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. The temperature prediction method of the audio playing device comprises the following steps: predicting an a priori estimated temperature of the audio playing device by using a temperature thermal model of the audio playing device in thermal response, wherein the temperature thermal model is used for characterizing temperature characteristics of the audio playing device in thermal response by using heat capacity and thermal resistance; converting the a priori estimated temperature into a predicted electric signal by using a first measurement matrix corresponding to the audio playing device, wherein the first measurement matrix is used for measuring at least one electric signal of the audio playing device in thermal response; obtaining a prediction error corresponding to the predicted electric signal; and performing smoothing processing on the a priori estimated temperature according to the prediction error, so as to obtain a predicted temperature of the audio playing device. The application can timely predict the temperature of the audio playing device.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of signal processing, and particularly relates to a temperature prediction method and system of an audio playing device and the audio playing device. BACKGROUND

[0002] In digital signal processing, the algorithm of speaker temperature protection needs a current or future temperature as a guide to trigger protection of different amplitudes at different temperatures

[0003] Audio playing devices such as micro speakers, loudspeakers and / or sound boxes are widely used in people's life and work. For example, micro speakers are important components of electronic devices such as mobile phones and tablet computers, and loudspeakers often appear in various conference sites and the like. The temperature protection algorithm of the voice coil temperature of the audio playing device needs a current or future temperature as a guide to trigger protection of the corresponding amplitude at the temperature. The existing scheme often mixes audio into a pilot tone (Pilot Tone) of a specified frequency and amplitude, and then uses a related algorithm to detect the component of the pilot tone in the voltage and current to obtain the resistance, and then uses the linear relationship between the resistance and the temperature to convert the real-time temperature. The temperature determination scheme has the problem of poor timeliness, which can easily lead to a delay in the corresponding temperature protection response. SUMMARY

[0004] In view of this, the present application provides a temperature prediction method and system of an audio playing device and the audio playing device to solve the problem of poor timeliness of the existing temperature acquisition scheme.

[0005] The present application provides a temperature prediction method of an audio playing device, comprising:

[0006] predicting a priori estimated temperature of the audio playing device by using a temperature thermal model of the audio playing device in thermal response, wherein the temperature thermal model uses heat capacity and thermal resistance to represent temperature characteristics of the audio playing device in thermal response;

[0007] converting the a priori estimated temperature into a predicted electric signal by using a first measurement matrix corresponding to the audio playing device, wherein the first measurement matrix is used to measure at least one electric signal of the audio playing device in thermal response;

[0008] obtaining a prediction error corresponding to the predicted electric signal;

[0009] smoothing the a priori estimated temperature according to the prediction error to obtain a predicted temperature of the audio playing device.

[0010] Optionally, the temperature thermal model uses at least three sets of heat capacity and thermal resistance to represent temperature variation characteristics of the audio playing device in thermal response.

[0011] Optionally, the temperature prediction formula corresponding to the temperature thermal model comprises: wherein, T represents the prior estimated temperature at the i th moment, Te i-1 represents the predicted temperature at the i-1 th moment, A represents the transfer matrix of the temperature thermal model, B represents the control matrix of the temperature thermal model, u i represents the power of the playing device at the i th moment.

[0012] Optionally, the conversion formula of the predicted electric signal comprises: wherein, represents the prior estimated temperature at the i th moment, H represents the first measurement matrix, De i represents the predicted electric signal at the i th moment.

[0013] Optionally, the predicted electric signal comprises a predicted current; and the obtaining of the prediction error corresponding to the predicted electric signal comprises: E i = If i -Ie i , wherein E i represents the prediction error at the i th moment, If i represents the pilot current at the i th moment, Ie i represents the predicted current at the i th moment.

[0014] Optionally, the predicted electric signal comprises a predicted resistance; and the obtaining of the prediction error corresponding to the predicted electric signal comprises: obtaining a detected resistance of the audio playing device; and determining the prediction error according to the difference between the detected resistance and the predicted resistance.

[0015] Optionally, the smoothing processing of the prior estimated temperature according to the prediction error to obtain the predicted temperature of the audio playing device comprises: wherein, represents the prior estimated temperature at the i th moment, Te i represents the predicted temperature at the i th moment, E i represents the prediction error at the i th moment, K i represents the smoothing processing coefficient at the i th moment.

[0016] Optionally, the determination process of the smoothing processing coefficient comprises: updating the prior error covariance at the i th moment according to the transfer matrix of the temperature thermal model and the error covariance at the i-1 th moment; and calculating the smoothing processing coefficient at the i th moment according to the prior error covariance at the i th moment, a preset noise covariance matrix and the first measurement matrix.

[0017] Optionally, the updating formula of the prior error covariance comprises: The calculation formula of the smoothing processing coefficient comprises: wherein, represents the prior error covariance at the i th moment, A represents the transfer matrix of the temperature thermal model, P i-1 represents the error covariance at the i-1 th moment, Q represents the process noise covariance matrix, K i represents the smoothing processing coefficient at the i th moment, H represents the first measurement matrix, R represents the noise covariance matrix, and the superscript T represents transposition.

