Method and device for detecting temperature of speaker voice coil, electronic equipment and storage medium

Abstract

The application provides a loudspeaker voice coil temperature detection method and device, electronic equipment and storage medium, and relates to the technical field of signal processing. The loudspeaker voice coil temperature detection method comprises: obtaining the current input power of the voice coil of the loudspeaker; inputting the current input power into a voice coil temperature detection model for representing the mapping relationship between the input power of the voice coil of the loudspeaker and the temperature of the voice coil of the loudspeaker, to obtain the target temperature of the voice coil of the loudspeaker output by the voice coil temperature detection model; wherein the voice coil temperature detection model is a second-order loudspeaker heat transfer model established based on target thermal parameters, and the target thermal parameters at least include a first target thermal parameter between the voice coil of the loudspeaker and the magnetic circuit of the loudspeaker, a second target thermal parameter between the magnetic circuit of the loudspeaker and the air, and a third target thermal parameter of the voice coil of the loudspeaker and the air convection. The technical scheme provided by the application realizes the detection of the voice coil temperature of the loudspeaker, and improves the precision of the voice coil temperature detection.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of signal processing, and particularly relates to a loudspeaker voice coil temperature detection method and device, electronic equipment and a storage medium. BACKGROUND

[0002] A loudspeaker is a transducer that converts electrical signals into acoustic signals, and is widely used in electronic devices such as mobile phones, computers and tablet computers that need to play audio. During the operation of the loudspeaker, the temperature of the voice coil changes with the working time and working state, etc. If the voice coil works in a high-temperature environment for a long time, the loudspeaker will malfunction or even be damaged. Therefore, it is particularly important to detect the temperature of the voice coil of the loudspeaker for temperature adjustment and control. The temperature change of the voice coil is affected by many factors, and the change is relatively complex. How to detect the temperature of the voice coil is a problem to be solved at present. SUMMARY

[0003] The present application provides a loudspeaker voice coil temperature detection method and device, electronic equipment and a storage medium to solve the problem of how to detect the temperature of the voice coil.

[0004] In a first aspect, the present application provides a loudspeaker voice coil temperature detection method, comprising:

[0005] obtaining the current input power of the voice coil of the loudspeaker;

[0006] inputting the current input power into a voice coil temperature detection model to obtain the target temperature of the voice coil of the loudspeaker output by the voice coil temperature detection model;

[0007] The voice coil temperature detection model is a second-order model of loudspeaker heat transfer established based on target thermal parameters, and the target thermal parameters at least include a first target thermal parameter between the voice coil of the loudspeaker and the magnetic circuit of the loudspeaker, a second target thermal parameter between the magnetic circuit of the loudspeaker and the air, and a third target thermal parameter of air convection of the voice coil of the loudspeaker. The voice coil temperature detection model is used to represent the mapping relationship between the input power of the voice coil of the loudspeaker and the temperature of the voice coil of the loudspeaker.

[0008] Optionally, the voice coil temperature detection model is established by the following steps:

[0009] constructing an equivalent circuit of the voice coil temperature detection model according to the heat dissipation path of the voice coil of the loudspeaker and the magnetic circuit of the loudspeaker;

[0010] establishing the voice coil temperature detection model based on the correlation between the equivalent circuit and the target thermal parameters.

[0011] Optionally, the equivalent circuit comprises a first thermal resistance between the voice coil of the loudspeaker and the magnetic circuit of the loudspeaker, a first thermal capacitance between the voice coil of the loudspeaker and the magnetic circuit of the loudspeaker, a second thermal resistance between the magnetic circuit of the loudspeaker and the air, a second thermal capacitance between the magnetic circuit of the loudspeaker and the air, and a third thermal resistance of the voice coil of the loudspeaker and the air convection;

[0012] The first thermal resistance and the first thermal capacitance are connected in parallel, and then connected in series with a parallel branch of the second thermal resistance and the second thermal capacitance to form a first power branch; the third thermal resistance is a second power branch connected in parallel with the first power branch; the first power branch and the second power branch are both connected in parallel with the heat power of the voice coil of the loudspeaker.

[0013] The first target thermal parameter comprises the first thermal resistance and the first thermal capacitance, the second target thermal parameter comprises the second thermal resistance and the second thermal capacitance, and the third target thermal parameter comprises the third thermal resistance.

[0014] Optionally, the voice coil temperature detection model is expressed by the following formula:

[0015] ;

[0016] wherein t represents a discrete time point, Te_pred[ ] represents a predicted temperature output by the voice coil temperature detection model, P_in[ ] represents a discrete value of input power, coefficients and are determined by converting the discrete domain transfer function H(z) and the continuous domain transfer function H(s) corresponding to the Te_pred[t] according to a bilinear transformation;

[0017] The discrete domain transfer function H(z) corresponding to the Te_pred[t] is: ;

[0018] The continuous domain transfer function H(s) is:

[0019] ;

[0020] wherein Rca represents the third thermal resistance, Rcm represents the first thermal resistance, Rma represents the second thermal resistance, Cma represents the second thermal capacitance, Ccm represents the first thermal capacitance, , .

[0021] Optionally, the optimal value of the target thermal parameter of the voice coil temperature detection model is determined by the following steps:

[0022] A parameter total number of the target thermal parameter is taken as a chromosome coding length, and a preset population quantity of chromosomes is generated;

[0023] In each iteration, an initial voice coil temperature detection model corresponding to each chromosome is determined based on an initial thermal parameter value corresponding to each chromosome in the preset population quantity of chromosomes;

[0024] The fitness of each chromosome is determined based on a real temperature of a voice coil of an experimental loudspeaker and a predicted temperature of the initial voice coil temperature detection model, wherein the experimental loudspeaker is of the same model as the loudspeaker;

[0025] The preset population quantity of chromosomes is iteratively optimized based on the fitness and genetic parameters until a preset iteration number is reached;

[0026] The fitness of each chromosome in the last generation of the preset population quantity is calculated;

[0027] An optimal value of the target thermal parameter is determined based on the fitness of each chromosome in the last generation.

[0028] Optionally, the fitness of each chromosome is determined based on the real temperature of the voice coil of the experimental loudspeaker and the predicted temperature of the initial voice coil temperature detection model, and includes:

[0029] The fitness of each chromosome is determined based on the real temperature of the voice coil of the experimental loudspeaker and the predicted temperature of the initial voice coil temperature detection model according to the following formula:

[0030] ;

[0031] wherein, represents the fitness, N represents a discrete sampling point number of the temperature, represents an i-th discrete sampling point of the real temperature of the voice coil of the experimental loudspeaker, represents an i-th discrete sampling point of the predicted temperature of the initial voice coil temperature detection model.

