Temperature prediction method and system of audio playing device, audio playing device

Abstract

The application discloses a temperature prediction method and system of an audio playing device, and the audio playing device; wherein the audio playing device comprises a voice coil circuit; the temperature prediction method comprises the following steps: obtaining a high-order time domain function of the voice coil circuit; obtaining a current measurable parameter of the voice coil circuit at a current prediction moment, and a group of prediction reference parameters corresponding to at least three prediction sampling moments before the current prediction moment respectively; and obtaining a prediction temperature of the voice coil at the current prediction moment according to the current measurable parameter, each group of prediction reference parameters and the high-order time domain function. The obtained prediction temperature has high timeliness and accuracy, and a corresponding temperature protection mechanism is enabled according to the prediction temperature, so that the working performance of the voice coil circuit can be improved on the basis of ensuring the working safety of the voice coil circuit, and the audio playing effect of the corresponding audio playing device can be improved.

Description

Technical Field

[0001] This application relates to the field of signal processing technology, specifically to a temperature prediction method and system for an audio playback device, and the audio playback device itself. Background Technology

[0002] Miniature speakers, loudspeakers, and / or audio equipment are widely used in people's lives and work. For example, miniature speakers are an important component of electronic devices such as mobile phones and tablets, and loudspeakers are frequently seen in various conference venues. During operation, the temperature of the circuit containing the voice coil in these audio playback devices changes with relevant operating parameters, affecting device performance such as sound quality. In many cases, excessively high voice coil temperatures can even cause the corresponding audio playback devices to malfunction. Therefore, these audio playback devices often require current or future temperature guidance to trigger corresponding protection mechanisms at different temperatures to protect the operating circuitry and ensure their performance and operational safety during operation.

[0003] Traditional solutions sometimes involve mixing existing audio data into a pilot tone with a specified frequency and amplitude. Then, algorithms are used to detect the pilot tone component in the voltage and current to determine the resistance. Finally, the linear relationship between resistance and temperature is used to calculate the real-time temperature, which is then used to determine the appropriate temperature protection mechanism. This approach can lead to poor timeliness of the temperature protection response. Other solutions use a second-order thermal model to predict the temperature of the circuit containing the voice coil. These solutions tend to underestimate the initial temperature rise, while overestimating the predicted temperature in the middle stages to account for the upper limit of the final temperature. Therefore, the accuracy of the prediction results is often low. Summary of the Invention

[0004] In view of this, this application provides a method and system for predicting the temperature of an audio playback device, as well as an audio playback device, to solve the problems of low timeliness and accuracy of existing voice coil temperature prediction schemes.

[0005] A first aspect of this application provides a temperature prediction method for an audio playback device, the audio playback device including a voice coil circuit; the temperature prediction method includes:

[0006] Obtain the higher-order time-domain function of the voice coil circuit; wherein, the higher-order time-domain function characterizes the relationship between the current operating parameters of the voice coil circuit at the current prediction time and the prediction reference parameters at least three prediction sampling times prior to the current prediction time; the current operating parameters include measurable parameters and the temperature of the voice coil;

[0007] Obtain the currently measurable parameters of the voice coil circuit at the current prediction time, and a set of prediction reference parameters corresponding to at least three prediction sampling times before the current prediction time;

[0008] The predicted temperature of the voice coil at the current prediction time is obtained based on the currently measurable parameters, each set of prediction reference parameters, and the higher-order time domain function.

[0009] Specifically, obtaining the higher-order time-domain function of the voice coil circuit includes: obtaining the higher-order temperature thermal model of the voice coil circuit; wherein the higher-order temperature thermal model uses thermal capacity and thermal resistance to characterize the temperature characteristics of the voice coil circuit; and determining the higher-order time-domain function based on the relationship between each set of thermal capacity and thermal resistance in the higher-order temperature thermal model.

[0010] Specifically, the high-order temperature thermal model uses at least three sets of thermal capacities and thermal resistances to characterize the temperature characteristics of the voice coil circuit.

[0011] Specifically, obtaining the higher-order time-domain function corresponding to the higher-order temperature thermal model includes: obtaining the s-domain transfer function of the higher-order temperature thermal model based on the relationship between each group of heat capacity and thermal resistance in the higher-order temperature thermal model; discretizing the s-domain transfer function to obtain the z-domain discrete function; and performing a time-domain transformation on the z-domain discrete function to obtain the higher-order time-domain function.

[0012] Specifically, the higher-order temperature thermal model includes a third-order temperature thermal model; the third-order temperature thermal model uses three sets of thermal capacities and thermal resistances to characterize the temperature characteristics of the voice coil circuit.

[0013] Specifically, the third-order temperature thermal model includes a power source, a first heat capacity, a second heat capacity, a third heat capacity, a first thermal resistance, a second thermal resistance, and a third thermal resistance; the power source, the first heat capacity, the second heat capacity, and the third heat capacity are connected in parallel; the first thermal resistance is connected between the first end of the first heat capacity and the first end of the second heat capacity; the second thermal resistance is connected between the first end of the second heat capacity and the first end of the third heat capacity; and the third thermal resistance is connected between the first end and the second end of the third heat capacity; the second ends of the first heat capacity, the second heat capacity, and the third heat capacity are respectively grounded.

