Loudspeaker voice coil temperature detection method and device, electronic equipment and storage medium

Abstract

The invention provides a loudspeaker voice coil temperature detection method and device, electronic equipment and a storage medium, and relates to the technical field of signal processing. The loudspeaker voice coil temperature detection method comprises the following steps: acquiring the current input power of a voice coil of a loudspeaker; inputting the current input power into a voice coil temperature detection model used for representing the mapping relation between the input power of the voice coil of the loudspeaker and the temperature of the voice coil of the loudspeaker, and obtaining the target temperature, output by the voice coil temperature detection model, of the voice coil of the loudspeaker; wherein the voice coil temperature detection model is a loudspeaker heat transfer second-order model established based on target heat parameters; the target thermal parameters at least comprise 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 between the voice coil of the loudspeaker and air convection. According to the technical scheme provided by the invention, the detection of the voice coil temperature of the loudspeaker is realized, and the precision of voice coil temperature detection is improved.

Description

Technical Field

[0001] The present application relates to the field of signal processing technology, and in particular to a method, device, electronic device, and storage medium for detecting the temperature of a loudspeaker voice coil. Background Art

[0002] Speakers are transducers that convert electrical signals into acoustic signals. They are widely used in electronic devices that need to play audio, such as mobile phones, computers, and tablets. During operation, the temperature of the voice coil fluctuates depending on the duration and operating conditions. Prolonged exposure to high temperatures can cause the speaker to malfunction or even damage. Therefore, it is crucial to monitor the temperature of the speaker's voice coil for easy temperature control. However, voice coil temperature fluctuations are complex and are influenced by a variety of factors. Therefore, measuring voice coil temperature is a pressing issue. Summary of the Invention

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

[0004] In a first aspect, the present application provides a method for detecting the temperature of a loudspeaker voice coil, comprising: Get the current input power of the speaker's voice coil; Inputting the current input power into a voice coil temperature detection model to obtain a target temperature of the voice coil of the speaker 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 target thermal parameters, wherein the target thermal parameters include at least 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 convection between the voice coil of the loudspeaker and the air; the voice coil temperature detection model is used to characterize the mapping relationship between the input power of the voice coil of the loudspeaker and the temperature of the voice coil of the loudspeaker.

[0005] Optionally, the voice coil temperature detection model is established through 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 speaker and the magnetic circuit of the speaker; The voice coil temperature detection model is established based on the correlation between the equivalent circuit and the target thermal parameter.

[0006] Optionally, the equivalent circuit includes a first thermal resistor between the voice coil of the speaker and the magnetic circuit of the speaker, a first thermal capacitor between the voice coil of the speaker and the magnetic circuit of the speaker, a second thermal resistor between the magnetic circuit of the speaker and the air, a second thermal capacitor between the magnetic circuit of the speaker and the air, and a third thermal resistor of convection between the voice coil of the speaker and the air; The first thermal resistor and the first thermal capacitor are connected in parallel and then in series with the parallel branch of the second thermal resistor and the second thermal capacitor to form a first power branch; the third thermal resistor serves as a second power branch and is connected in parallel with the first power branch; the first power branch and the second power branch are both connected in parallel with the heating power of the voice coil of the speaker; The first target thermal parameter includes the first thermal resistor and the first thermal capacitor, the second target thermal parameter includes the second thermal resistor and the second thermal capacitor, and the third target thermal parameter includes the third thermal resistor.

[0007] Optionally, the voice coil temperature detection model is expressed by the following formula: ; Where t represents a discrete time point, Te_pred[ ] represents the predicted temperature output by the voice coil temperature detection model, P_in[ ] represents the discrete value of the input power, and the coefficient and is 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 a bilinear transformation; The discrete domain transfer function H(z) corresponding to 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, and Ccm represents the first thermal capacitor. , .

[0008] Optionally, the optimal value of the target thermal parameter of the voice coil temperature detection model is determined by the following steps: The total number of parameters of the target thermal parameter is used as the chromosome encoding length to generate chromosomes of a preset population number; In each iteration, for each chromosome in the preset population of chromosomes, an initial thermal parameter value corresponding to each chromosome is obtained, and a corresponding initial voice coil temperature detection model is determined based on the initial thermal parameter value; Determining the fitness of each chromosome based on the actual temperature of the voice coil of an experimental speaker and the predicted temperature of the initial voice coil temperature detection model; wherein the experimental speaker is of the same model as the speaker; Iteratively optimizing the chromosomes of the preset population size based on the fitness and genetic parameters until the number of iterations reaches a preset number of iterations; Calculate the fitness of each of the last chromosomes of the last generation with a preset population size; The optimal value of the target thermal parameter is determined based on the fitness of each of the last generation chromosomes.

[0009] Optionally, determining the fitness of each chromosome based on the actual temperature of the voice coil of the experimental speaker and the predicted temperature of the initial voice coil temperature detection model includes: Based on the actual temperature of the voice coil of the experimental speaker and the predicted temperature of the initial voice coil temperature detection model, the fitness of each chromosome is determined according to the following formula: ; in, represents the fitness, N represents the number of discrete sampling points of temperature, represents the i-th discrete sampling point of the true 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.