[0018] Optionally, after smoothing the prior estimated temperature according to the prediction error to obtain the predicted temperature of the audio playback device, the temperature prediction method further comprises: updating the error covariance at the i th moment according to the smoothing processing coefficient at the i th moment, the prior error covariance at the i th moment, and the first measurement matrix.

[0019] Optionally, the update formula of the error covariance comprises: wherein, i represents the error covariance at the i th moment, represents the prior error covariance at the i th moment, I represents the unit matrix, K i represents the smoothing processing coefficient at the i th moment, and H represents the first measurement matrix.

[0020] The application also provides a temperature prediction system of an audio playback device, comprising:

[0021] a prediction module configured to predict a prior estimated temperature of the audio playback device by using a temperature thermal model of the audio playback device in thermal response, wherein the temperature thermal model is configured to represent temperature characteristics of the audio playback device in thermal response by using heat capacity and thermal resistance;

[0022] a conversion module configured to convert the prior estimated temperature into a predicted electrical signal by using a first measurement matrix corresponding to the audio playback device, wherein the first measurement matrix is configured to measure at least one electrical signal of the audio playback device in thermal response;

[0023] an acquisition module configured to acquire a prediction error corresponding to the predicted electrical signal;

[0024] a smoothing processing module configured to smooth the prior estimated temperature according to the prediction error to obtain a predicted temperature of the audio playback device.

[0025] The application also provides an audio playback device comprising a processor and a storage medium, wherein the storage medium stores program codes, and the processor is configured to call the program codes stored in the storage medium to execute any of the above-mentioned temperature prediction methods of the audio playback device.

[0026] The temperature prediction method and system of the audio playing device, and the audio playing device provided by the application predict the prior estimation temperature of the audio playing device by using the temperature thermal model of the audio playing device in thermal response, so that the obtained prior estimation temperature is prospective, the prior estimation temperature is converted into a predicted electrical signal by using a first measurement matrix for measuring at least one electrical signal of the audio playing device in thermal response, a prediction error corresponding to the predicted electrical signal is obtained, the prior estimation temperature is smoothed by using the prediction error, and the predicted temperature of the audio playing device is obtained, so that the predicted temperature is more smooth and stable, and the problem of affecting the subsequent control effect caused by jitter and / or mutation can be avoided.

[0027] The temperature thermal model is characterized by at least three sets of heat capacity and thermal resistance to represent the temperature change characteristics of the audio playing device in thermal response, so that the fitting degree between the represented temperature characteristics and the actual temperature characteristics of the voice coil circuit in the audio playing device can be improved, the temperature thermal model can accurately reflect each temperature characteristic of the voice coil circuit, and the obtained prior estimation temperature is more accurate.

[0028] The Kalman filtering method is used to smooth the prior estimation temperature, so that the smoothing effect can be improved, the delay of temperature response can be reduced in the case of removing disturbance, the timeliness of the temperature prediction process is further improved, and the timeliness of the subsequent related control and / or temperature protection process is improved.

[0029] It can be seen that the temperature prediction method of the audio playing device can improve the timeliness of the obtained predicted temperature from multiple aspects, and can also improve the smoothness and stability of the predicted temperature, and improve the effect of subsequent parameter control and / or temperature protection by using the predicted temperature. BRIEF DESCRIPTION OF DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the embodiments of the application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0031] Figure 1 is a temperature prediction method flowchart of the audio playing device in an embodiment of the application;

[0032] Figure 2 is a structure diagram of a three-order temperature thermal model in an embodiment of the application;

[0033] Figure 3 is a temperature prediction process diagram of an embodiment of the application;

[0034] Figure 4 is a temperature prediction process diagram of an embodiment of the application;

[0035] Figure 5 is a Kalman filtering process schematic diagram of an embodiment of the present application;

[0036] Figure 6a and Figure 6b is a temperature contrast analysis schematic diagram of an embodiment of the present application;

[0037] Figure 7 is a temperature prediction system structure schematic diagram of an audio playing device of an embodiment of the present application;

[0038] Figure 8 is an audio playing device structure schematic diagram of an embodiment of the present application. DETAILED DESCRIPTION

[0039] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application. In the case of no conflict, each of the following embodiments and technical features can be combined with each other.

[0040] The first aspect of the present application provides a temperature prediction method of an audio playing device, the audio playing device comprising a voice coil circuit (i.e. a circuit in which a voice coil is located), and the temperature prediction method of the audio playing device can be used for temperature parameters such as voice coil temperature of the voice coil circuit. Referring to Figure 1 The temperature prediction method of the audio playing device comprises steps S110 to S140.

[0041] S110, predicting a priori estimated temperature of the audio playing device by using a temperature thermal model of the audio playing device in thermal response, wherein the temperature thermal model uses thermal capacity and thermal resistance to represent temperature characteristics of the audio playing device in thermal response.

