Method and system for optimizing heat dissipation performance of Bluetooth sound box

Abstract

The invention discloses a heat dissipation performance optimization method and system for a Bluetooth loudspeaker box, and relates to the technical field of data analysis, and the method comprises the following steps: 1, setting a data collection frequency, and collecting loudspeaker box state data and environmental factor data; step 2, synchronizing and preprocessing the collected data, and performing feature extraction based on the preprocessed data; 3, modeling by combining thermodynamics and statistics, constructing a regression model based on the sound box state, the environmental factors and the historical temperature, and calculating the internal temperature of the sound box; and 4, quantifying the residual heat of the loudspeaker box, integrating the feature vector and the real temperature label to train the LSTM to predict the temperature, calculating a comprehensive heat load index and a dynamic temperature threshold, and defining a triggering active heat dissipation condition. According to the invention, the condition of poor applicability caused by single active heat dissipation condition of the Bluetooth sound box in the prior art can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and specifically to a method and system for optimizing the heat dissipation performance of a Bluetooth speaker. Background Art

[0002] As a portable device integrating audio playback and wireless connection functions, Bluetooth speakers are widely used in scenarios such as smart home and outdoor entertainment. During its operation, components such as the power amplifier module and the main control chip continuously generate heat, and the enclosed box structure and complex and changeable usage environment (such as high temperature and high humidity) are likely to cause heat accumulation. Excessive temperature inside the speaker will not only degrade the audio output quality and affect the sound quality performance, but also accelerate the aging of electronic components, shorten the service life of the device, and even cause failures in severe cases.

[0003] Currently, the heat dissipation design of most Bluetooth speakers mainly relies on passive heat dissipation methods, such as increasing heat dissipation holes and using heat-conducting materials. This method has low heat dissipation efficiency and is difficult to meet the heat dissipation requirements in high-power and long-term use scenarios. For some Bluetooth speakers with active heat dissipation functions, the setting of their heat dissipation trigger conditions is often based on a single temperature threshold, without fully considering the working state of the speaker (such as the input voltage of the power amplifier and the duty cycle of the audio signal), environmental factors (such as the air flow rate and humidity), and the impact of historical heat accumulation on the thermal state, resulting in a lack of accuracy and dynamic adaptability in heat dissipation control. In addition, in terms of temperature calculation and prediction in the prior art, simple empirical formulas or fixed models are mostly used, which cannot effectively cope with complex and changeable actual use scenarios, and are prone to misjudgment or delayed heat dissipation, reducing the heat dissipation performance and user experience of the speaker. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for optimizing the heat dissipation performance of a Bluetooth speaker to solve the problems raised in the prior art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: A method for optimizing the heat dissipation performance of a Bluetooth speaker, the method comprising the following steps: Step 1, set the data acquisition frequency, and acquire the speaker state data and environmental factor data; Step 2, synchronize and preprocess the acquired data, and perform feature extraction based on the preprocessed data; Step 3, combine thermodynamics and statistical modeling, construct a regression model based on the speaker state, environmental factors, and historical temperature, and calculate the internal temperature of the speaker; Step 4, quantify the residual heat of the speaker, integrate the feature vector and the true temperature label to train the LSTM to predict the temperature, calculate the comprehensive heat load index and the dynamic temperature threshold, and define the conditions for triggering active heat dissipation.

[0006] In step 1, set the data acquisition frequency according to the working characteristics and application scenarios of the speaker; The specific methods for collecting the speaker status data and environmental factor data are as follows: Arrange sensors inside the speaker and on the main control board to collect the speaker status data, expressed as: [D 1 (t), D 2 (t), …, D m (t)]; where m is a positive integer representing the number of speaker statuses; t represents the data acquisition time, and D 1 (t), D 2 (t), …, D m (t) respectively represent the 1st, 2nd, …, mth speaker status data collected at time t; The speaker status signals include but are not limited to the power amplifier input voltage, current, audio signal duty cycle, sound pressure level, etc.; Obtain the environmental factor data at the corresponding location by calling the meteorological API, expressed as: [E 1 (t), E 2 (t), …, E n (t)]; where n is a positive integer representing the number of environmental factors; E 1 (t), E 2 (t), …, E n (t) respectively represent the 1st, 2nd, …, nth environmental factor data obtained at time t; The environmental factor signals include but are not limited to environmental temperature, air flow rate, humidity, etc.

[0007] In step 2, the specific methods for synchronizing and preprocessing the collected data are as follows: Set the sliding window length w and the step size s; Apply median filtering with a window length of w to the collected speaker status data and environmental factor data to eliminate short-term spikes and jitters; Perform linear interpolation on the speaker status data and environmental factor data based on the same time reference to synchronize the data; The specific method for feature extraction based on the preprocessed data is as follows: within a sliding window with a length of w and a step size of s, calculate the short-term average D j of the speaker status data D j mean and the short-term peak D j max, as well as the short-term mean E k mean of the environmental factor data E k mean; where j is a positive integer, j ∈ {1, 2, …, m}, representing the speaker status number sequence, and D jRepresents the state data of the j-th speaker; k is a positive integer, k ∈ {1, 2, …, n}, representing the sequence of the number of environmental factors, E k Represents the k-th environmental factor data; Normalize the above features according to the maximum reference value to form a feature vector: X(t) = [D 1 (t)mean, D 1 (t)max, …, D m (t)mean, D m (t)max, E 1 (t)mean, …, E n (t)mean]; The average value reflects the "continuous power consumption" level of the power amplifier, battery or other components during this period, and has the greatest impact on long-term heat accumulation (cumulative temperature); the maximum value captures the short-term "power peak" or "transient impact", which is the key factor leading to an instantaneous jump in temperature; environmental temperature, air flow rate, humidity, etc. usually change slowly within seconds or even minutes, and the extreme value change range within a short time is limited, resulting in a small impact on the instantaneous temperature shock.

