Low-frequency biological wave frequency regulation control system
By constructing a low-frequency bio-wave frequency regulation and control system, real-time monitoring and processing of bioelectric signals, extraction of features and calculation of stimulation parameters, the problem of complexity and high cost of existing equipment is solved, realizing low-cost and efficient bio-wave frequency regulation, and improving the market penetration and user experience of intelligent sleep aid devices.
Patent Information
- Application Number
- CN202510748302.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-06
- Publication Date
- 2025-10-31
AI Technical Summary
Existing low-frequency biosignal modulation devices are complex and costly, which limits the widespread adoption of smart sleep aids in the market.
A low-frequency bio-wave frequency regulation and control system was designed, including data acquisition, data processing, biofeedback, control, and user interface modules. By monitoring and processing bioelectric signals in real time, extracting features and calculating stimulation parameters, and generating frequency regulation reports, the system reduces technical complexity and production costs.
It achieves low-cost and efficient bio-wave frequency regulation, increases the market penetration of intelligent sleep aid devices, and enhances user experience and device adaptability.
Smart Images
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Abstract
Description
Technical Field
[0001] This invention relates to the field of low-frequency biological signal modulation technology, specifically a low-frequency biological wave frequency modulation and control system. Background Technology
[0002] Low-frequency biowaves typically refer to brainwave signals in the range of 0.5Hz to 4Hz, mainly including Delta waves and Theta waves. These waveforms and frequencies are widely considered to be closely related to different physiological and psychological states and have important research value in fields such as medicine, psychology, and biofeedback. In modern home life, many product designs have begun to consider how to improve the living environment and enhance people's quality of life by regulating low-frequency biowaves, especially in terms of health and comfort. Some emerging smart home products, such as environmental therapy lamps and aromatherapy devices, can achieve psychological and physiological harmony by adjusting various sensory stimuli such as light, sound, and fragrance. All of this can be achieved by influencing biowaves, thereby creating a healthier, more comfortable, and spiritually enriching living space.
[0003] Currently, low-frequency bio-signal modulation usually involves complex biofeedback systems, including high-precision sensors and signal processing algorithms. This increases the technical complexity and production cost of the products, making them unacceptable to many home users and limiting the market penetration of smart sleep aids. Summary of the Invention
[0004] (a) Technical problems to be solved
[0005] To address the shortcomings of existing technologies, this invention provides a low-frequency bio-wave frequency adjustment and control system. This system includes a data acquisition module for acquiring bioelectrical signals Swdh, real-time monitoring of low-frequency bio-waves in the target organism, a data processing module for filtering and noise removal of the signal to obtain a pure bioelectrical signal Cdxh, and feature extraction for peak Bftz, trough Bgtz, average frequency Pjpl, and power spectral density Glpd. A biofeedback module adjusts the bio-wave frequency of the organism based on the analysis results of the data processing module, calculating the adjustment frequency value Tzpl, and then... The method of adjusting the frequency of bio-waves by numerical selection involves a control module that, based on the output of the biofeedback module, controls the parameter settings of the stimulation device and signal generator, calculates the stimulation frequency Cjpl, stimulation amplitude Cjfd, and system-set stimulation time, and generates a frequency adjustment report. The user interface module converts the frequency adjustment report into a spectrum graph and displays it on an electronic screen. Through these modules, a low-frequency bio-wave frequency adjustment control system is constructed. This system does not require the application of advanced technologies and auxiliary equipment, reducing the technical complexity and production cost of the product, increasing the market penetration of intelligent sleep aids, and solving the aforementioned problems.
[0006] (II) Technical Solution
[0007] To achieve the above objectives, the present invention provides the following technical solution: a low-frequency biological wave frequency regulation and control system, comprising a data acquisition module, a data processing module, a biofeedback module, a control module, and a user interface module;
[0008] The data acquisition module is used to acquire bioelectric signals Swdh, monitor low-frequency biowaves of the target organism in real time, and transmit the acquired signals to the data processing module.