[0032] Optionally, the preset population quantity of chromosomes is iteratively optimized based on the fitness and genetic parameters, and includes:

[0033] The preset population quantity of chromosomes is sorted from high to low according to the fitness, and a first chromosome in a preset elite preservation quantity is obtained from a sorting result;

[0034] According to the fitness, a second chromosome is selected from the preset population number of chromosomes by using a roulette selection method, and a sum of the second chromosome number and the preset elite preservation number is the preset population number;

[0035] The first chromosome and the second chromosome form a new first chromosome population;

[0036] According to a preset crossover probability, the first chromosome population is subjected to a crossover operation to obtain a second chromosome population;

[0037] According to a preset mutation probability, the second chromosome population is subjected to a mutation operation to obtain a third chromosome population, and the third chromosome population is used as a chromosome for the next iteration;

[0038] The genetic parameters include the preset population number, the preset elite preservation number, the preset crossover probability, the preset mutation probability, and the preset iteration number.

[0039] Optionally, the determining of the optimal value of the target thermal parameter based on the fitness of each of the last generation of chromosomes comprises:

[0040] determining a target chromosome with the minimum fitness in the last generation of chromosomes;

[0041] determining a value of a thermal parameter corresponding to the target chromosome as the optimal value of the target thermal parameter.

[0042] In a second aspect, the present application provides a temperature detection device for a voice coil of a loudspeaker, comprising:

[0043] an acquisition module configured to acquire a current input power of a voice coil of a loudspeaker;

[0044] a detection module configured to input the current input power into a voice coil temperature detection model to obtain a target temperature of the voice coil of the loudspeaker output by the voice coil temperature detection model;

[0045] The voice coil temperature detection model is a second-order model of heat transfer of a loudspeaker established based on target thermal parameters, the target thermal parameters at least including a first target thermal parameter between a voice coil of the loudspeaker and a magnetic circuit of the loudspeaker, a second target thermal parameter between the magnetic circuit of the loudspeaker and air, and a third target thermal parameter of convection between the voice coil of the loudspeaker and air; and the voice coil temperature detection model is used to represent a mapping relationship between an input power of the voice coil of the loudspeaker and a temperature of the voice coil of the loudspeaker.

[0046] In a third aspect, the present application provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program capable of running on the processor, and the processor implements the steps of the loudspeaker voice coil temperature detection method according to any one of the first aspect when executing the computer program.

[0047] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program implements the steps of the loudspeaker voice coil temperature detection method according to any one of the first aspect when executed by a processor.

[0048] The loudspeaker voice coil temperature detection method, device, electronic device and storage medium provided by the present application can obtain the current input power of the voice coil of the loudspeaker, input the current input power into the voice coil temperature detection model representing the mapping relationship between the input power of the voice coil of the loudspeaker and the temperature of the voice coil of the loudspeaker, and predict the temperature of the voice coil by using the voice coil temperature detection model, so as to obtain the target temperature of the voice coil of the loudspeaker output by the voice coil temperature detection model, and realize the detection of the temperature of the voice coil of the loudspeaker. The voice coil temperature detection model is a second-order loudspeaker heat transfer model established based on target thermal parameters, and the target thermal parameters at least include a first target thermal parameter between the voice coil of the loudspeaker and the magnetic circuit of the loudspeaker, a second target thermal parameter between the magnetic circuit of the loudspeaker and the air, and a third target thermal parameter of air convection of the voice coil of the loudspeaker. In this way, in addition to considering the influence of the heat conduction between the voice coil and the magnetic circuit and the heat conduction between the magnetic circuit and the air on the temperature of the voice coil, the air convection caused by the vibration of the voice coil in the magnetic gap is also considered, so that the accuracy of the voice coil temperature detection is improved. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 A flowchart of the loudspeaker voice coil temperature detection method provided by the embodiment of the present application is shown in the figure;

[0050] Figure 2 A principle diagram of the heat dissipation path of the voice coil and the magnetic circuit of the loudspeaker provided by the embodiment of the present application is shown in the figure;

[0051] Figure 3 A structure diagram of the equivalent circuit of the voice coil temperature detection model provided by the embodiment of the present application is shown in the figure;

[0052] Figure 4 A flowchart of the determination method of the optimal value of the target thermal parameter of the voice coil temperature detection model provided by the embodiment of the present application is shown in the figure;

[0053] Figure 5 A structure diagram of the loudspeaker voice coil temperature detection device provided by the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0054] In this application, "at least one" means one or more, and "multiple" means two or more. The association relationship of the associated objects is described, which means that there can be three relationships, for example, A and / or B, which means that A exists alone, A and B exist together, and B exists alone. Wherein A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects before and after it. "At least one" or similar expressions refer to any combination of these items, including any combination of single or multiple items. For example, at least one of a, b or c alone, which means a alone, b alone, c alone, combination of a and b, combination of a and c, combination of b and c, or combination of a, b and c. Wherein, a, b and c can be single or multiple. In addition, the terms "first", "second" are only for descriptive purposes, and cannot be understood as indicating or implying relative importance.

[0055] The terms "connected" and "connected" should be broadly understood, for example, the "connected" or "connected" of the circuit structure can mean physical connection, but also means electrical connection or signal connection, for example, it can be directly connected, that is, physically connected, or indirectly connected through at least one intermediate element, as long as the circuit is connected, it can also be the internal connection of two elements; Signal connection can be signal connection through circuit, or signal connection through media medium, such as radio wave. For those skilled in the art, the specific meaning of the above terms in this application can be understood according to the specific circumstances.

[0056] The loudspeaker is a transducer that converts electrical signals into acoustic signals, and is widely used in electronic devices such as mobile phones, computers, tablets and other devices that need to play audio. During the working process of the loudspeaker, the temperature of the voice coil will change with the working time and working state, etc. If the voice coil works in a high temperature environment for a long time, the loudspeaker will malfunction or even be damaged. Therefore, it is particularly important to detect the temperature of the voice coil of the loudspeaker to facilitate temperature adjustment and control.

[0057] In the related art, the temperature of the loudspeaker voice coil can be estimated by using the linear relationship between the thermal resistance and the temperature of the loudspeaker voice coil. Specifically, the voltage and current on the voice coil during the working process of the loudspeaker can be monitored in real time, the thermal resistance of the loudspeaker voice coil is estimated by the ratio of the effective values of the voltage and current, and the temperature of the voice coil is calculated by the material coefficient of the voice coil, so that the temperature of the loudspeaker voice coil can be indirectly obtained. But this method needs special hardware support to get current and voltage through feedback loop, and the hardware cost is high. Moreover, it can only be applied to the case where the voltage and current on the voice coil can be obtained, and the temperature of the voice coil cannot be determined in the case where the voltage and current cannot be obtained, so the range of adaptation is limited.