[0008] In step 3, the specific method of combining thermodynamics and statistical modeling is as follows: Combine thermodynamics and statistics for modeling and calculate the internal temperature T int (t): ; Among them, T int (t) represents the internal temperature of the speaker corresponding to the t-th moment, which is directly obtained by the sensor; a j represents the weight coefficient corresponding to the speaker state data D j , reflecting the unit sensitivity of this state to temperature; b k represents the weight coefficient corresponding to the environmental factor data E k , reflecting the thermal coupling strength of the environmental factor; c represents the weight coefficient of the historical temperature cumulative term, determining the contribution degree of exponential smoothing in the current temperature calculation; g k () represents the environmental factor mapping function, which converts the environmental factor data E k to the model space and is defined by the staff in combination with the sensor characteristics; The internal temperature of the speaker is determined by the dynamic balance between heat generation (power consumption) and heat dissipation (environmental conditions). The linear superposition in the formula directly reflects the physical contributions of the heat generation term (such as the Joule heat of the power amplifier voltage and current) and the heat dissipation term (such as the convective heat dissipation of wind speed and humidity).

[0009] Simplified modeling of statistical regression: The actual heat transfer process involves complex partial differential equations (such as the heat conduction equation), but the main factors can be extracted through a linear regression model, and the parameters can be fitted using a data-driven method to avoid complex calculations; ; Among them, EMA[T int (t) represents the exponentially weighted moving average, which is used to characterize the residual influence of past temperature on the current thermal state; α represents the smoothing factor, and by adjusting α, the EMA curve can achieve a balance between "tracking" and "smoothing". Usually, cross-validation is used to evaluate the MSE to determine the optimal α; Δt represents the time interval between two data acquisitions; Using D j (t), g k (E k (t)) and EMA[T int (t) as independent variables and T int (t) as the dependent variable to construct a regression model, and using the least squares method to solve for a j , b k and c.

[0010] In step 4, the specific method for quantifying the residual heat of the speaker is as follows: Quantify the residual heat of the speaker: ; Among them, H res (t) represents the residual heat index, indicating the heat that has not dissipated yet due to historical heat generation inside the speaker up to time t; H res (t - Δt) represents the residual heat value at the previous sampling moment; the initial residual heat H res (0) is calibrated manually; α represents the smoothing factor; T std represents the preset calibrated ambient temperature value; The recursive form of the residual heat (H(t) depends on the value at the previous moment) simulates the physical law of heat decay over time (similar to the discharge of an RC circuit). Heat capacity and heat decay: The heat capacity characteristics of the speaker material and structure cause a lag in temperature change. The smoothing factor in the formula is related to the thermal time constant. The larger it is, the slower the residual heat decays, corresponding to an environment with low heat dissipation efficiency (such as an enclosed space).

[0011] By taking the difference from the ambient calibration temperature (such as 25 °C), the baseline influence of the ambient temperature on the residual heat is eliminated, focusing on the heat generation accumulation of the speaker itself; The specific method for training the LSTM to predict temperature is as follows: Integrate the feature vector sequence X(t i ) and the corresponding true temperature label T int (t i ); Among them, i is a positive integer representing the data acquisition time sequence; X(t i ) represents the preprocessed speaker state and environmental factor features corresponding to time t i ; T int (ti ) represents the true temperature corresponding to time t i ; Construct sample label pairs: Each sample consists of a feature subsequence [X(t−τ+1),…,X(t)] of length τ; the label is the temperature T int (T + Δ) at a future time Select the long short-term memory network LSTM, and through training on the sample label pairs, output the predicted temperature T pre (t + Δ); The calculation of the comprehensive heat load index and the dynamic temperature threshold includes: Calculate the comprehensive heat load index S(t): ; where D j,max represents the reference maximum value for normalizing the state of the j-th speaker; T1 represents the safety critical temperature reference value, calibrated by the staff; w 1,j represents the weight corresponding to the j-th speaker state, reflecting the relative contribution of this state to the heat load; w 2 represents the weight of the predicted temperature term, measuring the influence degree of the future temperature on the current heat load judgment; w 3 represents the weight of the residual heat term, measuring the influence degree of the historical heat accumulation on the current heat load judgment; w 4,k represents the weight corresponding to the k-th environmental factor mapping term, reflecting the influence intensity of this environmental factor on the heat dissipation ability; Normalize and sum the weighted physical quantities (voltage, temperature, residual heat) with different dimensions, and convert them into a dimensionless index S(t) for setting a unified threshold; The significance of weight allocation: w 1,j : Reflect the contribution differences of different speaker states (such as amplifier voltage, sound pressure level) to heat generation, which can be determined by experimental calibration or principal component analysis (PCA). w 2 , w 3 : Balance the importance of the current state, future prediction, and historical residue (e.g., if the predicted temperature weight is high, it is more inclined to preventive heat dissipation).

