Self-powered intelligent snoring detection system based on intelligent pillow

Abstract

The invention belongs to the field of intelligent equipment, and particularly relates to a self-powered intelligent snoring detection system based on an intelligent pillow. An existing intelligent pillow has the problem that the recognition rate and accuracy of snoring sound are low. The invention provides a self-powered intelligent snoring detection system based on an intelligent pillow. The system comprises a power supply module, a snore detection module, a self-charging module and a rechargeable battery, the power supply module is electrically connected with the snore detection module and the rechargeable battery; the power supply module is used for supplying power to the snore detection module; the snore detection module is used for collecting an environment original signal when a sleeper sleeps; snore detection is carried out according to the environment original signal, and a snore signal of the sleeper in the sleeping activity process is obtained; and the self-charging module is electrically connected with the rechargeable battery and is used for charging the rechargeable battery when the sleeper sleeps, so that the problem of low recognition rate and accuracy of the snoring sound of the intelligent pillow is solved.

Description

Technical Field

[0001] The invention belongs to the field of intelligent devices, and in particular relates to a self-powered intelligent snoring detection system based on an intelligent pillow. Background Art

[0002] Snoring is a common problem in human sleep, and it becomes more common and often worsens with age. Existing medical research shows that people who snore are more likely to suffer from sleep apnea syndrome. This respiratory disease can interrupt sleep, reduce sleep quality, and even threaten life. It has become an important issue in the field of respiratory diseases.

[0003] In existing contact or non-contact snoring detection systems, snoring extraction is not sensitive enough, there is a lot of noise in the collected signal, and the recognition rate and accuracy of snoring are low. Summary of the invention

[0004] The purpose of the present invention is to solve the problem that the recognition rate and accuracy of snoring in smart pillows are low. A self-powered intelligent snoring detection system based on a smart pillow is provided, comprising:

[0005] Power supply module, snoring detection module, self-charging module and rechargeable battery;

[0006] The power supply module is electrically connected to the snoring detection module and the rechargeable battery;

[0007] The power supply module is used to supply the electric energy of the rechargeable battery to the snoring detection module;

[0008] The snoring detection module is used to collect the original environmental signal when the sleeper is sleeping; and perform snoring detection based on the original environmental signal to obtain the snoring signal when the sleeper is sleeping;

[0009] The self-charging module is electrically connected to the rechargeable battery and is used to charge the rechargeable battery when the sleeper is sleeping.

[0010] The snoring detection module comprises:

[0011] A main signal receiver, a first MEMS compensation receiver, a second MEMS compensation receiver, a first speaker, a second speaker, and a microprocessor;

[0012] The microprocessor is electrically connected to the main signal receiver, the first MEMS compensation receiver, the second MEMS compensation receiver, the first speaker and the second speaker;

[0013] The preferred microprocessor model is ATmega328P;

[0014] The self-charging module is a piezoelectric sensor;

[0015] The specific process of the self-charging module charging the rechargeable battery when the sleeper is sleeping is as follows:

[0016] When a sleeper performs sleeping activities, pressure is generated on the piezoelectric sensor, causing the piezoelectric sensor to deform, and the piezoelectric sensor generates electrical energy to charge the rechargeable battery.

[0017] The snoring detection module is used to collect the original environmental signal when the sleeper is sleeping; and perform snoring detection based on the original environmental signal to obtain the snoring signal when the sleeper is sleeping; the specific process is:

[0018] S1: Collects the original environmental signals when the sleeper is sleeping;

[0019] S2: performing noise elimination processing on the surrounding environment according to the original environmental signal, and then collecting the multi-source snoring signal after the noise elimination processing;

[0020] S3: performing snoring detection on the multi-source snoring signals after noise elimination processing to obtain the snoring signals of the sleeper during sleep activities;

[0021] The specific process of collecting the original environmental signal of the sleeper during sleep activity in S1 is:

[0022] Using a main signal receiver, a first MEMS compensation receiver, and a second MEMS compensation receiver to collect an original environmental signal when the sleeper is performing sleep activities;

[0023] The preferred main signal receiver model is MAX9814.

[0024] The beneficial effects of the present invention are:

[0025] The hardware system of the present invention uses a piezoelectric sensor to generate a circuit to power the system and store energy in a rechargeable battery to maintain the operation of the system. Through three sound receivers, a multi-source snoring signal containing noise is obtained, and the signal is processed through an initial weight. Two speakers emit an anti-phase sound signal to eliminate noise, and then the weights w1 and w2 are updated according to the anti-phase denoised signal, and the updated anti-phase sound signal is output to further reduce the difference signal e(n). The above steps are repeated until the difference between the current weight and the previous weight is close to zero, and the multi-source snoring signal y3(n) after denoising is obtained. y3(n) is input into two decomposition and recombination paths. One path decomposes it into multiple modal signals, and the ratio of the first k correlation factors to the total correlation factor is greater than 90%. The current k value is taken to determine the decomposition into k modal signals, which are composed of a data matrix to obtain a standard data matrix and a matrix P composed of j eigenvectors for signal reconstruction. The second path converts the multi-source snoring signal y3(n) into the frequency domain to obtain two dimensionality reduction matrices E ab 、F ba , iterate the matrix, stop iteration when convergence condition is met, complete decomposition, then perform overlap and sum to obtain reconstructed signal, merge two reconstructed signals to obtain merged signal R i , calculate R i The signal corresponding to the maximum correlation factor is selected from the correlation factors of one modal signal, and the best matching signal is cyclically screened to detect a more realistic snoring signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a schematic diagram of the weight update process of the inverse phase denoising of the present invention;

[0027] Figure 2 It is a schematic diagram of the double decomposition and recombination pathway process of the present invention;

[0028] Figure 3 It is a schematic diagram of the algorithm flow of the present invention;

[0029] Figure 4 It is a time domain schematic diagram of the original signal of the present invention;

[0030] Figure 5 This is a time domain schematic diagram of adding a random noise signal in the present invention;

[0031] Figure 6 It is a time domain schematic diagram of the output result of the present invention. DETAILED DESCRIPTION

[0032] Specific implementation method 1: Combination Figure 1-Figure 6 The present invention is described. The present invention is used in a smart pillow to identify the snoring sound of a sleeper during sleep. The present invention is a self-powered intelligent snoring detection system based on a smart pillow, comprising:

[0033] Power supply module, snoring detection module, self-charging module and rechargeable battery;

[0034] The power supply module is electrically connected to the snoring detection module and the rechargeable battery;

[0035] The power supply module is used to supply the electric energy of the rechargeable battery to the snoring detection module;

[0036] The snoring detection module is used to collect the original environmental signal when the sleeper is sleeping; and perform snoring detection based on the original environmental signal to obtain the snoring signal when the sleeper is sleeping;

[0037] The self-charging module is electrically connected to the rechargeable battery and is used to charge the rechargeable battery when the sleeper is sleeping.

[0038] Specific implementation method 2: The difference between this implementation method and specific implementation method 1 is that:

[0039] The snoring detection module comprises:

[0040] A main signal receiver, a first MEMS compensation receiver, a second MEMS compensation receiver, a first speaker, a second speaker, and a microprocessor;

[0041] The microprocessor is electrically connected to the main signal receiver, the first MEMS compensation receiver, the second MEMS compensation receiver, the first speaker and the second speaker;

[0042] The model of the microprocessor is ATmega328P;

[0043] The self-charging module is a piezoelectric sensor;

[0044] The specific process of the self-charging module charging the rechargeable battery when the sleeper is sleeping is as follows:

[0045] When the sleeper is sleeping, pressure is generated on the piezoelectric sensor, causing the piezoelectric sensor to deform. The piezoelectric sensor generates electrical energy to charge the rechargeable battery.

[0046] The other steps and parameters are the same as those in the first specific implementation.

[0047] Specific implementation method three: This implementation method is different from the specific implementation method one in that:

[0048] The snoring detection module is used to collect the original environmental signal when the sleeper is sleeping; and perform snoring detection based on the original environmental signal to obtain the snoring signal when the sleeper is sleeping; the specific process is:

[0049] S1: Collects the original environmental signals when the sleeper is sleeping;

[0050] S2: performing noise elimination processing on the surrounding environment according to the original environmental signal, and then obtaining a multi-source snoring signal after the noise elimination processing;

[0051] S3: Perform snoring detection on the multi-source snoring signals to obtain the snoring signals of the sleeper during sleep activities.

[0052] The other steps and parameters are the same as those in the first to second embodiments.

[0053] Specific implementation method 4: This implementation method is different from specific implementation methods 1 to 4 in that:

[0054] The specific process of collecting the original environmental signal of the sleeper during sleep activity in S1 is:

[0055] Using a main signal receiver, a first MEMS compensation receiver, and a second MEMS compensation receiver to collect an original environmental signal when the sleeper is sleeping; the original environmental signal includes snoring and noise from multiple sources to form a mixed sound input signal;

[0056] The model of the main signal receiver is MAX9814;

[0057] The other steps and parameters are the same as those in the first to third embodiments.

