such as power fluctuations to prevent distortion and system crashes, significantly enhancing the adaptability and overall stability of the audio system in different usage scenarios. Attached Figure Description

[0017] Figure 1 A flowchart of the processor operation provided for this invention;

[0018] Figure 2 This invention provides a schematic diagram of the structure of a smart speaker power amplifier;

[0019] In the attached diagram: 1. Power amplifier main body; 2. Power interface; 3. Audio signal input interface. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0021] In traditional power amplifier applications, dynamic range processing mechanisms employ fixed threshold or fixed ratio compression strategies. These strategies cannot adaptively adjust to the transient characteristics and low-frequency energy distribution of the audio signal. This results in a loss of detail in music with a large dynamic range during compression, while speech with a small dynamic range suffers from insufficient clarity under environmental noise. Furthermore, the signal load assessment fails to integrate the instantaneous peak factor and low-frequency energy integral ratio, leading to a mismatch between dynamic processing and actual audio characteristics. Thermal protection mechanisms rely solely on instantaneous temperature monitoring of the power amplifier chip, neglecting to consider the speaker voice coil's thermal state and cumulative temperature effects. This results in delayed protection responses and an inability to prevent long-term fatigue caused by repeated thermal stress.

[0022] For example, when the sound system plays electronic music at an outdoor music festival, the transient percussion signal is over-compressed due to the fixed compression ratio setting, which weakens the dynamic impact. At the same time, the continuous low-frequency signal input causes the speaker voice coil temperature to gradually accumulate and rise. In particular, when the ambient noise sound pressure level suddenly increases, the system fails to adjust the compression ratio based on the signal load factor and the environmental interference coefficient, resulting in a decrease in speech intelligibility in the audience area. Furthermore, the thermal state coefficient continues to rise but does not trigger protection, and long-term operation accelerates the aging of the voice coil.

[0023] If the above problems are not resolved, the system will frequently experience distortion and protective shutdown in complex audio scenarios. The speaker voice coil will suffer from reduced reliability due to accumulated thermal fatigue, and fluctuations in audio quality will affect the user experience. Furthermore, the mismatch between dynamic load and system tolerance will exacerbate device stress and shorten the equipment's lifespan.

[0024] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0025] like Figure 1 and Figure 2 As shown, an embodiment of the present invention provides a smart speaker power amplifier, including a power amplifier body 1, a power interface 2 and an audio signal input interface 3 installed on the power amplifier body 1, and a processor installed inside the power amplifier body 1. The processor, referring to the core control unit in the smart speaker power amplifier, is configured to execute a series of calculation and control instructions to achieve dynamic processing of audio signals and system protection. The processor can be a digital signal processor (DSP), a microcontroller (MCU), or any integrated circuit with computing capabilities. The processor is configured to perform the following steps:

[0026] The thermal state coefficient is calculated based on the power amplifier chip temperature and the speaker voice coil temperature. The thermal state coefficient is an indicator that quantifies the current thermal load and thermal risk of the system. This coefficient typically reflects the combined thermal state of the power amplifier chip and the speaker voice coil, and is used to guide subsequent dynamic range management.

[0027] The signal load factor is calculated based on the signal peak value, short-term RMS value, and low-frequency energy integral ratio (the ratio of low-frequency energy to full-frequency energy). The signal load factor is an indicator that quantifies the overall load demand of an audio signal on a power amplifier and speaker system. This factor comprehensively considers the transient, average energy, and low-frequency components of the signal.

[0028] Based on the thermal state coefficient, ambient noise sound pressure level, and signal load factor, the dynamic load-system tolerance matching degree is calculated. This matching degree is an indicator that quantifies the degree of matching between the current signal load and the system's thermal tolerance and environmental requirements. It reflects the "comfort" of the system in processing the current signal.

[0029] The dynamic response coefficient is calculated based on the acceleration of signal parameter changes (the second derivative of instantaneous peak factor with respect to time) and the power supply voltage drop rate. The dynamic response coefficient is an indicator that quantifies the urgency of the system's need for a rapid response. This coefficient reflects the urgency of the system needing to take immediate intervention measures when faced with sudden signal changes or power fluctuations.

[0030] Based on the signal load factor, dynamic load-system tolerance matching degree, and dynamic response coefficient, the target compression ratio is calculated and obtained, and the current compression ratio is adjusted to the target compression ratio.

[0031] Through the above process, the smart speaker power amplifier can dynamically adjust the compression ratio of the audio signal according to the real-time thermal state, signal characteristics, environmental noise and system response requirements, thereby effectively protecting the system hardware and optimizing the user's listening experience in complex environments while ensuring sound quality.

[0032] Traditional power amplifiers, when faced with user A playing high-dynamic-range rock music in a noisy environment and after the system has been running for an extended period, typically employ a fixed compression threshold or a simple thermal protection mechanism. For example, a traditional solution might only trigger a significant attenuation or shutdown when the amplifier chip temperature reaches a certain hard threshold. This "fuse-on" protection response is delayed and cannot provide preventative adjustments in advance. Furthermore, its dynamic range management may fail to distinguish between the transient characteristics of rock music and sustained low-frequency loads, leading to over-compression that results in a loss of musical expressiveness, or under-compression that causes distortion.

[0033] In contrast, this embodiment achieves a refined perception of system status and signal characteristics by comprehensively evaluating multiple dimensions, including thermal state coefficient, signal load factor, dynamic load-system tolerance matching degree, and dynamic response coefficient. For example, in the above example, the system not only considers the power amplifier chip temperature but also incorporates the speaker voice coil temperature, and combines the signal peak value, RMS value, and low-frequency energy integral ratio to comprehensively evaluate the signal load. This comprehensive consideration of multiple parameters enables the system to identify potential risks and demands earlier and more accurately.

[0034] Furthermore, this embodiment organically combines thermal state, environmental noise, and signal load by calculating the dynamic load-system tolerance matching degree, forming an index reflecting the system's "comfort." This enables the system to intelligently determine whether intervention is needed based on the actual working environment and signal characteristics. In addition, the introduction of the dynamic response coefficient allows the system to respond quickly and proactively to drastic signal changes and transient power supply fluctuations, avoiding the lag inherent in traditional solutions.

[0035] Ultimately, based on these multi-dimensional evaluation results, the system can calculate and adjust to a target compression ratio, achieving adaptive dynamic range management of the audio signal. This dynamic and intelligent compression adjustment mechanism avoids the drawbacks of traditional fixed compression strategies. It effectively protects the power amplifier chip and speaker voice coil from overheating and overload damage, while optimizing the listening experience according to environmental and signal characteristics. This ensures that the audio system always operates at its best in complex and ever-changing usage scenarios, thereby significantly improving the performance, reliability, and user experience of the smart speaker power amplifier.