[0058] In view of this, the embodiment of the present application establishes a voice coil temperature detection model of a voice coil of a loudspeaker, that is, a second-order model of heat transfer of the loudspeaker, uses the voice coil temperature detection model to represent the corresponding relationship between the input power of the voice coil of the loudspeaker and the temperature of the voice coil of the loudspeaker, and in the temperature detection of the voice coil, inputs the current input power of the voice coil of the loudspeaker into the voice coil temperature detection model, so as to use the voice coil temperature detection model to predict the temperature of the voice coil of the loudspeaker. Wherein, the optimal value of the target heat parameter of heat conduction of the loudspeaker required in the process of establishing the voice coil temperature detection model can be obtained by using a genetic algorithm.

[0059] The loudspeaker voice coil temperature detection method provided by the embodiment of the present application can be applied to an electronic device capable of playing audio, that is, an electronic device containing a loudspeaker, such as at least one of a mobile phone, a computer, a vehicle terminal, a tablet computer, a wearable device and a smart home device. The loudspeaker voice coil temperature detection method can also be applied to a loudspeaker voice coil temperature detection device provided in the electronic device, and the loudspeaker voice coil temperature detection device can be realized by software, hardware or a combination of both.

[0060] The loudspeaker voice coil temperature detection method provided by the embodiment of the present application can be applied to an electronic device capable of playing audio, that is, an electronic device containing a loudspeaker, such as at least one of a mobile phone, a computer, a vehicle terminal, a tablet computer, a wearable device and a smart home device. The loudspeaker voice coil temperature detection method can also be applied to a loudspeaker voice coil temperature detection device provided in the electronic device, and the loudspeaker voice coil temperature detection device can be realized by software, hardware or a combination of both. Figures 1-4 The loudspeaker voice coil temperature detection method provided by the embodiment of the present application can be applied to an electronic device capable of playing audio, that is, an electronic device containing a loudspeaker, such as at least one of a mobile phone, a computer, a vehicle terminal, a tablet computer, a wearable device and a smart home device. The loudspeaker voice coil temperature detection method can also be applied to a loudspeaker voice coil temperature detection device provided in the electronic device, and the loudspeaker voice coil temperature detection device can be realized by software, hardware or a combination of both.

[0061] Figure 1 The loudspeaker voice coil temperature detection method provided by the embodiment of the present application can be applied to an electronic device capable of playing audio, that is, an electronic device containing a loudspeaker, such as at least one of a mobile phone, a computer, a vehicle terminal, a tablet computer, a wearable device and a smart home device. The loudspeaker voice coil temperature detection method can also be applied to a loudspeaker voice coil temperature detection device provided in the electronic device, and the loudspeaker voice coil temperature detection device can be realized by software, hardware or a combination of both. Figure 1 The loudspeaker voice coil temperature detection method provided by the embodiment of the present application can be applied to an electronic device capable of playing audio, that is, an electronic device containing a loudspeaker, such as at least one of a mobile phone, a computer, a vehicle terminal, a tablet computer, a wearable device and a smart home device. The loudspeaker voice coil temperature detection method can also be applied to a loudspeaker voice coil temperature detection device provided in the electronic device, and the loudspeaker voice coil temperature detection device can be realized by software, hardware or a combination of both.

[0062] Step 110: Obtain the current input power of the voice coil of the loudspeaker.

[0063] In the working process of the loudspeaker, the electronic device can obtain the current input power of the voice coil of the loudspeaker.

[0064] For example, the electronic device can obtain the voltage and current of the voice coil, determine the voice coil thermal resistance according to the ratio of the effective values of the voltage and current, and then divide the square of the voltage of the input signal by the voice coil thermal resistance, so as to obtain the current input power of the voice coil of the loudspeaker.

[0065] Alternatively, the electronic device can use the relationship between the temperature change and the voice coil thermal resistance change, determine the voice coil thermal resistance change amount according to the change amount of the voice coil temperature obtained at the previous time, and then determine the voice coil thermal resistance at the current time, and then divide the square of the voltage of the input signal by the voice coil thermal resistance, so as to obtain the current input power of the voice coil of the loudspeaker.

[0066] Step 120: input the current input power into the voice coil temperature detection model to obtain a target temperature of the voice coil of the loudspeaker output by the voice coil temperature detection model.

[0067] The voice coil temperature detection model is a second-order model of heat transfer of the loudspeaker established based on the target thermal parameters. The voice coil temperature detection model can be used to represent a mapping relationship between the input power of the voice coil of the loudspeaker and the temperature of the voice coil of the loudspeaker. The target thermal parameters at least include a first target thermal parameter between the voice coil of the loudspeaker and the magnetic circuit of the loudspeaker, a second target thermal parameter between the magnetic circuit of the loudspeaker and the air, and a third target thermal parameter between the voice coil of the loudspeaker and the air convection.

[0068] Specifically, the voice coil temperature detection model can be established by the following steps: constructing an equivalent circuit of the voice coil temperature detection model according to the heat dissipation paths of the voice coil of the loudspeaker and the magnetic circuit of the loudspeaker; and establishing the voice coil temperature detection model based on the correlation of the equivalent circuit and the target thermal parameters.

[0069] Figure 2 A principle diagram of the heat dissipation paths of the voice coil and the magnetic circuit of the loudspeaker is shown, referring to Figure 2 As shown in the figure, there are heat dissipation paths between the voice coil and the magnetic circuit of the loudspeaker, between the voice coil and the air, and between the magnetic circuit and the air. The arrow direction represents the direction of heat dissipation. According to the physical characteristics of the voice coil and the magnetic circuit of the loudspeaker, there is heat conduction from the voice coil to the magnetic circuit, heat conduction from the magnetic circuit to the air, and heat convection from the voice coil to the air caused by the vibration of the voice coil. The heat conduction from the voice coil to the magnetic circuit can be represented by the thermal resistance and the thermal capacitance between the two, the heat conduction from the magnetic circuit to the air can be represented by the thermal resistance and the thermal capacitance between the two, and the heat convection from the voice coil to the air can be represented by the corresponding thermal resistance.

[0070] Based on this, in an embodiment, an equivalent circuit of the voice coil temperature detection model can be constructed according to the heat dissipation paths of the voice coil of the loudspeaker and the magnetic circuit of the loudspeaker, so as to represent the voice coil temperature detection model in the form of the equivalent circuit.