[0058] Specific implementation method 5: This implementation method is different from specific implementation methods 1 to 4 in that:

[0059] In S2, the surrounding environment is subjected to noise elimination processing according to the original environmental signal, and then a multi-source snoring signal after the noise elimination processing is obtained; the specific process is:

[0060] S2.1: Input the collected original environmental signal x(n) into a microprocessor to obtain a first anti-phase audio signal y1(n) and a second anti-phase audio signal y2(n);

[0061] S2.2: Output the first anti-phase audio signal y1(n) to the first speaker and transmit it to the environment for preliminary noise elimination processing;

[0062] Outputting the second anti-phase audio signal y2(n) to the second speaker and transmitting it into the environment for preliminary noise cancellation processing;

[0063] S2.3: Collect the environment signal e(n) after the preliminary noise elimination processing; update the first anti-phase audio signal y1 according to the environment signal e(n) after the preliminary noise elimination processing r and the second anti-phase audio signal y2 r,

[0064] S2.4: Update the first anti-phase audio signal y1 r The output is sent to the first speaker and emitted into the environment for noise cancellation processing;

[0065] The updated second anti-phase audio signal y2 r Output to the second speaker and transmit to the environment for noise elimination processing; then repeat steps S2.3-S2.4, stop the loop when the condition is met, and then collect the multi-source snoring signal after the noise elimination processing;

[0066] The other steps and parameters are the same as those in Specific Embodiments 1 to 4.

[0067] Specific implementation method 6: This implementation method is different from specific implementation methods 1 to 5 in that:

[0068] In S2.1, the collected original environmental signal x(n) is input into the microprocessor to obtain the first anti-phase audio signal y1(n) and the second anti-phase audio signal y2(n); the specific process is as follows:

[0069] S2.1.1: Preprocess the collected original environmental signal x(n) to obtain a preprocessed mixed sound signal y(n); the specific process is:

[0070] Set preprocessing parameters; sample, normalize and frame the original environmental signal x(n) in turn according to the processing parameters to obtain the preprocessed mixed sound signal y(n)

[0071] The preprocessing parameters include: sampling frequency L=12KHz, frame length of 20ms, frame shift of 120 sampling points and weight function a(n);

[0072] The formula of the weight function a(n) is expressed as

[0073]

[0074] N represents a constant, N=400,

[0075] S2.1.2: Convert the preprocessed mixed sound signal y(n) into a frequency domain signal Y(k);

[0076] S2.1.3: Set a first weight vector w1 and a second weight vector w2, and obtain a first anti-phase audio signal y1(n) and a second anti-phase audio signal y2(n) according to the first weight vector, the second weight vector and the original environmental signal; expressed as:

[0077]

[0078] y1(n)=[w 11 x(n)w 12 x(n)……w 1N x(n)]

[0079] y2(n)=[w 21 x(n)w 22 x(n)……w 2N x(n)]

[0080] Among them, w 1(N-1) represents the element in row N-1 of w1, w 2(N-1) represents the element in the N-1th row of w2, rand(-0.1,0.1) represents a random number in the interval (-0.1,0.1);

[0081] ; Other steps and parameters are the same as those in Specific Implementation Methods 1 to 5.

[0082] Specific implementation method 7: This implementation method is different from specific implementation methods 1 to 6 in that:

[0083] In the step S2.3, the environment signal e(n) after the preliminary noise elimination process is collected; the first anti-phase audio signal y1(n) and the second anti-phase audio signal y2(n) are updated according to the environment signal e(n) after the preliminary noise elimination process, which can be expressed as follows:

[0084] e(n)=x(n)+y1(n)+y2(n)

[0085] y 11 =e(n)*M 11 (n)

[0086] y 12 =e(n)*M 12 (n)

[0087] y 21 =e(n)*M 21 (n)

[0088] y 22 =e(n)*M 22 (n)

[0089]

[0090]

[0091] ρ=0.0001

[0092] y1 r =x(n)*w1 r

[0093] y2 r =x(n)*w2 r

[0094] Where y11, y12, y21 and y22 represent intermediate signals, m represents discrete time, x(m) represents the discrete time index of x(n), a[nm] represents the impulse response of a(n), e(n) represents the error signal, e(m) represents the discrete time signal, and M 11 [nm] means M 11 Compensation signal processing unit impulse response, M 21 [nm] means M 21 Compensation signal processing unit impulse response, M 12 [nm] means M 12 Compensation signal processing unit impulse response, M 22 [bm] stands for M 22 Compensation signal processing unit impulse response, M 11 (n), M 12 (n), M 21 (n) and M 22 (n) represents the compensation signal processing unit, f1(n) represents the first compensation signal, f2(n) represents the second compensation signal, w1 r represents the first weight vector after update, w2 r Represents the updated second weight vector, y1 r represents the updated first anti-phase audio signal, y2 r represents the updated second anti-phase audio signal; μ represents the step size, * represents the convolution operation; r represents the number of updates, and r is a positive integer;

[0095] The satisfying condition for stopping the cycle in S2.4 is expressed by the formula:

[0096] w1 r -w1 r-1 <0.0001

[0097] w2 r -w2 r-1 <0.0001

[0098] The multi-source snoring signal after the noise elimination process is collected; it is expressed by the formula:

[0099] y3(n)=x(n)+y1 r +y2 r

[0100] The other steps and parameters are the same as those in Specific Embodiments 1 to 6.