[0009] The data processing module filters the transmitted bioelectric signal Swdh and removes noise to obtain a pure bioelectric signal Cdxh. Then, based on the above signal, feature extraction is performed to extract the peak Bftz, trough Bgtz, average frequency Pjpl, and power spectral density Glpd. The processed signal and the extracted feature values are then transmitted to the biofeedback module.
[0010] The biofeedback module adjusts the biowave frequency of the organism based on the analysis results of the data processing module, calculates the adjustment frequency value Tzpl, selects a method for adjusting the biowave frequency based on the calculated value, and transmits the data to the control module.
[0011] The control module controls the parameter settings of the stimulation device and the signal generator based on the output of the biofeedback module, calculates the stimulation frequency Cjpl, stimulation amplitude Cjfd and the system-set stimulation time, and generates a frequency adjustment report to be sent to the user interface module.
[0012] The user interface module converts the frequency adjustment report into a spectrum graph, which is then displayed on the electronic display screen.
[0013] Preferably, the data processing module filters the transmitted bioelectric signal Swdh, and the filtering formula is as follows:
[0014]
[0015] In the formula, L(Swdh) represents filtering the bioelectric signal Swdh, w0 represents the specific frequency to be suppressed in radians per second, which is attenuated by the filter, and r represents the attenuation factor of the filter, which ranges from 0 to 1 and determines the bandwidth of the filter. The smaller the value of r, the narrower the range of suppressed frequencies.
[0016] Preferably, the data processing module filters the transmitted bioelectric signal Swdh and then removes noise to obtain a pure bioelectric signal Cdxh. The noise removal formula is as follows:
[0017]
[0018] In the formula, Cdxh represents the pure bioelectric signal, M represents the size of the denoising window, the summation formula represents the summation of all samples within the window from k=0 to k=M-1, and L(Swdh-K) represents the value of the filtered signal at time Swdh-K.
[0019] Preferably, the data processing module performs feature extraction based on the pure bioelectric signal Cdxh, extracting the peak Bftz. The feature extraction formula is as follows:
[0020]
[0021] In the formula, Bftz(n) represents a sample point in the pure bioelectric signal Cdxh(n) that is larger than the value of its neighboring samples, where 0 indicates that the current position is not a peak.
[0022] Preferably, the data processing module performs feature extraction based on the pure bioelectric signal Cdxh, extracting the trough Bgtz. The feature extraction formula is as follows:
[0023]
[0024] In the formula, Bgtz(n) represents the trough signal, and 0 indicates that the current position is not a trough.
[0025] Preferably, the data processing module performs feature extraction based on the pure bioelectric signal Cdxh, extracting the average frequency Pjpl. The feature extraction formula is as follows:
[0026]
[0027] In the formula, Pjpl represents the average frequency, f represents the frequency variable, and |X(f)| 2 The power spectrum of a signal in the frequency domain represents the signal intensity at frequency f, ∑ f f*|X(f)| 2 This represents the weighted sum of power at each frequency f, where frequency f corresponds to the power |X(f)|. 2 Multiplication reflects the contribution of that frequency to the overall signal energy, ∑ f |X(f)| 2 It represents the total energy of the signal across all frequencies.
[0028] Preferably, the data processing module performs feature extraction based on the pure bioelectric signal Cdxh, extracting the power spectral density Glpd. The feature extraction formula is as follows:
[0029]
[0030] In the formula, Glpd(f) represents the power spectral density at frequency f, and |X(f)| 2 This represents the power spectrum of the signal at frequency f, and N represents the number of samples used in the Fourier transform, which is used as a normalization factor.
