A method for measuring room temperature using an intelligent terminal based on linear frequency modulation acoustic signals

Abstract

The present invention discloses a method for measuring room temperature of an intelligent terminal based on a linear frequency modulation sound signal, which includes: using a speaker of the intelligent terminal to transmit a linear frequency modulation signal; performing environmental noise reduction on a dual-channel sound signal received by the upper and lower microphones of the intelligent terminal; dividing the interference of the signal according to distance and attenuation, and removing the interference item based on time delay calculation on the dual-channel sound signal after environmental noise reduction; then using guided search to calculate the relative time delay between the dual-channel sound signals, and then combining the sound path difference between the upper and lower microphones of the intelligent terminal to calculate the average sound speed of the environment around the intelligent terminal; finally, realizing room temperature measurement based on the mapping relationship of sound speed and temperature. The present invention can effectively filter out full-band noise in the environment, suppress the interference of sound reflection obstacles on the sound signal at the microphone, and realize room temperature measurement of the intelligent terminal from the perspective of sound signal processing. It is not only highly portable, but also has high accuracy, real-time and robustness.

Description

Technical Field

[0001] The invention relates to a method for measuring room temperature by an intelligent terminal based on a linear frequency modulation sound signal, and belongs to the technical field of voice signal processing. Background Art

[0002] Room temperature generally refers to the indoor temperature range within which humans can safely move around. It is a key factor influencing people's living comfort. Excessively high room temperatures can significantly worsen respiratory and psychiatric symptoms, and can even affect blood pressure stability. Therefore, clinical care requires close monitoring of room temperature fluctuations. Therefore, room temperature measurement is beneficial for applications such as building comfort assessment and clinical monitoring analysis.

[0003] Existing room temperature measurement methods often fall into the following three categories: 1. Using the thermal expansion and contraction properties of liquids to calibrate the temperature, such as red water thermometers, mercury thermometers, etc. The temperature measurement accuracy can be within 1°C, but the temperature measurement response time is too long, and the reading must be stable before it can be read; 2. Using the Seebeck effect, that is, when there is a temperature gradient in the electrical circuit, a thermoelectric potential will be generated. Contact temperature measurement tools such as thermocouples can achieve a large temperature range and high-precision measurement, and have good real-time performance, but the cost of use is high and a matching data analysis instrument is required to display the temperature; 3. Using the relationship between thermal radiation and temperature, such as infrared sensor temperature measurement, this method has good real-time performance and relatively reliable temperature measurement accuracy. It can also be integrated into some mobile devices, but the surface characteristics of the object (such as color, material, gloss, etc.) will affect the temperature measurement results. Surfaces with high reflectivity (such as metal) may even cause inaccurate readings. The above-mentioned temperature measurement methods often require the installation of additional external hardware equipment. In order to further balance the accuracy, portability and real-time performance of room temperature measurement, integrating the temperature measurement function into smart terminals by utilizing the acoustic characteristics of air has very broad application prospects. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for measuring room temperature by an intelligent terminal based on linear frequency modulation sound signals. The method can use ultrasound to realize room temperature measurement by an intelligent terminal without additionally installing an external temperature sensor on the intelligent terminal. This method can not only obtain the current room temperature range in a relatively real-time manner, but also take into account the integration requirements and portability of the intelligent terminal, and is expected to be put into practical use.

[0005] To achieve the above object, the technical solution adopted by the present invention is: a method for measuring room temperature by an intelligent terminal based on linear frequency modulation sound signals, which comprises the following steps:

[0006] S1: The smart terminal speaker transmits a linear frequency modulation signal x(t);

[0007] S2: When the speaker transmits the signal, it simultaneously collects the original two-channel audio signal y received by the upper and lower microphones of the smart terminal 10(t) and y 20 (t), and perform bandpass filtering on it, and then perform environmental noise reduction processing, and then upsample the noise-reduced signal by 1 times to obtain signal y 11 (t) and y 21 (t);

[0008] S3: Extract x(t), y 11 (t) and y 21 (t) Signal envelope and downsample the envelope signal by 1 / D times to obtain E X , E Y1 and E Y2 ; Using E X Respectively with E Y1 and E Y2 Perform a sliding window cross-correlation search and independently calculate the delay sets {τ1}, {τ2} and intensity sets {α1}, {α2} corresponding to the acoustic obstacle reflection signals at the upper and lower microphones of the smart terminal;

[0009] S4: respectively in y 11 (t) and y 21 (t) minus the set of time delay {τ} and intensity {α} calculated independently at each microphone, and obtain y after eliminating the interference of acoustic obstacles. 12 (t) and y 22 (t);

[0010] S5: Calculate y 12 (t) and y 22 The relative time delay Δτ between the two microphones (t) is combined with the acoustic path difference Δd between the upper and lower microphones of the smart terminal to guide the sliding window cross-correlation search to calculate the average sound speed c of the air around the smart terminal;

[0011] S6: Determine the indoor air temperature T based on the sound velocity-temperature mapping relationship c(T) in linear acoustics and the average sound velocity c of the air around the smart terminal.

