Filter bank frequency multiplication based on bci signal for suppressing heart rate calculation method, device, equipment and medium

Abstract

The application discloses a filter bank frequency multiplication suppression heart rate calculation method and device based on a BCG signal, equipment and a medium, and the method comprises the following steps: collecting a multi-channel BCG signal and performing window processing; calculating a body movement index based on a BCG voltage amplitude or an energy mutation, and marking a low-confidence window; performing channel quality scoring on a non-low-confidence window, and obtaining a reference BCG signal through weighted fusion; decomposing the reference BCG signal to obtain a plurality of sub-band BCG signals; generating a candidate heart rate and a confidence on each sub-band by combining a peak spacing method, an autocorrelation method and a frequency domain spectrum peak method; after clustering the candidate heart rate, weighting and correcting the candidate cluster score according to the sub-band source; performing harmonic consistency discrimination, when the frequency multiplication candidate is dominant and the fundamental frequency candidate has more than a threshold of supporting evidence in the low-frequency sub-band, performing frequency multiplication back-off adjustment scoring; combining the last window output to perform continuity constraint and suppress frequency multiplication jump. The application can actively identify and correct the frequency multiplication misjudgment caused by the harmonic energy being dominant.

Description

Technical Field

[0001] This invention relates to the field of medical technology, and in particular to a method, apparatus, device, and medium for calculating heart rate based on filter bank frequency doubling suppression of BCG signals. Background Technology

[0002] BCG (Ballistocardiogram / surface micro-vibration signal) signals reflect the weak mechanical vibrations caused by the human heartbeat, and have advantages such as being non-contact, imperceptible, and suitable for long-term sleep monitoring. Mattress-type piezoelectric film sensors can collect BCG signals and calculate heart rate without affecting the user's sleep, and have wide applications in sleep health monitoring, health care, and other fields.

[0003] However, due to factors such as the transmission characteristics of the mattress medium, changes in body position, superposition of respiratory components, and micro-body movements, BCG waveforms often exhibit phenomena such as double peaks, enhanced secondary peaks, and waveform distortion. This weakens the fundamental frequency component of the heartbeat cycle while enhancing the harmonic components, causing heart rate algorithms to easily lock onto the second or third harmonic of the fundamental frequency, resulting in harmonic misjudgments. For example, a true heart rate of 40 bpm may be calculated as 80 bpm or 120 bpm. Existing technologies commonly employing single bandpass filtering with peak detection or single autocorrelation / spectral main peak detection methods are prone to persistent misjudgments when harmonic energy is dominant, affecting the stability and reliability of applications such as mattress-based sleep monitoring.

[0004] Existing BCG heart rate calculation schemes typically employ peak detection after a single bandpass filter, or determine the main period using single autocorrelation and spectral main peak detection. These methods are susceptible to variations in the mattress medium transfer function, body position, breathing interference, and waveform bimodalities in mattress-based sensing scenarios. This causes the fundamental frequency component to weaken significantly within certain time windows, while the second or third harmonic components become the dominant peaks, leading to misjudgments of heart rate harmonics. Furthermore, without cross-band consistency verification and historical continuity constraints, this misjudgment may persist for multiple windows, resulting in a persistently elevated or abruptly increased heart rate curve that does not conform to physiological realities. Summary of the Invention

[0005] In view of the above problems, the present invention is proposed to provide a method, apparatus, device and medium for calculating heart rate based on filter bank frequency doubling suppression of BCG signals that overcomes or at least partially solves the above problems.

[0006] Other features and advantages of the invention will become apparent from the following detailed description, or may be learned in part by practice of the invention.

[0007] According to a first aspect of the present invention, a method for calculating filter bank frequency octave suppression heart rate based on BCG signals is provided, comprising: S1: Acquire the BCG signal of the mattress-type multi-channel piezoelectric thin film sensor, perform windowing processing on the BCG signal, and obtain a discrete sampling sequence formed by discrete sampling values ​​of multiple time windows; S2: Based on the discrete sampling sequence within each time window, calculate the body motion index of the BCG signal, and mark the time window as a low confidence window and a non-low confidence window according to whether the body motion index exceeds a preset threshold. S3: Perform channel quality scoring on the multi-channel BCG signals of the non-low confidence window, and after weighted fusion of the BCG signals based on the scoring results, determine the BCG signal of the target channel as the reference BCG signal; S4: The reference BCG signal is input into a filter bank and filtered and decomposed to obtain multiple sub-band BCG signals; S5: For each sub-band BCG signal, at least two of the following methods are combined: peak spacing method, autocorrelation method and frequency domain spectral peak method to generate candidate heart rates and corresponding candidate confidence scores. S6: Cluster all the sub-band BCG signals and all the generated candidate heart rates according to a preset tolerance to obtain at least one candidate cluster and its representative value. Calculate the initial score of the candidate cluster. Statistically analyze the sub-band source distribution, generation method source distribution, and candidate confidence of each candidate cluster to generate supporting evidence for that candidate cluster. Based on the supporting evidence, perform a weighted correction on the initial score of the candidate cluster to obtain a weighted candidate cluster score. In the weighted correction, the weight of low-frequency sub-bands is higher than the weight of high-frequency sub-bands. S7: Perform harmonic consistency discrimination on the candidate clusters, detect whether there is a first predetermined harmonic relationship between the representative values ​​of each candidate cluster, and for the fundamental frequency candidate clusters and harmonic candidate clusters that have the first predetermined harmonic relationship, perform backoff determination based on the supporting evidence, and perform harmonic backoff processing according to the backoff determination result; S8: After the candidate clusters have undergone frequency doubling back-off processing, a continuity constraint is imposed on the previous output heart rate of the final output of the previous time window. Based on whether there is a second predetermined frequency doubling relationship between the representative value of the current candidate cluster and the previous output heart rate, the candidate heart rates of the current time window are filtered to determine the final heart rate output of the current window.