[0071] Specifically, Figure 3 A structure diagram of the equivalent circuit of the voice coil temperature detection model is shown, referring to Figure 3 As shown in the figure, the equivalent circuit of the voice coil temperature detection model can include a first thermal resistance Rcm between the voice coil of the loudspeaker and the magnetic circuit of the loudspeaker, a first thermal capacitance Ccm between the voice coil of the loudspeaker and the magnetic circuit of the loudspeaker, a second thermal resistance Rma between the magnetic circuit of the loudspeaker and the air, a second thermal capacitance Cma between the magnetic circuit of the loudspeaker and the air, and a third thermal resistance Rca between the voice coil of the loudspeaker and the air convection.

[0072] The first thermal resistor Rcm and the first thermal capacitor Ccm are connected in parallel, and then connected in series with a parallel branch of the second thermal resistor Rma and the second thermal capacitor Cma to form a first power branch; the third thermal resistor Rca as a second power branch is connected in parallel with the first power branch. The first power branch and the second power branch are both connected in parallel with a heat generation power P of the voice coil of the loudspeaker, which can be considered as an input power of the voice coil of the loudspeaker.

[0073] Correspondingly, the first target thermal parameter includes the first thermal resistor Rcm and the first thermal capacitor Ccm, the second target thermal parameter includes the second thermal resistor Rma and the second thermal capacitor Cma, and the third target thermal parameter includes the third thermal resistor Rca.

[0074] The first thermal resistor Rcm and the first thermal capacitor Ccm are connected in parallel, and can represent heat conduction on a heat dissipation path of the voice coil and the magnetic circuit of the loudspeaker. Correspondingly, the first thermal resistor Rcm and the first thermal capacitor Ccm can represent heat dissipation parameters on the heat dissipation path of the voice coil and the magnetic circuit of the loudspeaker. The second thermal resistor Rma and the second thermal capacitor Cma are connected in parallel, and can represent heat conduction on a heat dissipation path from the magnetic circuit of the loudspeaker to the air (i.e. the external environment). Correspondingly, the second thermal resistor Rma and the second thermal capacitor Cma can represent heat dissipation parameters on the heat dissipation path from the magnetic circuit of the loudspeaker to the external environment. The third thermal resistor Rca represents heat convection on a heat dissipation path from the voice coil of the loudspeaker to the external environment. Correspondingly, the third thermal resistor Rca can represent heat dissipation parameters on the heat dissipation path from the voice coil of the loudspeaker to the external environment.

[0075] It can be understood that the target thermal parameters correspond to the thermal resistors and the thermal capacitors in the equivalent circuit, and the voice coil temperature detection model can be established based on the correlation between the equivalent circuit and the target thermal parameters. These target thermal parameters are closely related to the actual physical structure of the voice coil and the magnetic circuit of the loudspeaker. Therefore, the voice coil temperature detection model can be established according to the physical characteristics of the voice coil and the magnetic circuit of the loudspeaker in combination with the heat dissipation environment.

[0076] In Figure 3 the equivalent circuit, Tc represents the temperature of the voice coil, Ta represents the temperature of the air, Tm represents the temperature of the magnet, P coil represents the power of the first power branch, i.e. the sum of the power of the voice coil and the power of the magnetic circuit, P con represents the power of the heat convection. According to the equivalent circuit shown in Figure 3 , the temperature of the voice coil of the loudspeaker can be determined by establishing a mapping relationship between the input power of the voice coil of the loudspeaker and the temperature of the voice coil.

[0077] Specifically, according to Figure 3 , the continuous domain transfer function H coil (s) corresponding to the first power branch can be represented by the following formula (1):

[0078] (1)

[0079] wherein Tca(s) represents a transfer function of a temperature difference between the voice coil and the air in a continuous domain, P coil (s) represents P coil a transfer function in a continuous domain, Rcm represents a first thermal resistance, Rma represents a second thermal resistance, Cma represents a second thermal capacitance, and Ccm represents a first thermal capacitance.

[0080] A transfer function H con (s) corresponding to the second power branch in the continuous domain can be represented by the following formula (2):

[0081] (2)

[0082] wherein Tca(s) represents a transfer function of a temperature difference between the voice coil and the air in a continuous domain, P con (s) represents P con a transfer function in a continuous domain, Rca represents a third thermal resistance.

[0083] Since the first power branch and the second power branch are in parallel, then Figure 3 A transfer function H(s) of the equivalent circuit shown in FIG. 1 can be represented by the following formula (3):

[0084] (3)

[0085] Substituting the formula (1) and the formula (2) into the formula (3) and simplifying, the simplified H(s) can be represented by the following formula (4):

[0086] (4)

[0087] wherein , .

[0088] Further, the transfer function H(s) in the continuous domain can be bilinearly transformed to obtain a transfer function H(z) in a discrete domain by using a bilinear transformation formula shown in the following formula (5). The formula (5) is:

[0089] (5)

[0090] wherein T represents a sampling period, s represents a complex frequency variable in the continuous domain, and z represents a complex variable in the discrete domain.

[0091] The transfer function H(z) in the discrete domain can be represented by the following formula (6):

[0092] (6)

[0093] wherein the coefficients and are determined by converting the discrete-domain transfer function H(z) and the continuous-domain transfer function H(s) according to the bilinear transformation. According to the derivation process described above, it can be understood that the coefficients and can be represented based on the thermal parameters Rcm, Rma, Cma, Ccm and Rca, and by adjusting the thermal parameters Rcm, Rma, Cma, Ccm and Rca, the coefficients and can be adjusted. When the thermal parameters Rcm, Rma, Cma, Ccm and Rca take the optimal values, the optimal values of and can be obtained according to the corresponding relationship of the two.

[0094] Further, the voice coil temperature detection model can be represented by a difference equation corresponding to the discrete-domain transfer function H(z). Specifically, the voice coil temperature detection model can be represented as the following formula (7):

[0095] (7)

[0096] wherein t represents a discrete time point, Te_pred[ ] represents a predicted temperature output by the voice coil temperature detection model, and P_in[ ] represents a discrete value of the input power.

[0097] In the process of detecting the temperature of the voice coil of the loudspeaker by using the voice coil temperature detection model, the current input power P_in of the voice coil of the loudspeaker can be discretized and taken as the input of the formula (7), and the input is input into the voice coil temperature detection model, so that the predicted temperature Te_pred output by the voice coil temperature detection model can be obtained.