[0042] The audio playing device can comprise a miniature loudspeaker, a speaker, and / or a sound equipment for playing audio. The thermal response comprises a process of the audio playing device doing work and generating heat, i.e. a working process of the audio playing device. The temperature characteristics comprise temperature distribution characteristics, temperature conduction characteristics, and / or temperature change characteristics, etc. The temperature thermal model can also be referred to as an equivalent thermal circuit, and the number of groups of thermal capacity and thermal resistance included in the temperature thermal model can be set according to the required prediction accuracy and the structural characteristics of the corresponding voice coil circuit. The order of the temperature thermal model is consistent with the number of groups of thermal capacity and thermal resistance, for example, the temperature thermal model comprising two groups of thermal capacity and thermal resistance is a second-order temperature thermal model, the temperature thermal model comprising three groups of thermal capacity and thermal resistance is a third-order temperature thermal model, the temperature thermal model comprising four groups of thermal capacity and thermal resistance is a fourth-order temperature thermal model, and so on.

[0043] The aforementioned temperature thermal model specifically characterizes the relationship between the current temperature and the historical temperature at least one time prior to the current time. Step S110 inputs the historical temperature into the temperature thermal model to obtain the prior estimated temperature corresponding to the current time, thus improving the timeliness of the obtained prior estimated temperature.

[0044] S120, the prior estimated temperature is converted into a predicted electrical signal using a first measurement matrix corresponding to the audio playback device. The first measurement matrix is ​​used to measure at least one electrical signal of the audio playback device during thermal response. Specifically, the prior estimated temperature can be converted to obtain the desired electrical signal.

[0045] The first measurement matrix can be set according to the type of electrical signal to be measured. Optionally, when measuring a current signal, the first measurement matrix can be set according to the pilot voltage and / or pilot current of the audio playback device. In this case, the first measurement matrix H can include: H = [f(Vf)], where f(Vf) represents a function of the pilot voltage Vf as input. Specifically, the pilot current and pilot voltage can be obtained by means of pilot signal extraction, etc. For example, the process of obtaining the pilot current and pilot voltage includes: detecting the current and voltage during the thermal response of the audio playback device, obtaining the detected current and detected voltage, and extracting pilot signals from the detected current and detected voltage to obtain the pilot current and pilot voltage. Optionally, when measuring a resistance signal, the first measurement matrix can be set according to the impedance change characteristics of the audio playback device.

[0046] S130, obtain the prediction error corresponding to the predicted electrical signal; the prediction error may include parameters that characterize the error generated when obtaining the predicted electrical signal, such as the difference between the predicted electrical signal and the corresponding calculated value.

[0047] S140, the prior estimated temperature is smoothed based on the prediction error to obtain the predicted temperature of the audio playback device, making the obtained predicted temperature smoother and more stable, thus avoiding problems such as jitter and / or sudden changes that affect the subsequent control effect. Optionally, the smoothing method can be set according to factors such as the required filtering accuracy, and may include smoothing filtering methods such as correlated fuzzing and / or Kalman filtering.

[0048] The temperature prediction method of the audio playing device adopts a temperature thermal model of the audio playing device in thermal response to predict a priori estimated temperature of the audio playing device, so that the obtained priori estimated temperature has a forward-looking nature, converts the priori estimated temperature into a predicted electrical signal by using a first measurement matrix for measuring at least one electrical signal of the audio playing device in thermal response, obtains a prediction error corresponding to the predicted electrical signal, and smoothes the priori estimated temperature by using the prediction error to obtain a predicted temperature of the audio playing device, so that the predicted temperature is more smooth and stable, and the problem of affecting the subsequent control effect caused by jitter and / or mutation can be avoided. It can be seen that the temperature prediction method of the audio playing device not only improves the timeliness of the obtained predicted temperature, but also improves the smoothness and stability of the predicted temperature, thereby improving the effect of subsequent parameter control and / or temperature protection by using the predicted temperature.

[0049] In one embodiment, the temperature thermal model adopts at least three sets of heat capacity and thermal resistance to characterize the temperature change characteristics of the audio playing device in thermal response, so as to improve the fitting degree between the characterized temperature characteristics and the actual temperature characteristics of the voice coil circuit in the audio playing device, so that the temperature thermal model can accurately reflect each temperature characteristic of the voice coil circuit.

[0050] Optionally, the temperature thermal model includes three sets of heat capacity and thermal resistance, and the temperature thermal model is a third-order temperature thermal model. Referring to Figure 2 As shown, the third-order temperature thermal model includes a power supply DC, a first heat capacity C1, a second heat capacity C2, a third heat capacity C3, a first thermal resistance R1, a second thermal resistance R2, and a third thermal resistance R3. The power supply DC, the first heat capacity C1, the second heat capacity C2, and the third heat capacity C3 are connected in parallel. The first thermal resistance R1 is connected between the first end of the first heat capacity C1 and the first end of the second heat capacity C2. The second thermal resistance R2 is connected between the first end of the second heat capacity C2 and the first end of the third heat capacity C3. The third thermal resistance R3 is connected between the first end and the second end of the third heat capacity C3. The second ends of the first heat capacity C1, the second heat capacity C2, and the third heat capacity C3 are connected to ground. The corresponding voice coil circuit includes a voice coil and a magnet, as shown in Figure 2As shown, in the third-order temperature thermal model, through the power supply DC, a power P first passes through the first thermal capacity C1 and the first thermal resistance R1 representing the voice coil, causing the voice coil temperature Tvc to rise; then passes through the second thermal capacity C2 and the second thermal resistance R2 representing the voice coil gap, causing the loudspeaker voice coil gap temperature Tg to rise; then passes through the third thermal capacity C3 and the third thermal resistance R3 representing the magnet, causing the magnet temperature Tm to rise; and finally, the power P flows to the ambient temperature Ta, discharging heat to the external environment of the voice coil circuit. The above-mentioned third-order temperature thermal model has a simple structure, and on the basis of ensuring the overall fit between the temperature characteristics represented by the third-order temperature thermal model and the actual temperature characteristics of the voice coil circuit, the efficiency of predicting the corresponding priori estimated temperature using the third-order temperature thermal model can be improved. It should be noted that in some audio playback devices such as loudspeakers, the voice coil magnet and the voice coil magnet gap will not be damaged due to excessive temperature, so the guidance of these two temperatures is usually not required for the corresponding voice coil protection algorithm. In this case, the temperature to be predicted includes the voice coil temperature, so as to guide the subsequent voice coil protection algorithm using the predicted voice coil temperature.