[0036] This application further proposes that the step of calculating and obtaining the target compression ratio is as follows:

[0037] The signal load factor, dynamic load-system tolerance match, and dynamic response coefficient are acquired. These coefficients are key input parameters for evaluating the current system state and signal characteristics. They can be calculated by the processor through sensor data acquisition and preprocessing, or provided by other modules. For example, the signal load factor reflects the current audio signal's driving demand on the power amplifier, the dynamic load-system tolerance match reflects the power amplifier's capacity under current thermal conditions and environmental noise, and the dynamic response coefficient reflects the system's emergency response requirements to sudden signal changes or power fluctuations. Accurate acquisition of these parameters is fundamental to the subsequent precise calculation of the target compression ratio.

[0038] Based on the signal load factor, a linear interpolation is performed between the preset minimum compression ratio and the maximum compression ratio to obtain the base compression ratio; the specific calculation method is as follows: the signal load factor is imported into the formula. Obtain the base compression ratio This formula is intended to be based on the signal load factor. An initial compression ratio is determined within the linear range. Wherein, and These represent the minimum and maximum allowable compression ratios of the system, respectively. They can be preset fixed values, such as those determined based on the design limits of the power amplifier and speaker, or they can be parameters that are dynamically adjusted based on long-term operating data or user preferences. Signal load factor The value range is usually from 0 to 1, when When approaching 0, the basic compression ratio is close to This indicates that the signal load is low and no excessive compression is needed; when When the base compression ratio is close to 1, it is close to... This indicates a high signal load, requiring stronger compression. This step ensures that the baseline adjustment of the compression ratio is closely related to the characteristics of the signal itself.

[0039] The target compression ratio is obtained by multiplying the base compression ratio by a correction factor, which increases as the dynamic load-system tolerance matching degree decreases and the dynamic response coefficient increases. Specifically, the calculation method involves importing the base compression ratio, dynamic load-system tolerance matching degree, and dynamic response coefficient into the formula. Obtain the target compression ratio ,in, Based on the compression ratio, The matching degree weights range from 0 to 1. Used to balance dynamic load-system tolerance matching and dynamic response coefficient The impact on the target compression ratio. For example, when When the value is high, the system will place more emphasis on matching degree. To adjust the compression ratio; when When the value is low, more emphasis is placed on the dynamic response coefficient. To adjust, Specifically, values ​​can be assigned through preset strategies or dynamic algorithms. For dynamic load-system tolerance matching, This is the total correction strength coefficient, ranging from 0 to 1, used to control the overall influence of these correction factors on the final target compression ratio. When the value is low (indicating poor system matching, such as high thermal risk), The number of terms will increase, thereby increasing the target compression ratio; when A higher value (indicating an urgent need for response) will directly increase the target compression ratio. In this way, the target compression ratio not only considers the signal load, but also the thermal state of the power amplifier, environmental factors, and the system's response requirements to rapid changes, achieving multi-dimensional and adaptive compression ratio calculation. This represents the dynamic response coefficient.

[0040] This application's solution employs a step-by-step calculation approach. First, a base compression ratio is determined based on the signal load factor, directly reflecting the current audio signal's fundamental requirements for the system. Building upon this, a dynamic load-system tolerance matching degree and a dynamic response coefficient are further introduced, adjusting the base compression ratio through a weighted correction term. This two-stage calculation method ensures that the determination of the target compression ratio responds to the characteristics of the signal itself, fully considers the internal health of the power amplifier (e.g., thermal state) and the external environment (e.g., ambient noise), as well as the system's ability to handle transient changes. This is achieved by introducing a matching degree weight. and total correction strength coefficient The system can flexibly adjust the influence of different factors on the final compression ratio, thereby achieving fine-grained control of the power amplifier output and avoiding over- or under-compression that may result from a single-factor decision. This comprehensive calculation model enables the intelligent speaker power amplifier to determine the optimal compression ratio more intelligently and accurately to cope with complex and ever-changing operating conditions.

[0041] The following is a concrete example to illustrate this. Assume the processor in the aforementioned smart speaker power amplifier is a high-performance digital signal processor (DSP), internally running firmware that implements the aforementioned calculation logic. At a certain moment, the DSP obtains the signal load factor. The dynamic load-system tolerance matching degree is 0.6. A value of 0.4 (indicating a low match between system capacity and signal load, potentially indicating a thermal risk), dynamic response coefficient A value of 0.7 indicates rapid signal changes or power fluctuations, requiring a fast response. This assumes the system's preset minimum compression ratio. The maximum compression ratio is 1.0. The matching score is 8.0, with a weight of 8.0. The total corrected strength coefficient is 0.5. The initial compression ratio is 0.6. First, the DSP calculates a base compression ratio of 5.2. Next, the DSP calculates a target compression ratio of 7.228. Finally, the DSP uses the calculated target compression ratio of 7.228 as the current compression ratio value to be adjusted. In this way, even if the signal load factor is not at its highest, the target compression ratio is moderately increased due to the low system matching degree and the need for fast response, in order to better protect the system and maintain performance.

[0042] Through the above technical solution, the intelligent speaker power amplifier can achieve a more accurate and adaptive target compression ratio. This calculation method overcomes the limitations of traditional compression ratio adjustment strategies, which rely on only one or a few parameters, resulting in an inability to balance sound quality and system protection under complex operating conditions. This solution quantifies and organically combines signal load, system health status, and dynamic response requirements, enabling more refined and intelligent adjustment of the compression ratio. This helps to effectively prevent damage to the power amplifier and speakers due to overload, overheating, or transient impacts while ensuring sound quality, thereby extending the lifespan of the equipment and improving the user's listening experience in various usage scenarios. Simultaneously, the adjustable weighting coefficients provide the system with flexible configuration space, allowing it to adapt to the needs of different design goals and application environments.

[0043] This application further proposes that the step of calculating and obtaining the dynamic load-system tolerance matching degree is as follows:

[0044] Obtain the thermal state coefficient, ambient noise sound pressure level, and signal load factor; thermal state coefficient This characterizes the current thermal load status of the power amplifier chip and speaker voice coil. Its function is to quantify the potential risks arising from temperature increases within the system, providing crucial internal state information for subsequent dynamic load assessment. For example, the temperature of the power amplifier chip and speaker voice coil can be monitored in real time using temperature sensors, and calculated using preset temperature thresholds or models. The ambient noise sound pressure level reflects the noise level of the external environment in which the smart speaker operates. Its function is to assess the impact of the external environment on the speaker system's performance and the user's listening experience. For example, ambient sound can be collected in real time using a microphone array integrated into the smart speaker, and the collected sound signals can be analyzed to obtain the ambient noise sound pressure level. Signal load factor This characterizes the overall load demand of the current input audio signal on the power amplifier and speaker system. Its function is to quantify the stress exerted on the system by the characteristics of the signal itself. For example, by analyzing the input audio signal in real time, parameters such as peak value, short-term RMS value, and low-frequency energy integral ratio can be extracted, and the signal load factor can be calculated according to a preset algorithm.