[0098] The method for detecting the temperature of the voice coil of the loudspeaker provided in the embodiments of the present application comprises the following steps: obtaining the current input power of the voice coil of the loudspeaker; inputting the current input power into a voice coil temperature detection model representing the mapping relationship between the input power of the voice coil of the loudspeaker and the temperature of the voice coil of the loudspeaker; and predicting the temperature of the voice coil by using the voice coil temperature detection model. The target temperature of the voice coil of the loudspeaker output by the voice coil temperature detection model can be obtained, and the temperature of the voice coil of the loudspeaker is detected. The voice coil temperature detection model is a second-order loudspeaker heat transfer model established based on target thermal parameters. The target thermal parameters at least include a first target thermal parameter between the voice coil of the loudspeaker and the magnetic circuit of the loudspeaker, a second target thermal parameter between the magnetic circuit of the loudspeaker and the air, and a third target thermal parameter between the voice coil of the loudspeaker and the air convection. In this way, in addition to considering the influence of the heat conduction between the voice coil and the magnetic circuit and the heat conduction between the magnetic circuit and the air on the temperature of the voice coil, the air convection caused by the vibration of the voice coil in the magnetic gap is also considered. In particular, when the amplitude of the input signal is relatively large or the frequency is relatively low, a part of heat is directly introduced from the magnetic gap into the air, and the influence of the heat on the temperature of the voice coil is considered in the solution of the embodiments of the present application, thereby improving the accuracy of the voice coil temperature detection.

[0099] The optimal value of the target thermal parameter of the voice coil temperature detection model can be determined by establishing a cost function between the predicted temperature of the voice coil and the actual temperature of the voice coil. For example, the genetic algorithm can be used to optimize the thermal parameter of the voice coil temperature detection model to obtain the optimal value of the target thermal parameter.

[0100] Specifically, in one embodiment of the present application, Figure 4 A flowchart of the method for determining the optimal value of the target thermal parameter of the voice coil temperature detection model provided in the embodiments of the present application is shown in FIG. 4. As shown in FIG. 4, Figure 4 The optimal value of the target thermal parameter of the voice coil temperature detection model can be determined by the following steps 410-460.

[0101] Step 410: The total number of target thermal parameters is used as the length of chromosome coding, and chromosomes of a preset population number are generated.

[0102] In combination with the equivalent circuit of the voice coil temperature detection model shown in FIG. 3, Figure 3 In the embodiments of the present application, the target thermal parameters of the voice coil temperature detection model can include a first thermal resistance Rcm, a first thermal capacitance Ccm, a second thermal resistance Rma, a second thermal capacitance Cma, and a third thermal resistance Rca, a total of 5 thermal parameters. Therefore, the length of the chromosome coding can be 5.

[0103] In the initialization stage, the population size can be set, such as setting the preset population number as PopulationSize, and then generating PopulationSize chromosomes. Each chromosome represents a set of thermal parameters of the voice coil temperature detection model.

[0104] For example, in the initialization stage, the upper bound ub and the lower bound lb of the thermal parameter iteration of the voice coil temperature detection model can be set, and then a preset population number of chromosomes init_population can be generated based on the preset population number PopulationSize, the upper bound ub and the lower bound lb.

[0105] For example, the preset population number of chromosomes init_population can be expressed as: .

[0106] Wherein, Init_param=[0, 0, 0, 0, 0], rand() represents a random function, the upper bound ub can be expressed as ub=[max_Rcm, max_Ccm, max_Rma, max_Cma, max_Rca], and the lower bound lb can be expressed as lb=[min_Rcm, min_Ccm, min_Rma, min_Cma, min_Rca].

[0107] Wherein, max_Rcm represents the maximum value of the first thermal resistance Rcm, max_Ccm represents the maximum value of the first thermal capacitance Ccm, max_Rma represents the maximum value of the second thermal resistance Rma, max_Cma represents the maximum value of the second thermal capacitance Cma, max_Rca represents the maximum value of the third thermal resistance Rca, min_Rcm represents the minimum value of the first thermal resistance Rcm, min_Ccm represents the minimum value of the first thermal capacitance Ccm, min_Rma represents the minimum value of the second thermal resistance Rma, min_Cma represents the minimum value of the second thermal capacitance Cma, and min_Rca represents the minimum value of the third thermal resistance Rca.

[0108] Step 420: In each iteration, for each chromosome in the preset population number of chromosomes, the initial thermal parameter value corresponding to each chromosome is obtained, and the initial voice coil temperature detection model corresponding to the initial thermal parameter value is determined.

[0109] In each iteration, for each chromosome in the preset population number of chromosomes, the initial thermal parameter value of the chromosome is the value of the thermal parameter corresponding to the chromosome after the last iteration optimization, which can be used as the thermal parameter value of the voice coil temperature detection model in the current iteration optimization, and the initial voice coil temperature detection model with the initial thermal parameter value as the model parameter value is obtained.

[0110] Step 430: determining the fitness of each chromosome based on the real temperature of the voice coil of the experimental loudspeaker and the predicted temperature of the initial voice coil temperature detection model.

[0111] In the process of determining the optimal value of the target thermal parameter of the voice coil temperature detection model, the same type of experimental loudspeaker as the to-be-tested loudspeaker can be used for thermal parameter optimization.

[0112] Specifically, the fitness of each chromosome can be determined based on the real temperature of the voice coil of the experimental loudspeaker and the predicted temperature of the initial voice coil temperature detection model, using the following formula (8):

[0113] (8)

[0114] wherein, f represents the fitness, N represents the number of discrete sampling points of the temperature, represents the i-th discrete sampling point of the real temperature of the voice coil of the experimental loudspeaker, represents the i-th discrete sampling point of the predicted temperature of the initial voice coil temperature detection model.

[0115] Step 440: iteratively optimizing the chromosomes of the preset population number based on the fitness and genetic parameters until the number of iterations reaches a preset number of iterations.

[0116] After obtaining the fitness of each chromosome, the chromosomes of the preset population number can be iteratively optimized based on the fitness of each chromosome and the genetic parameters preset by the genetic algorithm. The genetic parameters can include the preset population number, the preset elite preservation number, the preset crossover probability, the preset mutation probability, and the preset number of iterations.

[0117] Specifically, the iterative optimization of the chromosomes can be performed through steps 441-445 as follows.

[0118] Step 441: sorting the chromosomes of the preset population number in descending order of fitness, and obtaining the first chromosomes from the top of the preset elite preservation number from the sorting result.

[0119] For example, there are 10 chromosomes of the preset population number, and the preset elite preservation number is 5. The fitness of each chromosome can be sorted in descending order, and then the top 5 chromosomes from the sorting result can be selected as the first chromosomes, which can be saved as elite chromosomes.