[0031] Preferably, the biofeedback module adjusts the biowave frequency of the organism based on the analysis results of the data processing module, and calculates the adjusted frequency value Tzpl, as shown in the following formula:
[0032]
[0033] In the formula, Tzpl represents the adjusted frequency value, Dqpl represents the current frequency value, and K... p The proportional gain is used to adjust the direct deviation between the current frequency and the target frequency. e represents the deviation between the target frequency and the current frequency, and K represents the proportional gain. i K represents the integral gain, used to eliminate steady-state bias. d ∫edt represents the differential gain, which reflects the rate of change of error and helps reduce the overshoot of the system.
[0034] Preferably, the control module controls the parameter settings of the stimulation device and the signal generator based on the output of the biofeedback module, and calculates the stimulation frequency Cjpl, as shown in the following formula:
[0035] Cjpl=Mbpl+Kn*(Fkpl-Dqpl)+Bhzy*Δf
[0036] In the formula, Cjpl represents the frequency of the stimulus, Mbpl represents the set target frequency, Kn represents the frequency adjustment gain, which is used to adjust the degree of influence of feedback on the target frequency, Fkpl represents the feedback frequency obtained by the biofeedback module, Dqpl represents the current frequency value, Bhzy represents the variable gain, which is used to handle the influence of frequency changes, and Δf represents the amount of change in the feedback frequency over a period of time.
[0037] Preferably, the control module controls the parameter settings of the stimulation device and the signal generator based on the output of the biofeedback module, and calculates the stimulation amplitude Cjfd, as shown in the following formula:
[0038] Cjfd=Mbfd+Ka*(Fkfd-Dqfd)+Bhfz*ΔA
[0039] In the formula, Cjfd represents the stimulus amplitude, Mbfd represents the target stimulus amplitude set by the system, Ka represents the amplitude adjustment gain, which is used to adjust the degree of influence of feedback on the target amplitude, Fkfd represents the feedback amplitude, which is the feedback signal from biofeedback, Dqfd represents the amplitude currently used by the system, Bhfz represents the gain of change on amplitude, and ΔA represents the amount of change in feedback amplitude over a period of time.
[0040] Compared with the prior art, the present invention provides a low-frequency biological wave frequency regulation and control system, which has the following beneficial effects:
[0041] This invention utilizes a data acquisition module to collect bioelectric signals Swdh, monitoring the low-frequency biowaves of the target organism in real time. A data processing module filters and removes noise from the signal to obtain a pure bioelectric signal Cdxh. Feature extraction is then performed on this signal, extracting peak Bftz, trough Bgtz, average frequency Pjpl, and power spectral density Glpd. A biofeedback module adjusts the biowave frequency of the organism based on the analysis results of the data processing module, calculating the adjustment frequency value Tzpl. A method for adjusting the biowave frequency is then selected based on the calculated value. A control module controls the parameter settings of the stimulation device and signal generator based on the output of the biofeedback module, calculating the stimulation frequency Gjpl, stimulation amplitude Cjfd, and system-set stimulation time, and generating a frequency adjustment report. A user interface module converts the frequency adjustment report into a spectrum graph, displaying it on an electronic screen. This system constructs a low-frequency biowave frequency adjustment control system that does not require advanced technology or auxiliary equipment, reducing the product's technical complexity and production costs, and increasing the market penetration of intelligent sleep aid devices. Attached Figure Description
[0042] Figure 1 This is a schematic diagram of the system flow of the present invention. Detailed Implementation
[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0044] To address the issue that current methods for low-frequency bio-signal modulation typically involve complex biofeedback systems, including high-precision sensors and signal processing algorithms, which increase product complexity and production costs, making them unacceptable to many home users and limiting the market penetration of smart sleep aids, a low-frequency bio-wave frequency modulation and control system is proposed. (See [link to relevant documentation]). Figure 1 The system includes a data acquisition module, a data processing module, a biofeedback module, a control module, and a user interface module.