[0012] Furthermore, in step S1, the linear frequency modulation signal x(t) emitted by the speaker is processed with a Hanning window, the signal duration is t0, ranging from 1 to 5s; the center frequency is f0, ranging from 16 to 20kHz; the bandwidth is B, ranging from 2 to 4kHz; the frequency range is [f0-B / 2, f0+B / 2]; the sampling rate is f s , usually 48kHz; and the front-end duration of the signal x(t) is set to τ n The blank period, where τ n Usually around 0.5s.

[0013] Furthermore, in step S2, y 10 (t) and y20 (t) is the original signal that has not been pre-filtered by the intelligent terminal, and the sampling rate is also f s The passband range of the bandpass filter is [f0-B / 2, f0+B / 2], and the filter coefficient is designed by the FIR window function method; at the same time, the environmental noise reduction is performed according to the spectrum subtraction method, and the reference noise segment selected by the spectrum subtraction method is y 10 (t) and y 20 (t) Each initial period [0,τ n ] within the blank noise signal, y 10 (t) and y 20 The corresponding amplitude spectrum of (t) is subtracted from the corresponding reference noise amplitude spectrum, and the noise-reduced signal is restored by inverse Fourier transform combined with the phase spectrum; In addition, the specific operation of I-fold upsampling is as follows: first, I-1 zero values ​​are inserted between every two sampling points of the original signal, and then the interpolated signal is subjected to a cutoff frequency of f s / (2I) is filtered by a low-pass FIR filter to obtain the corresponding up-sampled signal y 11 (t) and y 21 (t).

[0014] Furthermore, in step S3, the signal envelope is extracted by using Hilbert transform. In order to avoid the interference between the direct sound and the interference sound, the length t0 of the original excitation signal is used as the threshold setting standard. When the delay time Δt of the interference sound is greater than t0 / 2, a better distinction between the interference sound band and the direct sound band can be achieved. In addition, the specific operation of 1 / D downsampling is as follows: first, the original signal is subjected to a cutoff frequency of f s / (2D) low-pass FIR filter is used for filtering, and then the filtered signal is extracted at equal intervals, where the interval is D. The extracted signal is the corresponding down-sampled signal; the sliding window cross-correlation search and the calculation of the sound obstacle interference are respectively in E X With E Y1 、E X With E Y2 Between.

[0015] Furthermore, in step S3, the sliding window cross-correlation search selects the normalized cross-correlation coefficient as the basis for the signal similarity; specifically, the signal E X Shift, with E Y1 and E Y2 The cross-correlation operation is performed respectively. The number of shift points corresponding to the maximum cross-correlation coefficient is the time delay τ of the signal, and the cross-correlation coefficient corresponds to its signal strength α. In addition, the delay set {τ} and the strength set {α} are calculated independently for the signals at the upper and lower microphones of the smart terminal. The independent calculations are as follows: the excitation signal x(t) is respectively compared with the received signals y at the upper and lower microphones. 11(t) and y 21 (t) Calculate according to step S3 to obtain: the delay set {τ1} and intensity set {α1} corresponding to the excitation signal and the signal at the upper microphone, and the delay set {τ2} and intensity set {α2} corresponding to the excitation signal and the signal at the lower microphone.

[0016] Furthermore, in step S4, after eliminating the interference of the acoustic barrier, y 12 The expression of (t) is:

[0017]

[0018] where y 12 (t) is the signal after eliminating the acoustic interference, y 11 (t) is the signal containing the acoustic obstacle interference, α n1 is the nth value in the intensity set {α1}, τ n1 is the nth value in the time delay set {τ1}, i is the number of elements in the sets {α1} and {τ1};

[0019] y 21 (t) Repeat the above step S4, y 22 The specific expression of (t) is

[0020]

[0021] where y 22 (t) is the signal after eliminating the acoustic interference, y 21 (t) is the signal containing the acoustic obstacle interference, α n2 is the nth value in the intensity set {α2}, τ n2 is the nth value in the time delay set {τ2}, and j is the number of elements in the sets {α2} and {τ2}.