[0014] Specifically, the prediction reference parameters include measurable parameters and temperature at the prediction sampling time; the measurable parameters include power; the higher-order time-domain functions corresponding to the third-order temperature thermal model include:

[0015] T vc (i)=c1*P(i)+c2*P(i-1)+c3*P(i-2)+c4*P(i-3)+d1*T vc (i-1)+d2*T vc(i-2)+d3*T vc (i-3);

[0016] Among them, T vc (i) represents the predicted temperature at the current time, P(i) represents the power at the current time, P(i-1) represents the power at time (i-1), P(i-2) represents the power at time (i-2), P(i-3) represents the power at time (i-3), and T vc (i-1) represents the temperature at time (i-1), T vc (i-2) represents the temperature at time (i-2), T vc (i-3) represents the temperature at time (i-3), c1 represents the first power weight, c2 represents the second power weight, c3 represents the third power weight, c4 represents the fourth power weight, d1 represents the first temperature weight, d2 represents the second temperature weight, d3 represents the third temperature weight, and the symbol * represents multiplication.

[0017] Specifically, the process for determining the values ​​of the first heat capacity, the second heat capacity, the third heat capacity, the first thermal resistance, the second thermal resistance, and the third thermal resistance includes:

[0018] Obtain a set of debugging model parameters; the debugging model parameters include the initial or debugging values ​​of the model parameters of the high-order temperature thermal model; the model parameters include the parameters of the first heat capacity, the second heat capacity, the third heat capacity, the first thermal resistance, the second thermal resistance, and the third thermal resistance;

[0019] Using the parameters of the debugging model as the model parameters of the high-order temperature thermal model, the debugging temperature thermal model is obtained, and the debugging time-domain function corresponding to the debugging temperature thermal model is acquired.

[0020] The debugging working parameters at the debugging sampling time are obtained, as well as a set of debugging reference parameters corresponding to at least three debugging reference times before the debugging sampling time; wherein, the debugging working parameters include the measured temperature and measurable parameters of the voice coil circuit at the debugging sampling time; the debugging reference parameters include the temperature and measurable parameters of the voice coil circuit at the corresponding debugging reference time; each debugging reference time and the debugging sampling time are sequentially continuous.

[0021] The debugging temperature is obtained using the aforementioned debugging temperature thermal model, each set of debugging reference parameters, and the measurable parameters at the debugging sampling time. An error function is then constructed based on the measured temperature and the debugging temperature.

[0022] Obtain the optimal model parameters when the error function is optimal, and determine the values ​​of the first heat capacity, the second heat capacity, the third heat capacity, the first thermal resistance, the second thermal resistance, and the third thermal resistance based on the optimal model parameters.

[0023] Specifically, the process of determining the initial values ​​of the model parameters includes: obtaining reference model parameters; wherein the reference model parameters include at least one set of known model parameters; obtaining estimates of the reference model parameters using at least one instrumental variable, and using the estimates as the initial values ​​of the model parameters.

[0024] Specifically, obtaining the optimal model parameters when the error function is optimal includes: obtaining the cost function of the error function, and determining the model parameters when the cost function reaches its minimum value as the optimal model parameters.

[0025] Specifically, the cost function includes:

[0026]

[0027] Where C(θ) represents the cost function, n represents the number of debugging attempts, ∈(t, θ) t ) represents the error function at the sampling time t during debugging.

[0028] A second aspect of this application provides a temperature prediction system for an audio playback device, the audio playback device including a voice coil circuit; the temperature prediction system includes:

[0029] The first acquisition module is used to acquire the higher-order time-domain function of the voice coil circuit; wherein, the higher-order time-domain function characterizes the relationship between the current operating parameters of the voice coil circuit at the current prediction time and the prediction reference parameters at least three prediction sampling times prior to the current prediction time; the current operating parameters include measurable parameters and the temperature of the voice coil;

[0030] The second acquisition module is used to acquire the currently measurable parameters of the voice coil circuit at the current prediction time, and a set of prediction reference parameters corresponding to at least three prediction sampling times before the current prediction time.

[0031] The third acquisition module is used to acquire the predicted temperature of the voice coil at the current prediction time based on the currently measurable parameters, each set of prediction reference parameters and the higher-order time domain function.

[0032] A third aspect of this application provides an audio playback device, including a voice coil circuit, a processor, and a storage medium; the storage medium stores program code; the processor is used to call the program code stored in the storage medium to execute the temperature prediction method of any of the above-described audio playback devices.

[0033] The temperature prediction method and system for audio playback devices provided in this application, as well as the audio playback device itself, obtain the predicted temperature of the voice coil at the current prediction time by acquiring the higher-order time-domain function of the voice coil circuit, the currently measurable parameters at the current prediction time, and a set of prediction reference parameters corresponding to at least three prediction sampling times before the current prediction time. The obtained predicted temperature closely matches the actual temperature and has high accuracy and timeliness. Based on the predicted temperature, a corresponding temperature protection mechanism is activated, and the corresponding protection purpose is achieved by adjusting various operating parameters of the voice coil circuit. While ensuring the operational safety of the voice coil circuit, it can also improve the various operating performances of the voice coil circuit, thereby improving the audio playback effect of the corresponding audio playback device. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a schematic flowchart of a temperature prediction method for an audio playback device in one embodiment of this application;

[0036] Figure 2 This is a schematic diagram of a third-order temperature thermal model according to an embodiment of this application;

[0037] Figure 3 This is a schematic diagram showing a comparison and analysis of temperature results according to an embodiment of this application;

[0038] Figure 4 This is a schematic diagram of the temperature prediction system structure of an audio playback device in one embodiment of this application;

[0039] Figure 5 This is a schematic diagram of the structure of an audio playback device according to an embodiment of this application. Detailed Implementation

[0040] As described in the background section, in traditional solutions, the relevant temperature calculation method uses pilot tones to directly calculate the real-time temperature to guide the protection mechanism of audio playback devices such as speakers, which can easily affect the timeliness of the corresponding temperature protection response. In addition, the solution that uses a second-order thermal model to predict the temperature of the circuit where the voice coil is located often results in an underestimation of the initial temperature during the initial temperature rise phase. The second-order model often has an insufficient slope due to its low order, leading to an underestimation of the initial temperature. In the middle phase, the prediction value is overestimated because the upper limit of the final temperature needs to be taken into account, resulting in low accuracy of the prediction results.