[0012] Negative sign environmental term: Directly reflect the enhancement effect of environmental factors on heat dissipation (e.g., the higher the wind speed, the stronger the heat dissipation ability, equivalently reducing the heat load); Design the dynamic threshold T th (t): ; where l k represents the sensitivity coefficient corresponding to the environmental factor E k ; E k,std represents the preset calibration value corresponding to the environmental factor E k ; In a harsh environment (such as high temperature and high humidity), the heat dissipation ability of the speaker decreases, and it is necessary to lower the safety threshold to avoid the critical state. For example: when the environmental temperature E k (t)>E k,std (calibrated value), the threshold decreases and the heat dissipation is triggered in advance.

[0013] The linear sensitivity coefficient measures the influence gradient of environmental factors on the heat dissipation efficiency through experiments.

[0014] The specific method for defining the condition to trigger active heat dissipation is as follows: Define the trigger condition for active heat dissipation: 1. S(t)≥S th ; indicating that the comprehensive heat load index reaches or exceeds the preset trigger threshold S th ; 2. T int (t)≥T th (t); indicating that the actual internal temperature of the current speaker reaches or exceeds the dynamic temperature threshold Tth(t); 3. T pre (t + Δ)≥T th (t); indicating that the predicted temperature will reach or exceed the temperature threshold T th (t) within the next time step; When any of the conditions is met, active heat dissipation is triggered.

[0015] A heat dissipation performance optimization system for a Bluetooth speaker, which includes a data acquisition module, a preprocessing module, a temperature model module, and a heat dissipation prediction module; The data acquisition module is used to set the data acquisition frequency and collect the speaker status data and environmental factor data; The preprocessing module is used to synchronize and preprocess the collected data, and perform feature extraction based on the preprocessed data; The temperature model module is used to build a regression model by combining thermodynamics and statistics, and calculate the internal temperature of the speaker based on the speaker status, environmental factors, and historical temperature; The heat dissipation prediction module is used to quantify the residual heat of the speaker, integrate the feature vector and the real temperature label to train the LSTM to predict the temperature, calculate the comprehensive heat load index and the dynamic temperature threshold, and define the condition to trigger active heat dissipation.

[0016] The data acquisition module includes a frequency setting unit, a status acquisition unit, and an environment acquisition unit; The frequency setting unit is used to set the data acquisition frequency of the sensor and the API; The status acquisition unit is used to collect the speaker status data through the sensor; The environment acquisition unit is used to call the meteorological API to obtain environmental factor data.

[0017] The preprocessing module includes a filtering processing unit, a data synchronization unit, and a feature extraction unit; The filtering processing unit is used to apply median filtering to eliminate data jitter; The data synchronization unit is used to perform time alignment and linear interpolation on multi-source data; The feature extraction unit is used to calculate the short-term mean and peak of the data and generate a normalized feature vector.

[0018] The temperature model module includes a model construction unit and a parameter solving unit; The model construction unit is used to establish a thermodynamic and statistical hybrid model equation including EMA; The parameter solving unit is used to calculate the weight coefficient by the least squares method.

[0019] The heat dissipation prediction module includes a residual heat unit, a temperature prediction unit, an index calculation unit, and a threshold trigger unit; The residual heat unit is used to quantify the residual heat of the speaker; The temperature prediction unit is used to train an LSTM model to predict future temperatures; The index calculation unit is used to calculate the heat load index by integrating the state, prediction, and residual heat; The threshold trigger unit is used to dynamically calculate the temperature threshold and define the active heat dissipation trigger condition.

[0020] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention proposes a method for quantifying the residual heat of the speaker, constructs a comprehensive heat load index, normalizes physical quantities with different dimensions and then performs weighted summation, and also designs a dynamic threshold to reasonably trigger active heat dissipation, improve the efficiency and accuracy of the heat dissipation system, avoid energy waste caused by starting heat dissipation when it is unnecessary, and at the same time ensure that the speaker can obtain timely and effective heat dissipation protection under various environmental conditions; The present invention combines thermodynamics and statistics for modeling, constructs a regression model to calculate the internal temperature of the speaker through data collection, preprocessing, and feature extraction, and uses LSTM for temperature prediction, which can more comprehensively capture various factors affecting the temperature, thereby providing a more accurate basis for heat dissipation control, and helping to avoid problems such as performance degradation, shortened lifespan, and even failures of the speaker due to overheating. Description of the Drawings

[0021] Figure 1 It is a step schematic diagram of a method for optimizing the heat dissipation performance of a Bluetooth speaker according to the present invention; Figure 2 It is a process schematic diagram of a system for optimizing the heat dissipation performance of a Bluetooth speaker according to the present invention. Detailed Implementation Modes

[0022] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0023] Embodiment: As Figure 1 - Figure 2 shown, the present invention provides a technical solution, a method for optimizing the heat dissipation performance of a Bluetooth speaker, and the method includes the following steps: Step 1: Set the data acquisition frequency, and acquire the speaker state data and environmental factor data; Step 2: Synchronize and preprocess the acquired data, and perform feature extraction based on the preprocessed data; Step 3: Combine thermodynamics and statistical modeling, construct a regression model based on the speaker state, environmental factors, and historical temperature, and calculate the internal temperature of the speaker; Step 4: Quantify the residual heat of the speaker, integrate the feature vector and the true temperature label to train the LSTM to predict the temperature, calculate the comprehensive heat load index and the dynamic temperature threshold, and define the conditions for triggering active heat dissipation.