[0101] Specific implementation eight: This implementation differs from specific implementations one to seven in that:

[0102] In S3, snoring detection is performed on the snoring signals from multiple sources to obtain the snoring signals of the sleeper during sleep activities. The specific process is as follows:

[0103] S3.1: removing the signals other than the frequency band of 300-3400 Hz from the multi-source snoring signal y3(n) to obtain a retained signal, and decomposing the retained signal to obtain a first decomposed signal;

[0104] The retained signal is denoted as y4, and the first decomposed signal is denoted as y g (n);

[0105] S3.2: Decompose the retained signal to obtain a second decomposed signal; the second decomposed signal is represented by y h (n)

[0106] S3.3: Combine the first decomposed signal and the second decomposed signal to obtain a combined signal; the combined signal represents R i ;

[0107] S3.4: Obtaining a snoring signal based on the combined signal and the multi-source snoring signal

[0108] The other steps and parameters are the same as those in Specific Embodiments 1 to 7.

[0109] Specific implementation method 9: This implementation method is different from specific implementation methods 1 to 8 in that:

[0110] The specific process of decomposing the retained signal y4 in S3.1 to obtain the first decomposed signal and the second decomposed signal is as follows:

[0111] S3.1.1: Set the number of multi-channels m d , signal modal bandwidth constraint coefficient α d , the initial value of the center frequency w m , m d is a positive integer; α d =5000; w m =500;

[0112] S3.1.2: According to the set multi-channel number m d Decompose the retained signal y4 into m d signal mode; expressed by the formula:

[0113]

[0114] S1(n), S2(n), S3(n), S i (n), represents the decomposed signal mode;

[0115] S3.1.3: Calculate the correlation factor between the retained signal y4 and each of the decomposed signal modes, where the correlation factor between the i-th signal mode and the retained signal y4 is expressed as The formula is:

[0116]

[0117] represents the mean of the ith signal mode, represents the mean of the retained signal, y4(n) represents the retained signal, N represents the sequence length, N=500,

[0118] S3.1.4: Calculate the maximum relevance factor Corresponding to the optimal K value, the retained signal y4 is decomposed into K disbanded signals according to the optimal K value, and the K disbanded signals are expressed as

[0119] Positive frequency domain phase shift of K disintegrated signals and negative frequency domain phase shift Processing, we get S1(n), S2(n), S3(n), ..., S k (n) single-sided spectrum;

[0120] S3.1.5: Put S1(n), S2(n), S3(n),…,S k The unilateral spectrum of (n) is combined into a data matrix Q, and then the data matrix Q is standardized to obtain a standardized matrix Q std ; expressed as:

[0121]

[0122] Among them, s 11 represents the first coefficient of S1(n), Indicates S k-1 (n) the nth 1 -1 coefficient, n 1 is the number of rows of the data matrix Q, μ is the mean vector of each column of the data matrix Q, and Z d is the standard deviation vector matrix of each column of the data matrix Q, Represents Z d The inverse of

[0123] μ=[μ1 … μ k ]

[0124]

[0125]

[0126] In the formula, σ1, σ k represents the standard deviation vector;

[0127] S3.1.6: Calculate the normalization matrix Q std The data distribution matrix Q c , get the data distribution matrix Q c The matrix eigenvalue λ of k And the corresponding eigenvector v k , the eigenvalue λ k Sort by size from large to small; data distribution matrix Q c The formula is:

[0128]

[0129] Among them, Q std T Indicates Q std The transpose of

[0130] S3.1.7: Calculate the cumulative value of the first z eigenvectors before sorting, and calculate the ratio R of the cumulative value to the total eigenvector; which can be expressed as:

[0131]

[0132] The value range of z is (1, k). The calculation starts from z value 1 and the ratio R of the accumulated value to the total eigenvector is calculated.

[0133] If R≤0.9, the value of z is increased by one and the R value is recalculated.

[0134] If R>0.9, select the eigenvectors corresponding to the first z eigenvectors to form the matrix P; it can be expressed as:

[0135] P=[v1…v z ]

[0136] S3.1.8: By normalizing the matrix Q std Construct the first decomposed signal y with the matrix P g (n); expressed as

[0137] y g (n) = Q std PZ d +μ

[0138] where μ is the mean vector of each column of the data matrix Q, and Z d is the standard deviation vector matrix of each column of the data matrix Q

[0139] In S3.2, the retained signal is decomposed to obtain a second decomposed signal; the specific process is:

[0140] S3.2.1: The retained signal y4 is converted into a frequency domain mixed signal Y4(N) and frame segmented to obtain a frequency domain amplitude spectrum H representing the energy of the mixed signal at different times and frequencies.