[0041] To address the aforementioned issues, the temperature prediction method and system for audio playback devices provided in this application, along with the audio playback device itself, obtain the predicted temperature of the voice coil at the current prediction time by acquiring the higher-order time-domain function of the voice coil circuit, the currently measurable parameters at the current prediction time, and a set of prediction reference parameters corresponding to at least three prediction sampling times prior to the current prediction time. The obtained predicted temperature is highly accurate, and its timeliness meets the operational requirements of the corresponding audio playback devices.

[0042] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. In the absence of conflict, the following embodiments and their technical features can be combined with each other.

[0043] The first aspect of this application provides a method for predicting the temperature of an audio playback device, the audio playback device including a voice coil circuit (i.e., the circuit containing the voice coil); Reference Figure 1 As shown, the above temperature prediction method includes:

[0044] S100, Obtain the higher-order time-domain function of the voice coil circuit; wherein, the higher-order time-domain function characterizes the relationship between the current operating parameters of the voice coil circuit at the current prediction time and the prediction reference parameters at least three prediction sampling times prior to the current prediction time; the current operating parameters include measurable parameters and the temperature of the voice coil.

[0045] The aforementioned higher-order time-domain function can be determined based on the higher-order temperature thermal model corresponding to the voice coil circuit. The aforementioned current operating parameters include the measurable parameters and voice coil temperature at the current prediction time; the prediction reference parameters include the measurable parameters at the corresponding prediction sampling time and the temperature of the corresponding voice coil circuit, such as the voice coil temperature; each prediction sampling time and the current prediction time are sequentially continuous. If i represents the current prediction time, then the prediction sampling times before the current prediction time are arranged in the order of later times preceding earlier times, i-1, i-2, i-3, i-4, ... The aforementioned measurable parameters (including the measurable parameters at the current prediction time, each prediction sampling time, the debugging sampling time, and each debugging reference time) can refer to parameters that can be measured by relevant instruments or parameters obtained by calculation from measured parameters, such as electrical parameters and / or other parameters affecting the voice coil temperature. The electrical parameters can include voltage, current, impedance, and / or power of the voice coil circuit. Taking electrical parameters as an example, if power is included, the corresponding currently measurable parameter includes the power at the current prediction moment. In this case, other electrical parameters such as current and / or voltage of the voice coil circuit can be collected to calculate the corresponding power. In one example, if the measurable parameter is power, considering that the current is often too small for audio playback devices such as miniature speakers, calculating power or other parameters using current after converting floating-point to fixed-point numbers will result in significant errors. In this case, impedance calculation obtained through voltage and temperature impedance feedback can be used to calculate the power, reducing the error in the obtained power and mitigating the impact of real-time changes in the voice coil circuit resistance on the power.

[0046] It should be noted that the temperature of the voice coil circuit in an audio playback device can include parameters such as the voice coil temperature, the temperature of the gap between the voice coil and magnets, the magnet temperature, and the ambient temperature of the circuit. Specifically, because the voice coil temperature has a significant impact on the voice coil circuit, excessively high temperatures can easily cause voice coil failure. Therefore, predicting the voice coil temperature can ensure the validity of the predicted temperature.

[0047] S200, obtain the currently measurable parameters of the voice coil circuit at the current prediction time, and a set of prediction reference parameters corresponding to at least three prediction sampling times before the current prediction time.

[0048] In the above steps, the specific types of currently measurable parameters such as electrical parameters can be determined based on the characteristics of higher-order time-domain functions, and are usually determined to be the parameters used in the corresponding higher-order time-domain functions.

[0049] S300: Obtain the predicted temperature of the voice coil at the current prediction time based on the currently measurable parameters, each set of prediction reference parameters, and the higher-order time domain function.

[0050] The above steps directly substitute the currently measurable parameters and at least three sets of prediction reference parameters into a higher-order time-domain function to obtain the predicted temperature at the current prediction time, thereby improving the accuracy of the obtained predicted temperature and making it closer to the actual temperature. Notably, pilot signals are not used to detect temperature; that is, pilot tones are no longer needed to calculate real-time temperature. Instead, a higher-order time-domain function is used to predict the temperature at the current prediction time based on the currently measurable parameters and at least three sets of prediction reference parameters. This provides a forward-looking perspective on future temperatures and has significant guiding significance for temperature control of coil circuits in practical scenarios. Furthermore, no additional pilot tone generation module is needed, thus having minimal impact on the temperature of the corresponding coil circuit and ensuring the working performance of the corresponding audio playback device. Moreover, compared to the traditional second-order speaker thermal model, the higher-order time-domain function of the higher-order temperature thermal model has more polynomials to express the step response, resulting in a predicted temperature that is closer to the actual temperature.