[0024] In Step 1, set the data acquisition frequency according to the working characteristics and application scenarios of the speaker; The specific method for acquiring the speaker state data and environmental factor data is as follows: Arrange sensors inside the speaker and on the main control board to acquire the speaker state data, expressed as: [D 1 (t), D 2 (t), …, D m (t)]; where m is a positive integer representing the number of speaker states; t represents the data acquisition time, and D 1 (t), D 2 (t), …, D m (t) respectively represent the 1st, 2nd, …, mth speaker state data acquired at time t; The speaker state signals include but are not limited to the power amplifier input voltage, current, audio signal duty cycle, sound pressure level, etc.; Obtain the environmental factor data at the corresponding location through the meteorological API call, expressed as: [E 1 (t), E 2 (t), …, E n (t)]; where n is a positive integer representing the number of environmental factors; E 1 (t), E 2 (t), …, E n(t) respectively represent the first, second, …, nth environmental factor data obtained at time t; The environmental factor signals include, but are not limited to, environmental temperature, air flow rate, humidity, etc.

[0025] In step 2, the specific method for synchronizing and preprocessing the collected data is as follows: Set the sliding window length w and the step size s; Apply median filtering with window length w to the collected speaker state data and environmental factor data to eliminate short-term spikes and jitters; Perform linear interpolation on the speaker state data and environmental factor data based on the same time reference to synchronize the data; The specific method for feature extraction based on the preprocessed data is as follows: within a sliding window of length w and step size s, calculate the short-term average D j of the speaker state data D j mean and the short-term peak D j max, as well as the short-term mean E k of the environmental factor data E k mean; where j is a positive integer, j ∈ {1, 2, …, m}, representing the speaker state number sequence, D j represents the jth speaker state data; k is a positive integer, k ∈ {1, 2, …, n}, representing the environmental factor number sequence, E k represents the kth environmental factor data; Normalize the above features according to the maximum reference value to form a feature vector: X(t) = [D 1 (t)mean, D 1 (t)max, …, D m (t)mean, D m (t)max, E 1 (t)mean, …, E n (t)mean]; The average value reflects the "continuous power consumption" level of the power amplifier, battery or other components during this period, and has the greatest impact on long-term heat accumulation (cumulative temperature); the maximum value captures the short-term "power peak" or "transient impact", which is the key factor leading to an instantaneous temperature jump; environmental temperature, air flow rate, humidity, etc. usually change slowly within seconds or even minutes, and the extreme value change range within a short time is limited, having a small impact on the instantaneous temperature shock.

[0026] In step 3, the specific method for combining thermodynamic and statistical modeling is as follows: Combine thermodynamics and statistics for modeling to calculate the internal temperature T int (t) of the speaker: ; Among them, T int (t) represents the internal temperature of the speaker at time t, which is directly obtained by the sensor; a j represents the weight coefficient corresponding to the speaker status data D j , reflecting the unit sensitivity of this status to temperature; b k represents the weight coefficient corresponding to the environmental factor data E k , reflecting the thermal coupling strength of environmental factors; c represents the weight coefficient of the historical temperature cumulative term, determining the contribution degree of exponential smoothing in the current temperature calculation; g k () represents the environmental factor mapping function, which converts the environmental factor data E k to the model space and is defined by the staff in combination with the sensor characteristics; The internal temperature of the speaker is determined by the dynamic balance between heat generation (power consumption) and heat dissipation (environmental conditions). The linear superposition in the formula directly reflects the physical contributions of the heat generation term (such as the Joule heat of the amplifier voltage and current) and the heat dissipation term (such as the convective heat dissipation of wind speed and humidity).

[0027] Simplified modeling of statistical regression: The actual heat transfer process involves complex partial differential equations (such as the heat conduction equation), but the main factors can be extracted through a linear regression model, and the parameters are fitted in a data-driven manner to avoid complex calculations; ; Among them, EMA[T int (t) represents the exponential moving average, which is used to characterize the residual influence of past temperatures on the current thermal state; α represents the smoothing factor, and by adjusting α, the EMA curve reaches a balance between "tracking" and "smoothing". Usually, cross-validation is used to evaluate the MSE to determine the optimal α; Δt represents the time interval between two data acquisitions; Taking D j (t), g k (E k (t)) and EMA[T int (t) as independent variables and T int (t) as the dependent variable to construct a regression model, and using the least squares method to solve for a j , b k and c.