[0141] S3.2.2: The frequency domain amplitude spectrum H is divided into the decomposition signal E and the decomposition signal F; the specific process is:

[0142] Set the initial element values ​​of decomposition signal E and decomposition signal F to random numbers

[0143] Update the decomposed signal E and decomposed signal F through the iterative convergence function,

[0144] The iterative convergence function is expressed as:

[0145]

[0146]

[0147] Convergence condition ||H-EF|| is reached Fro <0.01, stop the iteration and get the decomposition signal E and decomposition signal F, || || Fro represents the norm, ← represents the update assignment, log represents the logarithm of the matrix, E T The transpose of E, F T represents the transpose of F

[0148] S3.2.3: Convert the decomposed signal E and the decomposed signal F to the time domain to obtain time domain signals e1 and f1;

[0149] Perform windowing on adjacent frames of time domain signals e1 and f1 to obtain windowed signals e2 and f2;

[0150] Use weight ε(n) to overlap and add the adjacent frames of the windowed signals e2 and f2 to obtain the second decomposed signal y h (n) signal;

[0151] The formula of the weight ε(n) is expressed as

[0152]

[0153] Where N1 is the length of the combined signal sequence, N1 = 400;

[0154] The other steps and parameters are the same as those in Specific Embodiments 1 to 8.

[0155] Specific implementation method 10: This implementation method is different from specific implementation methods 1 to 9 in that:

[0156] In S3.4, a snoring signal is obtained according to the combined signal and the multi-source snoring signal. The specific process is as follows:

[0157] S3.4.1: Combine the signal R i It is divided into T segment signals, and the T segment signal is represented by S R 1(n),S R 2(n),S R 3(n),…S R c (n)…、S R T (n),

[0158] Calculate the combined signal R i The correlation factor with the T segment signal,

[0159] The c-th segment signal S R c (n) and the combined signal R i The correlation factor table between The formula is:

[0160]

[0161] In the formula, R i (n) represents the combined signal, N1 represents the length of the combined signal sequence, and n represents the time variable;

[0162] Select the maximum correlation factor The corresponding segment signal S R c (n) as snoring signal M c ;

[0163] S3.4.3: The snoring signal M c Segmented processing is performed to obtain segmented snoring signals of segment O, wherein the segmented snoring signals of segment O are represented as S O 1(n),S O 2(n),S O 3(n),…S O l (n)…、S O O (n)

[0164] Calculate the correlation factor between the multi-source snoring signal y4(n) and each snoring segment signal;

[0165] The first segment signal S O l (n) and the combined signal R i The correlation factor table between The formula is:

[0166]

[0167] If the relevance factor of the current segment is less than the relevance factor corresponding to the next segment, the current segment is an irrelevant part and is normalized;

[0168] If the correlation factor of the current segment is not less than the correlation factor corresponding to the next segment, the current segment is retained until the correlation factor corresponding to the next segment signal is greater than the correlation factor of the current segment;

[0169] S3.4.4: Combine signal R i After segment processing, an O-segment merged segment signal is obtained, and the O-segment merged segment signal is expressed as: S OR 1(n),S OR 2(n),S OR 3(n),…S OR p (n)…、S OR O (n)

[0170] Calculate the correlation factor between the multi-source snoring signal y3(n) and each merged segment signal;

[0171] The pth segment signal S OR p (n) and the combined signal R i The correlation factor table between The formula is:

[0172]

[0173] Will satisfy the condition The corresponding merged segmented signal replaces the corresponding snoring segmented signal,

[0174] The replaced snoring segmented signals are combined into a snoring signal

[0175] The other steps and parameters are the same as those in Specific Implementations 1 to 9.

[0176] In summary, the hardware system of the present invention uses a piezoelectric sensor to generate a circuit to power the system and stores energy in a rechargeable battery to maintain the operation of the system. Through three sound receivers, a multi-source snoring signal containing noise is obtained, and the signal is processed through an initial weight. Two speakers emit an anti-phase sound signal to eliminate noise, and then the weights w1 and w2 are updated according to the anti-phase denoised signal, and the updated anti-phase sound signal is output to further reduce the difference signal e(n). The above steps are repeated until the difference between the current weight and the previous weight is close to zero, and the denoised multi-source snoring signal y3(n) is obtained. y3(n) is input into two decomposition and recombination paths. One path decomposes it into multiple modal signals, and the ratio of the first k correlation factors to the total correlation factor is greater than 90%. The current k value is taken to determine the decomposition into k modal signals, which are composed of a data matrix to obtain a standard data matrix and a matrix P composed of j eigenvectors for signal reconstruction. The second path converts the multi-source snoring signal y3(n) into the frequency domain to obtain two dimensionality reduction matrices E ab 、F ba , iterate the matrix, stop iteration when convergence condition is met, complete decomposition, then perform overlap and sum to obtain reconstructed signal, merge two reconstructed signals to obtain merged signal R i , calculate R i The signal corresponding to the maximum correlation factor is selected from the correlation factors of one modal signal, and the best matching signal is cyclically screened to detect a more realistic snoring signal.