[0045] The environmental noise sound pressure level is normalized to a minimum and maximum value to obtain the environmental interference coefficient. This process aims to transform the raw environmental noise sound pressure level data into a unified, dimensionless index. Its purpose is to eliminate differences in measurement units and ranges of different environmental noise levels, allowing for effective comparison and calculation with other normalized system parameters. For example, by setting a minimum environmental noise sound pressure level (e.g., 30 dB) and a maximum environmental noise sound pressure level (e.g., 90 dB), and then mapping the real-time measured environmental noise sound pressure level to the range of 0-1, the environmental interference coefficient can be obtained.

[0046] The thermal state coefficient, environmental interference coefficient, and signal load factor are imported into the formula. Obtain dynamic load-system tolerance matching degree , The output range is 0-1, reflecting the degree of matching between the current signal load and the system's thermal tolerance and environmental requirements. 1 indicates a perfect match, meaning the system can easily handle the current signal; 0 indicates a severe mismatch, requiring strong intervention. The thermal state coefficient, This represents the environmental interference coefficient. As an environmental noise weighting factor, it can be adjusted according to the actual application scenario and user experience requirements, or it can be assigned a value through a preset strategy or dynamic algorithm to balance the impact of environmental noise on the system tolerance assessment. For signal load factor, This is a very small positive constant, its purpose being to prevent division by zero errors in the denominator under extreme conditions, thereby ensuring the stability and robustness of the calculation. This step is to calculate the dynamic load-system tolerance matching degree. The core function of this formula is to comprehensively consider signal load, internal system thermal state, and external environmental interference to quantify the degree of matching between the current signal load and the system's processing capacity and environmental requirements. This formula uses the signal load factor... With system tolerance (derived from thermal state coefficient) and environmental interference coefficient (A joint decision) is made to compare and assess whether the system is overloaded or underloaded.

[0047] The solution in this application first obtains the internal thermal state coefficient of the system through the above steps. The environmental noise sound pressure level is normalized to an environmental interference coefficient. Simultaneously, the load factor of the current audio signal is obtained. The processor then substitutes these parameters into a preset formula to calculate the dynamic load-system tolerance matching degree. This formula cleverly incorporates the signal load. With system tolerance (by and (Together determined) comparison, when signal load When the load is less than the system tolerance, the dynamic load-system tolerance matching degree A value close to 1 indicates that the system can easily handle the current signal; when the signal load... When the load exceeds the system tolerance, the dynamic load-system tolerance matching degree This will decrease accordingly, indicating a risk of system mismatch. In this way, the dynamic load-system tolerance matching degree is determined. It is precisely calculated, with an output range of 0 to 1, and can intuitively reflect the degree of matching between the current signal load and the system's thermal tolerance and environmental requirements. This dynamic load-system tolerance matching degree... This was then used to calculate the target compression ratio, so that the adjustment of the compression ratio was no longer based solely on signal characteristics and thermal state, but also took into account interference from the external environment, thus enabling the power amplifier to perform dynamic range compression more intelligently and precisely.

[0048] The following is a concrete example to illustrate this. The processor in a smart speaker power amplifier can be a high-performance digital signal processor (DSP). This DSP is programmed to perform the steps described above for calculating the dynamic load-system tolerance matching. Specifically, the processor obtains the real-time temperatures of the amplifier chip and speaker voice coil via connected temperature sensors and calculates the thermal state coefficient according to a preset lookup table. Simultaneously, an ambient sound is acquired in real time using a microphone array integrated into the speaker, and the sound pressure level (SPL) of the acquired audio data is calculated to obtain the ambient noise SPL. This ambient noise SPL is then processed using a linear mapping function to perform max-min normalization, thereby obtaining the ambient interference coefficient. In addition, the processor performs real-time analysis of the input audio signal, extracting its instantaneous peak value, short-term RMS value, and low-frequency energy integral ratio, and calculates the signal load factor according to a preset weighted sum of squares formula. After obtaining , and The DSP then imports these parameters into its internal firmware to implement the formula. The calculations are performed in the middle.

[0049] Through the above technical solutions, intelligent speaker power amplifiers can more comprehensively and accurately assess the matching degree between the current signal load, system processing capacity, and external environment. When calculating the target compression ratio, a dynamic load-system tolerance matching degree is introduced. This allows compression strategies to be developed not just from a single perspective, but comprehensively considering the system's internal thermal state, external noise interference, and the signal's inherent load requirements. This enables power amplifiers to perform more intelligent and precise dynamic range compression based on actual operating conditions, avoiding aggressive signal amplification under high heat loads or noisy environments, thus effectively preventing the risks of overheating, distortion, or damage. Simultaneously, when system tolerance is sufficient and the environment is quiet, unnecessary compression can be reduced, maximizing the preservation of audio dynamic range and sound quality. This adaptive matching evaluation mechanism significantly improves the stability and reliability of smart speakers in various complex usage scenarios and optimizes the user's listening experience.

[0050] This application further proposes that the step of calculating and obtaining the signal load factor is as follows:

[0051] The acquisition of signal peak value, short-term RMS value, and low-frequency energy integral ratio is crucial. The signal peak value refers to the maximum instantaneous amplitude of an audio signal within a specific time window. It directly reflects the instantaneous impact of the signal and significantly influences the transient power output capability of the power amplifier and the mechanical stress of the speaker. This peak value can be obtained by real-time sampling of the input audio signal using a digital signal processor (DSP) and detecting and recording the maximum absolute value within a preset time window (e.g., a few milliseconds to tens of milliseconds). Alternatively, an analog peak detection circuit can be used to convert the analog signal into a digital signal for processing. The short-term RMS value refers to the root mean square value of the audio signal within a short time window. It reflects the average power level of the signal and is closely related to the continuous power output capability of the power amplifier and the thermal load of the speaker. This value can be calculated by squaring, summing, averaging, and taking the square root of the digital sampling points. The time window is typically tens to hundreds of milliseconds, or it can be achieved using an analog RMS-to-DC converter. The low-frequency energy integral ratio refers to the ratio of low-frequency energy to the total energy across the entire frequency range. Low-frequency signals typically place higher demands on speaker diaphragm displacement and power amplifier power supplies, making them a significant factor in system overload and damage. This ratio reflects the intensity of the low-frequency components of the signal. This ratio can be obtained by performing frequency domain analysis on the audio signal (such as Fast Fourier Transform, FFT), decomposing the signal into different frequency components, and then calculating the ratio of the total energy of a specific low-frequency band (e.g., 20Hz-200Hz) to the total energy of the entire audible frequency range (e.g., 20Hz-20kHz). Another approach is to use a digital filter bank to separate the signal into low-frequency and full-frequency components, and then calculate the energy of each component.