[0051] In one embodiment, step S100 above, obtaining the higher-order time-domain function of the voice coil circuit, includes:

[0052] S110, Obtain the high-order temperature thermal model of the voice coil circuit; wherein, the high-order temperature thermal model uses thermal capacity and thermal resistance to characterize the temperature characteristics of the voice coil circuit;

[0053] S120, determine the higher-order time-domain function based on the relationship between each group of heat capacity and thermal resistance in the higher-order temperature thermal model.

[0054] The aforementioned temperature characteristics include temperature distribution characteristics, temperature conduction characteristics, and / or temperature change characteristics. The higher-order temperature thermal model described above can also be called a higher-order equivalent thermal circuit diagram. The number of heat capacity and thermal resistance groups included can be set according to the required prediction accuracy and the structural characteristics of the corresponding voice coil circuit. The order of the higher-order temperature thermal model is consistent with the number of heat capacity and thermal resistance groups. For example, a temperature thermal model including three heat capacity and thermal resistance groups is a third-order temperature thermal model, and a temperature thermal model including four heat capacity and thermal resistance groups is a fourth-order temperature thermal model, and so on.

[0055] To ensure a close match between the temperature characteristics represented by the high-order thermal model and the actual temperature characteristics of the voice coil circuit, and to enable the high-order thermal model to accurately reflect the various temperature characteristics of the voice coil circuit, the aforementioned high-order thermal model uses at least three sets of thermal capacities and thermal resistances to represent the temperature characteristics of the voice coil circuit. In some cases, a third-order thermal model can be used to represent the temperature characteristics of the voice coil circuit. This not only ensures a high overall fit between the temperature characteristics it represents and the actual temperature characteristics of the voice coil circuit, making them essentially consistent, but also simplifies the structure of the high-order thermal model, improves its acquisition efficiency, and enhances the efficiency of predicting the temperature of the voice coil circuit using the third-order thermal model, thus contributing to ensuring the foresight, accuracy, and efficiency of temperature prediction.

[0056] This embodiment allows the high-order temperature thermal model to accurately characterize the temperature characteristics of the voice coil circuit by setting the connection relationships and specific values ​​of each group of thermal capacities and thermal resistances. The connection relationships of each group of thermal capacities and thermal resistances can be set according to the corresponding temperature prediction accuracy and efficiency. After determining the connection relationships of each group of thermal capacities and thermal resistances, the values ​​corresponding to each group of thermal capacities and thermal resistances can be determined by multiple adjustments, fitting multiple adjustment results, and / or processing multiple adjustment results using instrumental variables, thereby improving the accuracy of the determined values.

[0057] After determining the connection relationships and specific values ​​of each group of heat capacity and thermal resistance in the aforementioned high-order temperature thermal model, it has a corresponding high-order time-domain function. This high-order time-domain function can accurately characterize the relationship between the voice coil circuit parameters (current operating parameters) at the current prediction time and the voice coil circuit parameters (prediction reference parameters) at each prediction sampling time. Thus, by substituting each group of prediction reference parameters and the measurable parameters at the current prediction time into the aforementioned high-order time-domain function, the voice coil circuit temperature at the current prediction time can be obtained. Specifically, after determining the connection relationships and specific values ​​of each group of heat capacity and thermal resistance in the high-order temperature thermal model, the frequency domain transfer function of the high-order temperature thermal model can be obtained first. The frequency domain transfer function can then be discretized to obtain the corresponding discrete function, and finally, the high-order time-domain function corresponding to this discrete function can be obtained to ensure the accuracy of the obtained high-order time-domain function. In one example, step S120, determining the high-order time-domain function based on the relationship between each group of heat capacity and thermal resistance in the high-order temperature thermal model, includes:

[0058] S121, Obtain the s-domain transfer function of the high-order temperature thermal model based on the relationship between each group of heat capacity and thermal resistance in the high-order temperature thermal model;

[0059] S122, Discretize the s-domain transfer function to obtain the z-domain discrete function;

[0060] S123, perform a time-domain transformation on the z-domain discrete function to obtain the higher-order time-domain function.

[0061] In step S121 above, the s-domain transfer function can describe the relationship between temperature and power in the voice coil circuit in a high-order temperature thermal model. Since the magnets and the gap between the voice coil magnets in the voice coil circuit will not be damaged due to excessive temperature, predicting the voice coil temperature to guide the effectiveness of voice coil circuit protection is highly effective. Step S122 above can use the bilinear transform method to convert the s-domain transfer function into a z-domain discrete function. Step S123 above can use the difference method to transform the z-domain discrete function to the time domain, obtaining a high-order time-domain function.

[0062] In one embodiment, the higher-order temperature thermal model includes a third-order temperature thermal model; the third-order temperature thermal model uses three sets of thermal capacities and thermal resistances to characterize the temperature characteristics of the voice coil circuit.

[0063] Specifically, refer to Figure 2 As shown, the third-order temperature thermal model includes a power supply DC, a first thermal capacity C1, a second thermal capacity C2, a third thermal capacity C3, a first thermal resistance R1, a second thermal resistance R2, and a third thermal resistance R3. The power supply DC, the first thermal capacity C1, the second thermal capacity C2, and the third thermal capacity C3 are connected in parallel. The first thermal resistance R1 is connected between the first end of the first thermal capacity C1 and the first end of the second thermal capacity C2. The second thermal resistance R2 is connected between the first end of the second thermal capacity C2 and the first end of the third thermal capacity C3. The third thermal resistance R3 is connected between the first end and the second end of the third thermal capacity C3. The second ends of the first thermal capacity C1, the second thermal capacity C2, and the third thermal capacity C3 are respectively grounded.