[0008] In some embodiments of the present invention, calculating the body motion index of the BCG signal based on the discrete sampling sequence within each time window includes at least one of the following: calculating the difference between the maximum and minimum values ​​of the discrete sampled values ​​corresponding to the BCG signal within each time window to obtain a peak-to-peak value index; calculating the sum of squares of the discrete sampled values ​​corresponding to each sampling point of the BCG signal within each time window to obtain a window energy index; calculating the sum of squares of the differences between the discrete sampled values ​​corresponding to adjacent sampling points within each time window to obtain a first-order differential energy index; and when the body motion index exceeds a preset threshold of the corresponding type, the time window is marked as a low-confidence window.

[0009] In some embodiments of the present invention, the step of performing channel quality scoring on the multi-channel BCG signals of the non-low confidence window, and then weighting and fusing the BCG signals according to the scoring results to determine the BCG signal of the target channel as the reference BCG signal includes: Channel quality scoring is performed on the multi-channel BCG signal with non-low confidence window, and the preliminary quality score and preliminary candidate heart rate of each channel are obtained respectively. The median of the preliminary candidate heart rate for all channels is calculated. The preliminary quality score is corrected based on the fusion weight and the median. The maximum value in the corrected preliminary quality score is determined, the channel containing the maximum value is selected as the target channel, and the BCG signal of the target channel is determined as the reference BCG signal.

[0010] In some embodiments of the present invention, the reference BCG signal input is filtered and decomposed by a filter bank consisting of multiple partially overlapping bandpass filters to obtain multiple sub-band BCG signals.

[0011] In some embodiments of the present invention, in step S5: When generating candidate heart rates using the peak spacing method, the peak value of the BCG signal is detected and the coefficient of variation of the adjacent peak spacing sequence is calculated as the candidate confidence level. The smaller the coefficient of variation, the higher the confidence level. When generating candidate heart rates using the autocorrelation method, the autocorrelation function of the BCG signal is calculated, and the first significant peak outside of zero delay is selected. The amplitude of this significant peak is used as the candidate confidence level. When generating candidate heart rates using the frequency domain spectral peak method, the power spectrum is obtained by performing a Fourier transform on the BCG signal. The main spectral peak is then searched within a preset frequency range, and the ratio of the main peak power to the secondary peak power or the spectral kurtosis is used as the candidate confidence level.

[0012] In some embodiments of the present invention, calculating the initial score of the candidate cluster includes: obtaining the initial score of the candidate cluster by calculating the sum of the candidate confidence scores of all candidate heart rates within the candidate cluster.

[0013] In some embodiments of the present invention, before clustering all the sub-band BCG signals and all the generated candidate heart rates according to a preset tolerance, the method includes: performing a rationality screening on the candidate heart rates, determining whether the candidate heart rates are within a preset rationality threshold range, and removing all candidate heart rates outside the preset rationality threshold range.

[0014] According to a second aspect of the present invention, a filter bank frequency doubling suppression heart rate calculation device based on BCG signals is provided, the device comprising: A multi-channel BCG acquisition module is used to acquire BCG signals from a mattress-type multi-channel piezoelectric film sensor, and to perform windowing processing on the BCG signals to obtain a discrete sampling sequence formed by discrete sampled values ​​of multiple time windows. The windowing processing module is used to calculate the body motion index of the BCG signal based on the discrete sampling sequence within each time window, and to mark the time window as a low confidence window and a non-low confidence window according to whether the body motion index exceeds a preset threshold. The motion gating module is used to perform channel quality scoring on multi-channel BCG signals with non-low confidence windows, and to determine the target channel's BCG signal as the reference BCG signal after weighted fusion based on the scoring results. The signal decomposition module is used to filter and decompose the reference BCG signal into a filter bank to obtain multiple sub-band BCG signals. The candidate heart rate generation module is used to generate candidate heart rates and corresponding candidate confidence scores for each sub-band BCG signal by combining at least two of the following methods: peak spacing method, autocorrelation method and frequency domain spectral peak method. The clustering processing module is used to cluster all the sub-band BCG signals and all the generated candidate heart rates according to a preset tolerance to obtain at least one candidate cluster and its representative value, calculate the initial score of the candidate cluster, statistically analyze the sub-band source distribution, generation method source distribution and candidate confidence of the candidate heart rate of each candidate cluster to generate supporting evidence for the candidate cluster, and perform weighted correction on the initial score of the candidate cluster based on the supporting evidence to obtain a weighted candidate cluster score, wherein the weight of low frequency sub-bands is higher than the weight of high frequency sub-bands during weighted correction; The frequency doubling backoff processing module is used to perform harmonic consistency discrimination on the candidate clusters, detect whether there is a first predetermined frequency doubling relationship between the representative values ​​of the candidate clusters corresponding to each candidate cluster, and perform backoff determination based on the supporting evidence for the fundamental frequency candidate clusters and frequency doubling candidate clusters that have the first predetermined frequency doubling relationship, and perform frequency doubling backoff processing according to the backoff determination result. The heart rate output module is used to perform continuity constraints on the candidate clusters after frequency doubling back-off processing and the previous output heart rate of the final output of the previous time window, and to filter the candidate heart rates of the current time window based on whether there is a second predetermined frequency doubling relationship between the representative value of the current candidate cluster and the previous output heart rate, so as to determine the final heart rate output of the current window.

[0015] According to a third aspect of the present invention, a computer device is provided, including a processor and a memory, the memory storing computer program instructions executable by the processor, wherein when the processor executes the computer program instructions, it implements the instructions as described in any of the above methods.

[0016] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein computer program instructions are stored therein, the computer program instructions being loaded and executed by a processor to perform the operations performed by the method described in any of the preceding claims.