[0177] The above only describes the preferred embodiments of the present invention. It should be understood that the present invention is not limited to the above specific implementation methods. Although the present invention has been disclosed as above in the preferred embodiments, it is not used to limit the present invention. Any technician familiar with the profession can make some changes or modify the technical contents disclosed above into equivalent embodiments with equivalent changes without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent replacement and improvement made to the above embodiments without departing from the content of the technical solution of the present invention, based on the technical essence of the present invention, within the spirit and principles of the present invention, still fall within the protection scope of the technical solution of the present invention.

Claims

1. A self-powered intelligent snoring detection system based on a smart pillow, characterized in that: The system comprises: Power supply module, snoring detection module, self-charging module and rechargeable battery; The power supply module is electrically connected to the snoring detection module and the rechargeable battery; The power supply module is used to supply the electric energy of the rechargeable battery to the snoring detection module; The snoring detection module is used to collect the original environmental signal when the sleeper is sleeping; and perform snoring detection based on the original environmental signal to obtain the snoring signal when the sleeper is sleeping; The self-charging module is electrically connected to the rechargeable battery and is used to charge the rechargeable battery when the sleeper is sleeping.

2. The self-powered intelligent snoring detection system based on a smart pillow according to claim 1, characterized in that: The snoring detection module comprises: A main signal receiver, a first MEMS compensation receiver, a second MEMS compensation receiver, a first speaker, a second speaker, and a microprocessor; The microprocessor is electrically connected to the main signal receiver, the first MEMS compensation receiver, the second MEMS compensation receiver, the first speaker and the second speaker; The model of the microprocessor is ATmega328P.

3. The self-powered intelligent snoring detection system based on a smart pillow according to claim 2, characterized in that: The snoring detection module is used to collect the original environmental signal when the sleeper is sleeping; and perform snoring detection based on the original environmental signal. The specific process of obtaining the snoring signal when the sleeper is sleeping is as follows: S1: Collects the original environmental signals when the sleeper is sleeping; S2: performing noise elimination processing on the surrounding environment according to the original environmental signal, and then collecting the multi-source snoring signal after the noise elimination processing; S3: Performing snoring detection on the multi-source snoring signals after the noise elimination process to obtain the snoring signals of the sleeper during sleep activities.

4. The self-powered intelligent snoring detection system based on a smart pillow according to claim 3, characterized in that: The specific process of collecting the original environmental signal of the sleeper during sleep activity in S1 is: Using a main signal receiver, a first MEMS compensation receiver, and a second MEMS compensation receiver to collect an original environmental signal when the sleeper is performing sleep activities; The original environmental signal is represented by x(n); n represents a time variable; The model of the main signal receiver is MAX9814.

5. The self-powered intelligent snoring detection system based on a smart pillow according to claim 4, characterized in that: In S2, the surrounding environment is subjected to noise elimination processing according to the original environmental signal, and then a multi-source snoring signal after the noise elimination processing is obtained; the specific process is: S2.1: Input the collected original environmental signal x(n) into a microprocessor to obtain a first anti-phase audio signal y1(n) and a second anti-phase audio signal y2(n); S2.2: Output the first anti-phase audio signal y1(n) to the first speaker and transmit it to the environment for preliminary noise elimination processing; Outputting the second anti-phase audio signal y2(n) to the second speaker and transmitting it into the environment for preliminary noise cancellation processing; S2.3: Use the main signal receiver, the first MEMS compensation receiver and the second MEMS compensation receiver to collect the environmental signal e(r) after the preliminary noise cancellation processing; update the first anti-phase audio signal y1 according to the environmental signal e(n) after the preliminary noise cancellation processing r and the second anti-phase audio signal y2 r ; S2.4: Update the first anti-phase audio signal y1 r The output is sent to the first speaker and emitted into the environment for noise cancellation processing; The updated second anti-phase audio signal y2 r The output is sent to a second speaker and emitted into the environment for noise cancellation processing; Then, steps S2.3-S2.4 are repeated, and the cycle is stopped when the conditions are met, and then the multi-source snoring signal after noise elimination processing is acquired.

6. The self-powered intelligent snoring detection system based on a smart pillow according to claim 5, characterized in that: In S2.1, the collected original environmental signal x(n) is input into the microprocessor to obtain the first anti-phase audio signal y1(n) and the second anti-phase audio signal y2(n); the specific process is as follows: S2.1.1: Preprocess the collected original environmental signal x(n) to obtain a preprocessed mixed sound signal y(n); the specific process is: Set preprocessing parameters; sample, normalize and frame the original environmental signal x(n) in turn according to the processing parameters to obtain the preprocessed mixed sound signal y(n) The preprocessing parameters include: sampling frequency L=12KHz, frame length of 20ms, frame shift of 120 sampling points and weight function a(n); The formula of the weight function a(n) is expressed as N represents the sequence length; S2.1.2: Set a first weight vector w1 and a second weight vector w2, and obtain a first anti-phase audio signal y1(n) and a second anti-phase audio signal y2(n) according to the first weight vector, the second weight vector and the original environmental signal; expressed as follows: y1(n)=[w 11 x(n)w 12 x(n)……w 1N x(n)] y2(n)=[w 21 x(n)w 22 x(n)……w 2N x(n)] Among them, w 1(N-1) represents the element in row N-1 of w1, w 2(N-1) Represents the element in the N-1th row of w2, and rand(-0.1,0.1) means taking a random number in the interval (-0.1,0.1).