[0064] The above voice coil circuit includes a voice coil and a magnet. (Reference) Figure 2 As shown, in this third-order temperature thermal model, a power P, after passing through the DC power supply, first flows through the first thermal capacity C1 and the first thermal resistance R1 of the voice coil, causing the voice coil temperature Tvc to rise; then it flows through the second thermal capacity C2 and the second thermal resistance R2 of the voice coil magnet gap, causing the speaker voice coil magnet gap temperature Tg to rise; next, it flows through the third thermal capacity C3 and the third thermal resistance R3 of the magnet, causing the magnet temperature Tm to rise; finally, the power P flows to the ambient temperature Ta, dissipating heat to the external environment of the voice coil circuit. The above-mentioned third-order temperature thermal model has a simple structure, and while ensuring the overall fit between the temperature characteristics it represents and the actual temperature characteristics of the voice coil circuit, it can improve the efficiency of predicting the voice coil circuit temperature using this third-order temperature thermal model.

[0065] Specifically, the prediction reference parameters include measurable parameters and temperature at the prediction sampling time; the measurable parameters include power; the higher-order time-domain functions corresponding to the third-order temperature thermal model include:

[0066] T vc (i)=c1*P(i)+c2*P(i-1)+c3*P(i-2)+c4*P(i-3)+d1*T vc (i-1)+d2*T vc (i-2)+d3*T vc (i-3);

[0067] Among them, T vc(i) represents the predicted temperature at the current time, P(i) represents the power at the current time, P(i-1) represents the power at time (i-1), P(i-2) represents the power at time (i-2), P(i-3) represents the power at time (i-3), and T vc (i-1) represents the temperature at time (i-1), T vc (i-2) represents the temperature at time (i-2), T vc (i-3) represents the temperature at time (i-3), c1 represents the first power weight, c2 represents the second power weight, c3 represents the third power weight, c4 represents the fourth power weight, d1 represents the first temperature weight, d2 represents the second temperature weight, d3 represents the third temperature weight, and the symbol * represents multiplication.

[0068] Among them, the first power weight c1, the second power weight c2, the third power weight c3, the fourth power weight c4, the first temperature weight d1, the second temperature weight d2, and the third temperature weight d3 can be obtained by multiple transformations based on the parameters such as the first heat capacity, the second heat capacity, the third heat capacity, the first thermal resistance, the second thermal resistance, the third thermal resistance, and the power of the voice coil circuit in the third-order temperature thermal model.

[0069] In one example, the derivation process of the higher-order time-domain function corresponding to the third-order temperature thermal model may include: obtaining the s-domain transfer function of the third-order temperature thermal model; using the bilinear transformation method to transform the s-domain transfer function to the z-domain to obtain the z-domain discrete function; and using the difference method to perform a time-domain transformation on the z-domain discrete function to obtain the corresponding higher-order time-domain function.

[0070] The above s-domain transfer functions include:

[0071]

[0072] Q=s(R2C2+R3C2+R3C3+R1C1+R2C1+R3C1)+s 2 (R2R3C2C3+R1R2C1C2+R1R3C1C2+R1R3C1C3+R2R3C1C3)+s 3 (R1R2R3C1C2C3),

[0073] In the formula, s represents the independent variable in the s-domain, P represents the input power of the voice coil circuit, the first thermal resistance R1 represents the thermal resistance corresponding to the voice coil, the second thermal resistance R2 represents the thermal resistance corresponding to the voice coil-magnet gap, the third thermal resistance R3 represents the thermal resistance corresponding to the magnet, the first thermal capacity C1 represents the thermal capacity corresponding to the voice coil, the second thermal capacity C2 represents the thermal capacity corresponding to the voice coil-magnet gap, the third thermal capacity C3 represents the thermal capacity corresponding to the magnet, and T... vc This represents the voice coil temperature. The s-domain transfer function describes the relationship between voice coil temperature and power in a third-order temperature thermal model.

[0074] The above z-domain discrete functions include:

[0075]

[0076] In the formula, z represents the independent variable in the z-domain, and a1, a2, a3, a4, b1, b2, b3, and b4, as coefficients, can be obtained by transforming the input power through P, the first heat capacity C1, the second heat capacity C2, the third heat capacity C3, the first thermal resistance R1, the second thermal resistance R2, and the third thermal resistance R3. Specifically, the bilinear transformation method can be used to transform the s-domain transfer function to the z-domain, which is discretization. After the discretization process, a1, a2, a3, a4, b1, b2, b3, and b4 are processed by time-domain transformation methods such as the difference method, the first power weight c1, the second power weight c2, the third power weight c3, the fourth power weight c4, the first temperature weight d1, the second temperature weight d2, and the third temperature weight d3 can be obtained.

[0077] In one embodiment, the process of determining the values ​​of the first heat capacity, the second heat capacity, the third heat capacity, the first thermal resistance, the second thermal resistance, and the third thermal resistance includes:

[0078] S111, Obtain a set of debugging model parameters; the debugging model parameters include the initial or debugging values ​​of the model parameters of the high-order temperature thermal model; the model parameters include the parameters of the first heat capacity, the second heat capacity, the third heat capacity, the first thermal resistance, the second thermal resistance, and the third thermal resistance;

[0079] S112, using the parameters of the debugging model as the model parameters of the high-order temperature thermal model, the debugging temperature thermal model is obtained, and the debugging time-domain function corresponding to the debugging temperature thermal model is acquired.