[0022] Furthermore, in step S5, the shift y 12 (t), and y 22 (t) Perform normalized cross-correlation operation and search for the peak value of the cross-correlation coefficient Corr max , corresponding to the time shift of integer sample points Δn·f s , where f s is the signal y 12 (t) and y 22 The sampling frequency of (t); the cosine function interpolation is used to estimate the sub-precision time shift δ near the peak, denoted by Corr max The correlation coefficients of the two adjacent points on the left and right are Corr1 and Corr2 respectively, and the sub-precision time shift is:

[0023]

[0024] Among them, Corr max is the peak value of the mutual correlation coefficient, Corr1 and Corr2 are Corr max Correlation coefficient between two adjacent points on the left and right; from this, the relative time delay of the two signals can be obtained as Δτ=(Δn+δ)·f s .

[0025] Furthermore, the guided sliding window cross-correlation search uses the speed-of-sound-temperature mapping relationship c(T), which is derived from linear acoustic theory:

[0026]

[0027] Where c0 is 331.5 m / s, the reference temperature T0 is 273.15 K (corresponding to 0°C), and T is the indoor temperature in °C.

[0028] Furthermore, in the cross-correlation search of the guided sliding window, the mapping relationship c(T) between the speed of sound and the temperature and the indoor temperature range [T min ,T max ], we can know that the range of indoor air sound speed is [c min ,c max ], combined with the sound path difference Δd between the upper and lower microphones of the smart terminal to limit the sliding window cross-correlation search range:

[0029] Δd / c max <Δτ<Δd / c min

[0030] Where Δd is the sound path difference between the upper and lower microphones of the smart terminal, c min is the theoretical minimum value of the indoor air sound speed, c max is the theoretical maximum value of the indoor air sound speed, Δτ is the time delay corresponding to the sliding window cross-correlation search range; based on the above formula, guided search is achieved; the calculation expression of the average sound speed of the air around the smart terminal is c = Δd / Δτ.

[0031] Furthermore, in step S6, according to the mapping relationship c(T) between the speed of sound and the temperature, T(c) is obtained by an inverse function, and the average speed of sound c of the air around the smart terminal is substituted into the expression to estimate the current room temperature.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] The present invention provides a method for measuring room temperature using an intelligent terminal based on a linear frequency modulation acoustic signal, which can improve the signal-to-noise ratio of the excitation signal, suppress environmental noise, and eliminate acoustic interference signals. It also optimizes algorithm processing efficiency and improves delay estimation accuracy. The algorithm itself has low hardware requirements for intelligent terminals, providing broad ideas for integrated temperature measurement on mobile devices and having high commercial application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A flow chart of a method for measuring room temperature using a smart terminal based on linear frequency modulation acoustic signals;

[0035] Figure 2 Flowchart for eliminating interference signals from acoustic obstacles;

[0036] Figure 3 The figure shows the waveform and spectrum comparison of the original sound signal emitted in actual application of the present invention and the signal before and after noise reduction. DETAILED DESCRIPTION

[0037] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0038] like Figure 1 As shown, a method for measuring room temperature by an intelligent terminal based on a linear frequency modulation sound signal includes the following steps:

[0039] S1: The smart terminal speaker transmits a linear frequency modulation signal x(t);

[0040] S2: When the speaker transmits the signal, it simultaneously collects the original two-channel audio signal y received by the upper and lower microphones of the smart terminal 10 (t) and y 20 (t), perform band-pass filtering on it, and then perform environmental noise reduction processing according to spectral subtraction. Then, the noise-reduced signal is upsampled by 1 times to obtain y 11 (t) and y 21 (t);

[0041] S3: Extract x(t), y 11 (t) and y 21 (t) Signal envelope and downsample the envelope signal by 1 / D times to obtain E X , E Y1 and E Y2 ; Using E X Respectively with E Y1 and E Y2 Perform a sliding window cross-correlation search to independently estimate the delay set {τ} and intensity set {α} corresponding to the acoustic obstacle reflection signals at the upper and lower microphones of the smart terminal;

[0042] S4: respectively in y 11 (t) and y 21 Subtract the x(t) set corresponding to the delay {τ} and intensity {α} from (t) to obtain y after eliminating the interference of acoustic obstacles. 12 (t) and y 22 (t);

[0043] S5: Estimate y 12 (t) and y 22 The relative time delay Δτ between the two microphones (t) is combined with the acoustic path difference Δd between the upper and lower microphones of the smart terminal to calculate the average sound speed c of the air around the smart terminal;

[0044] S6: Determine the indoor air temperature T based on the sound velocity-temperature mapping relationship c(T) in linear acoustics and the average sound velocity c of the air around the smart terminal.