[0120] Step 442: selecting the second chromosomes from the chromosomes of the preset population number using the roulette selection method based on the fitness.

[0121] ​The sum of the number of the second chromosomes and the preset elite preservation number is the preset population number.

[0122] Specifically, for each chromosome in the preset population number of chromosomes, the cumulative probability of the chromosome can be determined based on the fitness of the chromosome using the following formula (9):

[0123] (9)

[0124] P[i] represents the cumulative probability from the first chromosome to the ith chromosome, P[j] represents the fitness of the jth chromosome, and PopulationSize represents the preset population number.

[0125] After the cumulative probability is determined, a number r between 0 and 1 can be randomly generated, and the index of the first element greater than or equal to r in the cumulative probability vector can be found. The chromosome individual corresponding to the index is the selected second chromosome. Repeat this process until the sum of the preset elite preservation number and the selected second chromosomes is the preset population number.

[0126] For example, assuming that the preset population number is 10 and the preset elite preservation number is 6, 4 second chromosomes need to be selected.

[0127] Step 443: Form a new first chromosome population with the first chromosomes and the second chromosomes.

[0128] For example, assuming that the preset population number is 10, 6 first chromosomes are selected, and 4 second chromosomes are selected, the 6 first chromosomes and the 4 second chromosomes are recombined to form a first chromosome population, and the first chromosome population also has 10 chromosomes.

[0129] Step 444: Perform a crossover operation on the first chromosome population according to a preset crossover probability to obtain a second chromosome population.

[0130] After obtaining the first chromosome population, the chromosomes that need to be crossed can be determined according to the preset crossover probability. For the chromosomes that need to be crossed, a crossover point crossover_point can be randomly selected on the gene code, and the gene fragments after the crossover point of the two parent individuals are exchanged to generate two offspring individuals.

[0131] For example, assuming that two chromosomes A and B in the first chromosome population before the crossover are respectively: A = [Rcm0, Ccm0, Rma0, Cma0, Rca0], B = [Rcm1, Ccm1, Rma1, Cma1, Rca1]. Then, after the crossover operation with crossover_point = 2, the chromosomes A and B can be changed to: A = [Rcm0, Ccm0, Rma1, Cma1, Rca1], B = [Rcm1, Ccm1, Rma0, Cma0, Rca0].

[0132] Step 445: performing a mutation operation on the second chromosome population according to a preset mutation probability to obtain a third chromosome population, and taking the third chromosome population as the chromosome of the next iteration.

[0133] For example, assuming that the standard deviation is Sigma, for each chromosome that needs to be mutated, a Gaussian distributed random value that satisfies the standard deviation Sigma can be added to the gene individual in the chromosome that satisfies the preset mutation probability MutationRate, to realize the mutation of the chromosome.

[0134] For example, assuming that the chromosome Q before mutation is Q = [Rcm3, Ccm3, Rma3, Cma3, Rca3], and the first and third gene individuals in the chromosome satisfy the mutation condition, then the chromosome Q after mutation can be expressed as where rand() represents a random function for generating a random number.

[0135] It can be understood that the chromosome after mutation satisfies the condition of the upper bound ub and the lower bound lb.

[0136] Step 450: calculating the fitness of each last-generation chromosome in the last-generation preset population.

[0137] Through continuous iteration optimization, until the number of iterations reaches the preset number of iterations, such as 100 times, the fitness of each chromosome in the last-generation preset population can be calculated according to the above formula (8).

[0138] Step 460: determining the optimal value of the target thermal parameter based on the fitness of each last-generation chromosome.

[0139] Specifically, determining the optimal value of the target thermal parameter based on the fitness of each last-generation chromosome can include: determining a target chromosome with the minimum fitness in the last-generation chromosome; and determining the value of the thermal parameter corresponding to the target chromosome as the optimal value of the target thermal parameter.

[0140] The thermal parameters include the first thermal resistance, the first thermal capacitance, the second thermal resistance, the second thermal capacitance, and the third thermal resistance in the equivalent circuit of the voice coil temperature detection model.

[0141] In this way, the value of the thermal parameter corresponding to the target chromosome is the optimal solution of the target thermal parameter of the voice coil temperature detection model.

[0142] The method for detecting the temperature of the voice coil of the loudspeaker provided in the embodiments of the present application can determine the optimal value of the target thermal parameter of the voice coil temperature detection model by using the genetic algorithm, has strong global search capability and is not prone to falling into a local optimal solution, thereby improving the accuracy of the voice coil temperature detection model and further improving the accuracy of the temperature detection of the voice coil of the loudspeaker.

[0143] The embodiments of the present application also provide a device for detecting the temperature of the voice coil of the loudspeaker, Figure 5 The structure schematic diagram of the device for detecting the temperature of the voice coil of the loudspeaker provided in the embodiments of the present application is shown, and the device for detecting the temperature of the voice coil of the loudspeaker can include an acquisition module 510 and a detection module 520, as shown in the drawing. Figure 5 The acquisition module 510 is configured to acquire the current input power of the voice coil of the loudspeaker.

[0144] The acquisition module 510 is configured to acquire the current input power of the voice coil of the loudspeaker.

[0145] The detection module 520 is configured to input the current input power into the voice coil temperature detection model to obtain the target temperature of the voice coil of the loudspeaker output by the voice coil temperature detection model.

[0146] The voice coil temperature detection model is a second-order model of the heat transfer of the loudspeaker established based on the target thermal parameter, and the target thermal parameter at least includes a first target thermal parameter between the voice coil of the loudspeaker and the magnetic circuit of the loudspeaker, a second target thermal parameter between the magnetic circuit of the loudspeaker and the air, and a third target thermal parameter of the convection between the voice coil of the loudspeaker and the air; and the voice coil temperature detection model is used to represent the mapping relationship between the input power of the voice coil of the loudspeaker and the temperature of the voice coil of the loudspeaker.

[0147] In one embodiment, the voice coil temperature detection model is established by the following steps: constructing an equivalent circuit of the voice coil temperature detection model according to the heat dissipation path of the voice coil of the loudspeaker and the magnetic circuit of the loudspeaker; and establishing the voice coil temperature detection model based on the correlation between the equivalent circuit and the target thermal parameter.