[0028] In step 4, the specific method for quantifying the residual heat of the speaker is as follows: Quantify the residual heat of the speaker: ; Among them, H res (t) represents the residual heat index, indicating the heat that has not dissipated yet due to historical heat generation inside the speaker up to time t; H res(t - Δt) represents the residual heat value at the previous sampling moment; the initial residual heat H res (0) is calibrated manually; α represents the smoothing factor; T std represents the preset calibrated ambient temperature value; The recursive form of the residual heat ((t) depends on the value at the previous moment) simulates the physical law of heat decaying exponentially over time (similar to the discharge of an RC circuit). Heat capacity and heat decay: The heat capacity characteristics of the speaker material and structure cause a lag in temperature change. The smoothing factor in the formula is related to the thermal time constant. The larger it is, the slower the residual heat decays, corresponding to an environment with low heat dissipation efficiency (such as an enclosed space).

[0029] By taking the difference from the calibrated ambient temperature (such as 25°C), the baseline influence of the ambient temperature on the residual heat is eliminated, focusing on the heat accumulation generated by the speaker itself; The specific way to train the LSTM to predict temperature is as follows: Integrate the feature vector sequence X(t i ) and the corresponding true temperature label T int (t i ); where i is a positive integer representing the data acquisition moment sequence; X(t i ) represents the characteristics of the speaker state and environmental factors after preprocessing corresponding to the moment t i ; T int (t i ) represents the true temperature corresponding to the moment t i ; Construct sample - label pairs: Each sample consists of a feature subsequence of length τ [X(t - τ + 1), …, X(t)]; the label is the temperature T int (t + Δ); Select the long - short - term memory network LSTM. By training on the sample - label pairs, output the predicted temperature T pre (t + Δ); The calculation of the comprehensive heat load index and the dynamic temperature threshold includes: Calculate the comprehensive heat load index S(t): ; where D j,max represents the reference maximum value for normalizing the j - th speaker state; T1 represents the safety critical temperature reference value, calibrated by the staff; w 1,j represents the weight corresponding to the j - th speaker state, reflecting the relative contribution of this state to the heat load; w 2 represents the weight of the predicted temperature term, measuring the influence degree of the future temperature on the current heat load judgment; w 3 represents the weight of the residual heat term, measuring the influence degree of the historical heat accumulation on the current heat load judgment; w 4,kDenote the weight corresponding to the k-th environmental factor item, reflecting the influence intensity of this environmental factor on the heat dissipation capacity; Normalize physical quantities with different dimensions (voltage, temperature, residual heat) and then perform weighted summation to convert them into a dimensionless index S(t), which is convenient for setting a unified threshold; The significance of weight allocation: w 1,j : Reflect the contribution differences of different speaker states (such as amplifier voltage, sound pressure level) to heat generation, which can be determined through experimental calibration or principal component analysis (PCA). w 2 , w 3 : Balance the importance of the current state, future prediction, and historical residue (for example, if the weight of the predicted temperature is high, it is more inclined to preventive heat dissipation).

[0030] Negative sign environmental item: Directly reflect the enhancement effect of environmental factors on heat dissipation (for example, the higher the wind speed, the stronger the heat dissipation capacity, equivalently reducing the heat load); Design a dynamic threshold T th (t): ; Among them, l k Denote the sensitivity coefficient corresponding to the environmental factor E k ; E k,std Denote the preset calibration value corresponding to the environmental factor E k ; In a harsh environment (such as high temperature, high humidity), the heat dissipation capacity of the speaker decreases, and the safety threshold needs to be reduced to avoid the critical state. For example: when the environmental temperature E k (t) > E k,std (calibration value), the threshold decreases and the heat dissipation is triggered in advance.

[0031] The linear sensitivity coefficient measures the influence gradient of environmental factors on the heat dissipation efficiency through experiments.

[0032] The specific method for defining the condition to trigger active heat dissipation is as follows: Define the trigger condition for active heat dissipation: 1. S(t) ≥ S th ; It means that the comprehensive heat load index reaches or exceeds the preset trigger threshold S th ; 2. T int (t) ≥ T th (t); It means that the actual internal temperature of the current speaker reaches or exceeds the dynamic temperature threshold Tth(t); 3. T pre (t + Δ) ≥ T th (t); It means that the predicted temperature will reach or exceed the temperature threshold T th (t) in the next time step; When any condition is met, active heat dissipation is triggered.

[0033] A heat dissipation performance optimization system for a Bluetooth speaker, the system includes a data acquisition module, a preprocessing module, a temperature model module and a heat dissipation prediction module; The data acquisition module is used to set the data acquisition frequency and collect speaker status data and environmental factor data; The preprocessing module is used to synchronize and preprocess the collected data, and perform feature extraction based on the preprocessed data; The temperature model module is used to combine thermodynamics and statistics to model, construct a regression model based on the speaker status, environmental factors and historical temperature, and calculate the internal temperature of the speaker; The heat dissipation prediction module is used to quantify the residual heat of the speaker, integrate the feature vector and the true temperature label to train the LSTM to predict the temperature, calculate the comprehensive heat load index and the dynamic temperature threshold, and define the conditions for triggering active heat dissipation.

[0034] The data acquisition module includes a frequency setting unit, a status acquisition unit and an environment acquisition unit; The frequency setting unit is used to set the data acquisition frequency of the sensor and the API; The status acquisition unit is used to collect speaker status data through the sensor; The environment acquisition unit is used to call the meteorological API to obtain environmental factor data.