[0017] The technical solutions provided in the embodiments of the present invention have at least the following technical effects or advantages: This invention provides a method, apparatus, device, and medium for calculating heart rate based on BCG signal frequency harmonic suppression using a filter bank. The method decomposes the BCG signal into multiple partially overlapping sub-bands using a filter bank. On each sub-band, three independent methods—peak spacing, autocorrelation, and frequency domain spectral peaks—are combined to generate candidate heart rates and confidence levels. Furthermore, through rules such as clustering and sub-band source weighting, harmonic consistency discrimination and frequency harmonic backoff, and continuity constraints, a multi-level verification closed loop is formed. This mechanism can actively identify and correct frequency harmonic misjudgments caused by harmonic energy dominance.

[0018] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A flowchart illustrating a method for calculating heart rate suppression based on filter bank frequency doubling of BCG signals, provided in an embodiment of the present invention; Figure 2The reference chart shows the fluctuations of the body motion index for low-confidence and non-low-confidence windows. Figure 3 This is a reference schematic diagram of multiple sub-band BCG signals obtained by filtering and decomposition; Figure 4 A schematic diagram of the principle structure of a filter bank frequency doubling suppression heart rate calculation device based on BCG signal provided in an embodiment of the present invention; Figure 5 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0021] Exemplary embodiments of this application will now be described in more detail with reference to the accompanying drawings.

[0022] The accompanying drawings illustrate various structural schematics according to embodiments of this application. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0023] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. In the context of this application, similar or identical parts may be represented by the same or similar reference numerals.

[0024] To better understand the above technical solutions, the following will describe the above technical solutions in detail with reference to specific implementation methods. It should be understood that the embodiments of this application and the specific features in the embodiments are detailed descriptions of the technical solutions of the present invention, rather than limitations on the technical solutions of the present invention. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0025] Figure 1 This is a flowchart illustrating a method for calculating heart rate based on filter bank frequency doubling suppression using BCG signals, as provided in an embodiment of the present invention. Figure 1 As shown, the filter bank frequency octave suppression heart rate calculation method based on BCG signals includes the following steps: S1: Acquire the BCG signal of the mattress-type multi-channel piezoelectric thin film sensor, perform windowing processing on the BCG signal, and obtain a discrete sampling sequence formed by discrete sampling values ​​of multiple time windows; In this embodiment of the invention, the BCG signal (ball-bed graph signal) of the human body is collected by a piezoelectric thin film sensor with multiple channels laid in the mattress. The number of channels m is, for example, 3. In other embodiments, other numbers can be selected according to actual application requirements.

[0026] When acquiring the BCG signal, this embodiment of the invention performs analog-to-digital conversion at a sampling rate of 200Hz to obtain a discrete sampling sequence formed by discrete sampled values ​​of multiple time windows. The discrete sampled values ​​can be, for example, voltage values ​​(voltage amplitudes), which realizes the conversion of the analog signal of the BCG signal into a discrete digital signal that is easy to process later.

[0027] In this embodiment of the invention, the BCG signal is processed by windowing. The length of each time window is, for example, 6 seconds. When performing analog-to-digital conversion at a sampling rate of 200Hz, it contains 200×6=1200 sampling points. At the same time, in order to maintain a smooth transition of heart rate output, the present invention sets a 3-second overlap between adjacent time windows, that is, the heart rate is calculated every 3 seconds. This overlapping window method ensures the continuity of heart rate changes and avoids data loss at the window boundaries.

[0028] S2: Based on the discrete sampling sequence within each time window, calculate the body motion index of the BCG signal, and mark the time window as a low confidence window and a non-low confidence window according to whether the body motion index exceeds a preset threshold. Combination Figure 2 The diagram shows the fluctuations of body motion indicators in low-confidence and non-low-confidence windows. Embodiments of this invention can collect these body motion indicators in various ways, including peak-to-peak value indicators, window energy indicators, first-order differential energy indicators, or combinations thereof. In this embodiment, the body motion indicators of the BCG signal are calculated based on the discrete sampling sequence within each time window, for example: the peak-to-peak value indicator is obtained by calculating the difference between the maximum and minimum values ​​of the discrete sampled values ​​corresponding to the BCG signal within each time window; the window energy indicator is obtained by calculating the sum of squares of the discrete sampled values ​​corresponding to each sampling point of the BCG signal within each time window; the first-order differential energy indicator is obtained by calculating the sum of squares of the differences between the discrete sampled values ​​corresponding to adjacent sampling points within each time window; and when the body motion indicator exceeds a preset threshold of the corresponding type (i.e., the sensor signal is at full scale, and the collected body motion indicator exceeds the sensor's range), the time window is marked as a low-confidence window, and the remaining time windows are determined as non-low-confidence windows, which can be used for subsequent steps.

[0029] In this embodiment of the invention, for low-confidence windows, since the quality of their window signals is unreliable, the strategy adopted is: when the confidence level of the highest candidate is less than the corresponding body movement threshold... Output the heart rate from the previous window in real time; or use a limit update strategy to restrict the heart rate update step size to no more than [the previous window's heart rate]. (Motion window limit update step size (bpm)).

[0030] S3: Perform channel quality scoring on the multi-channel BCG signals of the non-low confidence window, and after weighted fusion of the BCG signals based on the scoring results, determine the BCG signal of the target channel as the reference BCG signal; This invention provides an embodiment of channel quality scoring for multi-channel BCG signals with non-low confidence windows. Based on the scoring results, the BCG signals are weighted and fused to determine the target channel's BCG signal as a reference BCG signal. This includes: performing channel quality scoring on the multi-channel BCG signals with non-low confidence windows; obtaining a preliminary quality score and preliminary candidate heart rate for each channel; calculating the median of the preliminary candidate heart rates for all channels; correcting the preliminary quality scores based on the fusion weights and the median; determining the maximum value in the corrected preliminary quality scores; selecting the channel containing the maximum value as the target channel; and determining the target channel's BCG signal as the reference BCG signal.