[0080] S113, acquire the debugging working parameters at the debugging sampling time, and a set of debugging reference parameters corresponding to at least three debugging reference times before the debugging sampling time; wherein, the debugging working parameters include the measured temperature and measurable parameters of the voice coil circuit at the debugging sampling time; the debugging reference parameters include the temperature and measurable parameters of the voice coil circuit at the corresponding debugging reference time; each debugging reference time and the debugging sampling time are sequentially continuous. For example, if t represents the debugging reference time, then the debugging sampling times before the debugging reference time are arranged in the order of the later time being the first, in the following order: t-1, t-2, t-3, t-4, ...

[0081] S114, the debugging temperature is obtained by using the debugging temperature thermal model, each set of debugging reference parameters and the measurable parameters at the debugging sampling time, and an error function is constructed based on the measured temperature and the debugging temperature;

[0082] S115, obtain the optimal model parameters when the error function is optimal, and determine the values ​​of the first heat capacity, the second heat capacity, the third heat capacity, the first thermal resistance, the second thermal resistance, and the third thermal resistance according to the optimal model parameters.

[0083] This embodiment can set initial values ​​for model parameters, which are then used to determine the debugging temperature thermal model and construct an error function. If the error function does not reach its optimal state, the debugging values ​​of the model parameters are redefined to update the debugging model parameters. The process of determining the debugging temperature thermal model based on the debugging model parameters is then repeated until the optimal model parameters when the error function is optimal are obtained. Based on these optimal model parameters, the values ​​of the model parameters are determined to ensure high accuracy. The high-order temperature thermal model determined by these model parameter values ​​can accurately characterize the temperature characteristics of the voice coil circuit.

[0084] Specifically, the initial values ​​of the aforementioned model parameters can be determined by referencing empirical values ​​and / or by denoising the relevant empirical values. In one example, the process of determining the initial values ​​of the model parameters may include: obtaining reference model parameters; wherein the reference model parameters include at least one set of known model parameters (such as relevant empirical values ​​or test values, etc.); obtaining an estimate of the reference model parameters using at least one instrumental variable, and using the estimate as the initial value of the model parameters. This example uses at least one instrumental variable to filter the reference model parameters, which can effectively filter out disturbance components and improve the effectiveness of the obtained initial values ​​of the model parameters, thereby improving the efficiency of corresponding adjustments based on the initial values.

[0085] Specifically, step S115 above can determine the optimal state of the error function by multiple iterations, obtaining the state in which the error function remains at a certain minimum value, and / or obtaining the convergence state of the error function. In one example, obtaining the optimal model parameters when the error function is optimal may include: obtaining the cost function of the error function, and determining the model parameters when the cost function reaches its minimum value as the optimal model parameters.

[0086] The cost function includes:

[0087]

[0088] In the formula, C(θ) represents the cost function, n represents the number of debugging attempts, i.e., the number of sampling points in the entire debugging cycle, ∈(t, θ) t Let represent the error function at sampling time t, ∈(t, θ). t ) = T measured(t)-T predicted (t, θt), T measured (t) represents the measured temperature at sampling time t during debugging, T predicted (t,θ t ) represents the debugging temperature at the debugging sampling time t.

[0089] This example can use gradient descent or nonlinear solvers to minimize the cost function, thereby determining the model parameters when the cost function reaches its minimum value, resulting in optimal model parameters with higher accuracy and stability.

[0090] In one example, reference Figure 3 As shown, temperature detection or prediction was performed on miniature speakers used in portable electronic devices such as mobile phones using three methods: temperature measurement, temperature prediction based on the third-order temperature thermal model determined in this application (the illustrated third-order horn thermal model), and temperature prediction based on the traditional second-order temperature thermal model. The results are as follows. Figure 3 As shown, there is a significant deviation between the temperature curve predicted by the traditional second-order temperature thermal model and the actual measured temperature curve. The temperature curve predicted by the third-order temperature thermal model provided in this application basically coincides with the actual measured temperature curve. It can be seen that the temperature predicted by the third-order temperature thermal model determined in this application has high accuracy.

[0091] The above-described temperature prediction method for audio playback devices obtains the predicted temperature of the voice coil at the current prediction time by acquiring the higher-order time-domain function of the voice coil circuit, the currently measurable parameters at the current prediction time, and a set of prediction reference parameters corresponding to at least three prediction sampling times prior to the current prediction time. The resulting predicted temperature closely matches the actual temperature, exhibiting high accuracy and timeliness. Specifically, it eliminates the need for pilot signals to detect temperature, meaning it no longer requires pilot tones to calculate real-time temperature. Instead, it uses a higher-order time-domain function to predict the temperature at the current prediction time based on the currently measurable parameters and at least three sets of prediction reference parameters, providing a forward-looking perspective on future temperatures and offering significant guidance for temperature control of the coil circuit in practical scenarios. Furthermore, it eliminates the need for an additional pilot tone generation module, thus minimizing any impact on the temperature of the corresponding coil circuit and ensuring the operational performance of the audio playback device. Moreover, compared to the traditional second-order speaker thermal model, the higher-order time-domain function of the higher-order temperature model has more polynomials to express the step response, resulting in a predicted temperature that more closely approximates the actual temperature.