[0010] Optionally, the iterative optimization of the chromosomes of the preset population size based on the fitness and genetic parameters includes: Sorting the chromosomes of the preset population number according to the fitness from high to low, and obtaining the first chromosome of the preset elite preservation number from the sorting result; According to the fitness, a second chromosome is selected from the chromosomes of the preset population size by a roulette wheel selection method, where the sum of the number of the second chromosomes and the preset number of elites saved is the preset population size; forming a new first chromosome population from the first chromosome and the second chromosome; performing a crossover operation on the first chromosome population according to a preset crossover probability to obtain a second chromosome population; performing a mutation operation on the second chromosome population according to a preset mutation probability to obtain a third chromosome population, and using the third chromosome population as the chromosome for the next iteration; The genetic parameters include the preset population size, the preset elite preservation size, the preset crossover probability, the preset mutation probability and the preset number of iterations.

[0011] Optionally, determining the optimal value of the target thermal parameter based on the fitness of each of the last generation chromosomes includes: Determining a target chromosome with the smallest fitness among the last generation chromosomes; The value of the thermal parameter corresponding to the target chromosome is determined as the optimal value of the target thermal parameter.

[0012] In a second aspect, the present application provides a temperature detection device for a loudspeaker voice coil, comprising: An acquisition module, used to obtain the current input power of the voice coil of the speaker; 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 speaker 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 target thermal parameters, wherein the target thermal parameters include at least 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 convection between the voice coil of the loudspeaker and the air; the voice coil temperature detection model is used to characterize the mapping relationship between the input power of the voice coil of the loudspeaker and the temperature of the voice coil of the loudspeaker.

[0013] In a third aspect, the present application provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, the steps of the temperature detection method for the speaker voice coil as described in any one of the first aspects above are implemented.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for detecting the temperature of a loudspeaker voice coil as described in any one of the first aspects above.

[0015] The present application provides a method, apparatus, electronic device, and storage medium for detecting the temperature of a loudspeaker voice coil. By obtaining the current input power of the loudspeaker voice coil, inputting the current input power into a voice coil temperature detection model that characterizes the mapping relationship between the input power and the temperature of the loudspeaker voice coil, and using the voice coil temperature detection model to predict the voice coil temperature, a target temperature of the loudspeaker voice coil is obtained as output by the voice coil temperature detection model, thereby detecting the temperature of the loudspeaker voice coil. The voice coil temperature detection model is a second-order loudspeaker heat transfer model based on target thermal parameters. The target thermal parameters include at least a first target thermal parameter between the voice coil and the loudspeaker's magnetic circuit, a second target thermal parameter between the loudspeaker's magnetic circuit and air, and a third target thermal parameter for convection between the voice coil and air. This method not only considers the effects of heat conduction between the voice coil and the magnetic circuit, and heat conduction between the magnetic circuit and air on the voice coil temperature, but also takes into account air convection caused by the vibration of the voice coil in the magnetic gap, thereby improving the accuracy of voice coil temperature detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flow chart of a method for detecting the temperature of a loudspeaker voice coil according to an embodiment of the present application; Figure 2 A schematic diagram illustrating the principle of the heat dissipation path of the voice coil and magnetic circuit of the speaker provided in an embodiment of the present application; Figure 3 A schematic diagram of the structure of the equivalent circuit of the voice coil temperature detection model provided in an embodiment of the present application; Figure 4 A schematic flow chart of a method for determining the optimal value of a target thermal parameter of a voice coil temperature detection model provided in an embodiment of the present application; Figure 5 This is a schematic structural diagram of a temperature detection device for a loudspeaker voice coil provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] In this application, "at least one" means one or more, and "more" means two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c alone can mean: a alone, b alone, c alone, a and b combined, a and c combined, b and c combined, or a, b, and c combined. A, b, and c can be single or plural. In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance.

[0018] The terms "connected" and "connect" should be interpreted broadly. For example, "connected" or "connected" in a circuit structure can refer not only to a physical connection, but also to an electrical connection or a signal connection. For example, it can be a direct connection, i.e., a physical connection, or an indirect connection through at least one intermediate component, as long as the circuit is interconnected. It can also refer to internal connectivity between two components. Signal connection can refer not only to signal connection through circuits but also to signal connection through media, such as radio waves. Those skilled in the art will understand the specific meanings of the above terms in this application on a case-by-case basis.

[0019] Speakers are transducers that convert electrical signals into acoustic signals. They are widely used in electronic devices that need to play audio, such as mobile phones, computers, and tablets. During operation, the temperature of the speaker's voice coil fluctuates depending on the duration and operating conditions. Prolonged exposure to high temperatures can cause the speaker to malfunction or even damage. Therefore, it is crucial to monitor the temperature of the speaker's voice coil for easy temperature control.

[0020] In the related art, the linear relationship between the thermal resistance and temperature of the speaker voice coil can be used to estimate the temperature of the speaker voice coil. Specifically, the voltage and current on the voice coil during the operation of the speaker can be monitored in real time, and the thermal resistance of the speaker voice coil can be estimated by the ratio of the effective values ​​of the voltage and current. The temperature of the voice coil is then calculated by the material coefficient of the voice coil, and the temperature of the speaker voice coil can be indirectly obtained. However, this method requires special hardware support and obtains current and voltage through a feedback loop, which has a high hardware cost. Moreover, it can only be applied to situations where the voltage and current on the voice coil can be obtained. If the voltage and current cannot be obtained, the temperature of the voice coil cannot be determined, and the scope of application is limited.