[0148] In an embodiment, the equivalent circuit of the voice coil temperature detection model can include a first thermal resistance between the voice coil of the loudspeaker and the magnetic circuit of the loudspeaker, a first thermal capacitance between the voice coil of the loudspeaker and the magnetic circuit of the loudspeaker, a second thermal resistance between the magnetic circuit of the loudspeaker and the air, a second thermal capacitance between the magnetic circuit of the loudspeaker and the air, and a third thermal resistance of the voice coil of the loudspeaker and air convection. Wherein the first thermal resistance and the first thermal capacitance are connected in parallel, and then connected in series with the parallel branch of the second thermal resistance and the second thermal capacitance to form a first power branch; the third thermal resistance is connected in parallel with the first power branch as a second power branch; the first power branch and the second power branch are both connected in parallel with the heat generation power of the voice coil of the loudspeaker; the first target thermal parameter includes the first thermal resistance and the first thermal capacitance, the second target thermal parameter includes the second thermal resistance and the second thermal capacitance, and the third target thermal parameter includes the third thermal resistance.

[0149] In an embodiment, the voice coil temperature detection model is expressed by the following formula:

[0150] ;

[0151] Wherein t represents a discrete time point, Te_pred[ ] represents a predicted temperature output by the voice coil temperature detection model, P_in[ ] represents a discrete value of the input power, the coefficient and are determined by converting the discrete domain transfer function H(z) and the continuous domain transfer function H(s) corresponding to Te_pred[t] according to the bilinear transformation;

[0152] The discrete domain transfer function H(z) corresponding to Te_pred[t] is: ;

[0153] The continuous domain transfer function H(s) is:

[0154] ;

[0155] Wherein Rca represents the third thermal resistance, Rcm represents the first thermal resistance, Rma represents the second thermal resistance, Cma represents the second thermal capacitance, Ccm represents the first thermal capacitance, , .

[0156] In an embodiment, the temperature detection device of the voice coil of the loudspeaker can further include a model thermal parameter determination module for determining the optimal value of the target thermal parameter of the voice coil temperature detection model.

[0157] Specifically, the model thermal parameter determination module can include:

[0158] The generating unit is configured to generate chromosomes with a preset population quantity, with a parameter total number of the target thermal parameter as a chromosome coding length;

[0159] The first determining unit is configured to, in each iteration, obtain an initial voice coil temperature detection model corresponding to each chromosome in the chromosomes with the preset population quantity, and determine the initial voice coil temperature detection model based on an initial thermal parameter value corresponding to each chromosome.

[0160] The second determining unit is configured to determine the fitness of each chromosome based on a real temperature of a voice coil of an experimental loudspeaker and a predicted temperature of the initial voice coil temperature detection model, wherein the experimental loudspeaker is of the same model as the loudspeaker.

[0161] The iteration unit is configured to iteratively optimize the chromosomes with the preset population quantity based on the fitness and genetic parameters until the number of iterations reaches a preset number of iterations.

[0162] The calculating unit is configured to calculate the fitness of each last-generation chromosome in the chromosomes with the preset population quantity.

[0163] The third determining unit is configured to determine the optimal value of the target thermal parameter based on the fitness of each last-generation chromosome.

[0164] In an embodiment, the second determining unit is specifically configured to determine the fitness of each chromosome based on the real temperature of the voice coil of the experimental loudspeaker and the predicted temperature of the initial voice coil temperature detection model according to formula (8) as above.

[0165] In an embodiment, the iteration unit is specifically configured to: sort the chromosomes with the preset population quantity in a descending order of the fitness, and obtain a first chromosome from a top preset number of elite chromosomes in the sorting result; select a second chromosome from the chromosomes with the preset population quantity in a roulette selection manner according to the fitness, wherein the number of the second chromosomes and the total number of the preset number of elite chromosomes are equal to the preset population quantity; form a new first chromosome population from the first chromosome and the second chromosome; perform a crossover operation on the first chromosome population according to a preset crossover probability to obtain a second chromosome population; perform a mutation operation on the second chromosome population according to a preset mutation probability to obtain a third chromosome population, and use the third chromosome population as the chromosomes for the next iteration; and the genetic parameters include the preset population quantity, the preset number of elite chromosomes, the preset crossover probability, the preset mutation probability, and the preset number of iterations.

[0166] In an embodiment, the third determining unit is specifically configured to: determine a target chromosome with the minimum fitness in the last-generation chromosomes; and determine the optimal value of the target thermal parameter as a value of a thermal parameter corresponding to the target chromosome.

[0167] The temperature detection device of the loudspeaker voice coil provided by the embodiments of the present application has the same implementation principles and beneficial effects as the temperature detection method of the loudspeaker voice coil provided by the above-described embodiments, and thus will not be described herein again.

[0168] The device embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, i.e., can be located in one place or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiments according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0169] The embodiments of the present application further provide an electronic device including a memory and a processor, the memory storing a computer program capable of running on the processor, and the processor implements the steps of the temperature detection method of the loudspeaker voice coil according to any of the above-described method embodiments when executing the program, which will not be described herein again.

[0170] Based on the temperature detection method of the loudspeaker voice coil described in any of the above-described embodiments, the embodiments of the present application further provide a computer readable storage medium, for example, a non-transitory computer readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc. The storage medium stores computer instructions for executing the temperature detection method of the loudspeaker voice coil described in any of the above-described embodiments, which will not be described herein again.

[0171] Those skilled in the art can understand that all or part of the steps of the above-described embodiments can be completed by hardware, or by program instructing relevant hardware, and the program can be stored in a computer readable storage medium, and the above-mentioned storage medium can be a read-only memory, a magnetic disk or an optical disk, etc.

[0172] Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the content of the present disclosure. The present application is intended to cover any variations, uses or adaptations of the content of the specification and examples disclosed herein and includes all such variations, uses or adaptations in the general principles of the present application. The specification and examples are to be regarded as illustrative only, and the true scope and spirit of the present application is indicated by the claims.

Claims

1. A method for detecting the temperature of a loudspeaker voice coil, characterized in that, The method comprises: obtaining a current input power of a voice coil of a loudspeaker; inputting the current input power into a voice coil temperature detection model to obtain a target temperature of the voice coil of the loudspeaker output by the voice coil temperature detection model; wherein the voice coil temperature detection model is a second-order model of heat transfer of a loudspeaker established based on target thermal parameters, the target thermal parameters at least including a first target thermal parameter between the voice coil of the loudspeaker and a magnetic circuit of the loudspeaker, a second target thermal parameter between the magnetic circuit of the loudspeaker and air, and a third target thermal parameter of air convection of the voice coil of the loudspeaker; and the voice coil temperature detection model is used to represent a mapping relationship between an input power of the voice coil of the loudspeaker and a temperature of the voice coil of the loudspeaker; wherein the voice coil temperature detection model is established by the following steps: constructing an equivalent circuit of the voice coil temperature detection model according to a heat dissipation path of the voice coil of the loudspeaker and the magnetic circuit of the loudspeaker; establishing the voice coil temperature detection model based on a correlation relationship of the equivalent circuit and the target thermal parameters.