[0051] In one example, the temperature thermal model can predict the priori estimated temperature through a temperature prediction formula to simplify the prediction process and improve the prediction efficiency. Specifically, the temperature prediction formula corresponding to the temperature thermal model includes: wherein, represents the priori estimated temperature at the i-th moment (such as the current moment), Te i-1 represents the predicted temperature at the (i-1)-th moment, A represents the transition matrix of the temperature thermal model, B represents the control matrix of the temperature thermal model, and u i represents the power of the playback device at the i-th moment. Alternatively, the power u i at the i-th moment can be determined according to the detected current and / or predicted resistance at the i-th moment, for example, and the like, Im i represents the detected current at the i-th moment, which can be obtained by current detection of the audio playback device, and Re represents the predicted current at the i-th moment, which can be obtained by measurement or conversion using a corresponding measurement matrix (such as a first measurement matrix).

[0052] The parameters such as the transition matrix A and the control matrix B in the above-mentioned temperature prediction formula can be converted from the heat conduction function corresponding to the temperature thermal model. Taking Figure 2 the third-order temperature thermal model as an example, the heat conduction function of the third-order temperature thermal model in the s domain can be written as: wherein the conduction coefficients a1, a2, a3, b1, b2 and b3 can be obtained by transformation from the first thermal capacity C1, the second thermal capacity C2, the third thermal capacity C3, the first thermal resistance R1, the second thermal resistance R2 and the third thermal resistance R3, T vcrepresents a temperature to be predicted, such as a priori estimated temperature, and the like, and P represents a power corresponding to the third-order temperature thermal model. The heat conduction function is normalized to obtain the following identity equation: G(s) = C(sI-A) -1 B+D, which can also be referred to as a state space equation, where A represents a transition matrix of the temperature thermal model, B represents a control matrix of the temperature thermal model, C represents a second measurement matrix of the temperature thermal model, which is used to measure temperature parameters corresponding to the third-order temperature thermal model and can be determined according to the third-order temperature thermal model, and D = 0 for a single-input single-output audio playback device. The state space equation is discretized to obtain a corresponding discrete form, which includes:

[0053]

[0054] where x i represents an intermediate state of the i-th iteration, y i represents a measurement value of the intermediate state at the i-th iteration, u i represents a power at the i-th iteration.

[0055] Solving the discrete form of the state space equation can obtain the transition matrix A and the control matrix B of the temperature thermal model, and further determine a corresponding temperature prediction formula.

[0056] In an embodiment, the conversion formula of the predicted electrical signal includes: wherein, represents a priori estimated temperature at the i-th iteration, H represents a first measurement matrix, and De i represents a predicted electrical signal at the i-th iteration. The first measurement matrix H is used to accurately convert the a priori estimated temperature in this embodiment, so as to efficiently obtain the required predicted electrical signal. On the basis of ensuring the accuracy of the obtained predicted electrical signal, the corresponding prediction efficiency can be improved.

[0057] In an embodiment, the predicted electrical signal includes a predicted current; the corresponding predicted error of the predicted electrical signal includes: Figure 1 as shown in step S130, the acquisition of the predicted error of the predicted electrical signal includes: E i = If i -Ie i wherein, E i represents a predicted error at the i-th iteration, If i represents a pilot current at the i-th iteration, and Ie i represents a predicted current at the i-th iteration. The current is used as the predicted signal in this embodiment, and the difference between the pilot current and the predicted current can be directly determined as the predicted error, so as to improve the determination efficiency of the predicted error.