[0021] In view of this, embodiments of the present application establish a voice coil temperature detection model for a loudspeaker voice coil, namely, a second-order model of loudspeaker heat transfer. This model is used to characterize the relationship between the input power and the temperature of the loudspeaker voice coil. During voice coil temperature detection, the current input power of the loudspeaker voice coil is input into the model, and the model can be used to predict the temperature of the loudspeaker voice coil. A genetic algorithm can be used to optimize the optimal value of the target thermal parameter of loudspeaker heat transfer required during the establishment of the voice coil temperature detection model.

[0022] The speaker voice coil temperature detection method provided in the embodiments of the present application can be applied to at least one of electronic devices capable of playing audio, such as mobile phones, computers, vehicle-mounted terminals, tablet computers, wearable devices, and smart home devices, that is, electronic devices that include speakers. The speaker voice coil temperature detection method can also be applied to a speaker voice coil temperature detection device provided in the electronic device. The speaker voice coil temperature detection device can be implemented using software, hardware, or a combination of both.

[0023] The following combination Figures 1 to 4 , taking the execution subject as an electronic device as an example, the temperature detection method of the speaker voice coil provided in the embodiment of the present application is described in detail.

[0024] Figure 1 A schematic diagram showing the flow of the temperature detection method of the loudspeaker voice coil provided in an embodiment of the present application is shown. Figure 1 As shown, the temperature detection method of the loudspeaker voice coil may include the following steps 110 to 120.

[0025] Step 110: Obtain the current input power of the voice coil of the speaker.

[0026] During the operation of the speaker, the electronic device can obtain the current input power of the voice coil of the speaker.

[0027] For example, the electronic device can obtain the voltage and current of the voice coil, determine the voice coil thermal resistance based on the ratio of the effective values ​​of the voltage and current, and then divide the square of the input signal voltage by the voice coil thermal resistance to obtain the current input power of the speaker voice coil.

[0028] Alternatively, the electronic device can use the relationship between temperature change and voice coil thermal resistance change to determine the voice coil thermal resistance change based on the voice coil temperature change obtained before the current moment, and then determine the voice coil thermal resistance at the current moment. The square of the input signal voltage is then divided by the voice coil thermal resistance to obtain the current input power of the speaker's voice coil.

[0029] Step 120: Input the current input power into the voice coil temperature detection model to obtain the target temperature of the voice coil of the speaker output by the voice coil temperature detection model.

[0030] The voice coil temperature detection model is a second-order loudspeaker heat transfer model established based on target thermal parameters. This voice coil temperature detection model can be used to characterize the mapping relationship between the input power of the loudspeaker's voice coil and the temperature of the loudspeaker's voice coil. The target thermal parameters include at least a first target thermal parameter between the loudspeaker's voice coil and the loudspeaker's magnetic circuit, a second target thermal parameter between the loudspeaker's magnetic circuit and air, and a third target thermal parameter for convection between the loudspeaker's voice coil and air.

[0031] Specifically, the voice coil temperature detection model can be established through 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 speaker and the magnetic circuit of the speaker; and establishing the voice coil temperature detection model based on the correlation between the equivalent circuit and the target thermal parameters.

[0032] Figure 2 The schematic diagram shows the heat dissipation path of the speaker's voice coil and magnetic circuit, refer to Figure 2 As shown in the figure, heat dissipation paths exist between the voice coil and magnetic circuit, between the voice coil and air, and between the magnetic circuit and air. The arrows indicate the direction of heat dissipation. Due to the physical properties of the voice coil and magnetic circuit, heat conduction occurs from the voice coil to the magnetic circuit, heat conduction occurs from the magnetic circuit to the air, and heat convection occurs from the voice coil to the air due to voice coil vibration. Heat conduction from the voice coil to the magnetic circuit can be characterized by the thermal resistance and thermal capacitance between the two, heat conduction from the magnetic circuit to the air can be characterized by the thermal resistance and thermal capacitance between the two, and heat convection from the voice coil to the air can be characterized by the corresponding thermal resistance.

[0033] Based on this, in one embodiment, an equivalent circuit of the voice coil temperature detection model can be constructed according to the heat dissipation path of the voice coil of the speaker and the magnetic circuit of the speaker, and the voice coil temperature detection model can be represented in the form of an equivalent circuit.

[0034] Specifically, Figure 3 The schematic diagram of the equivalent circuit of the voice coil temperature detection model is shown. Figure 3 As shown, the equivalent circuit of the voice coil temperature detection model may include a first thermal resistor Rcm between the voice coil of the speaker and the magnetic circuit of the speaker, a first thermal capacitor Ccm between the voice coil of the speaker and the magnetic circuit of the speaker, a second thermal resistor Rma between the magnetic circuit of the speaker and the air, a second thermal capacitor Cma between the magnetic circuit of the speaker and the air, and a third thermal resistor Rca of convection between the voice coil of the speaker and the air.

[0035] The first thermal resistor Rcm and the first thermal capacitor Ccm are connected in parallel and then in series with the 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 serves as a second power branch and is connected in parallel with the first power branch. Both the first and second power branches are connected in parallel with the heat generation power P of the speaker's voice coil, which can be considered the input power to the speaker's voice coil.