[0031] Calculate the preliminary quality score for each channel separately. (For non-low confidence windows, preliminary quality scores are further calculated for each of the three BCG signals; let the BCG signal of the m-th channel be...) Its channel quality score It is composed of the rationality of subband energy, the effectiveness of peak detection, and the prominence of autocorrelation peaks: ; in, This reflects whether the effective bandwidth energy ratio of BCG is reasonable. This reflects whether the number, interval, and heart rate range of detected candidate J peaks conform to physiological constraints. Reflects whether the periodicity of the signal is significant near the heartbeat cycle; the weights satisfy... Ultimately based on The three-channel signals are ranked by quality, and the channel with the highest score is selected (or a multi-channel weighted fusion signal is constructed based on the score), along with preliminary candidate heart rates for each channel. For three channels , , The corresponding preliminary quality scores were calculated respectively. , , and their respective preliminary candidate heart rates , , ; Calculate preliminary candidate heart rates for all channels the median of Then there is ; According to the formula The preliminary quality score is revised, where the coefficients are... The value range is, for example, 0.2 to 0.5, and the preliminary quality score after correction is selected. The maximum value in the range is selected as the target channel, and the BCG signal of the target channel is determined as the reference BCG signal. Alternatively, the reference BCG signal is obtained by weighted fusion of the multi-channel signals according to the corrected score normalization weight.

[0032] ; In the formula, The reference BCG signal is obtained by weighted fusion of n channels. Let m be the discrete sampling sequence of the m-th channel. For the set of target channels, For the weighted fusion, and Positive correlation and normalization.

[0033] S4: The reference BCG signal is input into a filter bank and filtered and decomposed to obtain multiple sub-band BCG signals; In this embodiment of the invention, the reference BCG signal is input into a filter bank consisting of multiple partially overlapping bandpass filters (FIR filters) for filtering and decomposition to obtain multiple sub-band BCG signals, combined with... Figure 3The diagram shows a reference representation of multiple sub-band BCG signals obtained through filtering and decomposition. For example, sub-band 1 has a frequency range of 0.6–1.2 Hz, sub-band 2 has a frequency range of 0.9–1.8 Hz, sub-band 3 has a frequency range of 1.2–2.4 Hz, and sub-band 4 has a frequency range of 1.8–3.2 Hz. Each filter is responsible for retaining information from a specific frequency band in the signal while filtering out other frequencies. Furthermore, through partial overlap, the signal frequency components transition smoothly from one sub-band to an adjacent sub-band without information loss due to band gaps, ensuring that the entire heart rate frequency band is covered smoothly and continuously. The frequency range corresponding to the normal human heart rate is approximately 0.5–4 Hz (i.e., 30–240 Hz). The energy of the fundamental frequency (true heart rate) may be concentrated in a lower frequency band (such as sub-band 1), while the energy of its harmonic frequencies (2 times, 3 times the heart rate) will naturally fall into a higher frequency band (such as sub-band 3, sub-band 4). In this embodiment of the invention, by decomposing the signal into different sub-bands, the energy strength of the fundamental frequency and harmonic frequency components can be observed separately, providing a key basis for subsequent harmonic frequency suppression. For example, when the true heart rate is 40 bpm (0.67 Hz), its fundamental frequency energy is mainly in sub-band 1, while the harmonic frequency energy of 80 bpm (1.33 Hz) may be in sub-band 2 or 3.

[0034] S5: For each sub-band BCG signal, at least two of the following methods are combined: peak spacing method, autocorrelation method and frequency domain spectral peak method to generate candidate heart rates and corresponding candidate confidence scores. Since a single heart rate extraction method may have limitations on each sub-band signal, this embodiment of the invention combines at least two different methods to calculate candidate heart rates in parallel on each sub-band, and each candidate heart rate is accompanied by a candidate confidence score reflecting its reliability to improve robustness. Specifically: When generating candidate heart rates using the peak spacing method, the peak value of the BCG signal is detected and the coefficient of variation of the adjacent peak spacing sequence is calculated as the candidate confidence level. The smaller the coefficient of variation, the higher the confidence level. When generating candidate heart rates using the autocorrelation method, the autocorrelation function of the BCG signal is calculated, and the first significant peak outside of zero delay is selected. The amplitude of this significant peak is used as the candidate confidence level, and the autocorrelation delay search range is limited to 67 to 343 sampling points, corresponding to a heart rate of 35-180 bpm. When generating candidate heart rates using the frequency domain spectral peak method, the power spectrum is obtained by performing a Fourier transform on the BCG signal. The main spectral peak is searched within a preset frequency range (e.g., 0.583-3.0 Hz), and the ratio of the main peak power to the secondary peak power or the spectral kurtosis is used as the candidate confidence level.

[0035] The peak spacing method, autocorrelation method, and frequency domain spectral peak method are existing conventional calculation methods. The corresponding calculation parameters can be adjusted according to the actual situation to suit the corresponding scenario requirements. This embodiment of the invention does not limit this.

[0036] S6: Cluster all the sub-band BCG signals and all the generated candidate heart rates according to a preset tolerance to obtain at least one candidate cluster and its representative value. Calculate the initial score of the candidate cluster. Statistically analyze the sub-band source distribution, generation method source distribution, and candidate confidence of each candidate cluster to generate supporting evidence for that candidate cluster. Based on the supporting evidence, perform a weighted correction on the initial score of the candidate cluster to obtain a weighted candidate cluster score. In the weighted correction, the weight of low-frequency sub-bands is higher than the weight of high-frequency sub-bands. Based on a large number of candidate heart rates generated by various methods in step S5 on each subband, among the current large number of candidate heart rates, the candidate heart rate values ​​representing the same physiological heart rate are relatively close, while due to incorrect estimation or frequency doubling relationship, the candidate heart rate values ​​will differ greatly. In this embodiment of the invention, candidate clusters are obtained by clustering and weighting, and the cluster scores are corrected by weighting according to the source of the subband.