[0052] The instantaneous peak factor is obtained by comparing the signal peak value with the short-term RMS value. The instantaneous peak factor quantifies the dynamic range and transient characteristics of a signal, representing the intensity of its instantaneous impact relative to its average power. A high peak factor indicates that the signal has sharp transient components, posing a challenge to the transient response capability of power amplifiers and the impact resistance of loudspeakers. This ratio can be directly obtained by dividing the acquired signal peak value by the short-term RMS value.

[0053] The instantaneous peak factor is normalized to its minimum and maximum values ​​to obtain the instantaneous peak factor exponent. Minimum normalization is a common method for mapping data to a specific range (usually 0 to 1). The original value of the instantaneous peak factor may vary considerably; normalization converts it into a uniform, easily comparable, and subsequently calculated exponent. This helps eliminate the influence of differences in peak factor values ​​from different signal sources or system settings, giving it a more stable weight and meaning in subsequent formulas. The normalization formula is typically: Exponent = (Original value - Minimum value) / (Maximum value - Minimum value). The minimum and maximum values ​​can be preset empirical values ​​or obtained through long-term monitoring and statistics.

[0054] The signal load factor is obtained by taking the square root of the weighted sum of squares of the instantaneous peak factor exponent and the low-frequency energy integral ratio, where the weighting coefficients are used to adjust the relative contribution of the two to the overall load. Specifically, the instantaneous peak factor exponent and the low-frequency energy integral ratio are imported into the formula... Obtain the signal load factor , Output range 0-1, Used to characterize the overall load demand of the system on the signal; among which... The instantaneous peak factor exponent, For low-frequency energy integral ratio, This outputs weighting coefficients ranging from 0 to 1, parameters used to adjust the importance of different factors in decision-making. These values ​​can be assigned through preset strategies or dynamic algorithms. This is achieved by using the instantaneous peak factor exponent. (Reflecting transient impact and dynamic range) and low-frequency energy integral ratio The square root of the weighted sum of squares (reflecting the stress of low-frequency energy on speaker displacement and power amplifier power supply) effectively integrates these two key load factors. This integration method provides a more comprehensive assessment of the signal's stress on the system, avoiding the limitations of a single indicator. Among these factors, the weighting coefficients... (Value range 0-1) Allows the system to adjust the relative importance of the instantaneous peak factor exponent and the low-frequency energy integral ratio in the signal load factor according to actual needs or experience. For example, for subwoofer applications, the weight of the low-frequency energy integral ratio can be appropriately increased.

[0055] The solution in this application first acquires three key parameters of the audio signal: the signal peak value, the short-term RMS value, and the low-frequency energy integral ratio, to comprehensively capture the transient impact, average power, and low-frequency energy distribution of the signal. Subsequently, the signal peak value is compared with the short-term RMS value to obtain the instantaneous peak factor. This factor is then subjected to max-min normalization to form the instantaneous peak factor exponent. This is used to quantize the dynamic range and transient characteristics of a signal. Simultaneously, the low-frequency energy integral ratio... This directly reflects the intensity of the low-frequency components of the signal. Ultimately, these two processed indicators... and The signal load factor is calculated by incorporating the square root formula of a weighted sum of squares. This fusion of multi-dimensional parameters makes the signal load factor... This approach enables a more accurate and comprehensive characterization of the overall load demand of audio signals on intelligent speaker power amplifier systems. In this way, the system overcomes the limitations of evaluating signal load using a single indicator, paving the way for subsequent dynamic load-system tolerance matching. The calculations provide more accurate and reliable input, thus ensuring that the power amplifier can dynamically adjust based on more realistic load conditions, effectively avoiding overload and distortion.

[0056] The following is a concrete example. In a smart speaker power amplifier, the processor can be a high-performance digital signal processor (DSP), such as the Analog Devices ADAU1467. This processor receives digital audio streams either through its built-in analog-to-digital converter (ADC) or from an external ADC. When acquiring the signal peak value, short-term RMS value, and low-frequency energy integral ratio, the processor can continuously monitor the absolute value of the digital audio samples within each 10-millisecond sampling window and record the maximum absolute value within that window as the signal peak value. Simultaneously, within each 50-millisecond sampling window, the processor squares, sums, averages, and square roots the digital audio samples to obtain the short-term RMS value. For the low-frequency energy integral ratio, the processor can perform real-time Fast Fourier Transform (FFT) analysis on the digital audio signal, calculating the total energy in the 20Hz-200Hz frequency band and the total energy in the 20Hz-20kHz frequency band within each 100-millisecond analysis period, and then calculating the ratio between the two. When acquiring the instantaneous peak factor, the processor divides the acquired signal peak value by the short-term RMS value. For example, if the signal peak value is 0.8V and the short-term RMS value is 0.2V, then the instantaneous peak factor is 4. The processor then performs max-min normalization on the instantaneous peak factor to obtain the instantaneous peak factor exponent. Assume the preset minimum instantaneous peak factor is 1.414 (sine wave) and the maximum is 10 (extreme transient signal). If the calculated instantaneous peak factor is 4, then the instantaneous peak factor exponent is calculated to be 0.30. Finally, the processor calculates the instantaneous peak factor exponent... Low-frequency energy integral ratio (For example, 0.6) Substitute into the formula Assuming weighting coefficients Setting it to 0.5 results in a signal load factor of 0.47. The final signal load factor is then obtained. It is approximately 0.47.

[0057] Through the above technical solution, the intelligent speaker power amplifier can comprehensively calculate and obtain the signal load factor based on the signal peak value, short-term RMS value, and low-frequency energy integral ratio. This multi-dimensional and comprehensive evaluation method enables the system to have a more accurate and comprehensive understanding of the actual load caused by audio signals. Compared to relying on only a single signal parameter, this scheme can more accurately capture the stress on the system caused by transient signal impacts and sustained low-frequency energy, thus providing a basis for subsequent dynamic load-system tolerance matching. The calculations provide more reliable input. This helps smart speaker power amplifiers make more accurate dynamic range compression decisions under various complex audio signal conditions, effectively preventing overload, distortion, and equipment damage, while optimizing sound quality performance and avoiding unnecessary over-compression, thereby improving system stability and user experience.