[0036] Accordingly, the first target thermal parameter includes a first thermal resistor Rcm and a first thermal capacitor Ccm, the second target thermal parameter includes a second thermal resistor Rma and a second thermal capacitor Cma, and the third target thermal parameter includes a third thermal resistor Rca.

[0037] Among them, the first thermal resistor Rcm and the first thermal capacitor Ccm are connected in parallel, which can characterize the heat conduction on the heat dissipation path of the voice coil and the magnetic circuit of the speaker. Accordingly, the first thermal resistor Rcm and the first thermal capacitor Ccm can represent the heat dissipation parameters on the heat dissipation path of the voice coil and the magnetic circuit; the second thermal resistor Rma and the second thermal capacitor Cma are connected in parallel, which can characterize the heat conduction on the heat dissipation path from the magnetic circuit of the speaker to the air (that is, the external environment). Accordingly, the second thermal resistor Rma and the second thermal capacitor Cma can represent the heat dissipation parameters on the heat dissipation path from the magnetic circuit of the speaker to the external environment; the third thermal resistor Rca characterizes the heat convection on the heat dissipation path from the voice coil of the speaker to the external environment. Accordingly, the third thermal resistor Rca can represent the heat dissipation parameters on the heat dissipation path from the voice coil of the speaker to the external environment.

[0038] It's understandable that the target thermal parameters correspond to the thermal resistors and capacitors in the equivalent circuit. A voice coil temperature detection model can be established based on the relationship between the equivalent circuit and the target thermal parameters. These target thermal parameters are closely related to the actual physical structure of the speaker's voice coil and magnetic circuit. This allows for a targeted voice coil temperature detection model to be established based on the physical characteristics of the speaker's voice coil and magnetic circuit, combined with the heat dissipation environment.

[0039] exist Figure 3 In the equivalent circuit shown, Tc represents the temperature of the voice coil, Ta represents the temperature of the air, Tm represents the temperature of the magnet, and P coil Represents the power of the first power branch, that is, the sum of the power of the voice coil and the magnetic circuit, P con Indicates the power of heat convection. Figure 3 The equivalent circuit shown can determine the temperature of the voice coil of the loudspeaker by establishing a mapping relationship between the input power of the voice coil of the loudspeaker and the temperature of the voice coil.

[0040] Specifically, according to Figure 3 As shown, the continuous domain transfer function H corresponding to the first power branch coil (s) can be expressed as the following formula (1): (1) Where Tca(s) represents the transfer function of the temperature difference between the voice coil and the air in the continuous domain, P coil (s) indicates P coil In the transfer function of the continuous domain, Rcm represents the first thermal resistor, Rma represents the second thermal resistor, Cma represents the second thermal capacitor, and Ccm represents the first thermal capacitor.

[0041] The continuous domain transfer function H corresponding to the second power branch con (s) can be expressed as the following formula (2): (2) Where Tca(s) represents the transfer function of the temperature difference between the voice coil and the air in the continuous domain, P con (s) indicates P con In the continuous domain transfer function, Rca represents the third thermal resistor.

[0042] Since the first power branch and the second power branch are in parallel, Figure 3 The continuous domain transfer function H(s) of the equivalent circuit shown can be expressed as the following formula (3): (3) Substituting formula (1) and formula (2) into formula (3) for simplification, the simplified H(s) can be expressed as the following formula (4): (4) in, , .

[0043] Furthermore, the continuous domain transfer function H(s) can be bilinearly transformed by the bilinear transformation formula shown in the following formula (5) to obtain the discrete domain transfer function H(z). Wherein, formula (5) is: (5) Where T represents the sampling period, s represents the complex frequency variable in the continuous domain, and z represents the complex variable in the discrete domain.

[0044] The discrete domain transfer function H(z) can be expressed as the following formula (6): (6) Among them, the coefficient and It is 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 above derivation process, it can be understood that the coefficient and It can be expressed based on the thermal parameters Rcm, Rma, Cma, Ccm and Rca. By adjusting the thermal parameters Rcm, Rma, Cma, Ccm and Rca, the coefficient can be adjusted. and When the thermal parameters Rcm, Rma, Cma, Ccm and Rca take the optimal value, the corresponding relationship between the two can be obtained. and The optimal value of .

[0045] Furthermore, the voice coil temperature detection model can be expressed by the differential equation corresponding to the discrete domain transfer function H(z). Specifically, the voice coil temperature detection model can be expressed as the following formula (7): (7) Where t represents a discrete time point, Te_pred[ ] represents the predicted temperature output by the voice coil temperature detection model, and P_in[ ] represents the discrete value of the input power.

[0046] When using the voice coil temperature detection model to detect the temperature of the voice coil of the speaker, the current input power P_in of the voice coil of the speaker can be discretized and used as the input of formula (7) and input into the voice coil temperature detection model to obtain the predicted temperature Te_pred output by the voice coil temperature detection model.