[0058] Optionally, using the predicted current as the predicted electrical signal, the temperature prediction process for the audio playback device can be referenced. Figure 3 As shown, during the thermal response of the audio playback device, the device is detected to obtain the detection current Im and the detection voltage Vm. Pilot signals are extracted from the detection current Im and the detection voltage Vm to obtain the pilot current If and the pilot voltage Vf. The power u of the audio playback device is obtained based on the detection current Im, and the power u is input into the temperature thermal model to predict the prior estimated temperature Te. - The first measurement matrix H is determined based on the pilot voltage Vf, and the a priori estimated temperature Te is transformed using the first measurement matrix H. - The predicted current Ie is obtained, and the prediction error E is calculated according to E = If - Ie; the prediction error E is used to estimate the prior temperature Te. - Smoothing is performed to obtain the predicted temperature Te. Optionally, such as... Figure 3 As shown, the power calculation formula for the power acquisition process of the predicted temperature Te can also be used to determine the electrical signal corresponding to the predicted temperature Te, and the power u can be calculated based on these electrical signals. For example, the predicted temperature Te can be determined to correspond to the group Re(Te), where u = Im 2 *Re(Te) is used to ensure the accuracy of the obtained power u. Specifically, the temperature Te is estimated a priori using the first measurement matrix H transformation. - The conversion formulas corresponding to the predicted current Ie include: The first measurement matrix H is the equation for the pilot voltage Vf, i.e., H = [f(Vf)]. The principle behind this transformation formula can include: transforming the prior estimated temperature Te through a linear transformation. - The voltage is then converted to the corresponding resistor, and the predicted current Ie is obtained through voltage and resistance calculations.

[0059] In one embodiment, the predicted electrical signal includes a predicted resistance; corresponding to Figure 1 Step S130, as shown, involves obtaining the prediction error corresponding to the predicted electrical signal, which includes: obtaining the detection resistance of the audio playback device; and determining the prediction error based on the difference between the detection resistance and the predicted resistance. This embodiment uses the predicted resistance as the predicted electrical signal, making the form of the corresponding first measurement matrix simpler. This simplifies the first measurement matrix and reduces the computational load in the process of converting the prior estimated temperature into the predicted resistance using the first measurement matrix, thereby improving the corresponding temperature prediction efficiency. The aforementioned detection resistance can be calculated based on the pilot current and pilot voltage, for example, by determining the detection resistance based on the quotient of the pilot voltage and pilot current. The formula for calculating the prediction error may include: E i =Rm i -Re i , Rm i Re represents the detection resistance at time i. irepresents the predicted resistance at the i-th moment.

[0060] Optionally, the temperature prediction process of the audio playing device can refer to the predicted resistance as the predicted electric signal Figure 4 As shown, when the audio playing device is in thermal response, the audio playing device is detected to obtain a detection current Im and a detection voltage Vm, pilot signal extraction is performed on the detection current Im and the detection voltage Vm to obtain a pilot current If and a pilot voltage Vf, and the detection resistance Rm is calculated according to the pilot current If and the pilot voltage Vf; the power u of the audio playing device is obtained according to the detection current Im, and the prior estimated temperature Te is predicted by inputting the power u into the temperature thermal model - ; the first measurement matrix H is used to transform the prior estimated temperature Te - to obtain the predicted resistance Re, the prediction error E is calculated according to E=Rm-Re; the prediction error E is used to smooth the prior estimated temperature Te - to obtain the predicted temperature Te. Optionally, the first measurement matrix H can be directly determined according to the resistance characteristics corresponding to the temperature thermal model, and has a relatively simple form. The first measurement matrix H is used to transform the prior estimated temperature Te - to obtain the conversion formula corresponding to the predicted resistance Re, which includes: The principle of the conversion formula can include that the first measurement matrix H can transform the prior estimated temperature Te - to the predicted resistance Re through linear transformation.

[0061] In one embodiment, corresponding to Figure 1 Step S140 shown, the smoothing processing of the prior estimated temperature according to the prediction error to obtain the predicted temperature of the audio playing device includes: Wherein, represents the prior estimated temperature at the i-th moment, Te i represents the predicted temperature at the i-th moment, E i represents the prediction error at the i-th moment, K i represents the smoothing processing coefficient at the i-th moment. This embodiment realizes the smoothing processing of the prior estimated temperature through the weighted calculation mode, can simplify the smoothing processing process and improve the smoothing processing efficiency on the basis of ensuring the smoothing processing effect, so as to improve the temperature prediction efficiency. The smoothing processing coefficient K i at the i-th moment can be set according to the accuracy of the smoothing processing and other factors. Optionally, the smoothing processing coefficient K i can be set according to the relevant experience value to improve the setting efficiency; also set through multiple test processes to ensure the accuracy of the set smoothing processing coefficient K i ; also can be set in an iterative filtering mode to make the smoothing processing coefficient K iBased on the relevant operating parameters of the audio playback device (such as historical operating parameters, etc.), the smoothing effect is improved.

[0062] In one example, the process of determining the smoothing coefficients includes: updating the prior error covariance at time i based on the transition matrix of the temperature thermal model and the error covariance at time i-1; and calculating the smoothing coefficients at time i based on the prior error covariance at time i, a preset noise covariance matrix, and the first measurement matrix. This example updates the prior error covariance at time i based on the transition matrix of the temperature thermal model and the error covariance at time i-1, ensuring that the prior error covariance at time i is based on the relevant parameters of the temperature thermal model, thus improving the accuracy of the update process. Calculating the smoothing coefficients at time i based on the prior error covariance at time i, the preset noise covariance matrix, and the first measurement matrix improves the correlation between the smoothing coefficients and the relevant parameters and historical parameters of the temperature thermal model. Using Kalman filtering to smooth the prior estimated temperature improves the smoothing effect, saves on corresponding hardware costs, avoids errors caused by inaccuracies, and offers advantages such as low latency and low disturbance compared to temperature acquisition methods that only use pilot tones to calculate temperature.