[0058] This application further proposes that the step of calculating and obtaining the dynamic response coefficient is as follows:

[0059] The acceleration of signal parameter changes and the power supply voltage sag rate are obtained. The acceleration of signal parameter changes refers to the second derivative of the instantaneous peak factor of an audio signal with respect to time. This parameter reflects the severity and speed of dynamic changes in the signal. The acceleration of signal parameter changes is used to quantify the severity of transient changes in an audio signal. When the instantaneous characteristics of an audio signal (e.g., the instantaneous peak factor) change rapidly and significantly, it indicates that the system is facing a dynamic event requiring a rapid response. It can be obtained, for example, by taking the second derivative of the instantaneous peak factor of the signal, or by monitoring the RMS value of the signal or the second derivative of the rate of change of the peak value over a very short time. The power supply voltage sag rate is used to measure the stability of the power amplifier's power supply. When the power supply voltage drops rapidly due to high power output demands or insufficient load capacity of the power supply itself, it indicates that the system's power supply capacity may be insufficient, posing a potential risk of distortion or damage. It can be obtained, for example, by monitoring the power supply voltage in real time and calculating its percentage drop relative to a preset rated voltage or the voltage at the previous moment, or by comparing the current voltage with the average voltage over a period of time.

[0060] The acceleration of signal parameter changes is normalized by a maximum-minimum process to obtain the acceleration exponent. This process aims to convert the original acceleration values ​​into a unified, dimensionless exponent, typically ranging from 0 to 1. This normalization process allows for effective comparison and fusion of data from different sources and with different dimensions. For example, a linear mapping can be used to map the minimum acceleration value to 0, the maximum value to 1, and intermediate values ​​proportionally.

[0061] The current power supply voltage sag rate is compared to a voltage sag rate threshold, and then a min function with an upper limit of 1 is applied to obtain the power supply voltage sag rate index. This process quantifies the severity of the voltage sag and converts it into an index within the range of 0 to 1. By comparing this index with a preset voltage sag rate threshold, the relative severity of the sag can be determined. The upper limit of the min function (1) ensures that even if the sag is very severe, the index will not exceed 1, thus avoiding excessive and unreasonable weighting in subsequent calculations.

[0062] The larger of the signal parameter change acceleration exponent and the power supply voltage drop rate exponent is taken as the dynamic response coefficient. By comparing two indices (signal transient changes and power supply stability) and selecting the larger one, it is ensured that both drastic changes in the signal and sudden instability of the power supply can be captured by the system in a timely manner and used as the main basis for emergency response. The output range is 0-1, reflecting the urgency of the system's need for a rapid response. 0 indicates no emergency, while 1 indicates that the compression ratio needs to be increased significantly immediately to prevent distortion or crashes.

[0063] This application's solution constructs a rapid response mechanism for dynamic emergencies by introducing real-time monitoring and evaluation of signal parameter change acceleration and power supply voltage sag rate. The processor first acquires the current signal parameter change acceleration and power supply voltage sag rate, which reflect the transient pressure the system may face from the signal input and power supply ends, respectively. To enable effective comparison and comprehensive judgment of these two parameters with different properties, the signal parameter change acceleration is subjected to max-min normalization, resulting in a signal parameter change acceleration exponent ranging from 0 to 1. Simultaneously, the current power supply voltage sag rate is compared with a preset voltage sag rate threshold, and its upper limit is limited to 1 using a min function to obtain a power supply voltage sag rate exponent, also ranging from 0 to 1. Subsequently, the processor selects the larger value from these two exponents as the dynamic response coefficient. This selection mechanism ensures that whether it's a sudden, drastic change in signal or unexpected power instability, as long as either factor reaches an urgency level, the system can promptly detect it and respond with the highest level of urgency. This dynamic response coefficient... This information is then incorporated into the formula for calculating the target compression ratio. Working in conjunction with factors such as the base compression ratio and the dynamic load-system tolerance matching, the system can rapidly adjust the compression ratio based on real-time dynamic risks, enabling preventative intervention against transient overloads. In this way, the system can not only cope with long-term or slowly changing loads, but also provide rapid and effective protection against sudden, high-risk dynamic events, significantly improving the stability and sound quality of smart speakers in complex signal and power environments.

[0064] In one specific implementation, the processor can be a high-performance digital signal processor (DSP), such as Texas Instruments' (TI) C6000 series DSP. When acquiring the acceleration of signal parameter changes, the DSP can monitor the instantaneous peak factor (CPF) of the audio signal in real time, perform two differential operations on it, and then smooth it through a low-pass filter to eliminate high-frequency noise, thus obtaining the acceleration of signal parameter changes. For example, when the CPF rapidly increases from 3dB to 10dB within 1 millisecond, its second derivative will increase significantly. For acquiring the power supply voltage sag rate, the DSP can monitor the DC supply voltage of the power amplifier in real time at a high sampling rate (e.g., once every 100 microseconds) using a built-in analog-to-digital converter (ADC) or an external ADC. The acquired voltage value is compared with a preset rated voltage (e.g., 48V) to calculate the percentage of voltage sag. For example, if the rated voltage is 48V and the current voltage has dropped to 43.2V, the sag rate is 10%. The normalization of the signal parameter change acceleration exponent can be achieved using a linear mapping function, mapping the expected minimum change in acceleration to 0 and the maximum change to 1. The power supply voltage sag rate exponent can be calculated by setting a voltage sag rate threshold, for example, 15%. If the current sag rate is 10%, the exponent is 10% / 15% = 0.67; if the sag rate is 20%, the exponent is 20% / 15% = 1.33. In this case, the exponent is limited to 1 by a min function. Finally, the DSP compares these two normalized exponents. For example, if the signal parameter change acceleration exponent is 0.7 and the power supply voltage sag rate exponent is 0.9, the larger value of 0.9 is taken as the dynamic response coefficient. .

[0065] Through the above technical solution, the intelligent speaker power amplifier can effectively solve the problem of insufficient response of traditional compression mechanisms based on thermal state and average signal load in emergency situations such as drastic signal transient changes or sudden drops in power supply voltage. By monitoring the acceleration of signal parameter changes and the power supply voltage drop rate in real time, the system can detect potential overload or power insufficiency risks in advance and quantify them as dynamic response coefficients. This coefficient directly participates in the calculation of the target compression ratio, enabling the system to quickly and proactively adjust the compression ratio based on the dynamic urgency within a very short time. This not only effectively prevents transient distortion and clipping, significantly improving the sound quality performance of audio equipment when playing audio content with a wide dynamic range and high transient response requirements, but also protects the amplifier chip and speakers from damage during power fluctuations, thereby improving system stability and reliability and extending the lifespan of the equipment.

[0066] This application further proposes that the step of stating the thermal state coefficient is as follows:

[0067] Obtaining the power amplifier chip temperature and speaker voice coil temperature is crucial. The power amplifier chip temperature refers to the real-time operating temperature of the semiconductor chip inside the power amplifier used to amplify audio signals. This temperature can be measured using a temperature sensor integrated within the power amplifier chip or an external thermistor. The speaker voice coil temperature refers to the real-time operating temperature of the voice coil inside the speaker that drives the diaphragm to produce sound. The speaker voice coil temperature can be indirectly estimated using a temperature sensor (such as a PTC thermistor) installed near the voice coil or by measuring changes in the voice coil resistance. This temperature data forms the basis for assessing the system's thermal load.