[0045] The data acquisition module consists of a multi-channel bioelectric signal acquisition device capable of simultaneously acquiring signals from multiple electrodes at intervals. These electrodes can be connected to the organism via skin adhesion or implantation to ensure high-precision capture of changes in target biowaves. During signal acquisition, low-frequency biowave signals are often affected by environmental noise and electromagnetic interference. Therefore, the data acquisition module is typically equipped with amplifiers and filters to enhance the amplitude of the target signal and eliminate unwanted background noise, ensuring data accuracy and reliability. The collected analog signals need to be digitized using an analog-to-digital converter for subsequent calculations and analysis. In this process, the data acquisition module converts the signal into a digital format, laying the foundation for data transmission and subsequent processing. Once the data acquisition module successfully acquires and processes the biosignal, it transmits the signal to the data processing module via wired or wireless communication. This data transmission channel may utilize technologies such as Wi-Fi to ensure a balance between real-time performance and stability.
[0046] The data processing module filters the transmitted bioelectric signal Swdh and removes noise to obtain a pure bioelectric signal Cdxh. Then, feature extraction is performed on this signal to extract the peak Bftz, trough Bgtz, average frequency Pjpl, and power spectral density Glpd, where:
[0047] The signal filtering formula is as follows:
[0048]
[0049] Filtering can eliminate noise and interference, significantly improving the quality of bioelectric signals. Biosignals are often affected by environmental noise and motion artifacts. By using appropriate filtering techniques, these unnecessary signal components can be effectively reduced, resulting in clearer and more stable bioelectric signals. In the formula, L(Swdh) represents filtering the bioelectric signal Swdh, w0 represents the specific frequency to be suppressed in radians per second, which is attenuated by the filter, and r represents the attenuation factor of the filter, which ranges from 0 to 1. It determines the bandwidth of the filter. The smaller the value of r, the narrower the range of suppressed frequencies.
[0050] The signal denoising formula is as follows:
[0051]
[0052] By denoising, the system can more easily identify key features in biosignals, such as variations in specific frequencies, waveform shapes, and indications of physiological states. These features form the basis for further analysis, pattern recognition, and state assessment. Clearer signals help to extract and analyze important information more accurately. In the formula, Cdxh represents the pure bioelectric signal, M represents the size of the denoising window, the summation formula represents summing all samples within the window, from k=0 to k=M-1, and L(Swdh-K) represents the value of the filtered signal at time Swdh-K. Signal denoising not only improves signal quality but also reduces the burden on subsequent data processing stages. By reducing interference components in the data, the computational complexity of the algorithm can be significantly reduced, improving processing speed and efficiency and ensuring real-time processing capabilities.
[0053] The formula for peak feature extraction is as follows:
[0054]
[0055] The peaks of biowaves are often associated with specific physiological states. In electroencephalograms (EEGs), the peaks of alpha, beta, and theta waves may correspond to different brain functional states, such as relaxation, alertness, or sleep. By extracting peak features, effective evidence can be provided for psychological state assessment and health monitoring. In the formula, Bftz(n) represents the sample point in the pure bioelectric signal Cdxh(n) that is larger than the value of the surrounding neighboring samples, where 0 indicates that the current position is not a peak. In real-time biosignal monitoring, peak feature extraction provides an important means to quickly identify signal changes. This capability enables the control system to react in real time and adjust the frequency and intensity of biowaves in a timely manner, thereby improving the feedback effect and user experience.
[0056] The formula for extracting trough features is as follows:
[0057]
[0058] Troughs and peaks are usually complementary. Trough feature extraction can supplement the analysis of peak features. By analyzing peaks and troughs simultaneously, more comprehensive signal features can be obtained, enabling a full understanding of the dynamic changes of biological signals and their physiological meaning. In the formula, Bgtz(n) represents the trough signal, and 0 indicates that the current position is not a trough. Changes in trough features may be related to an individual's physiological state and can reflect changes such as fatigue, stress, or other health conditions. By monitoring trough features, changes in physiological state can be effectively assessed, which is helpful for health management.