[0037] Specifically, in this embodiment of the invention, candidate heart rates with differences less than a preset tolerance (e.g., ±3 bpm or ±5%) are grouped into a candidate cluster (representing the same physiological heart rate). The representative value of each candidate cluster can be the mean or median of all representative values ​​within that cluster. An initial score for the candidate cluster is obtained by summing the candidate confidence scores of all candidate heart rates within the cluster. Furthermore, the supporting evidence for each candidate cluster is generated by statistically analyzing the subband source distribution, generation method source distribution, and candidate confidence scores of the candidate heart rates. Based on this supporting evidence, the initial score of the candidate cluster is weighted and corrected to obtain a weighted candidate cluster score. Since the frequencies corresponding to the normal human heart rate range are concentrated in lower frequency bands, this embodiment of the invention limits the weight of low-frequency subbands to higher weights during weighting correction. Thus, by weighting and correcting based on the subbands from which the candidate heart rate originates, a weighted candidate cluster score is obtained, thereby enhancing the stability evidence of the fundamental frequency candidate in low heart rate scenarios.

[0038] The representative value of the candidate cluster is the numerical label of the candidate cluster, which is used to represent the heart rate value of the entire cluster. It is usually the median or average of all candidate heart rates in the cluster. For example, the representative value of the cluster may be 78.5 bpm, and the unit is bpm.

[0039] The initial score of the candidate cluster is obtained by calculating the sum of the candidate confidence scores of all candidate heart rates within the candidate cluster. That is, when two clusters contain the same number of candidates, the cluster with the higher sum of confidence scores will get a higher score and thus win in the final selection. The initial score of the candidate cluster is the quality score of the candidate cluster, which represents the credibility of the candidate heart rate represented by the cluster. After weighted correction, a more accurate weighted candidate cluster score is obtained.

[0040] The supporting evidence for the candidate cluster includes subband source, method source, and candidate confidence. The subband source indicates which subband candidate values ​​mainly constitute the candidate cluster. If it mainly comes from low-frequency subbands, it is strong evidence of the fundamental frequency. If it only comes from high-frequency subbands, it is evidence of the harmonic frequency. The method source indicates whether the candidate cluster is supported by multiple independent methods such as peak spacing method, autocorrelation method, and frequency domain method. The more independent methods supported, the stronger the evidence. The candidate confidence is the confidence level of each candidate heart rate supporting the candidate cluster. A candidate with high confidence provides stronger evidence than a candidate with low confidence. Through the aforementioned supporting evidence, it can be proven that a certain candidate cluster (or heart rate) is the objective factual basis for the true heart rate.

[0041] S7: Perform harmonic consistency discrimination on the candidate clusters, detect whether there is a first predetermined harmonic relationship between the representative values ​​of each candidate cluster, and for the fundamental frequency candidate clusters and harmonic candidate clusters that have the first predetermined harmonic relationship, perform backoff determination based on the supporting evidence, and perform harmonic backoff processing according to the backoff determination result; In this embodiment of the invention, harmonic consistency discrimination is performed on the candidate clusters. It detects whether a first predetermined harmonic relationship exists between the representative values ​​of each candidate cluster. Specifically, for fundamental frequency candidate clusters and harmonic candidate clusters with a first predetermined harmonic relationship, based on the supporting evidence, it further confirms whether the fundamental frequency candidate is mainly supported by evidence in low-frequency sub-bands (e.g., sub-bands 1 and 2) and whether the harmonic candidate is mainly supported by evidence in high-frequency sub-bands (e.g., sub-bands 3 and 4). If both the first predetermined harmonic relationship and the supporting evidence are satisfied, the current harmonic candidate cluster is determined to be a spurious harmonic estimation dominated by harmonics. In this case, harmonic backoff processing is performed, actively reducing the score of the harmonic candidate cluster (reducing the score of cluster B (80 bpm), for example, by subtracting a large penalty value P), while simultaneously increasing the score of the fundamental frequency candidate cluster (increasing the score of cluster A (40 bpm), for example, by adding a reward value B). After the score adjustment, the score of cluster A is greater than that of cluster B, so in the subsequent step S8 (selecting the candidate cluster with the highest score), the system will correctly output 40. bpm, through this mechanism, ensures that even in low heart rate scenarios (such as 40 bpm), the system can trace back and select the correct base frequency as the output based on the supporting evidence, even if the energy of the frequency multiplier (80 or 120 bpm) is stronger.

[0042] The first predetermined frequency multiplication factor is, for example, 2-3 times. In other embodiments, other frequency multiplication factors can be selected according to actual application requirements.

[0043] S8: After the candidate clusters have undergone frequency doubling back-off processing, a continuity constraint is imposed on the previous output heart rate of the final output of the previous time window. Based on whether there is a second predetermined frequency doubling relationship between the representative value of the current candidate cluster and the previous output heart rate, the candidate heart rates of the current time window are filtered to determine the final heart rate output of the current window.