[0068] The amplifier chip temperature and speaker voice coil temperature are subjected to maximum-minimum normalization to obtain the amplifier chip temperature index and speaker voice coil temperature index. The purpose of this step is to unify temperature values ​​of different dimensions or ranges into a standardized interval (e.g., 0 to 1) to facilitate subsequent comparisons and calculations. Maximum-minimum normalization is typically achieved by subtracting a preset minimum reference temperature from the current temperature value and then dividing by the difference between the preset maximum and minimum reference temperatures. For example, the minimum safe temperature for the amplifier chip can be set to 0°C, and the maximum allowable temperature to be 100°C; the normalized index will then reflect the relative position of the current temperature within the safe range. Another implementation method is to use the Sigmoid function or Tanh function for non-linear normalization to better reflect the impact of temperature changes on the risk level.

[0069] The fatigue factor is calculated based on the power amplifier chip temperature and the speaker voice coil temperature. The fatigue factor aims to quantify the cumulative damage or stress of a system under long-term or high-intensity thermal loads. It not only reflects instantaneous temperature but also considers the impact of factors such as temperature duration and rate of temperature change on component lifespan. One calculation method uses a thermal accumulation model, such as evaluating thermal stress by integrating the relationship between temperature and time. Another method estimates material fatigue based on the number of temperature cycles and temperature amplitude, for example, using Miner's rule or a simplified form of the Coffin-Manson equation, taking historical temperature data of the power amplifier chip and speaker voice coil as input to calculate an index reflecting the degree of cumulative thermal fatigue.

[0070] The maximum value among the power amplifier chip temperature index, the speaker voice coil temperature index, and the fatigue factor is taken as the thermal state coefficient. , Output range 0-1, A value of 0 indicates no thermal risk. A value of 1 indicates that the thermal protection limit has been reached, requiring maximum compression to reduce heat generation. This step aims to comprehensively consider multiple sources of thermal risk to the system and use the most severe risk as the current thermal state coefficient of the system. By taking the maximum value, it ensures that if the instantaneous temperature of any critical component becomes too high, or if the accumulated thermal fatigue of the system reaches a certain level, it will be reflected in a timely manner. This triggers the corresponding protection mechanism.

[0071] This application's solution accurately assesses the overall thermal state of a smart speaker power amplifier by comprehensively considering the amplifier chip temperature, speaker voice coil temperature, and accumulated thermal fatigue of the system. First, the amplifier chip temperature and speaker voice coil temperature are acquired in real time, as these are the most direct indicators of thermal load within the system and at the output. To eliminate inherent differences between different temperature sensors and components and to unify them to a comparable scale, these temperature values ​​are subjected to maximum-minimum normalization, resulting in the amplifier chip temperature index and speaker voice coil temperature index, which intuitively reflect the instantaneous thermal risk level of each component. Based on this, this solution further introduces the calculation of a fatigue factor. The fatigue factor does not simply reflect instantaneous temperature but quantifies the cumulative damage or lifespan loss of the system under continuous thermal stress through long-term monitoring and analysis of the amplifier chip temperature and speaker voice coil temperature. This enables the system not only to cope with sudden high temperatures but also to identify and manage potential risks from long-term operation. Finally, by comparing the amplifier chip temperature index, speaker voice coil temperature index, and fatigue factor, the maximum value is selected as the thermal state coefficient. This "maximum value" strategy ensures that as long as any critical hotspot or accumulated fatigue in the system reaches the warning level, This will accurately reflect the most pressing thermal risks. The output range is limited to between 0 and 1, where 0 indicates no thermal risk and 1 indicates that the thermal protection limit has been reached. This design allows... This can serve as a unified and comprehensive thermal risk indicator, effectively inputting it into subsequent dynamic load-system tolerance matching calculations. In this way, the thermal state coefficient... It can more comprehensively and accurately characterize the thermal load condition of the power amplifier, avoiding misjudgments that may result from relying on a single temperature index. A higher value indicates a higher thermal risk in the system, which will prompt a change in the dynamic load-system tolerance matching degree. This reduces the target compression ratio, thereby reducing output power, decreasing heat generation, and effectively protecting the power amplifier chip and speaker from overheating damage.

[0072] As a specific implementation, the processor in the smart speaker power amplifier can be configured to perform the following operations: First, acquire the power amplifier chip temperature using a digital temperature sensor integrated within the power amplifier chip. Simultaneously, install an NTC thermistor near the speaker voice coil, and acquire the speaker voice coil temperature by measuring its resistance value and combining it with a preset resistance-temperature reference table. Next, normalize the acquired power amplifier chip temperature and speaker voice coil temperature. For example, for the power amplifier chip temperature, its safe operating range can be set to 20℃ to 90℃, then the normalization formula can be (...). -20) / (90-20). For the speaker voice coil temperature, its safe operating range can be set to 25℃ to 120℃, then the normalization formula can be ( -25) / (120-25). This yields the power amplifier chip temperature index and the speaker voice coil temperature index, both ranging from 0 to 1. Subsequently, a fatigue factor is calculated based on this temperature data. For example, a simplified thermal accumulation model can be used, weighted by the average value and duration of the power amplifier chip temperature and speaker voice coil temperature over a period of time, or by tracking the accumulation time of temperatures exceeding a certain threshold to estimate fatigue. Specifically, a reference temperature threshold can be set; when any temperature exceeds this threshold, fatigue value accumulation begins, with the accumulation rate proportional to the degree to which the threshold is exceeded. Finally, the processor compares the power amplifier chip temperature index, the speaker voice coil temperature index, and the calculated fatigue factor, selecting the largest of these three values ​​as the thermal state coefficient. For example, if the power amplifier chip temperature index is 0.7, the speaker voice coil temperature index is 0.65, and the fatigue factor is 0.8, then the thermal state coefficient... It will be determined to be 0.8. The value was then used in subsequent dynamic load-system tolerance matching calculations to guide the adjustment of the compression ratio.

[0073] Through the above technical solution, the intelligent speaker power amplifier can more comprehensively and accurately assess its thermal load. Traditional temperature monitoring may only focus on a single hot spot or instantaneous temperature, easily overlooking the thermal stress accumulated in the system during long-term operation or the differences in thermal risk between different components. This solution introduces the power amplifier chip temperature index, the speaker voice coil temperature index, and the fatigue factor, and takes the maximum value of the three as the thermal state coefficient. This ensures that the system can simultaneously consider both instantaneous overheating risks and long-term thermal fatigue risks. This comprehensive evaluation method makes the thermal state coefficient... It can more sensitively and accurately reflect the most significant thermal threats facing the system. When When the value increases, the system can promptly identify and respond to potential thermal risks, reducing output power by adjusting the compression ratio. This effectively prevents the power amplifier chip and speaker voice coil from being damaged due to overheating, significantly improving the reliability and lifespan of the smart speaker, while avoiding situations where sound quality or performance is sacrificed due to excessive conservative protection.