[0047] The embodiment of the present application provides a method for detecting the temperature of a loudspeaker voice coil. By obtaining the current input power of the loudspeaker voice coil, the method inputs the current input power into a voice coil temperature detection model that characterizes the mapping relationship between the input power of the loudspeaker voice coil and the temperature of the loudspeaker voice coil. The voice coil temperature detection model is used to predict the temperature of the voice coil. The target temperature of the loudspeaker voice coil output by the voice coil temperature detection model can be obtained, thereby realizing the detection of the voice coil temperature of the loudspeaker. Among them, the voice coil temperature detection model is a second-order model of loudspeaker heat transfer established based on target thermal parameters. The target thermal parameters include at least 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 convection between the voice coil of the loudspeaker and the air. In this way, in addition to considering the influence of heat conduction between the voice coil and the magnetic circuit and heat conduction between the magnetic circuit and the air on the voice coil temperature, 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 low, it is easy to cause a part of the heat to enter the air directly from the magnetic gap. The solution of the embodiment of the present application takes into account the influence of this part of the heat on the voice coil temperature, thereby improving the accuracy of voice coil temperature detection.

[0048] For the voice coil temperature detection model provided in the embodiments of the present application, 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 voice coil temperature and the actual temperature. For example, a genetic algorithm can be used to optimize the thermal parameters of the voice coil temperature detection model to obtain the optimal value of the target thermal parameter.

[0049] Specifically, in one embodiment of the present application, Figure 4 A flow chart showing a method for determining the optimal value of the target thermal parameter of the voice coil temperature detection model provided in an embodiment of the present application is shown. Figure 4 As shown, the optimal value of the target thermal parameter of the voice coil temperature detection model can be determined through the following steps 410 to 460.

[0050] Step 410: Generate chromosomes of a preset population size using the total number of target thermal parameters as the chromosome encoding length.

[0051] Combine Figure 3 The equivalent circuit of the voice coil temperature detection model is shown. In the embodiment of the present application, the target thermal parameters of the voice coil temperature detection model may include a first thermal resistor Rcm, a first thermal capacitor Ccm, a second thermal resistor Rma, a second thermal capacitor Cma and a third thermal resistor Rca, a total of 5 thermal parameters, and the chromosome encoding length may be 5.

[0052] During the initialization phase, the population size can be set, for example, by setting the preset population size to PopulationSize, and then generating PopulationSize chromosomes, where each chromosome represents a set of thermal parameters for the voice coil temperature detection model.

[0053] For example, in the initialization phase, the upper bound ub and lower bound lb of the thermal parameter iteration of the voice coil temperature detection model may be set, and then chromosomes init_population of a preset population size are generated based on the preset population size PopulationSize, the upper bound ub and the lower bound lb.

[0054] For example, the chromosome init_population with a preset population size can be expressed as: .

[0055] Among them, Init_param=[0, 0, 0, 0, 0], rand() represents the 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].

[0056] Among them, max_Rcm represents the maximum value of the first thermal resistor Rcm, max_Ccm represents the maximum value of the first thermal capacitor Ccm, max_Rma represents the maximum value of the second thermal resistor Rma, max_Cma represents the maximum value of the second thermal capacitor Cma, max_Rca represents the maximum value of the third thermal resistor Rca, min_Rcm represents the minimum value of the first thermal resistor Rcm, min_Ccm represents the minimum value of the first thermal capacitor Ccm, min_Rma represents the minimum value of the second thermal resistor Rma, min_Cma represents the minimum value of the second thermal capacitor Cma, and min_Rca represents the minimum value of the third thermal resistor Rca.

[0057] Step 420: In each iteration, for each chromosome in a preset population of chromosomes, an initial thermal parameter value corresponding to each chromosome is obtained, and a corresponding initial voice coil temperature detection model is determined based on the initial thermal parameter value.

[0058] In each iteration, for each chromosome in the preset population size of chromosomes, the initial thermal parameter value of the chromosome is the value of the thermal parameter corresponding to the chromosome after the last iterative optimization. The initial thermal parameter value can be used as the thermal parameter value of the voice coil temperature detection model in this iterative optimization to obtain the initial voice coil temperature detection model with the initial thermal parameter value as the model parameter value.

[0059] Step 430: Determine the fitness of each chromosome based on the actual temperature of the voice coil of the experimental speaker and the predicted temperature of the initial voice coil temperature detection model.

[0060] The experimental speaker and the speaker have the same model. In the process of determining the optimal value of the target thermal parameter of the voice coil temperature detection model, the experimental speaker of the same model as the speaker to be tested can be used to optimize the thermal parameters.

[0061] Specifically, the fitness of each chromosome can be determined based on the actual temperature of the voice coil of the experimental speaker and the predicted temperature of the initial voice coil temperature detection model using the following formula (8): (8) in, represents fitness, N represents the number of discrete sampling points of temperature, represents the i-th discrete sampling point of the true 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.

[0062] Step 440: Iteratively optimize the chromosomes of the preset population size based on the fitness and genetic parameters until the number of iterations reaches the preset number of iterations.

[0063] After obtaining the fitness of each chromosome, iterative optimization can be performed on chromosomes of a preset population size based on the fitness of each chromosome and genetic parameters preset in the genetic algorithm. The genetic parameters may include a preset population size, a preset number of elites to be preserved, a preset crossover probability, a preset mutation probability, and a preset number of iterations.

[0064] Specifically, the chromosome can be iteratively optimized through the following steps 441 to 445.