[0044] To prevent sudden jumps in heart rate output that do not conform to physiological reality (e.g., a jump from 40 bpm to 80 bpm within a few seconds), this embodiment of the invention introduces a continuity constraint based on historical information. Specifically, the optimal candidate heart rate calculated in the current time window (the candidate cluster after frequency doubling backscaling) is compared with the final output heart rate of the previous window. This confirms whether a second predetermined frequency doubling relationship exists between the representative value of the candidate cluster corresponding to the current time window and the previous output heart rate. If such a relationship exists (e.g., 1.8-2.2 times or 2.8-3.2 times), a frequency doubling jump is determined to have occurred. Upon detecting a frequency doubling jump, this embodiment selects a candidate heart rate from the fundamental frequency candidate cluster as the final output heart rate. For example, approximately 1 / 2 or 1 / 3 of the representative value of the current candidate cluster is selected as the final output heart rate. Simultaneously, this embodiment can also limit the step size of heart rate changes (another continuity constraint). If the difference between the representative value of the current candidate cluster and the previous output heart rate is greater than a preset change threshold, the final output heart rate is forced to be within the range of the previous output heart rate ± the change threshold.

[0045] Before clustering all the sub-band BCG signals and all the generated candidate heart rates according to a preset tolerance, the embodiments of the present invention can also screen the candidate heart rates in advance based on the candidate range constraint, including: screening the candidate heart rates for rationality, determining whether the candidate heart rates are within the preset rationality threshold range (35-180 bpm), and removing all candidate heart rates outside the preset rationality threshold range.

[0046] By integrating candidate range constraints, body motion gating, subband source weighting, harmonic consistency discrimination and frequency doubling backoff, and continuity constraints, the heart rate represented by the candidate cluster with the highest score is selected from the candidate heart rates as the final output of the current time window. In order to further smooth the output curve, the embodiments of the present invention can also apply post-processing techniques such as exponential smoothing to the final result.

[0047] The filter bank frequency harmonic suppression heart rate calculation method based on BCG signals described in this embodiment of the invention has the following advantages compared with the prior art: 1. The BCG signal is decomposed into multiple partially overlapping sub-bands using a filter bank. On each sub-band, three independent methods, namely peak spacing, autocorrelation, and frequency domain spectral peaks, are combined to generate candidate heart rates and confidence scores. Then, through rules such as clustering and sub-band source weighting, harmonic consistency discrimination and octave backoff, and continuity constraints, a multi-level verification closed loop is formed. This mechanism can actively identify and correct octave misjudgments caused by harmonic energy dominance. 2. Body motion interference is judged directly from the abrupt changes in three dimensions of BCG signal: peak-to-peak value, window energy, and first-order differential energy. When any indicator exceeds the threshold, the window is automatically marked as low confidence, and a strategy of maintaining the heart rate of the previous window or limiting the update is adopted. It does not rely on independent body motion sensors such as accelerometers, which saves hardware costs and avoids false heart rate output of body motion window. 3. Mattress-type BCG signals are greatly affected by sleeping posture and body position. In this embodiment of the invention, the preliminary quality score of each channel is first calculated, and then the heart rate consistency between channels is penalized and corrected. Finally, the BCG reference signal is selected or weighted and fused to ensure that the current optimal signal can be obtained under any sleeping posture. 4. After candidate clustering, a weighted correction is performed based on the sub-band index of the candidate heart rate source. The weight of low-frequency sub-band is significantly higher than that of high-frequency sub-band. Through this prior weight allocation, even if the harmonic energy of a certain window is temporarily dominant, the fundamental frequency candidate cluster can obtain a higher correction score and thus win in the subsequent selection. This is more in line with the scenario in reality where the fundamental frequency energy of the real heart rate is naturally concentrated in the low-frequency sub-band. 5. By quantitatively evaluating the supporting evidence for each candidate cluster, including the distribution of subband sources (octave, fundamental frequency), diversity of method sources (whether it is supported by multiple independent methods), confidence statistics, etc., backtracking is triggered only when the octave candidate mainly originates from the high-frequency subband and the fundamental frequency candidate has supporting evidence exceeding the threshold in the low-frequency subband. This evidence-based decision-making mechanism avoids false backtracking (e.g., if the patient's true heart rate is 80 bpm, it will not be wrongly backtracked to 40 bpm). 6. In this embodiment of the invention, the final output heart rate of the previous window is used to detect whether there is an approximate 2-fold or 3-fold relationship between the current candidate and the historical value. If there is such a relationship and there is corresponding 1 / 2 or 1 / 3 heart rate supporting evidence, then the low-frequency heart rate is output first. The continuity and physiological rationality of the heart rate curve are greatly improved by the continuity constraint based on the time dimension, which is especially suitable for long-term operation of nighttime sleep monitoring.

[0048] Based on the above embodiments, as a supplement to the above... Figure 1 The present invention provides an embodiment of a filter bank frequency doubling suppression heart rate calculation device based on BCG signals, which is similar to the implementation of the method shown. Figure 1 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices, see reference. Figure 4 As shown, the filter bank frequency doubling suppression heart rate calculation device based on BCG signals includes: The multi-channel BCG acquisition module 100 is used to acquire the BCG signal of the mattress-type multi-channel piezoelectric film sensor, and to perform windowing processing on the BCG signal to obtain a discrete sampling sequence formed by discrete sampling values ​​of multiple time windows. The windowing processing module 200 is used to calculate the body motion index of the BCG signal based on the discrete sampling sequence within each time window, and to mark the time window as a low confidence window and a non-low confidence window according to whether the body motion index exceeds a preset threshold. The motion gating module 300 is used to perform channel quality scoring on multi-channel BCG signals with non-low confidence windows, and to determine the target channel's BCG signal as a reference BCG signal after weighted fusion of the BCG signals based on the scoring results. The signal decomposition module 400 is used to input the reference BCG signal into a filter bank for filtering and decomposition to obtain multiple sub-band BCG signals. The candidate heart rate generation module 500 is used to generate candidate heart rates and corresponding candidate confidence scores by combining at least two of the following methods among peak spacing method, autocorrelation method and frequency domain spectral peak method on each sub-band BCG signal. The clustering processing module 600 is used to cluster all the sub-band BCG signals and all the generated candidate heart rates according to a preset tolerance to obtain at least one candidate cluster and its representative value, calculate the initial score of the candidate cluster, statistically analyze the sub-band source distribution, generation method source distribution and candidate confidence of the candidate heart rate of each candidate cluster to generate supporting evidence for the candidate cluster, and perform weighted correction on the initial score of the candidate cluster based on the supporting evidence to obtain a weighted candidate cluster score, wherein the weight of low frequency sub-bands is higher than the weight of high frequency sub-bands during weighted correction; The frequency doubling backoff processing module 700 is used to perform harmonic consistency discrimination on the candidate clusters, detect whether there is a first predetermined frequency doubling relationship between the representative values ​​of the candidate clusters corresponding to each candidate cluster, and perform backoff determination based on the supporting evidence for the fundamental frequency candidate clusters and frequency doubling candidate clusters that have the first predetermined frequency doubling relationship, and perform frequency doubling backoff processing according to the backoff determination result. The heart rate output module 800 is used to perform continuity constraints on the candidate cluster after frequency doubling back-off processing and the previous output heart rate of the final output of the previous time window, and to filter the candidate heart rates of the current time window based on whether there is a second predetermined frequency doubling relationship between the representative value of the current candidate cluster and the previous output heart rate, so as to determine the final heart rate output of the current window.