[0063] Specifically, the update formula for the prior error covariance includes: The formulas for calculating the smoothing factor include: in, Let P represent the prior error covariance at time i, A represent the transition matrix of the temperature thermal model, and P represent the prior error covariance at time i. i-1 Let K represent the error covariance at time i-1, Q represent the process noise covariance matrix, and K represent the error covariance at time i-1. i Let H represent the smoothing coefficient at time i, H represent the first measurement matrix, R represent the noise covariance matrix, and the superscript T represent the transpose. Here, the smoothing coefficient can be calculated using the update formula for the prior error covariance and the calculation formula for the smoothing coefficient, thus achieving Kalman filtering corresponding to the prior estimated temperature and further improving the smoothing effect.

[0064] In one example, corresponding to Figure 1 As shown in step S140, after smoothing the prior estimated temperature based on the prediction error to obtain the predicted temperature of the audio playback device, the temperature prediction method further includes: updating the error covariance at time i based on the smoothing coefficient at time i, the prior error covariance at time i, and the first measurement matrix, so that the smoothing coefficient can be accurately and efficiently obtained at time i+1 based on the error covariance at time i, ensuring the continuity of the corresponding smoothing process and the filtering effect.

[0065] Specifically, the update formula for the error covariance includes: wherein P i represents the error covariance at the i-th moment, represents the prior error covariance at the i-th moment, I represents a unit matrix, and K i represents the smoothing coefficient at the i-th moment, and H represents the first measurement matrix.

[0066] In the process of Kalman filtering on the prior estimated temperature to achieve smoothing, the smoothing coefficient can also be referred to as Kalman gain. In one example, the smoothing coefficient is calculated by Figure 5 The above Kalman filtering process is described as shown in Figure 5 shown, first determine the process noise covariance matrix Q and the noise covariance matrix R by multiple debugging, input the noise covariance matrix into the update formula of the prior error covariance to calculate the error covariance, obtain the prior error covariance, input the prior error covariance and the noise covariance matrix R into the calculation formula of the smoothing coefficient to calculate the smoothing coefficient, obtain the smoothing coefficient at the current moment (such as the smoothing coefficient at the i-th moment), and then update the error covariance using the smoothing coefficient at the current moment to calculate the smoothing coefficient at the next moment according to the updated error covariance, and realize the corresponding Kalman filtering.

[0067] In one example, the inventors compare and analyze the temperature of the loudspeaker, an audio playing device, and specifically obtain the measured temperature of the voice coil circuit of the loudspeaker. The first predicted temperature of the voice coil circuit is obtained by using a third-order loudspeaker thermal model, the third predicted temperature of the voice coil circuit is obtained by using a second-order loudspeaker thermal model, and the fourth predicted temperature of the voice coil circuit is obtained by using the temperature prediction method provided in the application. In the temperature prediction method provided in the application, the prior estimated temperature is predicted by using a third-order temperature thermal model, and the prior estimated temperature is smoothed by using Kalman filtering. In an ideal state, the comparison chart of each temperature can refer to Figure 6a shown, in an actual situation, the comparison chart of each temperature can refer to Figure 6b shown, Figure 6b the meanings of the curves in Figure 6a shown, such as Figure 6a In the legend, the solid line arranged first represents the measured temperature, the dashed line arranged second represents the first predicted temperature obtained by using the third-order loudspeaker thermal model, the dashed line arranged third represents the third predicted temperature of the voice coil circuit obtained by using the second-order loudspeaker thermal model, and the dashed line arranged fourth represents the fourth predicted temperature of the voice coil circuit obtained by using the temperature prediction method provided in the application, Figure 6a and Figure 6bThe second-order horn heat model cannot fully and accurately express the horn heat behavior, the third-order horn heat model adopted in the application can accurately fit the actual measurement problem of the priori estimation temperature, and the response speed is faster than that of the second-order heat model when rising, the more the order of the temperature model, the more the curve characteristics can be expressed, and the more accurate the predicted temperature is; The temperature prediction method provided by the application can smooth the priori estimation temperature predicted by the third-order horn heat model, and the predicted temperature is relatively fast in response to the actual temperature in the early stage.

[0068] The temperature prediction method of the above audio playing device uses a temperature heat model to predict the priori estimation temperature of the audio playing device, so that the obtained priori estimation temperature has a forward-looking nature, and the priori estimation temperature is smoothed by using the prediction error, to obtain the predicted temperature of the audio playing device, so that the above predicted temperature is more smooth and stable, and the problem of affecting the subsequent control effect can be avoided. Control effect problem; wherein the temperature heat model uses at least three groups of heat capacity and thermal resistance to represent the temperature change characteristics of the audio playing device in thermal response, which can improve the fitting degree between the represented temperature characteristics and the actual temperature characteristics of the audio playing device. The coil circuit can accurately reflect each temperature characteristic of the coil circuit, and the predicted priori estimation temperature is more accurate; the Kalman filtering method is used to smooth the priori estimation temperature, which can improve the smoothing effect, reduce the delay of temperature response under the condition of removing disturbance, and further improve the timeliness of the temperature prediction process, thereby improving the timeliness of the subsequent related control and / or temperature protection process. It can be seen that the above temperature prediction method of the audio playing device can improve the timeliness of the obtained predicted temperature from multiple aspects, and can also improve the smoothness and stability of the predicted temperature, and improve the effect of subsequent parameter control and / or temperature protection using the predicted temperature.