[0065] Step 441: Sort the chromosomes of a preset population number according to the fitness from high to low, and obtain the first chromosome of the preset elite preservation number from the sorting result.

[0066] For example, if the preset population size is 10 chromosomes and the preset elite preservation number is 5, the fitness of each chromosome can be sorted from high to low, and then the first 5 chromosomes can be selected from the sorting results as the first chromosomes. These 5 first chromosomes can be preserved as elite chromosomes.

[0067] Step 442: Based on the fitness, a second chromosome is selected from a preset population of chromosomes using a roulette wheel selection method.

[0068] Among them, the sum of the number of second chromosomes and the preset number of elite reserves is the preset population size.

[0069] Specifically, for each chromosome in the preset population size, the cumulative probability of the chromosome can be determined based on the fitness of the chromosome using the following formula (9): (9) Among them, P[i] represents the cumulative probability from the first chromosome to the i-th chromosome, It represents the fitness of the j-th chromosome, and PopulationSize represents the preset population size.

[0070] After determining the cumulative probability, a random number r between 0 and 1 is generated. The index of the first element in the cumulative probability vector that is greater than or equal to r is then found. The chromosome individual corresponding to this index is the selected second chromosome. This process is repeated until the sum of the number of second chromosomes selected, plus the number of elites saved, equals the preset population size.

[0071] For example, assuming that the preset population size is 10 and the preset elite preservation size is 6, 4 second chromosomes need to be selected.

[0072] Step 443: The first chromosome and the second chromosome form a new first chromosome population.

[0073] For example, assuming that the preset population size is 10, 6 first chromosomes are selected, and 4 second chromosomes are selected, then the 6 first chromosomes and the 4 second chromosomes are reorganized into a first chromosome population, and the first chromosome population also has 10 chromosomes.

[0074] Step 444: Perform a crossover operation on the first chromosome population according to a preset crossover probability to obtain a second chromosome population.

[0075] After obtaining the first chromosome population, the chromosomes that need to undergo a crossover operation can be determined based on the preset crossover probability. For each chromosome that needs a crossover operation, a crossover point (crossover_point) can be randomly selected in the gene encoding. The gene segments after the crossover point of the two parent individuals are exchanged to generate two offspring individuals.

[0076] For example, suppose that the two chromosomes A and B in the first chromosome population before crossover are: A=[Rcm0, Ccm0, Rma0, Cma0, Rca0], B=[Rcm1, Ccm1, Rma1, Cma1, Rca1]. Then, after the crossover operation with crossover_point=2, chromosomes A and B can be changed to: A=[Rcm0, Ccm0, Rma1, Cma1, Rca1], B=[Rcm1, Ccm1, Rma0, Cma0, Rca0].

[0077] Step 445: 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 chromosome for the next iteration.

[0078] Taking Gaussian mutation as an example, assuming the standard deviation is Sigma, for each chromosome that needs to be mutated, a Gaussian distribution random value with the standard deviation of Sigma can be added to the gene individuals in the chromosome that meet the preset mutation probability MutationRate to achieve the mutation of the chromosome.

[0079] 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 meet the mutation conditions, the chromosome Q after mutation can be expressed as , where rand() represents a random function used to generate random numbers.

[0080] It can be understood that the mutated chromosome meets the conditions of the upper bound ub and the lower bound lb.

[0081] Step 450: Calculate the fitness of each of the last generation chromosomes of the last generation with a preset population size.

[0082] Through continuous iterative optimization, until the number of iterations reaches the preset number of iterations, such as 100 times, the fitness of each chromosome of the last generation of chromosomes with the preset population size can be calculated according to the above formula (8).

[0083] Step 460: Determine the optimal value of the target thermal parameter based on the fitness of each of the last generation chromosomes.

[0084] Specifically, determining the optimal value of the target thermal parameter based on the fitness of each last generation chromosome may include: determining the target chromosome with the smallest fitness among the last generation chromosomes; and determining the value of the thermal parameter corresponding to the target chromosome as the optimal value of the target thermal parameter.

[0085] The thermal parameters include a first thermal resistor, a first thermal capacitor, a second thermal resistor, a second thermal capacitor, and a third thermal resistor in the equivalent circuit of the voice coil temperature detection model.

[0086] 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.

[0087] The temperature detection method for the loudspeaker voice coil provided in the embodiment of the present application can use a genetic algorithm to determine the optimal value of the target thermal parameter of the voice coil temperature detection model. It has a strong global search capability and is not prone to falling into a local optimal solution. It improves the accuracy of the voice coil temperature detection model, thereby making the temperature detection of the loudspeaker voice coil more accurate.

[0088] The present application also provides a device for detecting the temperature of a loudspeaker voice coil. Figure 5 The schematic diagram of the structure of the temperature detection device of the loudspeaker voice coil provided by the embodiment of the present application is shown. Figure 5 As shown, the temperature detection device of the loudspeaker voice coil may include an acquisition module 510 and a detection module 520. An acquisition module 510 is configured to acquire a current input power of a voice coil of a loudspeaker; A detection module 520 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 speaker output by the voice coil temperature detection model; Among them, the voice coil temperature detection model is a second-order loudspeaker heat transfer model established based on target thermal parameters. The target thermal parameters include at least 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 convection between the voice coil of the loudspeaker and the air. The voice coil temperature detection model is used to characterize the mapping relationship between the input power of the voice coil of the loudspeaker and the temperature of the voice coil of the loudspeaker.