[0035] The preprocessing module includes a filtering processing unit, a data synchronization unit and a feature extraction unit; The filtering processing unit is used to apply median filtering to eliminate data jitter; The data synchronization unit is used to perform time alignment and linear interpolation on multi-source data; The feature extraction unit is used to calculate the short-term mean and peak of the data and generate a normalized feature vector.

[0036] The temperature model module includes a model construction unit and a parameter solving unit; The model construction unit is used to establish a thermodynamic and statistical hybrid model equation including EMA; The parameter solving unit is used to calculate the weight coefficient by the least squares method.

[0037] The heat dissipation prediction module includes a residual heat unit, a temperature prediction unit, an index calculation unit and a threshold trigger unit; The residual heat unit is used to quantify the residual heat of the speaker; The temperature prediction unit is used to train the LSTM model to predict the future temperature; The index calculation unit is used to calculate the heat load index by integrating the status, prediction and residual heat; The threshold trigger unit is used to dynamically calculate the temperature threshold and define the active heat dissipation trigger condition.

[0038] In this embodiment, a certain portable Bluetooth speaker is provided. The speaker is powered by a built-in lithium battery and is equipped with a digital power amplifier module. When playing music at a high volume for a long time in an outdoor high-temperature and high-humidity environment, it is prone to overheating problems. To optimize its heat dissipation performance, the above method is implemented, and the specific device configuration is as follows: Sensor arrangement: Install a voltage sensor, a current sensor, a temperature sensor, etc. on the main control board of the speaker; arrange a sound pressure level sensor inside the speaker cavity; at the same time, call the meteorological API through the device's GPS positioning information to obtain environmental data.

[0039] Collect the power amplifier input voltage D 1 (t), the power amplifier input current D 2 (t), the frequency output sound pressure level D 3 (t), and record them in the form of an array, such as [D 1 (t), D 2 (t), D 3 (t)].

[0040] At the moment t = 10, the collected power amplifier input voltage is 3.8V, the current is 0.5A, and the sound pressure level is 85dB, D 1 (10) = 3.8, D 2 (10) = 0.5, D 3 (10) = 85.

[0041] Obtain the environmental temperature E 1 (t), the air flow rate E 2 (t), the humidity E 3 (t), expressed as [E 1 (t), E 2 (t), E 3 (t)].

[0042] At the moment t = 10, the obtained environmental temperature is 30, the air flow rate is 1.2, and the humidity is 65, E 1 (10) = 30, E 2 (10) = 1.2, E 3 (10) = 65.

[0043] The sliding window length w = 60 (corresponding to 6 seconds of data, considering the change period of the audio signal and environmental factors), and the step size s = 10 (that is, the feature calculation is updated every 1 second).

[0044] Apply median filtering with a window length of 60 to the collected speaker status data and environmental factor data to eliminate abnormal data such as short-term voltage spikes; perform linear interpolation based on the same time reference.

[0045] Within a sliding window of length 60 and step size 10, calculate the short-term average value D j mean and short-term peak value D j max of the speaker status data D j , as well as the short-term average value E k mean of the environmental factor data E k .

[0046] For the power amplifier input voltage D 1 , within the window from t = 10 to t = 70, calculate the average value Dmean and maximum value D 1 max of the voltage data within this window; for the environmental temperature E 1 , calculate the average value E 1 mean of the temperature data within this window.

[0047] Normalize the above features according to the maximum reference value to form the feature vector X(t) = [D 1 (t)mean, D 1 (t)max, …, D 3 (t)mean, D 3 (t)max, E 1 (t)mean, …, E 3 (t)mean].

[0048] Combine thermodynamics and statistics for modeling to calculate the internal temperature T int (t); the initial values of a j , b k can be set as empirical values first, such as a 1 = 0.2, a 2 = 0.3, a 3 = 0.1, b 1 = 0.1, b 2 = 0.05, b 3 = 0.02, c = 0.4; the g k () function is defined according to the sensor characteristics. For example, for the environmental temperature E1, g 1 (E 1 (t)) = E 1 (t), and for the air flow rate E 2 , g 2 (E 2 (t)) = E 2 (t)^2 (considering that heat dissipation is approximately proportional to the square of the wind speed).

[0049] Calculate the Exponential Moving Average EMA[T int (t); α = 0.3, Δt = 0.1 second (data acquisition interval), and the initial EMA[T int (0) is set to the internal temperature value of the speaker collected for the first time.

[0050] Using D j (t), g k (E k (t)) and EMA[T int (t) as independent variables and T int (t) as the dependent variable to construct a regression model. By collecting data for a certain period (such as 1 hour) and using the least squares method to solve, more optimal values of a j , b k and c are obtained.

[0051] Quantify the residual heat of the speaker: Example: At t = 10, T int (10) = 37.5, H res (9) = 2 (the residual heat value at the previous moment), then H res (10) = 0.2×2 + (1 - 0.2)×(37.5 - 25) = 10.4.

[0052] Train the LSTM to predict temperature: Integrate the feature vector sequence X(t i ) and the corresponding true temperature label T int (t i ) to construct sample-label pairs. Each sample consists of a feature subsequence of length τ = 30 [X(t - 30 + 1), …, X(t)], and the label is the temperature T int (t + Δ), where Δ = 10 time steps (i.e., 1 second later).