[0059] The formula for extracting average frequency features is as follows:
[0060]
[0061] Average frequency feature extraction, through analysis of the original signal, can help reduce the impact of short-term fluctuations. Reducing noise and irregular changes can improve the efficiency and accuracy of subsequent analysis. In the formula, Pjpl represents the average frequency, f represents the frequency variable, and |X(f)| 2 The power spectrum of a signal in the frequency domain represents the signal intensity at frequency f, ∑ f f*|X(f)| 2 This represents the weighted sum of power at each frequency f, where frequency f corresponds to the power |X(f)|. 2 Multiplication reflects the contribution of that frequency to the overall signal energy, ∑ f |X(f)| 2 This represents the total energy of the signal across all frequencies;
[0062] The formula for extracting power spectral density features is as follows:
[0063]
[0064] Power spectral density provides the energy distribution of various frequency components of a signal, clearly showing its spectral characteristics. This analysis can reveal the main frequency components of biological signals, helping researchers understand their physiological mechanisms. In the formula, Glpd(f) represents the power spectral density at frequency f, and |X(f)| 2 The power spectrum of the signal at frequency f is represented by N, which represents the number of samples used in the Fourier transform. This value is used as a normalization factor. The power spectral density calculation can effectively isolate noise and unnecessary frequency components, helping to extract meaningful features from biological signals, which makes the signal analysis results more reliable.
[0065] Based on the analysis results from the data processing module, the biofeedback module adjusts the bio-wave frequency of the organism, calculates the adjustment frequency value Tzpl, and then selects a method for adjusting the bio-wave frequency based on the calculated value. This method employs sound therapy, using sound waves of a specific frequency to influence the user's physiological state and adjust the bio-wave frequency.
[0066] The formula for calculating the adjusted frequency value is as follows:
[0067]
[0068] By precisely calculating and adjusting the frequency value, the system can more effectively achieve biofeedback and optimize neurophysiological responses. This feedback not only improves the user's psychological state but also enhances the body's natural recovery ability. In the formula, Tzpl represents the adjusted frequency value, Dqpl represents the current frequency value, and K... p The proportional gain is used to adjust the direct deviation between the current frequency and the target frequency. e represents the deviation between the target frequency and the current frequency, and K represents the proportional gain.i K represents the integral gain, used to eliminate steady-state bias. d The differential gain represents the rate of change of error and helps reduce the overshoot of the system. ∫edt represents the integral with respect to e. Precise frequency adjustment can improve the user experience, making the adjustment process more comfortable and effective. Users may experience fewer side effects when adjusting, thereby increasing their acceptance and satisfaction.
[0069] The control module controls the parameter settings of the stimulation device and the signal generator based on the output of the biofeedback module, and calculates the stimulation frequency Cjpl, stimulation amplitude Cjfd, and system-set stimulation time.
[0070] The formula for calculating the frequency of stimuli is as follows:
[0071] Cjpl=Mbpl+Kn*(Fkpl-Dqpl)+Bhzy*Δf
[0072] Calculating the stimulation frequency allows the system to precisely adjust according to an individual's physiological state and specific needs. This precise frequency selection can more effectively influence biowaves, optimizing the recovery and regulation of various physiological functions. In the formula, Cjpl represents the stimulation frequency, Mbpl represents the set target frequency, Kn represents the frequency adjustment gain, used to adjust the degree of influence of feedback on the target frequency, Fkpl represents the feedback frequency obtained by the biofeedback module, Dqpl represents the current frequency value, Bhzy represents the change gain, used to handle the influence of frequency changes, and Δf represents the amount of change in feedback frequency over a period of time. By monitoring and calculating the stimulation frequency in real time, the system can dynamically adjust the stimulation parameters according to the user's feedback. This adaptability enhances the system's flexibility, enabling it to cope with changes in physiological state and ensure continuous and effective regulation.