[0092] This application provides a temperature prediction system for an audio playback device, the audio playback device including a voice coil circuit; Reference Figure 4 As shown, the temperature prediction system includes:

[0093] The first acquisition module 100 is used to acquire the higher-order time-domain function of the voice coil circuit; wherein, the higher-order time-domain function characterizes the relationship between the current operating parameters of the voice coil circuit at the current prediction time and the prediction reference parameters at least three prediction sampling times prior to the current prediction time; the current operating parameters include measurable parameters and the temperature of the voice coil;

[0094] The second acquisition module 200 is used to acquire the currently measurable parameters of the voice coil circuit at the current prediction time, and a set of prediction reference parameters corresponding to at least three prediction sampling times before the current prediction time.

[0095] The third acquisition module 300 is used to acquire the predicted temperature of the voice coil at the current prediction time based on the currently measurable parameters, each set of prediction reference parameters and the higher-order time domain function.

[0096] Specific limitations regarding the temperature prediction system for audio playback devices can be found in the above description of the temperature prediction method for audio playback devices, and will not be repeated here. Each module in the aforementioned temperature prediction system for audio playback devices can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in hardware or independent of the processor in the computer device, or stored in software in the memory of the computer device, so that the processor can call and execute the corresponding operations of each module.

[0097] This application provides an audio playback device in a third aspect, with reference to... Figure 5 As shown, the audio playback device may include a voice coil circuit 810, a processor 820, and a storage medium 830; the storage medium 830 stores program code; the processor 820 is used to call the program code stored in the storage medium 830 to execute the temperature prediction method of the audio playback device described in any of the above embodiments.

[0098] The aforementioned audio playback device may include independent audio playback devices such as speakers and audio amplifiers, audio playback modules mounted on other electronic devices such as miniature speakers, and electronic devices with audio playback modules such as mobile phones and tablets. This audio playback device obtains the predicted temperature of the voice coil at the current prediction time by acquiring the higher-order time-domain function of the voice coil circuit, the currently measurable parameters at the current prediction time, and a set of prediction reference parameters corresponding to at least three prediction sampling times prior to the current prediction time. Based on the obtained predicted temperature, a corresponding temperature protection mechanism is activated, achieving protection by adjusting various operating parameters of the voice coil circuit. While ensuring the operational safety of the voice coil circuit, it can also improve the various operating performance parameters of the voice coil circuit, thereby enhancing the audio playback effect of the corresponding audio playback device.

[0099] Although this application has been shown and described with respect to one or more implementations, equivalent variations and modifications will occur to those skilled in the art based on a reading and understanding of this specification and drawings. This application includes all such modifications and variations and is limited only by the scope of the appended claims. In particular, with respect to the various functions performed by the aforementioned components, the terminology used to describe such components is intended to correspond to any component (unless otherwise indicated) that performs the specified function of said component (e.g., is functionally equivalent to it), even if structurally not equivalent to the disclosed structure performing the functions in the exemplary implementations of this specification shown herein.

[0100] That is, the above description is only an embodiment of this application and does not limit the patent scope of this application. Any equivalent structural or procedural changes made using the content of this application’s specification and drawings, such as the combination of technical features between different embodiments, or direct or indirect application in other related technical fields, are similarly included within the patent protection scope of this application.

[0101] Furthermore, the terms "first," "second," "third," and "fourth" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first," "second," "third," or "fourth" may explicitly or implicitly include one or more features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0102] The above description is provided to enable any person skilled in the art to implement and use this application. Various details are set forth in the above description for purposes of explanation. It should be understood that those skilled in the art will recognize that this application can be implemented without using these specific details. In other embodiments, well-known processes will not be described in detail to avoid obscuring the description of this application with unnecessary detail. Therefore, this application is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed herein.

Claims

1. A method for predicting the temperature of an audio playback device, characterized in that, The audio playback device includes a voice coil circuit; the temperature prediction method includes: Obtain the higher-order time-domain function of the voice coil circuit; wherein, the higher-order time-domain function characterizes the relationship between the current operating parameters of the voice coil circuit at the current prediction time and the prediction reference parameters at least three prediction sampling times prior to the current prediction time; the current operating parameters include measurable parameters and the temperature of the voice coil; Obtain the currently measurable parameters of the voice coil circuit at the current prediction time, and a set of prediction reference parameters corresponding to at least three prediction sampling times before the current prediction time; The predicted temperature of the voice coil at the current prediction time is obtained based on the currently measurable parameters, each set of prediction reference parameters, and the higher-order time domain function. The step of obtaining the higher-order time-domain function of the voice coil circuit includes: obtaining the higher-order temperature thermal model of the voice coil circuit; wherein, the higher-order temperature thermal model uses thermal capacity and thermal resistance to characterize the temperature characteristics of the voice coil circuit; and determining the higher-order time-domain function based on the relationship between each set of thermal capacity and thermal resistance in the higher-order temperature thermal model. The high-order temperature thermal model uses at least three sets of thermal capacities and thermal resistances to characterize the temperature characteristics of the voice coil circuit.

2. The temperature prediction method for an audio playback device according to claim 1, characterized in that, The step of obtaining the higher-order time-domain function corresponding to the higher-order temperature thermal model includes: The s-domain transfer function of the high-order temperature thermal model is obtained based on the relationship between each set of heat capacity and thermal resistance in the high-order temperature thermal model. Discretize the s-domain transfer function to obtain the z-domain discrete function; The z-domain discrete function is transformed in the time domain to obtain the higher-order time-domain function.

3. The temperature prediction method for an audio playback device according to claim 1, characterized in that, The higher-order temperature thermal model includes a third-order temperature thermal model; the third-order temperature thermal model uses three sets of thermal capacity and thermal resistance to characterize the temperature characteristics of the voice coil circuit.