[0045] like Figure 2 As shown in the figure, eliminating the interference signal of the acoustic obstacle mainly includes: using Hilbert transform to extract the signal envelope, using E X With E Y1 Taking the cross-correlation operation as an example, the corresponding time delay τ0 and signal strength α0 can be obtained after the sliding window search, which corresponds to the delay and attenuation of the direct sound between the speaker and the microphone; in the original signal E Y1 The above corresponds to the signal E with the shift τ0 minus the amplitude α0 X , and obtain the envelope signal E composed of the acoustic obstacle interference signal Y11 Combined with E X With E Y11 Repeat the above sliding window search to obtain the time delay τ1 and signal strength α1, and execute the envelope signal E again. Y11 and the shifted weighted signal E X The subtraction operation obtains the envelope signal E Y12 ; Repeat the above process, and when the signal strength α i The cycle stops when it is less than a certain threshold γ, and γ is selected to be 0.1 to 0.2 times α0; through this cycle, the time delay set {τ} = {τ1, τ2, ..., τ i} and the strength set {α}={α1,α2,…,α i}. After eliminating the interference of sound obstacles, y 12 The expression of (t) is:

[0046]

[0047] where y 12 (t) is the signal after eliminating the interference of acoustic obstacles, y 11 (t) is the signal containing the acoustic obstacle interference, α nis the corresponding value in the intensity set {α}, τ n is the corresponding value in the time delay set {τ}, and i is the number of elements in the set. 21 (t) Repeat the above steps and obtain y using similar expressions as above. 22 (t), y 22 The specific expression of (t) is:

[0048]

[0049] where y 22 (t) is the signal after eliminating the acoustic interference, y 21 (t) is the signal containing the acoustic obstacle interference, α n2 is the nth value in the intensity set {α2}, τ n2 is the nth value in the time delay set {τ2}, and j is the number of elements in the sets {α2} and {τ2}.

[0050] like Figure 3 As shown in the figure, in the actual application of the temperature measurement scenario of the dual-channel microphone smart terminal, the sampling rate f of the smart terminal's speaker transmission signal and the dual-channel microphone receiving signal is s All are 48kHz. A Hanning window linear frequency modulation excitation signal with a center frequency f0 of 19kHz, a bandwidth B of 2kHz, and a duration of 4s is selected. The waveform (upper) and spectrum (lower) corresponding to the left column show that the waveform of the excitation signal is relatively smooth and slowly changing, which can reduce the frequency response defects of the smart terminal speaker. The waveform and spectrum of the unprocessed original audio signal received by the dual-channel microphone are shown in the middle column. It can be seen that the ambient noise components with frequencies below 10kHz and above 20kHz are obvious, the jitter of the frequency band where the effective signal is located (18-20kHz) is more severe, and the signal-to-noise ratio is low. After being processed by the noise reduction algorithm of the present invention, the waveform and spectrum of the signal are shown in the right column. It can be seen that the signal-to-noise ratio is enhanced in the entire frequency band and the interference of the acoustic reflection signal is more effectively suppressed. Compared with the waveform and spectrum of the excitation signal in the left column, it can be seen that the noise reduction method of the present invention can efficiently improve the signal-to-noise ratio, improve the accuracy of delay estimation and even sound speed calculation.

[0051] The main design advantages of the present invention lie in the following three points: 1. Linear frequency modulation and pulse compression technology can reduce the impact of additive noise and avoid acoustic attenuation that causes the received effective signal energy to be too low; 2. The filtering operations of the microphone signals at the upper and lower ends are based on the same set of filters, which helps to ensure the timing alignment of the signals, and the spectral subtraction operation is relatively low, which can effectively filter out environmental noise; 3. Before the delay estimation is calculated, the interference of acoustic obstacles is suppressed in the dual-channel signals, effectively improving the accuracy of the delay estimation.