[0073] The formula for calculating the stimulus amplitude is as follows:
[0074] Cjfd=Mbfd+Ka*(Fkfd-Dqfd)+Bhfz*ΔA
[0075] By calculating and adjusting the stimulus amplitude, user discomfort can be minimized. Excessive stimulation may cause pain or anxiety, while insufficient stimulation may fail to produce the desired effect. Precise amplitude calculation helps find the optimal range for comfortable stimulation. In the formula, Cjfd represents the stimulus amplitude, Mbfd represents the target stimulus amplitude set by the system, Ka represents the amplitude adjustment gain, used to adjust the degree of influence of feedback on the target amplitude, Fkfd represents the feedback amplitude, the feedback signal from biofeedback, Dqfd represents the amplitude currently used by the system, Bhfz represents the gain of change on amplitude, and ΔA represents the amount of change in feedback amplitude over a past period.
[0076] The stimulation time system is set to 20 minutes for anxiety treatment and 30 minutes for insomnia treatment. To ensure user safety and comfort, the maximum stimulation time limit should not exceed 60 minutes, and a frequency adjustment report is generated and sent to the user interface module.
[0077] The user interface module converts the frequency adjustment report into a spectrum graph, and uses the Matplotlib graphing tool to display the frequency components as curves or bar graphs. In the interface, different colors and densities are used to highlight specific frequency areas, enhancing the user's visual experience.
[0078] The above modules construct a low-frequency bio-wave frequency regulation and control system. This system does not require the application of high-precision technology and auxiliary equipment, which reduces the technical complexity and production cost of the product and increases the market penetration of intelligent sleep aid devices.
[0079] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.
Claims
1. A low-frequency biological wave frequency regulation and control system, characterized in that: It includes a data acquisition module, a data processing module, a biofeedback module, a control module, and a user interface module; The data acquisition module is used to acquire bioelectric signals Swdh, monitor low-frequency biowaves of the target organism in real time, and transmit the acquired signals to the data processing module. The data processing module filters the transmitted bioelectric signal Swdh and removes noise to obtain a pure bioelectric signal Cdxh. Then, based on the above signal, feature extraction is performed to extract the peak Bftz, trough Bgtz, average frequency Pjpl, and power spectral density Glpd. The processed signal and the extracted feature values are then transmitted to the biofeedback module. The biofeedback module adjusts the biowave frequency of the organism based on the analysis results of the data processing module, calculates the adjustment frequency value Tzpl, selects a method for adjusting the biowave frequency based on the calculated value, and transmits the data to the control module. The control module controls the parameter settings of the stimulation device and the signal generator based on the output of the biofeedback module, calculates the stimulation frequency Cjpl, stimulation amplitude Cjfd and the system-set stimulation time, and generates a frequency adjustment report to be sent to the user interface module. The user interface module converts the frequency adjustment report into a spectrum graph, which is then displayed on the electronic display screen.
2. The low-frequency biological wave frequency regulation and control system according to claim 1, characterized in that: The data processing module filters the transmitted bioelectric signal Swdh using the following filtering formula: In the formula, L(Swdh) represents filtering the bioelectric signal Swdh, w0 represents the specific frequency to be suppressed in radians per second, which is attenuated by the filter, and r represents the attenuation factor of the filter, which ranges from 0 to 1 and determines the bandwidth of the filter. The smaller the value of r, the narrower the range of suppressed frequencies.
3. The low-frequency biological wave frequency regulation and control system according to claim 2, characterized in that: The data processing module filters the transmitted bioelectric signal Swdh and removes noise to obtain the pure bioelectric signal Cdxh. The noise reduction formula is as follows: In the formula, Cdxh represents the pure bioelectric signal, M represents the size of the denoising window, the summation formula represents the summation of all samples within the window from k=0 to k=M-1, and L(Swdh-K) represents the value of the filtered signal at time Swdh-K.
4. The low-frequency biological wave frequency regulation and control system according to claim 3, characterized in that: The data processing module performs feature extraction based on the pure bioelectric signal Cdxh, extracting the peak Bftz. The feature extraction formula is as follows: In the formula, Bftz(n) represents a sample point in the pure bioelectric signal Cdxh(n) that is larger than the value of its neighboring samples, where 0 indicates that the current position is not a peak.