4. The temperature prediction method for an audio playback device according to claim 3, characterized in that, The third-order temperature thermal model includes a power source, a first heat capacity, a second heat capacity, a third heat capacity, a first thermal resistance, a second thermal resistance, and a third thermal resistance. The power supply, the first thermal capacitor, the second thermal capacitor, and the third thermal capacitor are connected in parallel. The first thermal resistor is connected between the first end of the first thermal capacitor and the first end of the second thermal capacitor. The second thermal resistor is connected between the first end of the second thermal capacitor and the first end of the third thermal capacitor. The third thermal resistor is connected between the first end and the second end of the third thermal capacitor. The second ends of the first thermal capacitor, the second thermal capacitor, and the third thermal capacitor are respectively grounded.

5. The temperature prediction method for an audio playback device according to claim 4, characterized in that, The prediction reference parameters include measurable parameters and temperature at the prediction sampling time; the measurable parameters include power; the higher-order time-domain functions corresponding to the third-order temperature thermal model include: ； in, This indicates the predicted temperature at the current moment. This represents the power at the current moment. Indicates the first Power at any moment Indicates the first Power at any moment Indicates the first Power at any moment Indicates the first Temperature at any moment Indicates the first Temperature at any moment Indicates the first Temperature at any moment Indicates the first power weight. Indicates the second power weight. Indicates the third power weight. Indicates the fourth power weight. Indicates the first temperature weight. Indicates the second temperature weight. Indicates the third temperature weight, symbol Indicates multiplication.

6. The temperature prediction method for an audio playback device according to claim 4, characterized in that, The process of determining the values ​​of the first heat capacity, the second heat capacity, the third heat capacity, the first thermal resistance, the second thermal resistance, and the third thermal resistance includes: Obtain a set of debugging model parameters; the debugging model parameters include the initial or debugging values ​​of the model parameters of the high-order temperature thermal model; the model parameters include the parameters of the first heat capacity, the second heat capacity, the third heat capacity, the first thermal resistance, the second thermal resistance, and the third thermal resistance; Using the parameters of the debugging model as the model parameters of the high-order temperature thermal model, the debugging temperature thermal model is obtained, and the debugging time-domain function corresponding to the debugging temperature thermal model is acquired. The debugging working parameters at the debugging sampling time are obtained, as well as a set of debugging reference parameters corresponding to at least three debugging reference times before the debugging sampling time; wherein, the debugging working parameters include the measured temperature and measurable parameters of the voice coil circuit at the debugging sampling time; the debugging reference parameters include the temperature and measurable parameters of the voice coil circuit at the corresponding debugging reference time; each debugging reference time and the debugging sampling time are sequentially continuous. The debugging temperature is obtained using the aforementioned debugging temperature thermal model, each set of debugging reference parameters, and the measurable parameters at the debugging sampling time. An error function is then constructed based on the measured temperature and the debugging temperature. Obtain the optimal model parameters when the error function is optimal, and determine the values ​​of the first heat capacity, the second heat capacity, the third heat capacity, the first thermal resistance, the second thermal resistance, and the third thermal resistance based on the optimal model parameters.

7. The temperature prediction method for an audio playback device according to claim 6, characterized in that, The process of determining the initial values ​​of the model parameters includes: Obtain reference model parameters; wherein, the reference model parameters include at least one set of known model parameters; Estimates of the reference model parameters are obtained using at least one instrumental variable, and these estimates are used as the initial values ​​of the model parameters.

8. The temperature prediction method for an audio playback device according to claim 6, characterized in that, The optimal model parameters for obtaining the optimal error function include: Obtain the cost function of the error function, and determine the model parameters when the cost function reaches its minimum value as the optimal model parameters.

9. The temperature prediction method for an audio playback device according to claim 8, characterized in that, The cost function includes: ， in, Represents the cost function, Indicates the number of debugging attempts. Indicates the debugging sampling time The error function.

10. A temperature prediction system for an audio playback device, characterized in that, The audio playback device includes a voice coil circuit; the temperature prediction system includes: The first acquisition module is used to acquire the higher-order time-domain function of the voice coil circuit; wherein, the higher-order time-domain function characterizes the relationship between the current operating parameters of the voice coil circuit at the current prediction time and the prediction reference parameters at least three prediction sampling times prior to the current prediction time; the current operating parameters include measurable parameters and the temperature of the voice coil; The second acquisition module is used to acquire the currently measurable parameters of the voice coil circuit at the current prediction time, and a set of prediction reference parameters corresponding to at least three prediction sampling times before the current prediction time. The third acquisition module is used to acquire the predicted temperature of the voice coil at the current prediction time based on the currently measurable parameters, each set of prediction reference parameters and the higher-order time domain function. The step of obtaining the higher-order time-domain function of the voice coil circuit includes: obtaining a higher-order temperature thermal model of the voice coil circuit; wherein the higher-order temperature thermal model uses thermal capacity and thermal resistance to characterize the temperature characteristics of the voice coil circuit; determining the higher-order time-domain function based on the relationship between each set of thermal capacity and thermal resistance in the higher-order temperature thermal model; and the higher-order temperature thermal model uses at least three sets of thermal capacity and thermal resistance to characterize the temperature characteristics of the voice coil circuit.

11. An audio playback device, characterized in that, The device includes a voice coil circuit, a processor, and a storage medium; the storage medium stores program code; the processor is used to call the program code stored in the storage medium to execute the temperature prediction method for an audio playback device as described in any one of claims 1 to 9.

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