[0089] 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 based on the heat dissipation path of the voice coil of the speaker and the magnetic circuit of the speaker; and establishing the voice coil temperature detection model based on the correlation between the equivalent circuit and the target thermal parameters.

[0090] In one embodiment, the equivalent circuit of the voice coil temperature detection model may include a first thermal resistor between the voice coil of the speaker and the magnetic circuit of the speaker, a first thermal capacitor between the voice coil of the speaker and the magnetic circuit of the speaker, a second thermal resistor between the magnetic circuit of the speaker and the air, a second thermal capacitor between the magnetic circuit of the speaker and the air, and a third thermal resistor for convection between the voice coil of the speaker and the air. The first thermal resistor and the first thermal capacitor are connected in parallel and then in series with the parallel branch of the second thermal resistor and the second thermal capacitor to form a first power branch; the third thermal resistor serves as a second power branch and is connected in parallel with the first power branch; the first power branch and the second power branch are both connected in parallel with the heating power of the voice coil of the speaker; the first target thermal parameter includes the first thermal resistor and the first thermal capacitor, the second target thermal parameter includes the second thermal resistor and the second thermal capacitor, and the third target thermal parameter includes the third thermal resistor.

[0091] In one embodiment, the voice coil temperature detection model is expressed using the following formula: ; Where t represents a discrete time point, Te_pred[ ] represents the predicted temperature output by the voice coil temperature detection model, P_in[ ] represents the discrete value of the input power, and the coefficient and is 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 a bilinear transformation; The discrete domain transfer function H(z) corresponding to 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, and Ccm represents the first thermal capacitor. , .

[0092] In one embodiment, the temperature detection device for a loudspeaker voice coil may further include a model thermal parameter determination module, wherein the model thermal parameter determination module is configured to determine an optimal value of a target thermal parameter of a voice coil temperature detection model.

[0093] Specifically, the model thermal parameter determination module may include: A generating unit, configured to generate chromosomes of a preset population size by taking the total number of parameters of the target thermal parameter as the chromosome encoding length; a first determining unit configured to obtain, in each iteration, for each chromosome in a preset population of chromosomes, an initial thermal parameter value corresponding to each chromosome, and determine a corresponding initial voice coil temperature detection model based on the initial thermal parameter value; a second determining unit, configured to determine the fitness of each chromosome based on a real temperature of a voice coil of an experimental speaker and a predicted temperature of an initial voice coil temperature detection model, wherein the experimental speaker and the speaker have the same model; An iteration unit, configured to iteratively optimize chromosomes of a preset population size based on fitness and genetic parameters until the number of iterations reaches a preset number of iterations; A calculation unit, used to calculate the fitness of each last generation chromosome of the last generation of a preset population size; The third determining unit is used to determine the optimal value of the target thermal parameter based on the fitness of each of the last generation chromosomes.

[0094] In one embodiment, the second determining unit is specifically configured to determine the fitness of each chromosome according to the above formula (8) based on the actual temperature of the voice coil of the experimental speaker and the predicted temperature of the initial voice coil temperature detection model.

[0095] In one embodiment, the iteration unit is specifically used to: sort the chromosomes of a preset population number in order of fitness from high to low, and obtain the first chromosome of the preset elite preservation number from the sorting result; select the second chromosome from the chromosomes of the preset population number by a roulette wheel selection method according to the fitness, 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 with 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 chromosome for the next iteration; wherein the genetic parameters include the preset population number, the preset elite preservation number, the preset crossover probability, the preset mutation probability and the preset number of iterations.

[0096] In one embodiment, the third determining unit is specifically configured to: determine a target chromosome having the smallest fitness among the last generation of chromosomes; and determine the value of the thermal parameter corresponding to the target chromosome as the optimal value of the target thermal parameter.

[0097] The temperature detection device for the loudspeaker voice coil provided in the embodiment of the present application has similar implementation principles and beneficial effects to the temperature detection method for the loudspeaker voice coil provided in the above embodiment, and will not be described in detail here.

[0098] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Those skilled in the art will be able to understand and implement the present invention without inventive effort.

[0099] An embodiment of the present application further provides an electronic device including a memory and a processor, wherein the memory stores a computer program that can be run on the processor. When the processor executes the program, the steps of the method for detecting the temperature of the loudspeaker voice coil described in any of the above method embodiments are implemented, which will not be repeated here.

[0100] Based on the loudspeaker voice coil temperature detection method described in any of the above embodiments, embodiments of the present application further provide a computer-readable storage medium. For example, the non-transitory computer-readable storage medium may be a read-only memory (ROM), random access memory (RAM), CD-ROM, magnetic tape, floppy disk, or optical data storage device. This storage medium stores computer instructions for executing the loudspeaker voice coil temperature detection method described in any of the above embodiments, which will not be further described here.

[0101] Those skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware, or may be accomplished by a program to instruct the relevant hardware, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0102] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the contents disclosed herein. This application is intended to cover any variations, uses, or adaptations herein that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered merely exemplary, and the true scope and spirit of this application are indicated by the claims.