5. A low-frequency bio-wave frequency regulation and control system according to claim 4, characterized in that: The data processing module performs feature extraction based on the pure bioelectric signal Cdxh, extracting the trough Bgtz. The feature extraction formula is as follows: In the formula, Bgtz(n) represents the trough signal, and 0 indicates that the current position is not a trough.
6. A low-frequency bio-wave frequency regulation and control system according to claim 5, characterized in that: The data processing module performs feature extraction based on the pure bioelectric signal Cdxh, extracting the average frequency Pjpl. The feature extraction formula is as follows: In the formula, Pjpl represents the average frequency, f represents the frequency variable, and |X(f)| 2 The power spectrum of a signal in the frequency domain represents the signal intensity at frequency f, ∑ f f*|X(f)| 2 This represents the weighted sum of power at each frequency f, where frequency f corresponds to the power |X(f)|. 2 Multiplication reflects the contribution of that frequency to the overall signal energy, ∑ f |X(f)| 2 It represents the total energy of the signal across all frequencies.
7. A low-frequency bio-wave frequency regulation and control system according to claim 6, characterized in that: The data processing module performs feature extraction based on the pure bioelectric signal Cdxh, extracting the power spectral density Glpd. The feature extraction formula is as follows: In the formula, Glpd(f) represents the power spectral density at frequency f, and |X(f)| 2 This represents the power spectrum of the signal at frequency f, and N represents the number of samples used in the Fourier transform, which is used as a normalization factor.
8. A low-frequency bio-wave frequency regulation and control system according to claim 7, characterized in that: The biofeedback module adjusts the biowave frequency of the organism based on the analysis results of the data processing module, and calculates the adjusted frequency value Tzpl. The calculation formula is as follows: In the formula, Tzpl represents the adjusted frequency value, Dqpl represents the current frequency value, and K... p The proportional gain is used to adjust the direct deviation between the current frequency and the target frequency. e represents the deviation between the target frequency and the current frequency, and K represents the proportional gain. i K represents the integral gain, used to eliminate steady-state bias. d ∫edt represents the differential gain, which reflects the rate of change of error and helps reduce the overshoot of the system.
9. A low-frequency bio-wave frequency regulation and control system according to claim 8, characterized in that: The control module controls the parameter settings of the stimulation device and the signal generator based on the output of the biofeedback module, and calculates the stimulation frequency Cjpl, as shown in the following formula: Cjpl=Mbpl+Kn*(Fkpl-Dqpl)+Bhzy*Δf In the formula, Cjpl represents the frequency of the stimulus, Mbpl represents the set target frequency, Kn represents the frequency adjustment gain, which is used to adjust the degree of influence of feedback on the target frequency, Fkpl represents the feedback frequency obtained by the biofeedback module, Dqpl represents the current frequency value, Bhzy represents the variable gain, which is used to handle the influence of frequency changes, and Δf represents the amount of change in the feedback frequency over a period of time.
10. A low-frequency bio-wave frequency regulation and control system according to claim 9, characterized in that: The control module controls the parameter settings of the stimulation device and the signal generator based on the output of the biofeedback module, and calculates the stimulation amplitude Cjfd, as shown in the following formula: Cjfd=Mbfd+Ka*(Fkfd-Dqfd)+BhfzΔA In the formula, Cjfd represents the stimulus amplitude, Mbfd represents the target stimulus amplitude set by the system, Ka represents the amplitude adjustment gain, which is used to adjust the degree of influence of feedback on the target amplitude, Fkfd represents the feedback amplitude, which is the feedback signal from biofeedback, Dqfd represents the amplitude currently used by the system, Bhfz represents the gain of change on amplitude, and ΔA represents the amount of change in feedback amplitude over a period of time.