[0052] It is worth noting that the present invention's method for measuring room temperature using a smart terminal based on linear frequency modulation (LFM) acoustic signals achieves robust room temperature measurement without the need for external hardware. The independent processing of dual-channel microphone signals allows for the use of a parallel programming structure, significantly improving the algorithm's computational efficiency. After the two threads complete independent denoising calculations, the speed of sound (c) is calculated based on the acoustic path difference and the relative time delay between the dual-channel microphone signals, yielding an estimated room temperature.

[0053] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the above embodiments do not limit the scope of protection of the present invention in any form. All technical solutions obtained by equivalent substitution, etc., fall within the scope of protection of the present invention. Parts not covered by the present invention are the same as the existing technology or can be implemented using existing technology.

Claims

1. A method for measuring room temperature using an intelligent terminal based on linear frequency modulation sound signals, characterized in that: The following steps are involved: S1: The smart terminal speaker transmits a linear frequency modulation signal x(t); S2: When the speaker transmits the signal, it simultaneously collects the original two-channel audio signal y received by the upper and lower microphones of the smart terminal 10 (t) and y 20 (t), and perform bandpass filtering on it, and then perform environmental noise reduction processing, and then upsample the noise-reduced signal by 1 times to obtain signal y 11 (t) and y 21 (t); S3: Extract x(t), y 11 (t) and y 21 (t) Signal envelope and downsample the envelope signal by 1 / D times to obtain E X , E Y1 and E Y2 ; Using E X Respectively with E Y1 and E Y2 Perform a sliding window cross-correlation search and independently calculate the delay sets {τ1}, {τ2} and intensity sets {α1}, {α2} corresponding to the acoustic obstacle reflection signals at the upper and lower microphones of the smart terminal; S4: respectively in y 11 (t) and y 21 (t) minus the set of time delay {τ} and intensity {α} calculated independently at each microphone, and obtain y after eliminating the interference of acoustic obstacles. 12 (t) and y 22 (t); S5: Calculate y 12 (t) and y 22 The relative time delay Δτ between the two microphones (t) is combined with the acoustic path difference Δd between the upper and lower microphones of the smart terminal to guide the sliding window cross-correlation search to calculate the average sound speed c of the air around the smart terminal; S6: Determine the indoor air temperature T based on the sound velocity-temperature mapping relationship c(T) in linear acoustics and the average sound velocity c of the air around the smart terminal.

2. The method for measuring room temperature using a smart terminal based on a linear frequency modulation sound signal according to claim 1, wherein: In step S1, the linear frequency modulation signal x(t) emitted by the speaker is processed with a Hanning window, the signal duration is t0, ranging from 1 to 5s; the center frequency is f0, ranging from 16 to 20kHz; the bandwidth is B, ranging from 2 to 4kHz; the frequency range is [f0-B / 2, f0+B / 2]; the sampling rate is f s ; and the front end of the signal x(t) is set to a duration of τ n blank period.

3. The method for measuring room temperature using a smart terminal based on a linear frequency modulation acoustic signal according to claim 1, wherein: In the step S2, y 10 (t) and y 20 (t) is the original signal that has not been pre-filtered by the intelligent terminal, and the sampling rate is f s The passband range of the bandpass filter is [f0-B / 2, f0+B / 2], and the filter coefficient is designed by the FIR window function method; at the same time, the environmental noise reduction is performed according to the spectrum subtraction method, and the reference noise segment selected by the spectrum subtraction method is y 10 (t) and y 20 (t) Each initial period [0,τ n ] within the blank noise signal, y 10 (t) and y 20 The corresponding amplitude spectrum of (t) is subtracted from the corresponding reference noise amplitude spectrum, and the noise-reduced signal is restored by inverse Fourier transform combined with the phase spectrum; In addition, the specific operation of I-fold upsampling is as follows: first, I-1 zero values ​​are inserted between every two sampling points of the original signal, and then the interpolated signal is subjected to a cutoff frequency of f s / (2I) is filtered by a low-pass FIR filter to obtain the corresponding up-sampled signal y 11 (t) and y 21 (t).

4. The method for measuring room temperature using a smart terminal based on a linear frequency modulation sound signal according to claim 1, wherein: In step S3, the signal envelope is extracted by using Hilbert transform; the length t0 of the original excitation signal is used as the threshold setting standard. When the delay time Δt of the interference sound is greater than t0 / 2, the interference sound band and the direct sound band can be distinguished; in addition, the specific operation of 1 / D times downsampling is: first, the original signal is subjected to a cutoff frequency of f s / (2D) low-pass FIR filter is used for filtering, and then the filtered signal is extracted at equal intervals, where the interval is D. The extracted signal is the corresponding down-sampled signal; the sliding window cross-correlation search and the calculation of the sound obstacle interference are respectively in E X With E Y1 、E X With E Y2 Between.