[0049] The modules in the aforementioned BCG signal-based filter bank frequency doubling suppression heart rate calculation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0050] The filter bank frequency doubling suppression heart rate calculation device based on BCG signal described in this embodiment can execute the filter bank frequency doubling suppression heart rate calculation method based on BCG signal provided in the above embodiment. The filter bank frequency doubling suppression heart rate calculation device based on BCG signal has the corresponding functional steps and beneficial effects of the filter bank frequency doubling suppression heart rate calculation method based on BCG signal described in the above embodiment. For details, please refer to the embodiment of the filter bank frequency doubling suppression heart rate calculation method based on BCG signal. The embodiments of this invention will not be repeated here.

[0051] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 5 As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface, such as a network interface card, is used for wired or wireless communication with external terminals. Wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. The display unit is used to create a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0052] Those skilled in the art will understand that Figure 5The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0053] In one exemplary embodiment, a chip is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the method in any of the above embodiments.

[0054] In one exemplary embodiment, a network interface card is provided, including a chip as described in any of the above embodiments and multiple interfaces, wherein the chip communicates externally through the interfaces.

[0055] In one embodiment, a computer device is also provided, including a processor, a chip in any of the above embodiments, or a network interface card in any of the above embodiments, wherein the chip or the network interface card is used to schedule packets to the processor or the chip or the network interface card itself for processing, and the processor is used to process the packets scheduled by the chip or the network interface card.

[0056] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0057] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0058] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory may include read-only memory (Read-Only Memory). Only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this application may include at least one of relational databases and non-relational databases. Non-relational databases may include distributed databases based on blockchain, etc., and are not limited thereto. The processors involved in the various embodiments provided in this application may be general-purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited thereto.

[0059] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0060] Similarly, it should be understood that, for the purpose of simplification and aiding understanding of one or more aspects of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof in the description of exemplary embodiments of the invention above. Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and it should be noted that the above embodiments are illustrative of the invention and not restrictive, and that alternative embodiments can be devised by those skilled in the art without departing from its scope.

Claims

1. A method for calculating heart rate suppression using filter banks based on BCG signals, characterized in that, include: S1: Acquire the BCG signal of the mattress-type multi-channel piezoelectric thin film sensor, perform windowing processing on the BCG signal, and obtain a discrete sampling sequence formed by discrete sampling values ​​of multiple time windows; S2: Based on the discrete sampling sequence within each time window, calculate the body motion index of the BCG signal, and mark the time window as a low confidence window and a non-low confidence window according to whether the body motion index exceeds a preset threshold. S3: Perform channel quality scoring on the multi-channel BCG signal with non-low confidence window, and after weighted fusion of the BCG signal based on the scoring results, determine the BCG signal of the target channel as the reference BCG signal; S4: The reference BCG signal is input into a filter bank and filtered and decomposed to obtain multiple sub-band BCG signals; S5: For each sub-band BCG signal, at least two of the following methods are combined: peak spacing method, autocorrelation method and frequency domain spectral peak method to generate candidate heart rates and corresponding candidate confidence scores. S6: Cluster all the sub-band BCG signals and all the generated candidate heart rates according to a preset tolerance to obtain at least one candidate cluster and its representative value. Calculate the initial score of the candidate cluster. Statistically analyze the sub-band source distribution, generation method source distribution, and candidate confidence of each candidate cluster to generate supporting evidence for that candidate cluster. Based on the supporting evidence, perform a weighted correction on the initial score of the candidate cluster to obtain a weighted candidate cluster score. In the weighted correction, the weight of low-frequency sub-bands is higher than the weight of high-frequency sub-bands. S7: Perform harmonic consistency discrimination on the candidate clusters, detect whether there is a first predetermined harmonic relationship between the representative values ​​of each candidate cluster, and for the fundamental frequency candidate clusters and harmonic candidate clusters that have the first predetermined harmonic relationship, perform backoff determination based on the supporting evidence, and perform harmonic backoff processing according to the backoff determination result; S8: After the candidate clusters have undergone frequency doubling back-off processing, a continuity constraint is imposed on the previous output heart rate of the final output of the previous time window. Based on whether there is a second predetermined frequency doubling relationship between the representative value of the current candidate cluster and the previous output heart rate, the candidate heart rates of the current time window are filtered to determine the final heart rate output of the current window.