[0074] This application further proposes that the specific method for calculating and obtaining the fatigue factor is as follows:

[0075] This system acquires the power amplifier chip temperature and speaker voice coil temperature at the current sampling point, as well as the fatigue accumulation value at the previous sampling point. Acquiring the power amplifier chip temperature and speaker voice coil temperature at the nth sampling point aims to monitor the temperature status of the core heat-generating components of the power amplifier in real time. The power amplifier chip temperature directly reflects the power amplifier's workload and heat dissipation efficiency, while the speaker voice coil temperature reflects the speaker's thermal load under prolonged high-power output. This temperature data can be obtained using temperature sensors integrated inside or outside the power amplifier chip, such as thermistors, thermocouples, or semiconductor temperature sensors. Another approach is to utilize infrared thermometry to non-contactly acquire the surface temperature of critical components.

[0076] The fatigue accumulation value of the previous sampling point is attenuated by a forgetting factor and then added to the temperature exceedance level of the current sampling point to obtain the fatigue accumulation value of the current sampling point. The temperature exceedance level is calculated based on the maximum value of the power amplifier chip temperature and the speaker voice coil temperature relative to the reference temperature threshold and the maximum allowable temperature. The specific calculation method is as follows: the power amplifier chip temperature and the speaker voice coil temperature of the current sampling point are imported into the formula. Obtain the cumulative fatigue value of the current sampling point. ,in, A forgetting factor with a value ranging from 0 to 1, used to balance historical fatigue accumulation values. and the current instantaneous heat load The impact, Values ​​can be assigned using preset strategies or dynamic algorithms. When the value is close to 1, the historical fatigue value has a greater impact, and the system is less sensitive to short-term temperature fluctuations; when... When the temperature is close to 0, the impact of the current instantaneous heat load is greater, and the system responds more quickly to temperature changes; This is the cumulative fatigue value from the previous sampling point. The current sampling point's power amplifier chip temperature, The current sampling point represents the speaker voice coil temperature. This is a reference temperature threshold; temperatures below this are generally considered not to cause significant fatigue. This is the maximum permissible temperature; reaching this temperature indicates the system is in a limiting state. The formula uses an exponentially weighted moving average (EWMA) form, combining historical fatigue conditions and the current instantaneous heat load. The formula... This indicates that the higher of the power amplifier chip temperature and the speaker voice coil temperature is taken to reflect the most critical hotspot. The formula obtains the instantaneous thermal stress value by processing the current heat load with reference and limit temperatures, and then uses it as... The weights are added to the historical fatigue accumulation value, thereby achieving dynamic accumulation of fatigue.

[0077] The fatigue factor is obtained by comparing the cumulative fatigue value of the current nth sampling point with the maximum cumulative fatigue value and then using the min function to limit the upper limit to 1. The aim is to transform the cumulative fatigue value into a standardized fatigue factor, making it comparable and operable.

[0078] The fatigue factor is obtained by comparing the current accumulated fatigue value with the maximum accumulated fatigue value and then limiting the upper limit to 1 using a min function. The maximum accumulated fatigue value is a preset threshold representing the system's fatigue limit. By comparing the current accumulated fatigue value with this maximum value, a proportional value reflecting the current fatigue level can be obtained. The upper limit is limited to 1 using a min function to ensure that the fatigue factor does not exceed 1. That is, when the accumulated fatigue value reaches or exceeds the maximum accumulated fatigue value, the fatigue factor is 1, indicating that the system has reached its fatigue limit. This fatigue factor is a value between 0 and 1, intuitively representing the potential risk accumulated by the system due to long-term thermal stress.

[0079] In the aforementioned intelligent speaker power amplifier, to more comprehensively assess the system's thermal risks and incorporate them into the dynamic compression ratio adjustment mechanism, this application introduces the calculation of a fatigue factor. The fatigue factor calculation first acquires the power amplifier chip temperature and speaker voice coil temperature in real time; these two temperatures are the most direct indicators of the system's internal thermal load. Then, using an accumulation formula with a forgetting factor, the current highest temperature is compared with a reference temperature threshold and the maximum allowable temperature, and combined with the fatigue accumulation value from the previous moment, the current fatigue accumulation value is dynamically calculated. This accumulation mechanism allows the system to not only focus on instantaneous temperature but also "memorize" and reflect the potential thermal stress accumulation caused by long-term, high-load operation. Finally, this dynamically accumulated fatigue value is ratioed to a preset maximum fatigue accumulation value and then limited to obtain a standardized fatigue factor within the range of 0 to 1. This fatigue factor is then incorporated into the thermal state coefficient. In the calculation, the temperature index of the power amplifier chip and the temperature index of the speaker voice coil are jointly determined. In this way, the thermal state coefficient This not only reflects the risk of excessively high instantaneous temperatures, but more importantly, it incorporates the fatigue risk resulting from the long-term accumulation of thermal stress in the system. When a system is under high thermal load for an extended period, even if the instantaneous temperature does not reach its limit, the accumulated fatigue factor will gradually increase, thereby raising the thermal state coefficient. . The improvement will further affect the dynamic load-system tolerance matching degree. The calculation is reduced. The value, which in turn affects the target compression ratio. The increase in compression ratio means that when the system shows signs of fatigue due to long-term thermal stress, even if other conditions remain unchanged, the system will proactively increase the compression ratio and reduce the output power, thereby effectively alleviating the thermal load and preventing performance degradation or equipment damage caused by fatigue. This mechanism enables power amplifiers to manage thermal risks more intelligently and proactively, extend equipment life, and ensure stable and reliable audio output under various operating conditions.

[0080] In one specific implementation, the processor in the smart speaker power amplifier can be configured to perform the following operations to calculate the fatigue factor. First, the processor acquires the power amplifier chip temperature at a frequency of 10 times per second using a digital temperature sensor (e.g., a DS18B20 or NTC thermistor in conjunction with an ADC converter) connected to the power amplifier chip and the speaker voice coil. and speaker voice coil temperature These temperature data are sent to the processor's digital signal processing module. This is used to calculate the cumulative fatigue value. At this time, the forgetting factor λ can be set to 0.95, which means that the historical fatigue accumulation value accounts for 95% of the weight, while the current instantaneous thermal stress accounts for 5% of the weight, thus giving the fatigue accumulation process a certain degree of smoothness and memory. Reference temperature threshold It can be set to 50 degrees Celsius, the maximum allowable temperature. The temperature can be set to 90 degrees Celsius. The system begins to accumulate fatigue when any temperature exceeds 50 degrees Celsius. For example, if the current amplifier chip temperature is 80 degrees Celsius and the speaker voice coil temperature is 70 degrees Celsius, then the maximum value of 80 degrees Celsius is used. In this case, the instantaneous thermal stress term is calculated to be 0.75. If the fatigue accumulation value at the previous sampling point... If it is 0.2, then the current cumulative fatigue value is... It will be updated to 0.2275. Then, to obtain the fatigue factor, the currently calculated cumulative fatigue value can be... The ratio is calculated with a preset maximum cumulative fatigue value. For example, the maximum cumulative fatigue value can be set to 100. If the current... If the value is 0.2275, then the ratio is 0.2275 / 100 = 0.002275. By limiting the upper limit to 1 using the min function, the final fatigue factor is 0.002275. As the system operates under high heat load for an extended period... The fatigue factor will gradually increase until it reaches 1, indicating that the system is at its fatigue limit.