[0053] Use the Keras library in Python to build an LSTM network, set appropriate network parameters (such as the number of neurons in the hidden layer is 64, the number of training epochs is 50, the batch size is 32, etc.), and through training on the sample-label pairs, output the predicted temperature T pre (t + Δ).

[0054] Calculate the comprehensive heat load index and the dynamic temperature threshold: Calculate the comprehensive heat load index S(t): w 1,1 = 0.3, w 1,2 = 0.2, w 1,3 = 0.1, w 2 = 0.2, w 3 = 0.1, w 4,1 = 0.05, w 4,2 = 0.03, w 4,3= 0.02; D 1,max = 5, D 2,max = 1, D 3,max = 100, T1 = 60 (safety critical temperature reference value), H max = 50 (maximum residual heat reference value).

[0055] At t = 10, D 1 (10) = 3.8, D 2 (10) = 0.5, D 3 (10) = 85, T pre (20) = 40 (predicted temperature 1 second later), H res (10) = 10.4, E 1 (10) = 30, E 2 (10) = 1.2, E 3 (10) = 65, substitute into the formula to calculate the value of S(10).

[0056] Design the dynamic threshold T th (t); At t = 10, substitute the data to calculate T th (10) = 60 - 0.5×(30 - 25) - 0.3×(1.2 - 2) - 0.2×(65 - 50) = 54.74; Define the trigger condition for active heat dissipation: When S(t) ≥ S th (assuming S th = 0.8 is the preset trigger threshold), trigger active heat dissipation.

[0057] When T int (t) ≥ T th (t), trigger active heat dissipation. At t = 10, T int (10) = 37.5, T th (10) = 54.74, at this time this condition is not satisfied.

[0058] When T pre (t + Δ) ≥ T th (t), trigger active heat dissipation. At t = 10, T pre (20) = 40, T th (10) = 54.74, at this time this condition is not satisfied.

[0059] When any of the above conditions is met, start the active heat dissipation device of the speaker, such as turning on the built-in fan, adjusting the power amplifier power, etc., to reduce the internal temperature of the speaker and optimize the heat dissipation performance.

[0060] It is obvious to those skilled in the art that the present invention is not limited to the details of the above-described exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention. Therefore, in any respect, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Thus, all changes that fall within the meaning and scope of the equivalent elements of the claims are intended to be embraced within the present invention. Any reference signs in the claims should not be construed as limiting the claims concerned.

Claims

1. A method for optimizing the heat dissipation performance of a Bluetooth speaker, characterized in that: The method comprises the following steps: Step 1: Set the data collection frequency to collect speaker status data and environmental factor data; Step 2: Synchronize and preprocess the collected data, and perform feature extraction based on the preprocessed data; Step 3: Combining thermodynamics and statistical modeling, a regression model is constructed based on the speaker status, environmental factors and historical temperature to calculate the internal temperature of the speaker; Step 4: Quantify the residual heat of the speaker, integrate the feature vector and the real temperature label to train the LSTM to predict the temperature, calculate the comprehensive heat load index and dynamic temperature threshold, and define the conditions for triggering active heat dissipation.

2. The method for optimizing the heat dissipation performance of a Bluetooth speaker according to claim 1, characterized in that: In step 1, the specific method of collecting the speaker status data and environmental factor data is: Sensors are arranged inside the speaker and on the main control board to collect speaker status data, expressed as: [D1(t),D2(t),…,D m (t)]; where m is a positive integer, indicating the number of speaker states; t indicates the data collection time, D1(t), D2(t),…, D m (t) represents the 1st, 2nd, ..., mth speaker status data collected at time t respectively; By calling the meteorological API, we can obtain the environmental factor data of the corresponding location, which is expressed as: [E1(t), E2(t),…, E n (t)]; where n is a positive integer, indicating the number of environmental factors; E1(t), E2(t),…, E n (t) represents the 1st, 2nd, ..., nth environmental factor data obtained at time t respectively.

3. The method for optimizing the heat dissipation performance of a Bluetooth speaker according to claim 2, characterized in that: In step 2, the specific method of synchronizing and preprocessing the collected data is: Set the sliding window length w and step size s; Apply a median filter with a window length of w to the collected speaker status data and environmental factor data to eliminate short-term spikes and jitters; Linearly interpolate the speaker status data and environmental factor data based on the same time reference to synchronize the data; The specific method of extracting features based on the preprocessed data is as follows: within a sliding window with a length of w and a step size of s, the speaker status data D is calculated. j The short-term average value D j mean and short-time peak value D j max, and environmental factor data E k The short-term mean E k mean; where j is a positive integer, j∈{1,2,…,m}, representing the sequence of speaker state quantities, D j represents the j-th speaker status data; k is a positive integer, k∈{1,2,…,n}, representing the sequence of environmental factors, E k Represents the kth environmental factor data; Normalize the above features according to the maximum reference value to form a feature vector: X(t)=[D1(t)mean,D1(t)max,…,D m (t)mean,D m (t)max,E1(t)mean,…,E n (t)mean].