2. The method for calculating heart rate based on filter bank frequency harmonic suppression using BCG signals according to claim 1, characterized in that, The calculation of the body motion index of the BCG signal based on the discrete sampling sequence within each time window includes at least one of the following: calculating the difference between the maximum and minimum values ​​of the discrete sampling values ​​corresponding to the BCG signal within each time window to obtain the peak-to-peak value index; The window energy index is obtained by calculating the sum of squares of the discrete sampled values ​​corresponding to each sampling point of the BCG signal within each time window. The first-order differential energy index is obtained by calculating the sum of squares of the differences between the discrete sampled values ​​of adjacent sampling points within each time window; and when the body motion index exceeds the preset threshold of the corresponding type, the time window is marked as a low confidence window.

3. The method for calculating heart rate based on filter bank frequency harmonic suppression using BCG signals according to claim 1, characterized in that, The step of performing channel quality scoring on multi-channel BCG signals with non-low confidence windows, and then weighting and fusing the BCG signals based on the scoring results to determine the target channel's BCG signal as a reference BCG signal includes: Channel quality scoring is performed on the multi-channel BCG signal with non-low confidence window, and the preliminary quality score and preliminary candidate heart rate of each channel are obtained respectively. The median of the preliminary candidate heart rate for all channels is calculated. The preliminary quality score is corrected based on the fusion weight and the median. The maximum value in the corrected preliminary quality score is determined, the channel containing the maximum value is selected as the target channel, and the BCG signal of the target channel is determined as the reference BCG signal.

4. The method for calculating heart rate based on filter bank frequency harmonic suppression using BCG signals according to claim 1, characterized in that: The reference BCG signal input is filtered and decomposed by a filter bank consisting of multiple partially overlapping bandpass filters to obtain multiple sub-band BCG signals.

5. The method for calculating heart rate based on filter bank frequency doubling suppression using BCG signals according to claim 1, characterized in that, In step S5: When generating candidate heart rates using the peak spacing method, the peak value of the BCG signal is detected and the coefficient of variation of the adjacent peak spacing sequence is calculated as the candidate confidence level. The smaller the coefficient of variation, the higher the confidence level. When generating candidate heart rates using the autocorrelation method, the autocorrelation function of the BCG signal is calculated, and the first significant peak outside of zero delay is selected. The amplitude of this significant peak is used as the candidate confidence level. When generating candidate heart rates using the frequency domain spectral peak method, the power spectrum is obtained by performing a Fourier transform on the BCG signal. The main spectral peak is then searched within a preset frequency range, and the ratio of the main peak power to the secondary peak power or the spectral kurtosis is used as the candidate confidence level.

6. The method for calculating heart rate based on filter bank frequency doubling suppression using BCG signals according to claim 1, characterized in that, The calculation of the initial score of the candidate cluster includes: obtaining the initial score of the candidate cluster by summing the candidate confidence scores of all candidate heart rates within the candidate cluster.

7. The method for calculating heart rate based on filter bank frequency harmonic suppression using BCG signals according to claim 1, characterized in that, Before clustering all the sub-band BCG signals and all the generated candidate heart rates according to a preset tolerance, the method includes: performing a rationality screening on the candidate heart rates, determining whether the candidate heart rates are within a preset rationality threshold range, and removing all candidate heart rates outside the preset rationality threshold range.

8. A filter bank frequency doubling suppression heart rate calculation device based on BCG signals, applied to the method described in any one of claims 1-7, characterized in that, The device includes: A multi-channel BCG acquisition module is used to acquire BCG signals from a mattress-type multi-channel piezoelectric film sensor, and to perform windowing processing on the BCG signals to obtain a discrete sampling sequence formed by discrete sampling values ​​of multiple time windows. The windowing processing module is used to calculate the body motion index of the BCG signal based on the discrete sampling sequence within each time window, and to mark the time window as a low confidence window and a non-low confidence window according to whether the body motion index exceeds a preset threshold. The motion gating module is used to perform channel quality scoring on multi-channel BCG signals with non-low confidence windows, and to determine the target channel's BCG signal as the reference BCG signal after weighted fusion based on the scoring results. The signal decomposition module is used to filter and decompose the reference BCG signal into a filter bank to obtain multiple sub-band BCG signals. The candidate heart rate generation module is used to generate candidate heart rates and corresponding candidate confidence scores for each sub-band BCG signal by combining at least two of the following methods: peak spacing method, autocorrelation method and frequency domain spectral peak method. The clustering processing module is used to cluster all the sub-band BCG signals and all the generated candidate heart rates according to a preset tolerance to obtain at least one candidate cluster and its representative value, calculate the initial score of the candidate cluster, statistically analyze the sub-band source distribution, generation method source distribution and candidate confidence of the candidate heart rate of each candidate cluster to generate supporting evidence for the candidate cluster, and perform weighted correction on the initial score of the candidate cluster based on the supporting evidence to obtain a weighted candidate cluster score, wherein the weight of low frequency sub-bands is higher than the weight of high frequency sub-bands during weighted correction; The frequency doubling backoff processing module is used to perform harmonic consistency discrimination on the candidate clusters, detect whether there is a first predetermined frequency doubling relationship between the representative values ​​of the candidate clusters corresponding to each candidate cluster, and perform backoff determination based on the supporting evidence for the fundamental frequency candidate clusters and frequency doubling candidate clusters that have the first predetermined frequency doubling relationship, and perform frequency doubling backoff processing according to the backoff determination result. The heart rate output module is used to perform continuity constraints on the candidate clusters after frequency doubling back-off processing and the previous output heart rate of the final output of the previous time window, and to filter the candidate heart rates of the current time window based on whether there is a second predetermined frequency doubling relationship between the representative value of the current candidate cluster and the previous output heart rate, so as to determine the final heart rate output of the current window.

9. A computer device comprising a processor and a memory, characterized in that, The memory stores computer program instructions that can be executed by the processor, and when the processor executes the computer program instructions, it implements the instructions of the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions that are loaded and executed by a processor to perform the operations described in any one of claims 1-7.

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