[0081] Through the aforementioned technical solution, the intelligent speaker power amplifier, when calculating the thermal state coefficient, not only considers instantaneous temperature but also incorporates an assessment of the system's long-term accumulated thermal stress. This introduction of a fatigue factor allows the system to more comprehensively and accurately perceive potential thermal risks, avoiding misjudgments that might occur based solely on instantaneous temperature. When the system accumulates fatigue due to prolonged high-load operation, even if the instantaneous temperature has not yet reached a dangerous threshold, the fatigue factor will cause the thermal state coefficient to rise, thus triggering an adjustment in the compression ratio and reducing output power in advance. This effectively prevents equipment performance degradation, shortened lifespan, or even damage caused by thermal fatigue. This significantly improves the reliability and durability of the power amplifier, ensuring that the audio system can operate continuously and stably under various usage scenarios, especially during prolonged high-intensity use, providing users with a more durable and higher-quality audio experience.

[0082] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart speaker power amplifier, comprising a power amplifier body, and a power interface and an audio signal input interface mounted on the power amplifier body, characterized in that, It also includes a processor installed inside the power amplifier body, the processor being configured to perform the following steps: The thermal state coefficient is calculated based on the power amplifier chip temperature and the speaker voice coil temperature. The signal load factor is calculated based on the signal peak value, short-term RMS value, and low-frequency energy integral ratio. Based on the thermal state coefficient, ambient noise sound pressure level, and signal load factor, the dynamic load-system tolerance matching degree is calculated and obtained. The dynamic response coefficient is calculated based on the acceleration of signal parameter changes and the power supply voltage drop rate. Based on the signal load factor, dynamic load-system tolerance matching degree, and dynamic response coefficient, the target compression ratio is calculated and obtained, and the current compression ratio is adjusted to the target compression ratio.

2. The intelligent speaker power amplifier according to claim 1, characterized in that, The steps for calculating and obtaining the target compression ratio are as follows: Obtain the signal load factor, dynamic load-system tolerance matching degree, and dynamic response coefficient; Based on the signal load factor, a linear interpolation is performed between the preset minimum compression ratio and the maximum compression ratio to obtain the base compression ratio; The target compression ratio is obtained by multiplying the base compression ratio by a correction factor, which increases as the dynamic load-system tolerance matching degree decreases and the dynamic response coefficient increases.

3. The intelligent speaker power amplifier according to claim 2, characterized in that, The steps for calculating and obtaining the dynamic load-system tolerance matching degree are as follows: Obtain the thermal state coefficient, ambient noise sound pressure level, and signal load factor; The environmental noise sound pressure level is normalized to its maximum and minimum values ​​to obtain the environmental interference coefficient. The thermal state coefficient, environmental interference coefficient, and signal load factor are imported into the formula. Obtain dynamic load-system tolerance matching degree , The output range is 0-1, reflecting the degree of matching between the current signal load and the system's thermal tolerance and environmental requirements. 1 indicates a perfect match, meaning the system can easily handle the current signal; 0 indicates a severe mismatch, requiring strong intervention. The thermal state coefficient, The environmental interference coefficient is... Environmental noise weighting factor, For signal load factor, It is a very small positive number.

4. The intelligent speaker power amplifier according to claim 3, characterized in that, The steps for calculating and obtaining the signal load factor are as follows: Acquire signal peak value, short-term RMS value, and low-frequency energy integral ratio; The instantaneous peak factor is obtained by comparing the signal peak value with the short-term RMS value. The instantaneous peak factor is normalized to its maximum and minimum values ​​to obtain the instantaneous peak factor exponent. The signal load factor is obtained by taking the square root of the weighted sum of squares of the instantaneous peak factor exponent and the low-frequency energy integral ratio, where the weighting coefficient is used to adjust the relative contribution of the two to the overall load.

5. The intelligent speaker power amplifier according to claim 3, characterized in that, The steps for calculating and obtaining the dynamic response coefficients are as follows: Acquire the acceleration of signal parameter changes and the power supply voltage drop rate; The acceleration of signal parameter changes is subjected to maximum-min normalization to obtain the exponent of the acceleration of signal parameter changes. The current power supply voltage drop rate is compared with the voltage drop rate threshold, and the upper limit is limited to 1 by the min function to obtain the power supply voltage drop rate index. The larger of the signal parameter change acceleration exponent and the power supply voltage drop rate exponent is taken as the dynamic response coefficient. , The output range is 0-1, reflecting the urgency of the system's need for a rapid response. 0 indicates no emergency, while 1 indicates that the compression ratio needs to be increased significantly immediately to prevent distortion or crashes.

6. The intelligent speaker power amplifier according to claim 5, characterized in that, The steps for obtaining the thermal state coefficient are as follows: Obtain the power amplifier chip temperature and the speaker voice coil temperature; The power amplifier chip temperature and the speaker voice coil temperature are subjected to maximum-min normalization to obtain the power amplifier chip temperature index and the speaker voice coil temperature index. The fatigue factor is calculated based on the power amplifier chip temperature and the speaker voice coil temperature. The maximum value among the power amplifier chip temperature index, the speaker voice coil temperature index, and the fatigue factor is taken as the thermal state coefficient. , Output range 0-1, A value of 0 indicates no thermal risk. A value of 1 indicates that the thermal protection limit has been reached, and maximum compression is required to reduce heat generation.

7. The intelligent speaker power amplifier according to claim 6, characterized in that, The specific method for calculating and obtaining the fatigue factor is as follows: Obtain the power amplifier chip temperature and speaker voice coil temperature at the current sampling point, as well as the fatigue accumulation value at the previous sampling point; The fatigue accumulation value of the previous sampling point is attenuated by a forgetting factor and then superimposed with the temperature exceedance level of the current sampling point to obtain the fatigue accumulation value of the current sampling point. The temperature exceedance level is calculated based on the maximum value of the power amplifier chip temperature and the speaker voice coil temperature relative to the reference temperature threshold and the maximum allowable temperature. The fatigue factor is obtained by comparing the cumulative fatigue value of the current sampling point with the maximum cumulative fatigue value and then using the min function to limit the upper limit to 1.

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