7. The self-powered intelligent snoring detection system based on a smart pillow according to claim 6, characterized in that: In S2.3, the first anti-phase audio signal y1(n) and the second anti-phase audio signal y2(n) are updated according to the environment signal e(n) after the preliminary noise elimination process, which can be expressed as follows: e(n)=x(n)+y1(n)+y2(n) y 11 =e(n)*M 11 (n) y 12 =e(n)*M 12 (n) y 21 =e(n)*M 21 (n) y 22 =e(n)*M 22 (n) ρ=0.0001 y1 r =x(n)*w1 r y2 r =x(n)*w2 r In the formula, y 11 ,y 12 ,y 21 and 22 represents the intermediate signal, m represents the discrete time index, x(m) represents the discrete time signal, a[nm] represents the impulse response of a(n), e(m) represents the discrete time signal, M 11 (nm) represents M 11 Compensation signal processing unit impulse response, M 21 (nm) represents M 21 Compensation signal processing unit impulse response, M 12 (nm) represents M 12 Compensation signal processing unit impulse response, M 22 (nm) represents M 22 Compensation signal processing unit impulse response, M 11 (n), M 12 (n), M 21 (n) and M 22 (n) represents the compensation signal processing unit, f1(n) represents the first compensation signal, f2(n) represents the second compensation signal, w1 r represents the first weight vector after update, w2 r Represents the updated second weight vector, y1 r represents the updated first anti-phase audio signal, y2 r represents the updated second anti-phase audio signal; μ represents the step size, * represents the convolution operation; r represents the number of updates, and r is a positive integer; The satisfying condition for stopping the cycle in S2.4 is expressed by the formula: w1 r -w1 r-1 <0.0001 w2 r -w2 r-1 <0.0001 The multi-source snoring signal after the noise elimination process is collected; it is expressed by the formula: y3(n)=x(n)+y1 r +y2 r 。 8. The self-powered intelligent snoring detection system based on a smart pillow according to claim 7, characterized in that: In S3, snoring detection is performed on the snoring signals from multiple sources to obtain the snoring signals of the sleeper during sleep activities. The specific process is as follows: S3.1: removing the signals other than the frequency band of 300-3400 Hz from the multi-source snoring signal y3(n) to obtain a retained signal, and decomposing the retained signal to obtain a first decomposed signal; The retained signal is denoted as y4, and the first decomposed signal is denoted as y g (n); S3.2: Decompose the retained signal to obtain a second decomposed signal; the second decomposed signal is represented by y h (n) S3.3: Combine the first decomposed signal and the second decomposed signal to obtain a combined signal; the combined signal represents R i ; S3.4: Obtain a snoring signal according to the combined signal and the multi-source snoring signals.