[0069] In a second aspect, the application provides a temperature prediction system of an audio playing device, as shown in Figure 7 The temperature prediction system comprises:

[0070] The prediction module 210 is configured to predict the priori estimation temperature of the audio playing device by using the temperature heat model of the audio playing device in thermal response, and the temperature heat model is configured to represent the temperature characteristics of the audio playing device in thermal response by using heat capacity and thermal resistance.

[0071] The conversion module 220 is configured to convert the priori estimation temperature into a predicted electrical signal by using the first measurement matrix corresponding to the audio playing device, and the first measurement matrix is configured to measure at least one electrical signal of the audio playing device in thermal response.

[0072] The acquisition module 230 is configured to acquire the prediction error corresponding to the predicted electrical signal.

[0073] The smoothing processing module 240 is configured to smooth the priori estimated temperature according to the prediction error, to obtain a predicted temperature of the audio playing device.

[0074] The specific definitions of the temperature prediction system of the audio playing device can refer to the definitions of the temperature prediction method of the audio playing device, which will not be repeated here. The various units in the above-mentioned temperature prediction system of the audio playing device can be realized by software, hardware and combinations thereof. The above-mentioned various units can be embedded in or independent of the operation module in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the operation module to perform the operations corresponding to the above-mentioned various units.

[0075] In a third aspect, the present application provides an audio playing device, referring to Figure 8 The audio playing device includes a processor 610 and a storage medium 620; the storage medium 620 stores program codes; and the processor 610 is configured to call the program codes stored in the storage medium 620 to execute the temperature prediction method of the audio playing device according to any of the above-mentioned embodiments.

[0076] The above-mentioned audio playing device can include independent audio playing devices such as loudspeakers and sound systems, can include audio playing modules on other electronic devices such as micro speakers, and can include electronic devices with audio playing modules such as mobile phones and tablet computers. Optionally, the audio playing device can further include a voice coil circuit for audio playing.

[0077] The audio playing device provided by the present application can timely and accurately predict the temperature of the voice coil and other related parameters and / or temperature parameters required in the temperature control process, execute corresponding control and / or protection mechanisms according to the temperature parameters, and ensure the stability of the corresponding performance in the audio playing process; the predicted temperature is smoother, which can avoid the problem of jitter in the subsequent control process affecting the audio playing effect, and is beneficial to improve the audio playing effect.

[0078] Although the present application has been shown and described with respect to one or more implementations, equivalent alterations and modifications will occur to others skilled in the art based on the foregoing description and accompanying drawings. The present application includes all such modifications and alterations and is limited only by the scope of the following claims. In particular, with reference to the various functions performed by the above-described components, the terms used to describe such components are intended to correspond, unless otherwise indicated, to any component which performs the specified function of the component (e.g., that is functionally equivalent), even though not structurally equivalent to the disclosed structure which performs the function in the exemplary implementation illustrated by the present specification. The components shown in the various figures are not necessarily drawn to scale.

[0079] That is, the above merely describes the embodiments of the present application, and does not limit the patent scope of the present application, and any equivalent structure or equivalent process conversion using the content of the present application specification and drawings, such as the mutual combination of technical features between embodiments, or direct or indirect application in other related technical fields, are also included in the patent protection scope of the present application.

[0080] In addition, the terms "first" and "second" and the like are used only for descriptive purposes, and are not to be construed as indicating or implying relative importance or implying the number of indicated technical features. Therefore, the "first" and "second" and the like features can explicitly or implicitly include one or more features. In the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise specifically limited.

[0081] The above description is given for enabling any person skilled in the art to carry out and use the present application. In the above description, various details are listed for the purpose of explanation. It should be understood that those skilled in the art can recognize that the present application can be implemented without using these specific details. In other embodiments, well-known processes will not be described in detail to avoid unnecessary details making the description of the present application obscure. Therefore, the present application is not intended to be limited to the embodiments shown, but is consistent with the broadest scope consistent with the principles and features disclosed in the present application.