4. The method for optimizing the heat dissipation performance of a Bluetooth speaker according to claim 3, characterized in that: In step 3, the specific method of combining thermodynamics and statistical modeling is: Combining thermodynamics and statistics to model and calculate the internal temperature T of the speaker int (t): ； Among them, T int (t) represents the internal temperature of the speaker at time t, which is directly obtained by the sensor; a j Indicates the speaker status data D j The corresponding weight coefficient reflects the unit sensitivity of the state to temperature; b k Indication and environmental factor data E k The corresponding weight coefficient reflects the thermal coupling strength of environmental factors; c represents the weight coefficient of the historical temperature accumulation term, which determines the contribution of exponential smoothing in the current temperature calculation; g k () represents the environmental factor mapping function, which transforms the environmental factor data E k Transformed into model space, defined by the staff in combination with sensor characteristics; ； Among them, EMA[T int ](t) represents the exponential moving average, which is used to characterize the residual effect of past temperature on the current thermal state; α represents the smoothing factor; Δt represents the time interval between two data collections; D j (t), g k (E k (t)) and EMA[T int ](t) is the independent variable, T int (t) Build a regression model for the dependent variable and use the least squares method to solve it to get a j , b k and c.

5. The method for optimizing the heat dissipation performance of a Bluetooth speaker according to claim 4, characterized in that: In step 4, the specific method of quantifying the residual heat of the speaker is: The residual heat index H is calculated using the heat accumulation model res (t), the model is based on historical temperature data and heat dissipation characteristic parameters; The specific method of training LSTM to predict temperature is: integrating the pre-processed speaker status and environmental factor characteristics and the corresponding real temperature label; selecting a long short-term memory network LSTM for training, and outputting the predicted temperature T pre (t+Δ); The calculation of the comprehensive heat load index and the dynamic temperature threshold comprises: Calculate the comprehensive heat load index S(t): Use a comprehensive evaluation function to calculate S(t), which includes speaker status index items, temperature prediction items, residual heat index items and environmental factor mapping items, each of which is equipped with a corresponding weight coefficient; Design dynamic threshold T th (t): Dynamically adjust the temperature threshold based on environmental factors and preset parameters, where the preset parameters include environmental factor sensitivity coefficient, preset calibration value and safety critical temperature reference value; The specific method of defining the triggering active heat dissipation condition is as follows: Define the trigger conditions for active cooling: Condition 1: S(t)≥S th ; Indicates that the comprehensive heat load index reaches or exceeds the preset trigger threshold S th ; Condition 2: T int (t)≥T th (t); indicates that the actual internal temperature of the current speaker has reached or exceeded the dynamic temperature threshold value Tth(t); Condition 3: T pre (t+Δ)≥T th (t); indicates that the predicted temperature will reach or exceed the temperature threshold T in the next time step th (t); When any of the conditions is met, active cooling is triggered.

6. A Bluetooth speaker heat dissipation performance optimization system, applied to a Bluetooth speaker heat dissipation performance optimization method according to any one of claims 1 to 5, characterized in that: The system includes a data acquisition module, a preprocessing module, a temperature model module and a heat dissipation prediction module; The data acquisition module is used to set the data acquisition frequency and collect the speaker status data and environmental factor data; The preprocessing module is used to synchronize and preprocess the collected data, and to extract features based on the preprocessed data; the temperature model module is used to combine thermodynamics and statistical modeling, build a regression model based on the speaker status, environmental factors and historical temperature, and calculate the internal temperature of the speaker; the heat dissipation prediction module is used to quantify the residual heat of the speaker, integrate feature vectors and real temperature labels to train LSTM to predict temperature, calculate the comprehensive heat load index and dynamic temperature threshold, and define the conditions for triggering active heat dissipation.

7. The heat dissipation performance optimization system of a Bluetooth speaker according to claim 6, characterized in that: The data acquisition module includes a frequency setting unit, a state acquisition unit and an environment acquisition unit; The frequency setting unit is used to set the data collection frequency of the sensor and API; the status collection unit is used to collect the speaker status data through the sensor; and the environment acquisition unit is used to call the meteorological API to obtain environmental factor data.

8. The heat dissipation performance optimization system of a Bluetooth speaker according to claim 7, characterized in that: The preprocessing module includes a filtering processing unit, a data synchronization unit and a feature extraction unit; The filtering processing unit is used to apply median filtering to eliminate data jitter; the data synchronization unit is used to perform time alignment and linear interpolation on multi-source data; and the feature extraction unit is used to calculate the short-time mean and peak value of the data and generate a normalized feature vector.

9. The heat dissipation performance optimization system of a Bluetooth speaker according to claim 8, characterized in that: The temperature model module includes a model building unit and a parameter solving unit; The model building unit is used to establish a thermodynamic and statistical mixed model equation containing EMA; and the parameter solving unit is used to calculate the weight coefficient by the least square method.

10. The heat dissipation performance optimization system of a Bluetooth speaker according to claim 9, characterized in that: The heat dissipation prediction module includes a residual heat unit, a temperature prediction unit, an index calculation unit and a threshold trigger unit; The residual heat unit is used to quantify the residual heat of the speaker; the temperature prediction unit is used to train the LSTM model to predict future temperature; the indicator calculation unit is used to calculate the thermal load indicator based on the comprehensive status, prediction and residual heat; the threshold trigger unit is used to dynamically calculate the temperature threshold and define the active heat dissipation trigger condition.

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