9. The self-powered intelligent snoring detection system based on a smart pillow according to claim 8, characterized in that: The specific process of decomposing the retained signal y4 in S3.1 to obtain the first decomposed signal and the second decomposed signal is as follows: S3.1.1: Set the number of multi-channels m d , signal modal bandwidth constraint coefficient α d , the initial value of the center frequency w m , m d is a positive integer; α d =5000; w m =500; S3.1.2: According to the set multi-channel number m d Decompose the retained signal y4 into m d signal mode; expressed by the formula: S1(n), S2(n), S3(n), S i (n), represents the decomposed signal mode; S3.1.3: Calculate the correlation factor between the retained signal y4 and each of the decomposed signal modes, where the correlation factor between the i-th signal mode and the retained signal y4 is expressed as The formula is: represents the mean of the ith signal mode, represents the mean of the retained signal, y4(n) represents the retained signal, N represents the sequence length, N=500, S3.1.4: Calculate the maximum relevance factor Corresponding to the optimal K value, the retained signal y4 is decomposed into K disbanded signals according to the optimal K value, and the K disbanded signals are expressed as Positive frequency domain phase shift of K disintegrated signals and negative frequency domain phase shift Processing, we get S1(n), S2(n), S3(n), ..., S k (n) single-sided spectrum; S3.1.5: Put S1(n), S2(n), S3(n),…,S k The unilateral spectrum of (n) is combined into a data matrix Q, and then the data matrix Q is standardized to obtain a standardized matrix Q std ; expressed as: Among them, s 11 represents the first coefficient of S1(n), Indicates S k-1 (n) the nth 1 -1 coefficient, n 1 is the number of rows of the data matrix Q, μ is the mean vector of each column of the data matrix Q, and Z d is the standard deviation vector matrix of each column of the data matrix Q, Represents Z d The inverse of μ=[μ1…μ k ] Where, σ1, σ k represents the standard deviation vector; S3.1.6: Calculate the normalization matrix Q std The data distribution matrix Q c , get the data distribution matrix Q c The matrix eigenvalue λ of k And the corresponding eigenvector v k , the eigenvalue λ k Sort by size from large to small; data distribution matrix Q c The formula is: Among them, Q std T Indicates Q std The transpose of S3.1.7: Calculate the cumulative value of the first z eigenvectors before sorting, and calculate the ratio R of the cumulative value to the total eigenvector; which can be expressed as: The value range of z is (1, k). The calculation starts from z value 1 and the ratio R of the accumulated value to the total eigenvector is calculated. If R≤0.9, the value of z is increased by one and the R value is recalculated. If R>0.9, select the eigenvectors corresponding to the first z eigenvectors to form the matrix P; it can be expressed as: P=[v1…v z ] S3.1.8: By normalizing the matrix Q std Construct the first decomposed signal y with the matrix P g (n); expressed as and g (n)=Q std PZ d +μ where μ is the mean vector of each column of the data matrix Q, and Z d is the standard deviation vector matrix of each column of the data matrix Q In S3.2, the retained signal is decomposed to obtain a second decomposed signal; the specific process is: S3.2.1: The retained signal y4 is converted into a frequency domain mixed signal Y4(N) and frame segmented to obtain a frequency domain amplitude spectrum H representing the energy of the mixed signal at different times and frequencies. S3.2.2: The frequency domain amplitude spectrum H is divided into the decomposition signal E and the decomposition signal F; the specific process is: Set the initial element values ​​of decomposition signal E and decomposition signal F to random numbers Update the decomposed signal E and decomposed signal F through the iterative convergence function, The iterative convergence function is expressed as: Convergence condition ||H-EF|| is reached Fro <0.01, stop the iteration and get the decomposition signal E and decomposition signal F, || || Fro represents the norm, ← represents the update assignment, log represents the logarithm of the matrix, E T The transpose of E, F T represents the transpose of F; S3.2.3: Convert the decomposed signal E and the decomposed signal F to the time domain to obtain time domain signals e1 and f1; Perform windowing on adjacent frames of time domain signals e1 and f1 to obtain windowed signals e2 and f2; Use weight ε(n) to overlap and add the adjacent frames of the windowed signals e2 and f2 to obtain the second decomposed signal y h (n) signal; The formula of the weight ε(n) is expressed as Wherein N1 is the length of the combined signal sequence, N1=400.

10. The self-powered intelligent snoring detection system based on a smart pillow according to claim 9, characterized in that: In S3.4, a snoring signal is obtained according to the combined signal and the multi-source snoring signal. The specific process is as follows: S3.4.1: Combine the signal R i (n) is divided into T segment signals, and the T segment signal is represented by S R 1(n),S R 2(n),S R 3(n),…S R c (n)…、S R T (n), Calculate the combined signal R i The correlation factor between the T segment signal, The c-th segment signal S R c (n) and the combined signal R i The correlation factor table between (n) is The formula is: In the formula, R i (n) represents the combined signal, N1 represents the combined signal length, Select the maximum correlation factor The corresponding segment signal S R c (n) as snoring signal M c ; S3.4.3: The snoring signal M c After segment processing, an O-segment snoring signal is obtained, where O=T. The O-segment snoring signal is represented by S O 1(n),S O 2(n),S O 3(n),…S O l (n)…、S O O (n); Calculate the correlation factor between the multi-source snoring signal y4(n) and each snoring segment signal; The first segment signal S O l (n) and the combined signal R i The correlation factor table between The formula is: If the relevance factor of the current segment is less than the relevance factor corresponding to the next segment, the current segment is an irrelevant part and is normalized; If the correlation factor of the current segment is not less than the correlation factor of the next segment, the current segment is retained until the correlation factor corresponding to the next segment signal is greater than the correlation factor of the current segment; finally, the maximum correlation factor is obtained. S3.4.4: Combine the signal R i After segment processing, an O-segment merged segment signal is obtained, and the O-segment merged segment signal is expressed as: S OR 1(n),S OR 2(n),S OR 3(n),…S OR p (n)…、S OR O (n); Calculate the correlation factor between the multi-source snoring signal y4(n) and each merged segment signal; The pth segment signal S OR p (n) and the combined signal R i The correlation factor table between The formula is: Will satisfy the condition The corresponding merged segmented signals replace the corresponding snoring segmented signals, and the replaced snoring segmented signals form a snoring signal.

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