Claims

1. A temperature prediction method of an audio playback device, characterized by, The temperature prediction method comprises: predicting an a priori estimated temperature of the audio playing device by using a temperature thermal model of the audio playing device in thermal response, the temperature thermal model using heat capacity and thermal resistance to represent temperature characteristics of the audio playing device in thermal response; converting the a priori estimated temperature into a predicted electrical signal by using a first measurement matrix corresponding to the audio playing device, the first measurement matrix being used to measure at least one electrical signal of the audio playing device in thermal response; obtaining a prediction error corresponding to the predicted electrical signal; smoothing the a priori estimated temperature according to the prediction error to obtain a predicted temperature of the audio playing device; a determination process of a smoothing coefficient comprises: updating an a priori error covariance at an i-th moment according to a transition matrix of the temperature thermal model and an error covariance at an (i-1)-th moment; and calculating the smoothing coefficient at the i-th moment according to the a priori error covariance at the i-th moment, a preset noise covariance matrix and the first measurement matrix. 2.The temperature prediction method of an audio playback device according to claim 1, wherein, The temperature thermal model uses at least three sets of heat capacity and thermal resistance to represent temperature variation characteristics of the audio playing device in thermal response. 3.The temperature prediction method of an audio playback device according to claim 2, wherein, A temperature prediction formula corresponding to the temperature thermal model comprises: wherein, represents the prior estimate temperature at the i-th time instant, Te i-1 represents the predicted temperature at the i-1-th time instant, A represents a transition matrix of the temperature thermal model, B represents a control matrix of the temperature thermal model, u i represents the power of the playing device at the i-th time instant. 4.The temperature prediction method of an audio playback device according to claim 1, wherein, A conversion formula of the predicted electrical signal comprises: wherein, represents the prior estimation temperature at the i-th moment, H represents the first measurement matrix, De i represents the predicted electric signal at the i-th moment. 5.The temperature prediction method of an audio playback device according to claim 1, wherein, The predicted electrical signal includes a predicted current; the obtaining of a prediction error corresponding to the predicted electrical signal includes: E i = If i - Ie i , wherein E i represents a prediction error at the i th moment, If i represents a pilot current at the i th moment, and Ie i represents a predicted current at the i th moment. 6.The temperature prediction method of an audio playback device according to claim 1, wherein, The predicted electrical signal comprises a predicted resistance; and the obtaining of the prediction error corresponding to the predicted electrical signal comprises: obtaining a detected resistance of the audio playing device; determining the prediction error according to a difference between the detected resistance and the predicted resistance. 7.The temperature prediction method of an audio playback device according to claim 1, wherein, The smoothing of the a priori estimated temperature according to the prediction error to obtain the predicted temperature of the audio playing device comprises: wherein, represents the prior estimation temperature at the i-th time, Te i represents the predicted temperature at the i-th time, E i represents the prediction error at the i-th time, K i represents the smoothing processing coefficient at the i-th time. 8.The temperature prediction method of an audio playback device according to claim 1, wherein, The update formula of the prior error covariance comprises: The calculation formula of the smoothing processing coefficient comprises: wherein, Pi represents the prior error covariance at the i th moment, A represents a transition matrix of the temperature thermal model, Pi-1 represents the error covariance at the i-1 th moment, Q represents a process noise covariance matrix, Ki represents the smoothing processing coefficient at the i th moment, H represents a first measurement matrix, R represents a noise covariance matrix, and a superscript T represents transposition. i-1 Pi represents the prior error covariance at the i th moment, A represents a transition matrix of the temperature thermal model, Pi-1 represents the error covariance at the i-1 th moment, Q represents a process noise covariance matrix, Ki represents the smoothing processing coefficient at the i th moment, H represents a first measurement matrix, R represents a noise covariance matrix, and a superscript T represents transposition. i Pi represents the prior error covariance at the i th moment, A represents a transition matrix of the temperature thermal model, Pi-1 represents the error covariance at the i-1 th moment, Q represents a process noise covariance matrix, Ki represents the smoothing processing coefficient at the i th moment, H represents a first measurement matrix, R represents a noise covariance matrix, and a superscript T represents transposition. 9.The temperature prediction method of an audio playback device according to claim 1, wherein, After the smoothing of the a priori estimated temperature according to the prediction error to obtain the predicted temperature of the audio playing device, the temperature prediction method further comprises: updating an error covariance at an i-th moment according to the smoothing coefficient at the i-th moment, the a priori error covariance at the i-th moment and the first measurement matrix. 10.The temperature prediction method of an audio playback device according to claim 9, wherein, The update formula of the error covariance includes: wherein P i represents the error covariance at the i-th moment, represents the prior error covariance at the i-th moment, I represents a unit matrix, K i represents the smoothing processing coefficient at the i-th moment, and H represents the first measurement matrix.

11. A temperature prediction system of an audio playback device, the system comprising: comprises: a prediction module configured to predict an a priori estimated temperature of the audio playing device by using a temperature thermal model of the audio playing device in thermal response, the temperature thermal model using heat capacity and thermal resistance to represent temperature characteristics of the audio playing device in thermal response; a conversion module configured to convert the a priori estimated temperature into a predicted electrical signal by using a first measurement matrix corresponding to the audio playing device, the first measurement matrix being used to measure at least one electrical signal of the audio playing device in thermal response; an obtaining module configured to obtain a prediction error corresponding to the predicted electrical signal; a smoothing module configured to smooth the a priori estimated temperature according to the prediction error to obtain a predicted temperature of the audio playing device; a determination process of a smoothing coefficient comprises: updating an a priori error covariance at an i-th moment according to a transition matrix of the temperature thermal model and an error covariance at an (i-1)-th moment; and calculating the smoothing coefficient at the i-th moment according to the a priori error covariance at the i-th moment, a preset noise covariance matrix and the first measurement matrix. ​ 12. An audio playback device, comprising: The audio playback device comprises a processor and a storage medium; the storage medium stores program codes; the processor is configured to invoke the program codes stored in the storage medium to execute the temperature prediction method of the audio playback device according to any one of claims 1 to 10.

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