2. The method of claim 1, wherein, The equivalent circuit comprises a first thermal resistor between the voice coil of the loudspeaker and the magnetic circuit of the loudspeaker, a first thermal capacitor between the voice coil of the loudspeaker and the magnetic circuit of the loudspeaker, a second thermal resistor between the magnetic circuit of the loudspeaker and air, a second thermal capacitor between the magnetic circuit of the loudspeaker and air, and a third thermal resistor of air convection of the voice coil of the loudspeaker; wherein the first thermal resistor and the first thermal capacitor are connected in parallel, and then connected in series with a parallel branch of the second thermal resistor and the second thermal capacitor to form a first power branch; the third thermal resistor is connected in parallel with the first power branch as a second power branch; and the first power branch and the second power branch are both connected in parallel with a heat generation power of the voice coil of the loudspeaker. Correspondingly, the first target thermal parameter of the voice coil temperature detection model comprises the first thermal resistor and the first thermal capacitor, the second target thermal parameter comprises the second thermal resistor and the second thermal capacitor, and the third target thermal parameter comprises the third thermal resistor.

3. The method of claim 2, wherein the temperature of the voice coil is determined by measuring the resistance of the voice coil. The voice coil temperature detection model is represented by the following formula: ； where t denotes a discrete time point, Te_pred[ ] denotes a predicted temperature output by the voice coil temperature detection model, P_in[ ] denotes a discrete value of input power, coefficients and are determined by converting the discrete-domain transfer function H(z) and the continuous-domain transfer function H(s) corresponding to the Te_pred[t] according to a bilinear transformation; The discrete domain transfer function H(z) corresponding to the Te_pred[t] is: ; The continuous domain transfer function H(s) is: ； wherein Rca represents the third thermal resistor, Rcm represents the first thermal resistor, Rma represents the second thermal resistor, Cma represents the second thermal capacitor, Ccm represents the first thermal capacitor, , .

4. The method of claim 1 to 3, wherein, The optimal value of the target thermal parameter of the voice coil temperature detection model is determined by the following steps: taking a total number of parameters of the target thermal parameter as a chromosome coding length to generate a preset number of chromosomes; in each iteration, for each chromosome in the preset number of chromosomes, obtaining an initial thermal parameter value corresponding to the chromosome, and determining a corresponding initial voice coil temperature detection model based on the initial thermal parameter value; determining the fitness of each chromosome based on a real temperature of a voice coil of an experimental loudspeaker and a predicted temperature of the initial voice coil temperature detection model; wherein the experimental loudspeaker is of the same model as the loudspeaker; iteratively optimizing the preset number of chromosomes based on the fitness and genetic parameters until the number of iterations reaches a preset number of iterations; calculating the fitness of each chromosome of the last generation of the preset number of chromosomes. Determine the optimal value of the target thermal parameter based on the fitness of each of the last generation of chromosomes.

5. The method for detecting the temperature of a loudspeaker voice coil according to claim 4, characterized in that, The fitness of each chromosome is determined based on the real temperature of the voice coil of the experimental loudspeaker and the predicted temperature of the initial voice coil temperature detection model. The fitness of each chromosome is determined based on the real temperature of the voice coil of the experimental loudspeaker and the predicted temperature of the initial voice coil temperature detection model according to the following formula: ； wherein, represents the fitness, N represents the number of discrete sampling points of temperature, represents the i-th discrete sampling point of the real temperature of the voice coil of the experimental speaker, represents the i-th discrete sampling point of the predicted temperature of the initial voice coil temperature detection model.

6. The method of claim 4, wherein the temperature of the voice coil is determined by measuring the resistance of the voice coil. The chromosomes of the preset population number are iteratively optimized based on the fitness and genetic parameters, including: Sort the chromosomes of the preset population number in descending order of the fitness, and obtain the first chromosomes from the sorting results, the number of which is the preset elite preservation number; According to the fitness, the second chromosomes are selected from the chromosomes of the preset population number using a roulette selection method, and the sum of the number of the second chromosomes and the preset elite preservation number is the preset population number; Form a new first chromosome population from the first chromosomes and the second chromosomes; According to a preset crossover probability, perform a crossover operation on the first chromosome population to obtain a second chromosome population; According to a preset mutation probability, perform a mutation operation on the second chromosome population to obtain a third chromosome population, and use the third chromosome population as the chromosomes for the next iteration. The genetic parameters include the preset population number, the preset elite preservation number, the preset crossover probability, the preset mutation probability, and the preset iteration number.

7. The method of claim 4, wherein the temperature of the voice coil is determined by measuring the resistance of the voice coil. Determine the optimal value of the target thermal parameter based on the fitness of each of the last generation of chromosomes, including: Determine the target chromosome with the minimum fitness from the last generation of chromosomes; Determine the value of the thermal parameter corresponding to the target chromosome as the optimal value of the target thermal parameter.

8. A temperature detecting device for a speaker voice coil, characterized by, Including: An acquisition module is configured to acquire a current input power of a voice coil of a loudspeaker; A detection module is configured to input the current input power into a voice coil temperature detection model to obtain a target temperature of the voice coil of the loudspeaker output by the voice coil temperature detection model. The voice coil temperature detection model is a second-order loudspeaker heat transfer model established based on a target thermal parameter, and the target thermal parameter at least includes a first target thermal parameter between the voice coil of the loudspeaker and a magnetic circuit of the loudspeaker, a second target thermal parameter between the magnetic circuit of the loudspeaker and air, and a third target thermal parameter of air convection with the voice coil of the loudspeaker. The voice coil temperature detection model is used to represent the mapping relationship between the input power of the voice coil of the loudspeaker and the temperature of the voice coil of the loudspeaker. The voice coil temperature detection model is established by the following steps: constructing an equivalent circuit of the voice coil temperature detection model according to the heat dissipation path of the voice coil of the loudspeaker and the magnetic circuit of the loudspeaker; and establishing the voice coil temperature detection model based on the correlation between the equivalent circuit and the target thermal parameter.

9. An electronic device comprising a memory and a processor, said memory storing a computer program operable on said processor, characterized in that, The processor executes the computer program to implement the steps of the loudspeaker voice coil temperature detection method according to any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by a processor, implements the steps of the method for detecting the temperature of a voice coil of a loudspeaker according to any one of claims 1 to 7.

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