Claims

1. A method for detecting the temperature of a loudspeaker voice coil, characterized in that: include: Get the current input power of the speaker's voice coil; Inputting the current input power into a voice coil temperature detection model to obtain a target temperature of the voice coil of the speaker 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 target thermal parameters, wherein the target thermal parameters include at least 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 convection between the voice coil of the loudspeaker and the air. The voice coil temperature detection model is used to characterize a mapping relationship between the input power of the voice coil of the loudspeaker and the temperature of the voice coil of the loudspeaker.

2. The method for detecting the temperature of a loudspeaker voice coil according to claim 1, wherein: The voice coil temperature detection model is established through 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 speaker and the magnetic circuit of the speaker; The voice coil temperature detection model is established based on the correlation between the equivalent circuit and the target thermal parameter.

3. The method for detecting the temperature of a loudspeaker voice coil according to claim 2, wherein: The equivalent circuit includes a first thermal resistor between the voice coil of the speaker and the magnetic circuit of the speaker, a first thermal capacitor between the voice coil of the speaker and the magnetic circuit of the speaker, a second thermal resistor between the magnetic circuit of the speaker and air, a second thermal capacitor between the magnetic circuit of the speaker and air, and a third thermal resistor of convection between the voice coil of the speaker and air; The first thermal resistor and the first thermal capacitor are connected in parallel and then in series with the parallel branch of the second thermal resistor and the second thermal capacitor to form a first power branch; the third thermal resistor serves as a second power branch and is connected in parallel with the first power branch; the first power branch and the second power branch are both connected in parallel with the heating power of the voice coil of the speaker; Correspondingly, the first target thermal parameter of the voice coil temperature detection model includes the first thermal resistor and the first thermal capacitor, the second target thermal parameter includes the second thermal resistor and the second thermal capacitor, and the third target thermal parameter includes the third thermal resistor.

4. The method for detecting the temperature of a loudspeaker voice coil according to claim 3, wherein: The voice coil temperature detection model is expressed by the following formula: ； Where t represents a discrete time point, Te_pred[ ] represents the predicted temperature output by the voice coil temperature detection model, P_in[ ] represents the discrete value of the input power, and the coefficient and is 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 a bilinear transformation; The discrete domain transfer function H(z) corresponding to 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, and Ccm represents the first thermal capacitor. , .

5. The method for detecting the temperature of a loudspeaker voice coil according to any one of claims 1 to 4, wherein: The optimal value of the target thermal parameter of the voice coil temperature detection model is determined by the following steps: The total number of parameters of the target thermal parameter is used as the chromosome encoding length to generate chromosomes of a preset population number; In each iteration, for each chromosome in the preset population of chromosomes, an initial thermal parameter value corresponding to each chromosome is obtained, and a corresponding initial voice coil temperature detection model is determined based on the initial thermal parameter value; Determining the fitness of each chromosome based on the actual temperature of the voice coil of an experimental speaker and the predicted temperature of the initial voice coil temperature detection model; wherein the experimental speaker is of the same model as the speaker; Iteratively optimizing the chromosomes of the preset population size based on the fitness and genetic parameters until the number of iterations reaches a preset number of iterations; Calculate the fitness of each of the last chromosomes of the last generation with a preset population size; The optimal value of the target thermal parameter is determined based on the fitness of each of the last generation chromosomes.

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

7. The method for detecting the temperature of a loudspeaker voice coil according to claim 5, wherein: The iterative optimization of the chromosomes of the preset population size based on the fitness and genetic parameters includes: Sorting the chromosomes of the preset population number according to the fitness from high to low, and obtaining the first chromosome of the preset elite preservation number from the sorting result; According to the fitness, a second chromosome is selected from the chromosomes of the preset population size by a roulette wheel selection method, where the sum of the number of the second chromosomes and the preset number of elites saved is the preset population size; forming a new first chromosome population from the first chromosome and the second chromosome; performing a crossover operation on the first chromosome population according to a preset crossover probability to obtain a second chromosome population; performing a mutation operation on the second chromosome population according to a preset mutation probability to obtain a third chromosome population, and using the third chromosome population as the chromosome for the next iteration; The genetic parameters include the preset population size, the preset elite preservation size, the preset crossover probability, the preset mutation probability and the preset number of iterations.

8. The method for detecting the temperature of a loudspeaker voice coil according to claim 5, wherein: Determining the optimal value of the target thermal parameter based on the fitness of each of the last generation chromosomes includes: Determining a target chromosome with the smallest fitness among the last generation chromosomes; The value of the thermal parameter corresponding to the target chromosome is determined as the optimal value of the target thermal parameter.

9. A temperature detection device for a loudspeaker voice coil, characterized in that: include: An acquisition module, used to obtain the current input power of the voice coil of the speaker; 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 speaker 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 target thermal parameters, wherein the target thermal parameters include at least 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 convection between the voice coil of the loudspeaker and the air. The voice coil temperature detection model is used to characterize a mapping relationship between the input power of the voice coil of the loudspeaker and the temperature of the voice coil of the loudspeaker.

10. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, wherein: When the processor executes the computer program, the steps of the method for detecting the temperature of the loudspeaker voice coil according to any one of claims 1 to 8 are implemented.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for detecting the temperature of a loudspeaker voice coil according to any one of claims 1 to 8 are implemented.

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