5. The method for measuring room temperature using a smart terminal based on a linear frequency modulation sound signal according to claim 1, wherein: In step S3, the sliding window cross-correlation search selects the normalized cross-correlation coefficient as the basis for the signal similarity; specifically, the signal E X Shift, with E Y1 and E Y2 The cross-correlation operation is performed respectively. The number of shift points corresponding to the maximum cross-correlation coefficient is the time delay τ of the signal, and the cross-correlation coefficient corresponds to its signal strength α. In addition, the delay set {τ} and the strength set {α} are calculated independently for the signals at the upper and lower microphones of the smart terminal. The independent calculations are as follows: the excitation signal x(t) is respectively compared with the received signals y at the upper and lower microphones. 11 (t) and y 21 (t) Calculate according to step S3 to obtain: the delay set {τ1} and intensity set {α1} corresponding to the excitation signal and the signal at the upper microphone, and the delay set {τ2} and intensity set {α2} corresponding to the excitation signal and the signal at the lower microphone.

6. The method for measuring room temperature using a smart terminal based on a linear frequency modulation acoustic signal according to claim 1, wherein: In step S4, after eliminating the interference of the acoustic barrier, y 12 The expression of (t) is: where y 12 (t) is the signal after eliminating the acoustic interference, y 11 (t) is the signal containing the acoustic obstacle interference, α n1 is the nth value in the intensity set {α1}, τ n1 is the nth value in the time delay set {τ1}, i is the number of elements in the sets {α1} and {τ1}; y 21 (t) Repeat the above step S4, y 22 The specific expression of (t) is where y 22 (t) is the signal after eliminating the acoustic interference, y 21 (t) is the signal containing the acoustic obstacle interference, α n2 is the nth value in the intensity set {α2}, τ n2 is the nth value in the time delay set {τ2}, and j is the number of elements in the sets {α2} and {τ2}.

7. The method for measuring room temperature using a smart terminal based on a linear frequency modulation sound signal according to claim 1, wherein: In step S5, the shift y 12 (t), and y 22 (t) Perform normalized cross-correlation operation and search for the peak value of the cross-correlation coefficient Corr max , corresponding to the time shift of integer sample points Δn·f s , where f s is the signal y 12 (t) and y 22 The sampling frequency of (t); the cosine function interpolation is used to estimate the sub-precision time shift δ near the peak, denoted by Corr max The correlation coefficients of the two adjacent points on the left and right are Corr1 and Corr2 respectively, and the sub-precision time shift is: in Among them, Corr max is the peak value of the mutual correlation coefficient, Corr1 and Corr2 are Corr max Correlation coefficient between two adjacent points on the left and right; from this, the relative time delay of the two signals can be obtained as Δτ=(Δn+δ)·f s .

8. The method for measuring room temperature using a smart terminal based on a linear frequency modulation acoustic signal according to claim 7, wherein: The guided sliding window cross-correlation search uses the mapping relationship between sound speed and temperature c(T), which is derived from linear acoustic theory: Where c0 is 331.5 m / s, the reference temperature T0 is 273.15 K (corresponding to 0°C), and T is the indoor temperature in °C.

9. The method for measuring room temperature using a smart terminal based on a linear frequency modulation acoustic signal according to claim 8, wherein: In the cross-correlation search of the guided sliding window, the mapping relationship between the speed of sound and the temperature c(T) and the indoor temperature range [T min ,T max ], we can know that the range of indoor air sound speed is [c min ,c max ], combined with the sound path difference Δd between the upper and lower microphones of the smart terminal to limit the sliding window cross-correlation search range: Δd / c max <Δτ<Δd / c min Where Δd is the sound path difference between the upper and lower microphones of the smart terminal, c min is the theoretical minimum value of the indoor air sound speed, c max is the theoretical maximum value of the indoor air sound speed, Δτ is the time delay corresponding to the sliding window cross-correlation search range; based on the above formula, guided search is achieved; the calculation expression of the average sound speed of the air around the smart terminal is c = Δd / Δτ.

10. The method for measuring room temperature using a smart terminal based on a linear frequency modulation acoustic signal according to claim 9, wherein: In step S6, according to the mapping relationship c(T) between the speed of sound and the temperature, T(c) is obtained by an inverse function, and the average speed of sound c of the air around the smart terminal is substituted into the expression to estimate the current room temperature.

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